esa-sceva/satcom-chunk-collection huggingface dataset:
# An Unsupervised Image Denoising Method Using a Nonconvex Low-Rank Model with TV Regularization
## 4 Experimental Results and Analysis
### Natural Image Denoising
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Noise Type** & **Images** & **RPCA** & **AMlogtv** & **NonRPCA** & **WNNM\(-\)RPCA** & **TDV** & **Ours** \\ \hline \multirow{8}{*}{Impulse noise} & Butterfly & 32.5246 & 20.8745 & 33.8365 & 30.9500 & 18.4085 & 41.6482 \\  & House & 32.5501 & 24.5450 & 35.0507 & 31.1299 & 24.3206 & 41.7090 \\  & Peppers & 30.9569 & 22.1821 & 31.9384 & 26.9935 & 21.8727 & 34.1587 \\  & Lena & 33.1704 & 22.6855 & 34.8620 & 30.4863 & 22.0347 & 41.1695 \\  & Room & 33.3740 & 22.9321 & 35.3441 & 33.8834 & 21.4471 & 41.4602 \\  & Bird & 30.4850 & 21.6382 & 30.4429 & 28.0903 & 20.8948 & 33.2591 \\  & Camera & 30.8366 & 21.7976 & 30.7196 & 30.9059 & 21.2003 & 33.4341 \\  & Starfish & 30.2611 & 21.9611 & 32.7050 & 31.8355 & 20.7648 & 33.7062 \\  & **Average** & **31.7698** & **22.3270** & **33.1124** & **30.5343** & **21.3679** & **37.5681** \\ \hline \multirow{8}{*}{Poisson noise} & Butterfly & 33.1761 & 25.2643 & 37.8854 & 37.5122 & 19.3446 & 41.8914 \\  & House & 33.1059 & 30.1854 & 35.1456 & 34.7763 & 25.9563 & 37.3046 \\ \cline{1-1}  & Peppers & 31.8290 & 25.5136 & 32.0457 & 31.9485 & 23.3223 & 33.0805 \\ \cline{1-1}  & Lena & 34.3953 & 26.2260 & 35.4915 & 35.3024 & 23.0865 & 37.6541 \\ \cline{1-1}  & Room & 35.2429 & 25.7317 & 35.5204 & 35.2885 & 22.2211 & 37.6050 \\ \cline{1-1}  & Bird & 31.0893 & 26.0181 & 31.4407 & 31.3215 & 21.9071 & 34.0229 \\ \cline{1-1}  & Camera & 30.6940 & 25.4728 & 32.7069 & 32.5430 & 22.0588 & 33.8362 \\ \cline{1-1}  & Starfish & 27.1146 & 25.6538 & 28.4586 & 28.8304 & 20.8408 & 29.3038 \\ \cline{1-1}  & **Average** & **32.0808** & **26.2582** & **33.5868** & **33.4403** & **22.3421** & **35.5873** \\ \hline \multirow{8}{*}{Gaussian noise} & Butterfly & 33.2683 & 23.6624 & 37.4013 & 38.9586 & 20.4690 & 42.1629 \\ \cline{1-1}  & House & 33.1171 & 28.5725 & 37.9599 & 38.4267 & 25.7665 & 42.1794 \\ \cline{1-1}  & Peppers & 31.0499 & 24.5416 & 33.4540 & 34.0774 & 23.0379 & 34.6498 \\ \cline{1-1}  & Lena & 33.6107 & 25.2392 & 37.5569 & 38.8699 & 23.0261 & 42.3696 \\ \cline{1-1}  & Room & 33.6502 & 25.0528 & 37.4442 & 37.9283 & 22.0668 & 42.1284 \\ \cline{1-1}  & Bird & 30.6875 & 24.7570 & 32.3656 & 33.0949 & 21.7068 & 33.6199 \\ \cline{1-1}  & Camera & 30.7358 & 24.6608 & 32.5424 & 32.5958 & 21.9053 & 33.6987 \\ \cline{1-1}  & Starfish & 30.7621 & 24.6617 & 32.8105 & 33.5210 & 20.7648 & 34.0942 \\ \cline{1-1}  & **Average** & **32.1102** & **25.1435** & **35.1919** & **35.9340** & **22.3429** & **38.1129** \\ \hline \end{tabular} The Average values are in bold black.  
\end{table} 
Table 1: PSNR comparisons of RPCA, AMlogtv, NonRPCA, WNNNM\(-\)RPCA, TDV, and the proposed method for noise removal in natural test images (optimal value: red line; suboptimal value: cyan line). Figure 7: Histogram of evaluation indicators of natural images. (**a**) Average PSNR; (**b**) average SSIM; (**c**) average FSIM.
# Enhancements and Challenges in CoAP--A Survey
## 3 Applications of CoAP
### Interoperability and Integration
#### 3.1.5 Integration with OSGP  
Open Smart Grid Protocol (OSGP) is the communication protocol used for information exchange between devices in Smart Grids (SG). However, OSGP does not support integration with constrained IoT devices running CoAP which is used for communication in most IoT devices. In order to map the data packets between CoAP and OSGP, [13] proposes a solution called CoAP and OSGP Integration for the Internet of Things (COIIoT). A function mapping layer is applied to each request/response individually. CoAP uses MicroCoAP library to execute a GET method and this message is then mapped to an OSGP partial read request. The request type, message id and packet size are extracted from the CoAP packets and analyzed for match between CoAP request and OSGP request. Afterwards, the two fields of OSGP packet (count and offset) are filled with the CoAP message payload. The count field is embedded with message size whereas the offset field includes message contents. For the reverse mapping, the Pending Event Descriptor (PED) holds the OSGP packet and is included in options field of CoAP packet and the payload size field of CoAP is filled with count value of OSGP packet and the payload field of CoAP is filled with offset/data value of OSGP packet. However, performing mapping for each request/response individually may add additional overhead and latency, therefore, it needs to be experimentally evaluated.
## 2 Materials and Methods
### Statistical Analysis  
The statistical analyses were descriptive and performed in MATLAB R2020b (Mathworks, Natick, MA, USA). Variables were summarized using their median, minimum-maximum and interquartile range. Outliers were included in the statistical tests. For all patients, means and standard deviations of OEF, CBF and CMRO\({}_{2}\) were calculated in the ROIs set out in Figure 2. In patients with multiple cMETs, perfusion and oxygenation parameters were not assessed on a metastasis-by-metastasis basis but averaged across all metastases present in the respective patient's brain. GBM patients were not stratified according to IDH or MGMT promoter methylation status.  
Non-parametric Mann-Whitney-Wilcoxon tests for two populations were applied to properly compare the distributions of explanatory variables between the two groups. Two-tailed Wilcoxon signed-rank tests were used for intra-individual comparisons between CET and NET2. \(p\)-values \(<\) 0.05 were considered significant. Due to the explorative nature of this study, \(p\)-values were not adjusted for multiple comparisons and power analysis for determination of required sample size was not performed [35].  
The primary learning objective was the binary classification of GBM vs. cMET. For this, a well-established maximum margin classifier, a linear kernel support-vector machine [36], was fitted to the following features: OEF, CBF and CMRO\({}_{2}\) in CET and NET2. Additional secondary measures, i.e., the ratios of said features in CET divided by NET2, were also calculated and used for fitting [17]. We performed five-fold cross-validation with 10 repeats to assess the robustness of these explanatory variables [37]. Receiver operating characteristic analysis was performed to calculate the area under the receiver operating characteristic curve (AUC) accuracy metrics and derivatives of the confusion matrix, averaged over the five folds [37]. In an effort to compare the classification performance of the linear kernel support-vector machine with other common binary classifiers, ROC analysis was performed for naive Bayes, weighted k-nearest neighbor, decision trees and for quadratic as well as Gaussian kernel support-vector machines.
## 2 Materials and Methods  
**Cells, virus and chemicals.** Vero E6 cells (African green monkey kidney epithelial cells, ATCC CRL-1586) purchased from ATCC were propagated in Dulbecco's modified Eagle's minimum essential medium (DMEM, Corning) supplemented with 10% fetal bovine serum (FBS). SARS-CoV-2 (TCDC#4, hCoV-19/Taiwan/4/2020) was provided by the Taiwan CDC and amplified by infecting Vero E6 cells in a biosafety level 3 facility. The culture supernatant was harvested when the cytopathic effects were fully developed. The virus titer was determined by the standard 50% tissue culture infectious dose (TCID\({}_{50}\)) method and expressed as TCID\({}_{50}\)/mL. The human anti-SARS-CoV-2 N protein antibody was kindly provided by Dr. [PERSON] (Genomics Research Center, Academia Sinica).  
**Disinfection test.** Three UV light prototype devices, a UVC LED (275 nm), a mercury lamp (254 nm), and an excimer lamp (222 nm), designed and produced by Everlight Electronics, were used in this disinfection test. The UV devices were positioned 11 cm above the SARS-CoV-2-containing medium and the radiation intensities of the UVC LED (275 nm), the mercury lamp (254 nm), and the excimer lamp (222 nm) at the medium'ssurface were measured by UVR-300 (Topcon, Tokyo, Japan) as 79.8, 850, and 7.0 \(\upmu\)W/cm\({}^{2}\), respectively. The depth of the medium was 1 cm, and the radiation intensities of the UVC LED (275 nm), mercury lamp (254 nm), and excimer lamp (222 nm) at the medium's bottom were 28.9, 305.1, and 1.6 \(\upmu\)W/cm\({}^{2}\), respectively. Table 1 shows the UV light accumulative exposure of the three prototype devices. After irradiation, the viral samples were 10-fold serially diluted and added to Vero E6 cells for 4-day incubation. The cells were then fixed with 10% formaldehyde overnight and stained with 0.5% crystal violet for 20 min. After washing, the plates were scored for infection. The Reed and Muench Method [17] was used to determine the virus titer as 50% tissue culture infectious dose per ml (TCID\({}_{50}\)/mL).  
**Immunofluorescent assay.** To verify viral infectivity, we also used an immunofluorescent assay to detect SARS-CoV-2 N protein expression. Briefly, the irradiated samples were added to Vero E6 cells for 1 day infection. Cells were fixed and immunostained with an anti-SARS-CoV-2 N protein antibody plus a goat anti-human IgG-Alexa Fluor 488 (A11013, Invitrogen). In addition, the cell nucleus was stained with DAPI (4',6-Diamidino-2-phenylindole dihydrochloride, D1306, Invitrogen). The signal was observed and photographed by using an immunofluorescent microscope.  
**Calculation of disinfection ability.** The Log reduction value (LRV) for the virus titer was calculated using the formula: LRV = Log\({}_{10}\) (input virus) \(-\) Log\({}_{10}\) (output virus). The term \"Log reduction\" is a mathematical expression for the relative number of living microbes that are eliminated by disinfection. A 1-Log reduction means 90% of a target microbe is inactivated with a 10-fold reduction in the microbe count. A 2-Log reduction results in a 99% reduction, or a 100-fold reduction in microbe count, and so on.
## 2 Materials and Methods
### Generation of ANN Training Data  
#### 2.1.1 Overview of Geometric Parameters for Generation of ANN Training Data  
The training data for the ANN was generated using finite element analysis (FEA) for a range of parameters for API 5L X52, X65, and X80 pipe grades. The material properties are tabulated in Table 3, and the geometric parameters of the corroded pipe is tabulated in Table 4. The material properties of the pipe body are represented by a nonlinear true stress-strain curve of the materials during finite element simulations, as illustrated in Figure 2. FEM has proven to be a reliable tool for structural analysis and many researchers have utilised this method to generate training data for the development of ANN [4,25]. However, prior to FEA, FEM was validated against full-scale burst tests to ensure that the methodology and applied boundary conditions are correct. The results of the finite element analysis used as the training data for the developed ANN can be found in Supplementary Materials. A total of 1353 datasets were generated.
# A Power-Gated 8-Transistor Physically Unclonable Function Accelerates Evaluation Speeds
## 4 8-Transistor PUF Performance Measurements
### Uniqueness: Inter-Distance  
Since PUFs are the hardware sources for identifying individual devices or generating _Secret Keys_, the extracted information from each PUF should be unique. The fractional Hamming distance was computed between different PUF devices. In order to successfully identify each PUF device, this value should be close to 50%. In the experiments, the _Response_ bit strings of different PUF clusters were compared to calculate the inter-distance. The average inter-distance of the 20 8T PUF clusters is 50.67%.
## 2 Experimental Methods  
The schematic diagram of the LIF setup is presented in Figure 1. A Q-switched Nd: YAG laser (Quantel, Q-Smart 850) emitting at 266 nm with an output power of 30 mJ/pulse was used as the excitation source and the repetition rate of the laser is 1 Hz. The pulse laser beam irradiated onto the soil surface perpendicularly and a plano-convex quartz lens with a 50 mm focal length was used for focusing the fluorescence spectra. The fluorescence spectrum was collected by a 1 m long fiber optic which central fiber is 800 m diameter, each spectrum detected by a grating spectrometer (Ocean, Maya-2000 pro) and recorded by a computer. The spectrometer covered an overall range 200 nm-1100 nm with a spectral resolution of 1.1 nm. The soil samples were placed on a rotary platform which was synchronized rotation with the pulse laser to avoid the inhomogeneity of spectral detection.  
Soil samples were collected from the both sides of a national highway with a depth of 20-30 cm and all sampling points are yellowish red soil. All of the collected samples were dried, grinded, and sieved through 100-mesh to remove roots, then they were measured by gas chromatography to choose the one without petroleum hydrocarbons as the experimental matrix. In order to obtain the diffusion regularity of diesel oil in soils, the experiments were divided into two sets of experiments, and to imitate the actual soil conditions, we impacted the soils by a hydraulic press. Firstly, for the quantitative analysis experiments, ten dried soil samples were prepared by mixing different concentrations of diesel oil and the concentrations are 0.5%, 1%, 1.5%, 2%, 2.5%, 3%, 3.5%, 4%, 4.5%, and 5%. Secondly, for the three-dimensional diffusion analysis, several samples with different gravimetric soil moisture ranging from 0% to 25% were prepared, then 5.3 g diesel oil was quickly dropped on three soil samples with gravimetric moisture of 0%, 5%, and 10% by a pipette, and the density of the soils is about 2 g per cubic centimeter.  
For improving the stability of the LIF spectra, each measured spectrum was the accumulation of 40 laser shots at different locations of the same soil sample every sample was detected 5 times under the same conditions to confirm the experimental reproducibility.
## 4 Results and Discussion
### Discussion
#### 4.2.4 Need for Time-Series Composition  
In Table 5, as the time intervals for time-series composition increased, the cloud/shadow coverage percentages decreased dramatically. Similarly, classification accuracies declined from 84.21% for OA and 0.76 for F1 scores when using 5-day raw data to 74.81% for OA and 0.58 for F1 scores when using 60-day composited data. This was somewhat surprising and contradictory to previous studies, which showed that the time-series monitoring of vegetation can be most effective when the composition period is near the length of phenological stages [43; 44]. Since vegetation is usually assumed to be stable over 10 days [44], the best results should have been obtained utilizing a 10-day composition.  
When investigating the causes, it was found that the time-series composition operation used satellite images captured on different days to produce one image. This could lead to the temporal confusion of crop growth in the composited image sequences, which is not favorable to crop classification. In addition, contrastive learning can fully explore the inherent time-series properties of raw data with a greater cloud/shadow percentage. In light of this, we may deduce that time-series composition is neither necessary nor detrimental in contrastive-learning-based feature representation.
# FOG De-Noising Algorithm Based on Augmented Nonlinear Differentiator and Singular Spectrum Analysis
## 3 Simulation
EMD-FLP algorithm is a hybrid de-noising algorithm, by using which noisy signal is decomposed into intrinsic mode functions (IMFs) first, and then each IMF is de-noised by forward linear prediction (FLP) [18]. The comparison results are shown in Table 2, and we can see that the SNR can be improved by using SSA-AND algorithm effectively from SNR's range from + 20 dB to \(-\)20 dB, which also proves the excellent robustness of the proposed method. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**SNR (dB)** & \(-\)**20** & \(-\)**16** & \(-\)**9** & \(-\)**3** & **0** & **3** & **6** & **9** & **11** & **13** & **16** \\ \hline EMD-FLP & \(-\)14.51 & \(-\)10.23 & \(-\)3.63 & 1.76 & 3.29 & 7.85 & 9.67 & 11.96 & 15.79 & 18.34 & 21.15 \\ DFA-VMD & \(-\)16.55 & \(-\)12.35 & \(-\)5.59 & 0.38 & 6.54 & 6.48 & 12.77 & 12.47 & 16.73 & 18.90 & 22.56 \\ ARKF & \(-\)12.71 & \(-\)9.21 & \(-\)0.41 & 4.69 & 7.12 & 10.30 & 13.49 & 15.68 & 19.59 & 21.30 & 24.87 \\ SSA-AND & \(-\)10.3 & \(-\)4.78 & 0.75 & 4.33 & 9.02 & 11.87 & 12.93 & 16.75 & 17.33 & 19.90 & 21.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the singular spectrum analysis-augmented nonlinear differentiator (SSA-AND) to other de-noising methods with different signal-to-noise power ratios (SNRs). EMD-FLP: empirical mode decomposition-forward linear prediction; DFA-VMD: detrended fluctuation analysis-variational mode decomposition; ARKF: adaptive robust Kalman filter. Figure 3: Reconstruction results and comparisons: (**a**) original signal; (**b**) original signal added with noise; (**c**) de-noised signal by using SSA-AND; (**d**) residual.
## 5 Multi-Objective Optimization
### Optimization Objectives and Boundary Conditions  
For this shrink tube anti-climb device, the technical requirements were as follows: an energy absorption of 228 kJ, an energy absorption stroke of 395 mm, an average force range of 600 kN \(\pm\) 7.5%, a small gap between the peak force and the average force and an original average force efficiency of more than 93%. The space for optimization of mean force efficiency was limited. In the crashworthiness design, the energy-absorbing structure is required to have the highest possible specific energy absorption \(E_{\rm SA}\). Therefore, the first objective of the optimization was to maximize the specific energy absorption \(E_{\rm SA}\), and at the same time, the peak force \(F_{\rm PC}\) needed to be reduced as much as possible in order to avoid excessive deceleration during the collision, which could lead to heavy casualties among occupants. Hence, the second objective of the optimization was to minimize the peak force \(F_{\rm PC}\). In addition, for this shrink tube design, the mean force is required to be as close as  
Figure 8: Multi-objective optimization process.  
possible to 600 kN while meeting the first two targets. Therefore, the third target of the optimization was to take the absolute value of the difference between the mean force \(F_{\text{MC}}\) and 600 kN as the target of the mean force and to minimize it. Referring to the main cause analysis results, only two input variables, the cone angle \(\alpha\) and the shrink tube thickness \(T\), were considered in the optimal design. The friction coefficient \(u\) is related to the lubrication method used in the actual working conditions. This paper selected the original value of the friction coefficient \(u\) as 0.1 for the case of no lubrication, and the axial length of the friction cone \(S\) had a relatively small influence on the three indexes, and an intermediate value of 25 mm was used. In summary, this paper carried out the mathematical description of the multi-objective optimization problem and the boundary conditions, as shown in the following equation:  
\[\left\{\begin{array}{l}\text{Max }E_{SA}(\alpha,T)\\ \text{Min }F_{PC}(\alpha,T)\\ \text{Min }|F_{MC}-600|\\ 5^{\circ}\leq\alpha\leq 25^{\circ}\\ 3\text{ mm}\leq T\leq 12\text{ mm}\end{array}\right. \tag{8}\]
# Test Bed of Semantic Interaction of Smart Objects in the Web of Things
## 3 Architecture Deployed
### Interaction Layer  
The interaction layer supports the tools that allow the objects to communicate with applications and with other objects. It is based on the Eclipse Paho broker server (iot.eclipse.org) that manages the MQTT protocol [26]. The broker server is the intermediary where objects and users can communicate their states, requirements, and queries. All the messaging work of the elements that interact with the broker are supported by XML documents, which were coded to take into account the Open Group ODF standard [27] in order to define a common and interoperable information protocol by any type of smart object. The interactions defined on this layer are:  
* Object to object: the event object shares the status of one of its resources to the action object.
* Object to user: the object responds to the user about the result of an internal operation.
* User to object: the user makes a query about the metadata of the object by means of a query to it. The user communicates the structure of a new interaction service to the event and action object by sending them an ECA. The user requests the change of status of a basic service (on/off). The user requests the status change of an interaction service (on/off). The user requests the elimination of an interaction service to the event and action object (eliminates ECA of each object).  
Among the documents developed, those that intervene in the creation of an interaction service can be highlighted:  
* MetaData.xml: contains the information of the object and its resources, Figure 3 presents a representation of the main elements.
* ECA.xml: supports the creation of an interaction service, Figure 4 presents a representation of the main elements.  
Figure 3: MetaData.xml.
## 3 Experimental Platform  
As shown in Figure 5, the planetary rover wheel-on-limb system consists of a mechanical arm control subsystem, an experimental wheel sensing subsystem, and a wheel sinkage detection subsystem. Among them, the mechanical arm control subsystem is used for terrain following and constant force tracking; the experimental wheel sensing subsystem is used to sense the wheel-terrain contact force; the wheel sinkage detection subsystem is used to detect the wheel sinkage depth in real time to judge the planetary rover passability [7]. The actual wheel radius in this paper is 80 mm. Since a fixed load is applied to the wheels during the movement of the planetary rover, the wheel-on-limb system simulates a real wheel-passing scenario by applying the same load to the perceived wheels via the mechanical arm. Detecting the sinkage of the experimental wheel in real time provides an important basis for the planetary rover's passability. The wheel sinkage detection subsystem consists of three parts: a vision acquisition module, a data processing module, and auxiliary tools. To capture real-time images of the detection wheel and the wheel-terrain interface, the visual acquisition module must cover the entire wheel and the surrounding area on the wheel side. This coverage should extend at least as far as the diameter, length, and radius width of the wheel. The data processing module processes the collected visual image information in real-time to output the values of wheel sinkage, as well as the entry and departure angles of the wheel-terrain contact. During data processing, auxiliary tools are employed to determine the installation position of the visual acquisition module and its related parameters. The distortion-free USB camera ov5640 was chosen as the vision acquisition module based on its characteristics, as shown in Table 1. Measurement results confirm that the camera meets the requirements for measuring wheel sinkage.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Maximum Resolution** & **Pixel** & **Focal Length** & **Field of View** & **Capture Rate** \\ \hline
4K & 8 million & 2.2 mm & 140\({}^{\circ}\) & 30 fps \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed parameters of USB camera ov5640.