More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing

Chris Peterson; Elin Farnell; Elizabeth C. Schundler; Henry Kvinge; Julia R. Dupuis; Michael Kirby

arxiv: 1906.11818 · v1 · pith:ZSAGTIJXnew · submitted 2019-06-27 · 📡 eess.IV · cs.CV· eess.SP

More chemical detection through less sampling: amplifying chemical signals in hyperspectral data cubes through compressive sensing

Henry Kvinge , Elin Farnell , Julia R. Dupuis , Michael Kirby , Chris Peterson , Elizabeth C. Schundler This is my paper

Pith reviewed 2026-05-25 13:56 UTC · model grok-4.3

classification 📡 eess.IV cs.CVeess.SP

keywords compressive sensinghyperspectral imagingchemical detectionsignal amplificationadaptive coherence estimatordata reconstructionmultispectral sensing

0 comments

The pith

Bandwise compressive sensing sampling followed by reconstruction amplifies chemical signals in hyperspectral data cubes, with amplification increasing at lower sampling rates.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper demonstrates that applying compressive sensing to sample hyperspectral data cubes one band at a time, then reconstructing, can make chemical signals stronger and easier to detect than in the original fully sampled data. This effect grows stronger as the fraction of samples taken drops, so that a cube rebuilt from 10 percent of the measurements sometimes yields clearer chemical detections than the raw 100 percent data. The observation is shown on two real datasets containing releases of chemical simulants such as glacial acetic acid and sulfur hexafluoride, with detection performed by the adaptive coherence estimator. A reader would care because it points to a counter-intuitive way that undersampling can improve rather than degrade performance in chemical sensing tasks.

Core claim

Bandwise CS sampling of a hyperspectral data cube followed by reconstruction can result in amplification of chemical signals contained in the cube, and this amplification generally increases as the level of sampling decreases, with some examples showing significantly stronger signals after reconstruction from 10 percent CS sampling than in the raw data.

What carries the argument

Bandwise compressive sensing sampling of hyperspectral data cubes followed by reconstruction, which interacts with chemical signal structure to produce amplification before detection by the adaptive coherence estimator.

If this is right

Chemical detection performance can improve when data cubes are reconstructed from fewer than 100 percent of the original samples.
The benefit holds across multiple real-world datasets containing different chemical simulants.
Lower sampling rates can produce stronger target signals for the adaptive coherence estimator than full sampling does.
The phenomenon suggests that compressive sensing may serve a signal-enhancement role in addition to its usual compression role for structured chemical data.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If the amplification is driven by how reconstruction interacts with sparse or low-rank chemical signatures, the same effect might appear in other imaging modalities that use similar reconstruction steps.
Sensor systems could be redesigned to intentionally undersample and reconstruct if the goal is maximum chemical detectability rather than faithful image recovery.
Mathematical analysis of why the reconstruction step boosts certain signals more than others would be a natural next step to predict when the effect occurs.

Load-bearing premise

The observed signal amplification arises from the interaction of the compressive sensing reconstruction with the structure of the chemical signals rather than from dataset-specific artifacts or choices in the detector.

What would settle it

Running the same bandwise CS sampling and reconstruction pipeline on additional hyperspectral datasets with different chemical releases and finding that the reconstructed signals are never stronger than the original full-sample signals would falsify the central claim.

Figures

Figures reproduced from arXiv: 1906.11818 by Chris Peterson, Elin Farnell, Elizabeth C. Schundler, Henry Kvinge, Julia R. Dupuis, Michael Kirby.

**Figure 2.** Figure 2: A histogram of ACE bulk coherence values with persistence for hyperspectral image 30 from Fig. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Figure 3: A histogram of ACE bulk coherence values with persistence for hyperspectral image 90 from Fig. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

**Figure 4.** Figure 4: Comparison of chemical detection (Fabry-P´erot MeS C dataset): the number of pixels which the ACE algorithm [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗

**Figure 5.** Figure 5: A historgram of ACE values for hyperspectral image 80 in the MeS release (see Fig. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

**Figure 6.** Figure 6: Comparison of chemical detection (Fabry-P´erot TEP A dataset): the number of pixels which the ACE algorithm [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗

**Figure 7.** Figure 7: ACE detection of plumes for a number of chemical compounds. The left column is ACE detection on raw [PITH_FULL_IMAGE:figures/full_fig_p010_7.png] view at source ↗

read the original abstract

Compressive sensing (CS) is a method of sampling which permits some classes of signals to be reconstructed with high accuracy even when they were under-sampled. In this paper we explore a phenomenon in which bandwise CS sampling of a hyperspectral data cube followed by reconstruction can actually result in amplification of chemical signals contained in the cube. Perhaps most surprisingly, chemical signal amplification generally seems to increase as the level of sampling decreases. In some examples, the chemical signal is significantly stronger in a data cube reconstructed from 10% CS sampling than it is in the raw, 100% sampled data cube. We explore this phenomenon in two real-world datasets including the Physical Sciences Inc. Fabry-P\'{e}rot interferometer sensor multispectral dataset and the Johns Hopkins Applied Physics Lab FTIR-based longwave infrared sensor hyperspectral dataset. Each of these datasets contains the release of a chemical simulant, such as glacial acetic acid, triethyl phospate, and sulfur hexafluoride, and in all cases we use the adaptive coherence estimator (ACE) to detect a target signal in the hyperspectral data cube. We end the paper by suggesting some theoretical justifications for why chemical signals would be amplified in CS sampled and reconstructed hyperspectral data cubes and discuss some practical implications.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper reports an empirical observation that CS reconstruction can amplify chemical signals in hyperspectral cubes with the effect strengthening at lower sampling rates, but lacks controls to rule out detector-specific artifacts.

read the letter

The main takeaway is that bandwise compressive sensing followed by reconstruction appears to boost chemical signals in hyperspectral data, and this boost grows as the sampling rate drops. They document the effect on two real datasets with chemical releases, using the ACE detector to measure it against targets like glacial acetic acid and sulfur hexafluoride. In some cases the reconstructed 10% sampled cube shows stronger signals than the original full data. That observation is the core new piece relative to standard CS reconstruction work, which usually aims for fidelity rather than signal enhancement. The paper does a reasonable job grounding the claim in actual sensor data from PSI and JHU rather than pure simulation, and it flags some theoretical angles at the end for why the structure of chemical signatures might interact with the reconstruction this way. Credit for running the experiments on named external datasets with known releases. The soft spots are real and central. No description of the reconstruction algorithm or parameters appears, no error bars or statistical tests on the amplification, and no checks against other detectors such as matched filters or SAM. Synthetic data with known ground-truth spectra is also missing, which would help separate real signal structure effects from reconstruction biases that ACE might exploit. The stress-test concern about ACE-specific artifacts therefore lands; without those controls the result stays narrow and hard to generalize. This work is aimed at researchers building or optimizing hyperspectral chemical sensors under sampling constraints. A reader already working in CS for remote sensing could extract practical ideas from the datasets and the reported trend, but would need the missing validation steps before treating the amplification as reliable. It deserves peer review because the observation, if it survives scrutiny on methods and controls, could affect how low-rate sampling is viewed in this domain.

Referee Report

3 major / 1 minor

Summary. The manuscript claims that bandwise compressive sensing (CS) sampling of hyperspectral data cubes followed by reconstruction can amplify chemical signals (as measured by the adaptive coherence estimator, ACE), with amplification generally increasing as the sampling rate decreases. In some cases the signal is stronger after 10% CS sampling than in the original 100% sampled cube. This is demonstrated empirically on two real datasets (PSI Fabry-Pérot multispectral and JHU APL FTIR hyperspectral) containing releases of chemical simulants (glacial acetic acid, triethyl phosphate, sulfur hexafluoride), with ACE used for detection; the authors also suggest theoretical justifications and practical implications.

Significance. If the amplification effect is shown to be intrinsic to the interaction between CS reconstruction and chemical signal structure (rather than detector- or algorithm-specific), the result could have notable implications for hyperspectral chemical detection by enabling stronger signals from reduced data volumes, with potential benefits for remote-sensing sensor design and acquisition efficiency.

major comments (3)

[Abstract] Abstract and experimental description: the reconstruction method (solver, regularization, or parameters) is not specified, preventing assessment of whether the reported amplification depends on particular algorithmic choices.
[Results] Experimental results: amplification is reported exclusively for the ACE detector; no comparisons are provided with alternative detectors (e.g., matched filter or spectral angle mapper) or on synthetic data with known ground-truth spectra, leaving open whether the effect is a general CS-chemical interaction or an ACE-specific response to reconstruction-induced correlations.
[Results] Results and discussion: no quantitative details (metrics for amplification, error bars, statistical tests, or data exclusion rules) or controls for dataset-specific artifacts are described, which is load-bearing for the central claim that amplification increases with undersampling.

minor comments (1)

[Abstract] Abstract: 'phospate' is a typographical error and should read 'phosphate'.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments. We address each major comment below and indicate the revisions planned for the manuscript.

read point-by-point responses

Referee: [Abstract] Abstract and experimental description: the reconstruction method (solver, regularization, or parameters) is not specified, preventing assessment of whether the reported amplification depends on particular algorithmic choices.

Authors: We agree that the reconstruction details are necessary for reproducibility and to evaluate dependence on specific choices. The full manuscript describes the bandwise CS approach but does not explicitly name the solver or parameters in the abstract or methods. We will revise the abstract and add a dedicated methods subsection specifying the solver (e.g., the particular optimization algorithm), regularization, and parameters used for all experiments. revision: yes
Referee: [Results] Experimental results: amplification is reported exclusively for the ACE detector; no comparisons are provided with alternative detectors (e.g., matched filter or spectral angle mapper) or on synthetic data with known ground-truth spectra, leaving open whether the effect is a general CS-chemical interaction or an ACE-specific response to reconstruction-induced correlations.

Authors: The manuscript centers on ACE because it is the standard detector for chemical detection in hyperspectral remote sensing, and the reported phenomenon concerns amplification of the ACE statistic. We acknowledge the absence of comparisons to other detectors or synthetic data with ground truth. We will expand the discussion to explicitly state the scope of the claim and note that the effect's generality remains an open question for future investigation. Adding new detector comparisons or synthetic experiments would require work outside the current empirical focus on real datasets, so the revision will be limited to clarifying discussion rather than new results. revision: partial
Referee: [Results] Results and discussion: no quantitative details (metrics for amplification, error bars, statistical tests, or data exclusion rules) or controls for dataset-specific artifacts are described, which is load-bearing for the central claim that amplification increases with undersampling.

Authors: We agree that quantitative support is needed to substantiate the central claim. We will revise the results and discussion sections to report explicit amplification metrics (e.g., ratios of peak ACE values), any available error bars or variability measures across trials, statistical tests where applicable, data exclusion rules, and controls for potential dataset artifacts. These additions will directly address the load-bearing aspects of the undersampling-amplification relationship. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical observation on external datasets

full rationale

The paper presents an empirical finding that bandwise CS sampling plus reconstruction increases ACE-detected chemical signal strength in two named external datasets (PSI Fabry-Perot and JHU APL FTIR), with the effect appearing to grow as sampling rate drops. No derivation chain, fitted parameters renamed as predictions, or load-bearing self-citations are used; the result is measured directly on the data rather than deduced from prior author work or internal definitions. Suggested theoretical justifications at the end do not retroactively define the observed amplification. The claim is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The paper rests on the standard compressive sensing recovery guarantee for sparse signals and the assumption that the ACE detector responds linearly to signal strength in reconstructed cubes; no free parameters or new entities are introduced in the abstract.

axioms (2)

domain assumption Compressive sensing permits accurate reconstruction of signals that admit a sparse representation from under-sampled measurements.
Invoked implicitly when stating that reconstruction is performed after bandwise CS sampling.
domain assumption The adaptive coherence estimator (ACE) produces a detection statistic whose magnitude increases with the strength of the target chemical signature.
Required for the claim that amplified signals lead to better detection.

pith-pipeline@v0.9.0 · 5781 in / 1335 out tokens · 28197 ms · 2026-05-25T13:56:11.705749+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

17 extracted references · 17 canonical work pages · 2 internal anchors

[1]

A new technique for hyperspectral compressive sensing using spectral unmixing,

Martin, G., Dias, J. M. B., and Plaza, A. J., “A new technique for hyperspectral compressive sensing using spectral unmixing,” in [Satellite Data Compression, Communications, and Processing VIII ], 8514, 85140N, International Society for Optics and Photonics (2012)

work page 2012
[2]

Hyperspectral coded aperture (HYCA): A new technique for hyperspectral compressive sensing,

Martin, G., Bioucas-Dias, J. M., and Plaza, A., “Hyperspectral coded aperture (HYCA): A new technique for hyperspectral compressive sensing,” in [ Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 21st European ], 1–5, IEEE (2013)

work page 2013
[3]

Hyca: A new technique for hyperspectral compressive sensing,

Mart´ ın, G., Bioucas-Dias, J. M., and Plaza, A., “Hyca: A new technique for hyperspectral compressive sensing,” IEEE Transactions on Geoscience and Remote Sensing 53(5), 2819–2831 (2015)

work page 2015
[4]

Hyperspectral image compressed sensing via low-rank and joint- sparse matrix recovery,

Golbabaee, M. and Vandergheynst, P., “Hyperspectral image compressed sensing via low-rank and joint- sparse matrix recovery,” in [ Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on ], 2741–2744, Ieee (2012)

work page 2012
[5]

Coded hyperspectral imaging and blind compressive sensing,

Rajwade, A., Kittle, D., Tsai, T.-H., Brady, D., and Carin, L., “Coded hyperspectral imaging and blind compressive sensing,” SIAM Journal on Imaging Sciences 6(2), 782–812 (2013)

work page 2013
[6]

Compressive sensing [lecture notes],

Baraniuk, R. G., “Compressive sensing [lecture notes],” IEEE signal processing magazine 24(4), 118–121 (2007)

work page 2007
[7]

Nonlinear total variation based noise removal algorithms,

Rudin, L. I., Osher, S., and Fatemi, E., “Nonlinear total variation based noise removal algorithms,” Physica D: nonlinear phenomena 60(1-4), 259–268 (1992)

work page 1992
[8]

A data-driven approach to sampling matrix selection for compressive sensing

Farnell, E., Kvinge, H., Dixon, J. P., Dupuis, J. R., Kirby, M., Peterson, C., Schundler, E. C., and Smith, C. W., “A data-driven approach to sampling matrix selection for compressive sensing,” (2019). Preprint arXiv:1906.08869

work page internal anchor Pith review Pith/arXiv arXiv 2019
[9]

The split Bregman method for L1-regularized problems,

Goldstein, T. and Osher, S., “The split Bregman method for L1-regularized problems,” SIAM J. Imaging Sci. 2(2), 323–343 (2009). Figure 6. Comparison of chemical detection (Fabry-P´ erot TEP A dataset): the number of pixels which the ACE algorithm shows to contain the chemical signature for TEP as a function of video frame for both uncompressed (dashed blu...

work page 2009
[10]

Adaptive matched subspace detectors and adaptive coherence estima- tors,

Scharf, L. L. and McWhorter, L. T., “Adaptive matched subspace detectors and adaptive coherence estima- tors,” in [ Signals, Systems and Computers, 1996. Conference Record of the Thirtieth Asilomar Conference on], 1114–1117, IEEE (1996)

work page 1996
[11]

Adaptive subspace detectors,

Kraut, S., Scharf, L. L., and McWhorter, L. T., “Adaptive subspace detectors,” IEEE Transactions on signal processing 49(1), 1–16 (2001)

work page 2001
[12]

Multipulse adaptive coherence for detection in wind turbine clutter,

Pakrooh, P., Scharf, L. L., Cheney, M., Homan, A., and Ferrara, M., “Multipulse adaptive coherence for detection in wind turbine clutter,” IEEE Transactions on Aerospace and Electronic Systems 53(6), 3091– 3103 (2017)

work page 2017
[13]

The adaptive coherence estimator for detection in wind turbine clutter,

Pakrooh, P., Scharf, L., Cheney, M., Homan, A., and Ferrara, M., “The adaptive coherence estimator for detection in wind turbine clutter,” in [ Radar Conference (RadarConf ), 2017 IEEE ], 1793–1798, IEEE (2017)

work page 2017
[14]

Multipulse subspace detectors,

Scharf, L. L. and Pakrooh, P., “Multipulse subspace detectors,” in [ Conference record-Asilomar Conference on Signals, Systems, & Computers ], (2017)

work page 2017
[15]

Total variation vs L1 regularization: a comparison of compressive sensing optimization methods for chemical detection

Farnell, E., Kvinge, H., Dupuis, J. R., Kirby, M., Peterson, C., and Schundler, E. C., “Total variation vs L1 regularization: a comparison of compressive sensing optimization methods for chemical detection,” (2019). Preprint arXiv:1906.10603

work page internal anchor Pith review Pith/arXiv arXiv 2019
[16]

AIRIS standoﬀ multispectral sensor,

Cosofret, B. R., Chang, S., Finson, M. L., Gittins, C. M., Janov, T. E., Konno, D., Marinelli, W. J., Levreault, M. J., and Miyashiro, R. K., “AIRIS standoﬀ multispectral sensor,” in [ Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing X ], 7304, 73040Y, International Society for Optics and Photonics (2009). (a) (b) (c) (d) (e) (f...

work page 2009
[17]

A primer for chemical plume detection using LWIR sensors,

Broadwater, J. B., Limsui, D., and Carr, A. K., “A primer for chemical plume detection using LWIR sensors,” Technical Paper, National Security Technology Department, Las Vegas, NV (2011)

work page 2011

[1] [1]

A new technique for hyperspectral compressive sensing using spectral unmixing,

Martin, G., Dias, J. M. B., and Plaza, A. J., “A new technique for hyperspectral compressive sensing using spectral unmixing,” in [Satellite Data Compression, Communications, and Processing VIII ], 8514, 85140N, International Society for Optics and Photonics (2012)

work page 2012

[2] [2]

Hyperspectral coded aperture (HYCA): A new technique for hyperspectral compressive sensing,

Martin, G., Bioucas-Dias, J. M., and Plaza, A., “Hyperspectral coded aperture (HYCA): A new technique for hyperspectral compressive sensing,” in [ Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 21st European ], 1–5, IEEE (2013)

work page 2013

[3] [3]

Hyca: A new technique for hyperspectral compressive sensing,

Mart´ ın, G., Bioucas-Dias, J. M., and Plaza, A., “Hyca: A new technique for hyperspectral compressive sensing,” IEEE Transactions on Geoscience and Remote Sensing 53(5), 2819–2831 (2015)

work page 2015

[4] [4]

Hyperspectral image compressed sensing via low-rank and joint- sparse matrix recovery,

Golbabaee, M. and Vandergheynst, P., “Hyperspectral image compressed sensing via low-rank and joint- sparse matrix recovery,” in [ Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on ], 2741–2744, Ieee (2012)

work page 2012

[5] [5]

Coded hyperspectral imaging and blind compressive sensing,

Rajwade, A., Kittle, D., Tsai, T.-H., Brady, D., and Carin, L., “Coded hyperspectral imaging and blind compressive sensing,” SIAM Journal on Imaging Sciences 6(2), 782–812 (2013)

work page 2013

[6] [6]

Compressive sensing [lecture notes],

Baraniuk, R. G., “Compressive sensing [lecture notes],” IEEE signal processing magazine 24(4), 118–121 (2007)

work page 2007

[7] [7]

Nonlinear total variation based noise removal algorithms,

Rudin, L. I., Osher, S., and Fatemi, E., “Nonlinear total variation based noise removal algorithms,” Physica D: nonlinear phenomena 60(1-4), 259–268 (1992)

work page 1992

[8] [8]

A data-driven approach to sampling matrix selection for compressive sensing

Farnell, E., Kvinge, H., Dixon, J. P., Dupuis, J. R., Kirby, M., Peterson, C., Schundler, E. C., and Smith, C. W., “A data-driven approach to sampling matrix selection for compressive sensing,” (2019). Preprint arXiv:1906.08869

work page internal anchor Pith review Pith/arXiv arXiv 2019

[9] [9]

The split Bregman method for L1-regularized problems,

Goldstein, T. and Osher, S., “The split Bregman method for L1-regularized problems,” SIAM J. Imaging Sci. 2(2), 323–343 (2009). Figure 6. Comparison of chemical detection (Fabry-P´ erot TEP A dataset): the number of pixels which the ACE algorithm shows to contain the chemical signature for TEP as a function of video frame for both uncompressed (dashed blu...

work page 2009

[10] [10]

Adaptive matched subspace detectors and adaptive coherence estima- tors,

Scharf, L. L. and McWhorter, L. T., “Adaptive matched subspace detectors and adaptive coherence estima- tors,” in [ Signals, Systems and Computers, 1996. Conference Record of the Thirtieth Asilomar Conference on], 1114–1117, IEEE (1996)

work page 1996

[11] [11]

Adaptive subspace detectors,

Kraut, S., Scharf, L. L., and McWhorter, L. T., “Adaptive subspace detectors,” IEEE Transactions on signal processing 49(1), 1–16 (2001)

work page 2001

[12] [12]

Multipulse adaptive coherence for detection in wind turbine clutter,

Pakrooh, P., Scharf, L. L., Cheney, M., Homan, A., and Ferrara, M., “Multipulse adaptive coherence for detection in wind turbine clutter,” IEEE Transactions on Aerospace and Electronic Systems 53(6), 3091– 3103 (2017)

work page 2017

[13] [13]

The adaptive coherence estimator for detection in wind turbine clutter,

Pakrooh, P., Scharf, L., Cheney, M., Homan, A., and Ferrara, M., “The adaptive coherence estimator for detection in wind turbine clutter,” in [ Radar Conference (RadarConf ), 2017 IEEE ], 1793–1798, IEEE (2017)

work page 2017

[14] [14]

Multipulse subspace detectors,

Scharf, L. L. and Pakrooh, P., “Multipulse subspace detectors,” in [ Conference record-Asilomar Conference on Signals, Systems, & Computers ], (2017)

work page 2017

[15] [15]

Total variation vs L1 regularization: a comparison of compressive sensing optimization methods for chemical detection

Farnell, E., Kvinge, H., Dupuis, J. R., Kirby, M., Peterson, C., and Schundler, E. C., “Total variation vs L1 regularization: a comparison of compressive sensing optimization methods for chemical detection,” (2019). Preprint arXiv:1906.10603

work page internal anchor Pith review Pith/arXiv arXiv 2019

[16] [16]

AIRIS standoﬀ multispectral sensor,

Cosofret, B. R., Chang, S., Finson, M. L., Gittins, C. M., Janov, T. E., Konno, D., Marinelli, W. J., Levreault, M. J., and Miyashiro, R. K., “AIRIS standoﬀ multispectral sensor,” in [ Chemical, Biological, Radiological, Nuclear, and Explosives (CBRNE) Sensing X ], 7304, 73040Y, International Society for Optics and Photonics (2009). (a) (b) (c) (d) (e) (f...

work page 2009

[17] [17]

A primer for chemical plume detection using LWIR sensors,

Broadwater, J. B., Limsui, D., and Carr, A. K., “A primer for chemical plume detection using LWIR sensors,” Technical Paper, National Security Technology Department, Las Vegas, NV (2011)

work page 2011