esa-sceva/satcom-chunk-collection huggingface dataset:
# Mycoremediation of PCBs by _Pleurotus ostreatus_: Possibilities and Prospects
## 1 Introduction  
The synthetic compounds obtained through chlorination of biphenyls are called polychlorinated biphenyls (PCBs), which are composed of a biphenyl molecule (two benzene rings linked by a C-C bond) that carries one to ten chlorine atoms. PCBs as mixtures are commercialized with trade names Aroclor, Clophen, Delor, etc. These commercialized PCBs consist of a mixture of congeners distinguished based on the number and position of chlorines on the biphenyl nucleus. PCBs find place in numerous industrial applications, such as heat transfer fluids, dielectric fluids, hydraulic fluids, flame-retardants, solvent extenders, and organic diluents.  
The use of PCBs is expanding and widespread these days, and these compounds are contributing more than enough damage to the environment with their percolation into soil and sedimenting, as there are inadequacies in their waste disposal [1; 2]. According to a recent finding, traces of PCBs were still detected in places where production was carried on decades before, in spite of their restricted application then--and this does not even take into account the situation now with large-scale production, of late, to meet the large-scale applications. Currently, PCBs are considered as one of the most hazardous contaminants in the world, and hence, they are the topmost public health concern [3]. The teratogenic, carcinogenic, and endocrine-disrupting aspects of these xenobiotics have been well documented [4; 5; 6; 7; 8; 9]. The most alarming adverse property of PCBs is their tendency toward bioaccumulation in lipid tissues and organic components of the soil and adipose tissue of animals and humans [10].  
This review briefly dwells on the available bioremediation-based technologies for decontamination of PCBs with special focus on white-rot fungi and more so with _Pleurotus ostreatus_. The milestones achieved so far with _P. ostreatus_ and their future prospects are presented.
# The Effect of Carbon Nanofibers Surface Properties in Hydrogenation and Dehydrogen Reactions
## 3 Results
### Catalysts Characterisation
Figure 4: Representative transmission electron microscopy (TEM) micrographs of the four catalysts used before reaction: (**a**) Pd/CNFs-PS-OX, (**b**) Pd/CNFs-PS, (**c**) Pd/CNFs-LHT and (**d**) Pd/CNFs-HHT. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Catalyst**} & \multicolumn{3}{c}{**Fresh**} & \multicolumn{3}{c}{**Used**} \\ \cline{2-5}  & **Mean (nm)** & **Std. Dev. (nm)** & **Pd\({}_{\text{SURF}}\) (\%)** & **Mean (nm)** & **Std. Dev. (nm)** \\ \hline Pd/CNFs-PS-OX & 5.8 & 1.1 & 20.7 & 5.7 & 2.4 \\ Pd/CNFs-PS & 6.9 & 1.8 & 17.6 & 6.8 & 2.0 \\ Pd/CNFs-LHT & 5.7 & 1.3 & 21.1 & 7.5 & 2.8 \\ Pd/CNFs-HHT & 5.4 & 0.9 & 22.1 & 5.9 & 2.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Particle size and size distribution of the four catalysts before and after reaction. Surface Pd percentage (Pd\({}_{\text{SURF}}\)) is also reported for the fresh catalysts.
# Mini-Crack Detection of Conveyor Belt Based on Laser Excited Thermography
###### Abstract  
For a belt conveyor, a belt with mini-cracks influences the safety of the operation of the belt conveyor devices significantly. The lumpy material rubbing against a belt with mini-cracks will lead to detrimental tears on the belt while the conveyor works at high speeds. In order to detect mini-cracks in a timely manner, before the occurrence of large tears, a new mini-crack detection method for conveyor belts based on laser excited thermography is proposed. A laser beam is applied to generate a heat wave on the belt surface, so the corresponding thermal images can be recorded by an infrared camera. Then, an algorithm for extracting and fusing the features of mini-crack belt's thermal images is firstly proposed by using time sequence analysis. It could transform multiple sequential thermal images into a single thermal image according to the frame difference method, so that the infrared thermal response of the mini-crack over time could be recorded quickly. To reveal the features of mini-crack, the power function image enhancement algorithm is proposed. After that, the threshold segmentation and Hough transform algorithm are used to determine the size of the mini-crack. The experimental results show that the mini-cracks with length of 50-140 mm, width of 1-10 mm, and depth of 5 mm can be successfully detected, and the detection accuracy could reach 96%. The detection results of different sizes of mini-crack show that the laser excited thermography has high damage sensitivity (1 mm diameter mini-crack detection), high accuracy (96%), lower power consumption (15 W laser excitation), and high SNR (signal-to-noise ratio).  
laser excited thermography; rapid detection; mini-crack; conveyor belt +
Footnote †: journal: Applied Sciences  
1
###### Abstract  
We explore the feasibility of classifying perceived pain intensity--despite the stimulus being identical--using multimodal physiological signals and self-reported emotional ratings. A total of 112 healthy participants watched the same anger-inducing video, yet reported varying pain intensities (5, 6, or 7 on a 7-point scale). We recorded electrocardiogram, skin conductance (SC), respiration, photoplethysmogram results, and finger temperature, extracting 12 physiological features. Participants also rated their valence and arousal. Using a random forest model, we classified pain versus baseline and distinguished intensity levels. Compared to baseline, the painful stimulus altered heart rate variability, SC, respiration, and pulse transit time (PTT). Higher perceived pain correlated with more negative valence, higher arousal, and elevated SC, suggesting stronger sympathetic activation. The classification of baseline versus pain using SC and respiratory features reached an F1 score of 0.83. For intensity levels 6 versus 7, including PTT and skin conductance response along with valence achieved an F1 score of 0.73. These findings highlight distinct psychophysiological patterns that reflect perceived intensity under the same stimulus. SC features emerged as key biomarkers, while valence and arousal offered complementary insights, supporting the development of personalized, psychologically informed pain assessment systems.  
emotional pain; perceived pain intensity; physiological signals; autonomic nervous system; valence; arousal; classification; machine learning Article  
1
## 5 Experiments and Results
### Experimental Parameters  
We initially aimed to compare the performance of the centralized version of federated learning (FedAVG and FedSGD) with the distributed P2P architecture (FedAVGP2P and  
Figure 6: Block diagram of the ensemble network, illustrating the internal architecture of the individual networks composing the SNN. The CNN utilized is EfficientNetV2S, while the ViT employed is BEiT [17].  
FedSGDP2P). The objective was to use a high number of clients (C = 100, 200, 300, etc.) and a large number of communications (round = 100, 200, 300, etc.) in our experiments to obtain the most relevant results for the purpose of analysis. However, we soon realized that due to resource constraints, the computation times were excessively high, primarily because of the heavy deep learning models used and described earlier, which we also had to substitute for a computation-friendly backbone.  
As a result, we decided to limit the maximum number of clients to 20 and the maximum number of rounds to 10. In the case of FedAVG, each round consists of two steps: selecting the clients that receive the aggregated model from the central server, and selecting the clients that send updates of their local models to the central server. In the case of FedAVGP2P and FedSGDP2P, during each communication round, we evaluate the clients' models on the test data. The round concludes when a client receives updates from all its neighbors. The training data are distributed among the clients, considering both IID and non-IID data distribution scenarios.  
To evaluate the performance of the three heuristics (n lastest, f1-score, and score cosine), we vary the fraction of clients C with values of 0.1, 0.2, 0.5, and 1.0. As a result, each client communicates with 2, 4, 10, or 20 neighboring clients in each round. After each round, we assess all clients' performance on the test data. During experimentation, the backbones initially proposed could not be used because of resource limitations. We were forced to change the backbones of the ensemble model architecture to a combination of [\"MobileNet\", \"MobileNetV2\"]. Table 1 shows the additional parameters used.
## 3 Results and Discussion  
In this, the proposed method is implemented using PBC with A-elliptic curve cryptography under a Windows 10 environment. The proposed method is evaluated using the following two terms:  
* Communication overhead;
* Computational overhead.  
These two metrics are analyzed for the proposed method and the process is compared with the existing lightweight break-glass access (LIBAC) and multi-authority MCP-ABE-BG approach.
# Analysis of Bullet Impact Locations in the 10 m Air Pistol Men's Competition Based on Covariance
## 5 Discussion
### Competition Analysis  
Based on actual competition cases from ISSF tournaments, we conducted an analysis of each player's bullet impact locations. Through dispersion analysis of hit position variance, we identified each player's shooting style and stability, allowing for a quantitative comparison and evaluation of their bullet impact location pattern and performance. What sets this study apart is the presentation of new performance metrics through dispersion analysis, enabling the derivation of individualized training and strategic plans to enhance players' performance. By analyzing bullet impact locations image coordinates from real competitions, we could examine shooting patterns such as left-right and up-down stability for each player, utilizing quantitative data on bullet impact locations that were previously unobservable.
# Algorithmic Coverage Quantification and Visualization in Range-Free Sensor Networks
## 4 Discussion
As shown in Figure 7, the blind zones caused by the nearby \(TR_{2}\) and \(TR_{3}\) combine into one blind zone for \(SR_{1}\) and hence \(B_{1}=3\). Scenario 3 illustrated the full capabilities of the tool, with the triangulated detection areas shown in transparent red. For this specific random setup, the total triangulated detection area was calculated equal to 9591 km\({}^{2}\) (84.88% of the AoI). Finally, Scenario 4 illustrated the impact of a node failure on the coverage area and its visualization. The tool automatically displayed the reduction in the triangulated coverage area (shown as reduced transparent red sub-areas in Figure 9). The failure of the fifth SR resulted in a reduction in the triangulated coverage area by 3191 km\({}^{2}\) (28.23% of the AoI). In [7], similar results are presented regarding triangulated coverage, where the maximum relative coverage does not exceed 85.56% of the area of interest. This led the authors to explore optimizing sensor ranges by using three different ranges (small, medium, and large) to increase the coverage percentage. Additionally, the authors in [7] demonstrate how activating additional groups of sensors near blind areas improves network performance, which is beyond the scope of the present paper. The four example scenarios demonstrate that the tool presented here is highly adaptable to diverse conditions: the users can adjust SR parameters, such as detection range, beam openings, geographic coordinates, in order to visualize and calculated coverage areas in real-time. This adaptability allows for informed decisions to be made on the optimal final SR network configuration, under various setups. Although further research could explore the limitations related to computational complexity in large WSN, our study demonstrates that the proposed tool has a significant potential in decision-making for network configuration, across various applications. To maximize the triangulated coverage area and improve WSN performance, future research should also address the problem of optimizing SR placement. This optimization should consider the number of available SRs and the number of available locations, which can vary depending on the application and the setup.
## 6 Synthetic Model Inversion Results and Analysis  
To verify the accuracy and effectiveness of the inversion algorithm, we designed a 3-D geoelectric model as shown in Figure 5. Figure 6 shows the horizontal view of the source and the subsurface anomaly. The resistivity of the background medium was 100 \(\Omega\cdot\)m, and a block-shaped anomaly was placed in the underground at \(x\in[100,350]\) m, \(y\in[-100,100]\) m, and \(z\in[50,180]\) m. This example employed a large-loop source, with the shape being a square and the side length was 420 m. The unit step-off waveform was adopted for transmitting the primary field. The survey consisted of 21 profiles, each with 21 sites, totaling 441 measurement points. The intervals between the profiles and sites were 50 m, and the survey area spanned \(x,y\in[-500,500]\) m, as indicated by the yellow points in Figure 5. The db\({}_{z}\)/dt response for each measurement point was computed within the time range from \(10^{-5}\)s to \(10^{-2}\)s, with a total of 31 logarithmically spaced time points. The inversion area is \(x\in[-2000,2000]\) m, \(y\in[-2000,2000]\) m, \(z\in[0,500]\) m. Gaussian random noise with a 5% standard deviation was added to the true model responses to generate synthetic data for the inversion test.  
This example consisted of several inversion tests to validate the reliability of the algorithm and to demonstrate the necessity of considering the impact of magnetic permeability  
Figure 5: Horizontal view of the source and the subsurface anomaly.  
Figure 6: Top view of the surface source and subsurface anomalies.  
in the 3-D TEM inversion. These tests include: (1) Resistivity model inversion, (2) Magnetic permeability model inversion, (3) 3-D TEM inversion considering the impact of magnetic permeability, and (4) 3-D TEM inversion ignoring the impact of magnetic permeability.
## 3 The mmWave Band
### Foliage Loss  
The Weissberger model has been used in this work to define the signal propagation through vegetation in mmWave networks [52]. The Weissberger foliage loss is described by the following:  
\[L_{f}=0.45\ f^{0.284}\Big{(}d_{f}\Big{)}\ for\ 0\leq d\leq 14\ \text{m} \tag{3}\]  
\[L_{f}=1.33\ f^{0.284}(d)^{0.588}\ for\ 14\leq d\leq 400\ \text{m}\]  
where \(f\) represents the center frequency in GHz, applied for 230 MHz to 95 GHz, and \(d\) is the depth of the foliage in meters. The overall propagation loss is equal to the path loss (described earlier) added to the Weissberger foliage loss:  
\[L_{T}=P_{L}+L_{f} \tag{4}\]  
The ITU foliage model is a different model for characterizing foliage loss, which was developed by (CCIR Rpt 236-2) [53]. The ITU foliage loss model is defined as follows:  
\[L_{F}=0.2f^{0.3}d^{0.6} \tag{5}\]  
where \(L_{F}\) is the foliage penetration loss in dB, \(f\) is the designated frequency in MHz, and \(d\) is the depth of the foliage in meters. In Figure 7, we present the plotted foliage loss for both the ITU and Weissberger models, considering two different foliage depths: 3 m and 15 m. The graph illustrates that the ITU model yields relatively consistent results with  
Figure 6: SINR mapping with/without shadow fading, the DBS architecture (upper snap), and CBS architecture (lower snap), where the color bar represents the range of SINR in dB.  
minimal variation when compared to the Weissberger model. The foliage loss values for the specified models and foliage depths are depicted, providing valuable insights into the performance of the respective models under different foliage conditions.
# High-Reliability Wireless Pressure Measurement System Based on FEC Algorithm
## 3 Key Hardware Circuit Design  
### Trigger Circuit Design  
After ignition, the front-end memory of the testing system can receive a signal synchronized with that of the control center. This signal is used to ensure the real-time performance and time consistency of data acquisition. Before the test starts, although the system is powered on and already recording data, it will only use the received signal as the starting mark. Once the signal is received, the system will accurately timestamp the subsequent data, enabling the data at different time points to have an accurate correspondence on the time axis. The externally connected cable contains two channels, which receive +5 V and \(-\)5 V level signals, respectively, when triggered. Once the system receives the trigger signal from the main control module through the optocoupler, it will timestamp the data, and the frame structure timing of the data will change from \"0000\" to \"FFFF\", marking the starting point of the test. The hardware parameters of the standby trigger channel are symmetric to those of the main channel. When the signal level of the main channel is abnormal and the abnormality persists for longer than the preset time, the FPGA automatically switches to the standby channel.  
The optocoupler circuit, due to the absence of electrical connection between the input and output, offers strong anti-interference capabilities and protective functions for the circuit, thereby enhancing reliability and preventing test accidents caused by misoperations [21,22]. In the testing system, the timing trigger signal is isolated and received using the optocoupler OLH6000, which has a conduction voltage of 1.9 V and an operating current of 12 mA. According to [PERSON]'s law, the resistance values of R8 and R9 in the circuit can be calculated. When the acquisition command is initiated, ST1_P and ST1_N conduct, outputting a low-level signal. Under the control of the FPGA, the system determines the start and end of acquisition by detecting changes in the ST1 level. Figure 3 illustrates the circuit design principle of the trigger receiving module.
## 3 Key Findings
### RQ2: What Kinds of Learning Algorithms Are Being Deployed Towards the Security of IIoT?
#### 3.2.1 Machine Learning-Based Technique  
In the study of [PERSON] et al. [9], the authors present an ML-IDS-based framework for detecting misuse and anomaly detection for Edge-Based IIoTsec. K-nearest neighbor (KNN) and Principal Component Analysis (PCA) techniques were evaluated on NSL-KDD and Bot-IoT datasets. While KNN was adopted to improve accuracy for admirable decisions, PCA was used for an enhanced feature engineering and fitting process. The model had a better performance result on the NSL-KDD dataset, that is, 99.10% accuracy, 98.4% detection rate, and a 2.7% false alarm rate (FAR). In addition, the authors in [70] also proposed a KNN algorithm for intrusion detection in the IIoT ecosystem. To reduce computation complexity and speed up processing time, PCA, univariate statistics, and genetic algorithms (GA) were selected for feature engineering. The model was validated with a Bot-IoT dataset with an accuracy rate of 99.99%. Another study by [PERSON] et al. [62] identified a increasing number of emerging technologies and their hybridization in the IIoT ecosystem as a direct proportionate to threats and vulnerabilities. The authors therefore proposed an ML-boosted tree algorithms-based smart vulnerability detector. The experimental result revealed the efficiency of the detector as it recorded a mean efficiency of 95%-99% in the F1 score metric.  
[PERSON] et al. [31] implemented two bioinspired-based feature selection algorithms--particle swarm optimization (PSO) and bat algorithm (BA)--toward the reduction in computational time and FAR while also advancing the detection rate of attacks in an IIIoT ecosystem. Three weak learners were validated with the WUSTL-IIOT-2021 dataset. Consequently, Random Forest (RF) had the best values in performance metrics, which therefore makes hybridization with BA the best model for the study. From another perspective, [PERSON] et al. [1] argued that in a heterogeneous environment of IoT, like industrial IoT, security of routing protocol for low-power and lossy networks (RPL) remains paramount. Unfortunately, most of the present IDS approaches are deficient in identifying novel RPL assaults. Hence, the authors proposed a federated-transfer-learning-assisted customized distributed IDS (FT-CID) model to identify RPL in such an environment. The model has an accuracy of 85.2% in detecting RPL intrusions. The model also proved better when compared with logistic regression (LR), multilayer perceptron (MLP), and CNN-based RPL-IIoT IDS with accuracies of 56.12%, 58.04%, and 74.89%, respectively. However, the authors did not consider the intrusion behaviors of the intruders, as expected to enhance the study, based on the features of FT-CID.
## 4 Results and Discussion
### Evaluation of the Trap Dynamic Sealing Capacity
#### 4.4.2 Evaluation of the Static Sealing Capacity of Cap Rock
These operating pressure limits are crucial factors in determining the capacity of natural gas in the underground gas storage during the construction process. In particular, some scholars believe that the upper operating pressure of underground gas storage can exceed the original fluid pore pressure, even up to 140% of the original pore pressure, potentially increasing the gas storage capacity to 400% of the original capacity [37]. Therefore, it is necessary to conduct further research on the maximum limit pressure that exceeds the original reservoir pore pressure under the influence of cyclic loading during subsequent injection and extraction stages, based on rock fatigue damage theory. Furthermore, under these extreme pressure operating conditions, the design of compatible injection and extraction schemes and innovative research and development of surface equipment will also be a future research focus. This will effectively increase the actual operating pressure of underground gas storage and significantly expand the underground gas storage volume. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Fault**} & **Adjacent** & \multirow{2}{*}{**Dip Angle**} & \multirow{2}{*}{**Location**} & \multirow{2}{*}{**Current State**} & \multicolumn{2}{c}{**Maximum**} & \multirow{2}{*}{
\begin{tabular}{c} **Maximum** \\ **Formation Stress** \\ **Modulus (SG)** \\ \end{tabular} } & \multirow{2}{*}{**Depth**} \\  & & & & & & **Modulus (SG)** & & **Stress (MPa)** \\ \hline F3 & SS2-7 & 58 & 65 & stable & 1.59 & 47.5 & 3050 \\ \hline F7 & SS2-12 & 70 & 65 & stable & 1.50 & 43.4 & 2950 \\ \hline F8 & SS2-17 & 86 & 336 & stable & 1.56 & 43.6 & 2850 \\ \hline F17 & SS202 & 52 & 282 & stable & 1.78 & 50.6 & 2900 \\ \hline F11 & SS202 & 68 & 90 & stable & 1.80 & 50.3 & 2850 \\ \hline F22 & SS2-25 & 62 & 44 & stable & 1.54 & 45.3 & 3000 \\ \hline F40 & SS2-25 & 60 & 60 & stable & 1.29 & 37.9 & 3000 \\ \hline F259 & SS2-5 & 65 & 280 & stable & 1.35 & 40.4 & 3050 \\ \hline F2 & SS2-7 & 71 & 150 & stable & 1.52 & 42.5 & 2850 \\ \hline F329 & SS202 & 62 & 265 & stable & 1.78 & 49.7 & 2850 \\ \hline F365 & SS2-19 & 58 & 242 & stable & 1.30 & 37.7 & 2900 \\ \hline F383 & SS202 & 64 & 118 & stable & 1.73 & 49.2 & 2900 \\ \hline F5 & SS2 & 80 & 340 & stable & 1.29 & 37.9 & 3000 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical table of fault analysis results.