esa-sceva/satcom-chunk-collection huggingface dataset:
# Super-Resolution Lensless Imaging of Cells Using Brownian Motion
## 2 Materials and Methods  
In this study, the observation window of the microfluidic chip located above the CIS was used to collect the multi-frame images of blood cells. The diluted blood was injected into the microfluidic chip with a syringe, and the diluent flow in the observation window was stopped for a period of time. Cell images can only be acquired by the Brownian motion of the cells suspended in the diluent. Due to the large volume of cells, cells basically moved in a balanced position, the displacement was small, and the speed was slow. In this case, the motion blur of the image was not serious during the exposure of 400 ms, which was beneficial for high-precision image registration.  
The Brownian motion of cells was used to determine the sub-pixel displacement. The direction and speed of the motion were random. It was necessary to use multi-frame motion estimation to complete the image registration. In this study, the blind estimation method was used to estimate the cell displacement between two frames. First, the target detection algorithm was used to capture the cell position in the field of view (FOV) region. According to the system structure shown in Figure 1, due to the diffraction, the size of the cell diffraction image was four times larger than the focused cell image. There were several cells in a FOV, and every cell was independent, which is different from the super-resolution displacement based on the light source. In the whole image, only cells are displaced; the background is not. This issue can be solved by segmenting the cell and its background region. Here, the whole image was segmented according to each cell and its surrounding area, and the segmented area formed a sub-image with only one cell. High-resolution cell images could be obtained by separately attaining the super-resolution of these sub-images. This method is better for cell motion estimation. Without super-resolution of the whole image, the amount of data in image processing will be greatly reduced.  
We describe the details of the algorithm that was used for the super-resolution in this manuscript. The most important problem we encountered in achieving multi-frame super-resolution was estimating the displacement of the cells. Here, we used the blind estimation method and Keren's method to estimate the motion. We simultaneously needed to study the Brownian motion of the live cell and determine the mode of Brownian motion of the cell and the statistical distribution of the displacement.
## 4 Simulation Results
### Short-Circuit Tests
With the Substep model, the overall RTS should occupy four cores of an RTDS as shown in Figure 17a. In contrast, the RTS with the proposed model holds fewer cores as shown in Figure 17b. Consequently, the proposed model achieved lower absolute computational load and higher flexibility in the use of the processor unit while configuring a large-scale electric railway system. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & \multicolumn{3}{c}{S/S} & & SP \\ \hline \multirow{3}{*}{**Time 6:14:03 AM**} & **Upward** & **Downward** & **Upward** & **Downward** & **Upward** & **Downward** & **Upward** & **Downward** \\  & **Line** & **Line** & **Line** & **Line** & **Line** & **Line** & **Line** & **Line** \\  & **Voltage** & **Voltage** & **Current** & **Current** & **(kA)** & **Power** & **Power** & **Voltage** & **Voltage** \\  & **(kV)** & **(kV)** & **(kA)** & **(kA)** & **(MW)** & **(MW)** & **(MW)** & **(kV)** & **(kV)** \\ \hline Actual Measurement & 48.552 & 48.570 & 0.3501 & 0.3520 & 16.5850 & 16.6245 & 47.3179 & 47.3215 \\ \hline Substep Model & 49.22 & 49.22 & 0.3528 & 0.3549 & 16.74 & 16.78 & 48.03 & 48.03 \\ \hline Proposed Model & 49.11 & 49.11 & 0.352 & 0.3541 & 16.67 & 16.70 & 47.92 & 47.92 \\ \hline Library Model & 52.75 & 52.75 & 0.3781 & 0.3803 & 19.22 & 19.26 & 51.46 & 51.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Case study result (comparison of actual measurement data with the RTDS simulation results obtained using each model (the Substep model, the proposed model, and the Library model)). Figure 16: Comparison of accuracy between the simulation results of each model and the actual measurement data.
## 4 Experimental Results of the Proposed Systems
### Results of ANN Classifier Based on Fusion of Features
#### 4.5.1 Validation Checks and Gradient  
The validation check and gradient are tools for assessing the performance of the ANN based on the two tuberculosis X-ray datasets. Validation and gradient values are recorded in each epoch, and the least error is calculated to obtain the best network performance. Figure 9 describes the validation check and gradient generated by the ANN for diagnosing the two tuberculosis datasets. For the first dataset, it is noted that the ANN network with ResNet-50, GLCM, DWT and LBP features reached the best network performance at a gradient of 0.030079 at epoch 33 and a validation check with a value of 6 at epoch 33. Meanwhile, the network with GoogLeNet, GLCM, DWT and LBP features reached the best network performance at a gradient of 0.0009887 at epoch 26 and a validation check with a value of 6 at epoch 26. For the second dataset, we note that an ANN network with features of ResNet-50, GLCM, DWT and LBP achieved the best network performance at a gradient of 0.00059127 at epoch 47 and validation check with a value of 6 at epoch 47, while with GoogLeNet, GLCM, DWT and LBP features, the ANN network achieved the best performance at a gradient of 0.00028374 at epoch 34 and a validation check with a value of 6 at epoch 34.  
Figure 9: Display validation checks and gradient of the ANN to classify the two datasets based on the hybrid features. (**a**) ResNet-50, GLCM, DWT first dataset and LBP. (**b**) GoogLeNet, GLCM, DWT and LBP first dataset. (**c**) ResNet-50, GLCM, DWT and LBP second dataset. (**d**) GoogLeNet, GLCM, DWT and LBP second dataset.
# A Computational Platform for Automatic Signal Processing for Bender Element Sensors +
## 1 Introduction
The robustness of the GeoHyTE predictions to sub-optimal user input (in terms of the initial estimation of the arrival time and coarse refinement of the numerical model) is also assessed. The main advantage of the GeoHyTE platform is that it provides shear modulus estimates that are, at the same time, _objective_ and _physically consistent_. The objectivity comes from the minimal user intervention required for the interpretation of the output signal, and means that multiple users obtain the same shear modulus prediction. The physical consistency lays in the use of numerical models especially tailored for the problem that is being solved, taking into account the multi-phase nature of geomaterials.
# Economic and Environmental Impact of Energy Saving in Healthcare Buildings
## 3 Results
### Thermal Insulation Improvement  
In a building of more than 20 years, it is estimated that, with thermal rehabilitation, a saving of 50% of the energy consumed for heating and/or cooling [46] could be achieved. A suitable thermal insulation can be a significant annual savings because it minimizes energy losses and air leakage from the building.  
Figure 6: Potential of energy saving derived from maintenance management.  
With the objective of determining the positive economic and environmental impacts thanks to thermal insulation improvement, the annual average saving and investment for the whole set of buildings was quantified for a three-year period.  
Figure 7 represents the potential average energy saving per built surface area (m\({}^{2}\)) through the upgrade of the thermal insulation improvement in healthcare buildings with less than 5000 m\({}^{2}\) (no beds) and hospitals larger than 5000 m\({}^{2}\) (with beds). A potential average saving between 0.65 kWh and 0.85 kWh per built surface area is observed, in a payback period less than nine years.  
The proposed measures include the following actions: increasing the insulation level of roofs, sealing doors and windows to prevent infiltrations, installing blinds to reduce direct solar radiation, installing overhangs, replacing single glazing with double glazing, and applying protective glazing films. The main thermal losses in hallways, waiting rooms, and corridors were observed to occur when access doors remained open, provided that the most usual type of access to healthcare centres consists of windshield partition doors, a system that loses effectiveness as doors might even remain open during the busiest periods in the building.
## 3 Hardware and System
### System Overview
#### 3.2.2 Control Unit  
The control unit is composed of a coordinator node connected to the control system. The coordinator has to organize the network and its communications. It has also the capability to add or remove any node from the network, provide addresses and create/control the topology. The algorithms used by coordinator, like the topology construction algorithm, have to be sent by the control system to the coordinator.
# Few-Shot Continuous Authentication for Mobile-Based Biometrics
## 5 Experimental Result
### Impact of the Number of impostor Profiles and Sample Size for Training  
This experiment analyzes the impact of the number of impostor profiles and samples for training. By default, we use ten samples for each impostor profile, where there are ten profiles in total. Here, we analyze the sensitivity of our model with respect to the number of impostor profiles and samples per profile. The following experiments are based on the HMOG dataset.  
Figure 7 illustrates the averaged EERs for the different numbers of impostor profiles involved in training. We observe that the EERs tend to decrease as the number of profiles increases. The LFP-TCN without data augmentation shows EER values of 10.91% and 12.25% when using 40 and 10 impostor profiles, respectively, which represents a performance degradation of 1.34%. On the other hand, the LFP-TCN with the random pattern mixing shows EER values of 9.86% and 10.95% when using 40 and 10 impostor profiles, respectively, in which the performance degradation remains 1.09%. This suggests that the random pattern mixing alleviates the negative impact on performance due to using only a few impostor profiles.  
Figure 8 illustrates the averaged EERs for the different training sample sizes per impostor profile. Similar to Figure 7, the EERs constantly decrease as the sample size increases. The sample size ranges from 10 to 80. The LFP-TCN without data augmentation achieves an EER of 9.64% with 80 samples. Then, the EER degrades to 12.25% with ten samples, which is an increase of 2.61%. On the other hand, the LFP-TCN with random pattern mixing achieves values of EER of 9.33% and 10.95% with 80 and 10 samples, in which the performance degradation remains 1.62%. This shows that the random pattern mixing improves the sample efficiency, diversifying impostor patterns for the training even with limited impostor data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Sensor**} & \multirow{2}{*}{**Feature**} & \multirow{2}{*}{**Supervision**} & \multicolumn{2}{c}{**Dataset**} \\ \cline{3-3}  & & & & **HMOG** & **BBMAS** \\ \hline AUToSen [10] & & Raw & Fully-supervised & 22.13 & 15.34 \\ DAKOTA [13] & To. \({}^{1}\), Acc. \({}^{2}\), Gyro. \({}^{3}\) & Hand-crafted & Fully supervised & 11.71 & 14.06 \\ [PERSON] et al. [14] & & Hand-crafted & Fully supervised & 15.59 & 15.04 \\ Ours & & Raw & Weakly supervised & **10.95** & **12.85** \\ \hline CNNAuth [4] & & Raw & Semi-supervised & 31.60 & 38.11 \\ DeFusion [6] & & Raw & Semi-supervised & 26.40 & 36.55 \\ [PERSON] et al. [3] & Acc. \({}^{2}\), Gyro. \({}^{3}\) & Raw & Fully supervised & 26.36 & 23.28 \\ DeepAuthen [9] & & Raw & Fully supervised & 27.29 & 24.86 \\ Ours & & Raw & Weakly supervised & **18.32** & **22.98** \\ \hline \hline \end{tabular}  
* \({}^{1}\) Touch screen, \({}^{2}\) accelerometer, \({}^{3}\) gyroscope.  
\end{table}
Table 4: Averaged EERs (%) of SOTA methods.  
Figure 7: Averaged EERs (%) for different numbers of impostor profiles for training.
## 4 Computational Results  
The benchmark sets found in [58] are selected to evaluate the proposed methodology. At the moment of writing, there were three benchmark sets divided by the number of activities (30, 50 and 100 activities); each set contains 270 instances, and every instance uses three renewable, three non-renewable and three doubly-constrained resources. Furthermore, every activity can be executed in anywhere from three to five different modes, except for the dummy (initial and final) activities, which have only one mode with no duration and no resource consumption.  
The experiments conducted were very straight-forward, following the procedures described in Section 3, and the only additional information used was the parameters used to evaluate the formulas, which are detailed below. The results from the methodology proposed in this study are not compared to the results of other approaches because the main objective is not to minimize makespan. The main objective for this study is introduce a procedure that generates a robust baseline schedule that lie within a lower and upper bound and which is capable of absorbing variations caused by uncertainty; to the best of the authors' knowledge, there are no other papers that present such results. The sole point of comparison is against the best-known optima, and the results are reported based on the deviation against these Best-Known Optima (BKO). Additionally, even though the main objective was not to minimize the makespan, in most of the instances, the best-known solution was reached as the lower bound. Furthermore, the results presented are based on the schedules' makespan, but for every makespan, there was an actual schedule (\(M\), \(S\)) generated.
# Review on Structural Health Evaluation with Acoustic Emission
## 7 AE Inspection of Miscellaneous Processes  
Some reports have appeared covering various processes. Since these are rare, yet useful, we list them below.  
Tank-bottom inspection by AE has been widely practiced. For this testing, [PERSON] et al. (2020) presented the statistical distribution data of their many inspection results. Naturally, such outcomes depend on specific sets of tested tanks, but this information should be of value to other inspectors and tank owners.  
[PERSON] et al. (2003) monitored a blast furnace for steelmaking with AE for detecting cracks in its steel shell and leaks from hot air blower pipes. Burst and continuous signals were detected, respectively, providing a baseline data for devising an SHM system.  
[PERSON] et al. (2004) monitored the cold forming of aircraft wing panels using AE and seeking optimized forming parameters. The final aim is to automate the operation to get resultant panels equivalent to those made by qualified manual processes.  
[PERSON] et al. (2005) used AE to gain understanding of thermal spraying processes as AE signals are related to coating thickness achieved and to crack density of coatings. Different spray guns and their conditions must be selected properly and AE results provided guidelines to select the processes.  
Figure 8: Applied stress vs. rupture time curves of composite-overwrapped pressure vessels with glass, carbon, and Kevlar fibers. Stress is expressed in percent of the short-time rupture pressure. Reproduced from ([PERSON] et al., 2003), originally from [PERSON] (2005). Reproduced with permission.  
Figure 9: Applied stress vs. rupture time curves of SiC fiber bundles (blue) and SiC composites (red) at 500 \({}^{\circ}\)C. Shaded zone denoted monotonic fracture is for the short-time fracture stress of the SiC composite (1.85–2.25 GPa). From [PERSON] et al. (198). Reproduced with permission.  
[PERSON] et al. [206] examined fracture of automobile windshields with AE and evaluated their fracture processes under static and dynamic loading together with finite element modeling. They identified the initiation point of fracture, which is crucial in proper modeling and support design.  
[PERSON] et al. [207] correlated low frequency AE during quenching of steel components to quenching cracks. Reliability of hydrophone detector was established and the system is ready for real component tests.
###### Abstract  
The super-resolution generative adversarial network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied by unpleasant artifacts. To further enhance the visual quality, we propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The method is based on depthwise separable convolution super-resolution generative adversarial network (DSCSRGAN). A new depthwise separable convolution dense block (DSC Dense Block) was designed for the generator network, which improved the ability to represent and extract image features, while greatly reducing the total amount of parameters. For the discriminator network, the batch normalization (BN) layer was discarded, and the problem of artifacts was reduced. A frequency energy similarity loss function was designed to constrain the generator network to generate better super-resolution images. Experiments on several different datasets showed that the peak signal-to-noise ratio (PSNR) was improved by more than 3 dB, structural similarity index (SSIM) was increased by 16%, and the total parameter was reduced to 42.8% compared with the original model. Combining various objective indicators and subjective visual evaluation, the algorithm was shown to generate richer image details, clearer texture, and lower complexity.  
Keywords:single image super-resolution; deep learning; depthwise separable convolution; generative adversarial networks; image processing +
Footnote †: journal: Applied
# Path Loss Model for Outdoor Parking Environments at 28 GHz and 38 GHz for 5G Wireless Networks
## 4 Proposed Path Loss Model  
The parking lot path loss model (PLM) is proposed by determining the compensation factors for single and dual parking lots, where the path loss exponent value was set to 2 as a free space path loss exponent. The path loss exponent was set to 2 to model the propagation of the 5G channel based on the effect of reflection from cars and other objects in the measurement environment. This model was extracted with respect to 1 m reference; thus, it is a physical-based model and it is defined as:  
\[PL^{PLM}(f,d)[dB]=FSPL(f,d_{0})+10n\log_{10}\biggl{(}\frac{d}{d_{0}}\biggr{)} +k_{f}+\chi_{\sigma}^{PLM} \tag{4}\]  
where \(n\) is the free space path loss exponent of 2, \(k_{f}\) is giving as the path compensation factor with respect to number of the parking lot and \(\chi_{\sigma}^{PLM}\) is the shadowing factor with standard deviation (\(\sigma\)) in dB. With \(N\) number of data points, the MMSE was used to estimate the \(k_{f}\) and minimum standard deviation as follows:  
\[\chi_{\sigma}^{PLM}=PL^{PLM}(d)[dB]-FSPL(f,d_{0})-10n\log_{10}\biggl{(}\frac{ d}{d_{0}}\biggr{)}-k_{f}=Z-S-k_{f}, \tag{5}\]  
where \(Z=PL^{PLM}(d)[dB]-FSPL(f,d_{0})\) and \(S=20\log_{10}\biggl{(}\frac{d}{d_{0}}\biggr{)}\).  
The standard deviation \(\sigma^{PLM}\) for the PLM model becomes:  
\[\sigma^{PLM}=\sqrt{\frac{\Sigma\chi_{\sigma}^{PLM^{2}}}{N}}=\sqrt{\frac{ \Sigma\left(Z-S-k_{f}\right)^{2}}{N}} \tag{6}\]In order to minimize \(\sigma^{PLM}\), the term \(\left(Z-S-k_{f}\right)^{2}\) should be minimized by making the derivative of \(\left(Z-S-k_{f}\right)^{2}\) with respect to \(k_{f}\) zero, such that:  
\[\begin{array}{c}\frac{\partial\sum\left(Z-S-k_{f}\right)^{2}}{\partial k_{f}} =\sum 2(k_{f}+S-Z)=2(k_{f}\times N+\sum S-\sum Z)=\\ 2(k_{f}\times N+\frac{\sum SZ\sum S}{\sum S^{2}}-\sum Z)=0\end{array} \tag{7}\]  
From (7):  
\[k_{f}=\frac{\sum Z}{N}-\frac{\sum SZ\sum S}{N\sum S^{2}} \tag{8}\]  
Therefore, the minimum standard deviation for the PLM proposed model is:  
\[\sigma^{PLM}_{\text{min}}==\sqrt{\left(S-S\frac{\sum SZ}{S^{2}}-\frac{\sum Z}{ N}+\frac{\sum SZ\sum S}{N\sum S^{2}}\right)^{2}/N} \tag{9}\]  
The Z and S can be considered column vectors and the optimum \(k_{f}\) can also be expressed in matrix form as:  
\[k_{f}=\sqrt{(\sum Z-\left(Z^{\text{T}}(S^{\text{T}}S)^{-1}S\right)\sum S)/N} \tag{10}\]  
where the \((.)^{\text{T}}\) operator represents the transpose of the column vector.
## 5 Enhancements to ShinyAnonymizer Data Encryption Techniques
### Technical Details of Encryption Techniques
#### 5.1.4 Aes512
By applying those encryption techniques, a medical facility can securely transmit and evaluate data. Scientists can use them in an exploratory data analysis, receive perspectives on patient profiles and health trends, and help lead to advancements in medicine while respecting patient privacy. The enhanced ShinyAnonymizer finds an essential compromise between value for data and privacy, enabling medical institutions to profit from their data resources while adhering to strict privacy laws. ShinyAnonymizer uses advanced encryption techniques for the latest and most secure data protection and confidentiality. Some suitable algorithms and approaches are DES, XDES, Blowfish, and AES512, going to enormous detail in applying the very process of encryption itself to guarantee that sensitive data are shielded from unauthorized access \begin{table}
\begin{tabular}{c c c c} \hline \hline
**PatientId** & **PatientGender** & **PatientRace** & **PatientMaritalStatus** & **PatientLanguage** \\
**4D9-8464-497B0982F0F0** & & & \\  & & & \\  & & & \\  & & & \\  & & & \\  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Encrypted Dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**PatientId** & **PatientGender** & **PatientRace** & **PatientMaritalStatus** & **PatientLanguage** \\ \hline FB2 ABB23-C9D0-4D09-8464-49B70B982F0F0 & Male & Unknown & Married & Icelandic \\
64182B95-EB72-4E2B-BE77-8050B71498 CE & Male & African-American & Separated & English \\ DB22A4D9-7E4D-485C-916A-9 CD1386507B & Female & Asian & Married & English \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Original Dataset. but retaining the analytical value of that data. This approach ensures that all the privacy concerns are met, thus providing room for secure data management and research.
# Regeneration of Activated Carbons Spent by Waste Water Treatment Using KOH Chemical Activation
###### Abstract  
In this study, spent activated carbons (ACs) were collected from a waste water treatment plant (WWTP) in Incheon, South Korea, and regenerated by heat treatment and KOH chemical activation. The specific surface area of spent AC was 680 m\({}^{2}\)/g, and increased up to 710 m\({}^{2}\)/g through heat treatment. When the spent AC was activated by the chemical agent potassium hydroxide (KOH), the surface area increased to 1380 m\({}^{2}\)/g. The chemically activated ACs were also washed with acetic acid (CH\({}_{3}\)COOH) to compare the effect of ash removal during KOH activation. The low temperature N\({}_{2}\) adsorption was utilized to measure the specific surface areas and pore size distributions of regenerated ACs by heat treatment and chemical activation. The functional groups and adsorbed materials on ACs were also analyzed by X-ray photoelectron spectroscopy and X-ray fluorescence. The generated ash was confirmed by proximate analysis and elementary analysis. The regenerated ACs were tested for toluene adsorption, and their capacities were compared with commercial ACs. The toluene adsorption capacity of regenerated ACs was higher than commercial ACs. Therefore, it is a research to create high value-added products using the waste.  
activated carbon; KOH; regeneration; heat treatment; chemical activation; toluene adsorption; ash removal Article