esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The reliability of every electrical reticulation system heavily depends on the health of its connected transformers as they play a key role in the transmission and distribution of electricity. However, over time, transformers tend to degrade and become susceptible to failures and breakdowns which may be caused by various elements such as lack of maintenance, environmental conditions, operation malfunctions, etc. One of the commonly adopted methods used to assess the condition of the transformers is the dissolved gas analysis (DGA) method. DGA is used to detect the presence of combustible gases such as hydrogen (H\({}_{2}\)), methane (CH\({}_{4}\)), ethylene (C\({}_{2}\)H\({}_{4}\)), ethane (C\({}_{2}\)H\({}_{6}\)), acetylene (C\({}_{2}\)H\({}_{2}\)), carbon monoxide (CO), and carbon dioxide (CO\({}_{2}\)) in the transformer oil [1], and detect changes in their concentration [2]. The application of DGA has enabled researchers and professionals to conduct early diagnoses of transformers and to intervene with the necessary remedial methods to prolong the life of transformers [2; 3; 4]. The commonly used methods for assessing transformer oil concentration include Key Gas Analysis, Dornenberg and Rogers ratio methods, IEC Ratio, Duval triangle, and CIGRE method. However, various studies conducted on the application of the traditional interpretation methods have indicated their limitations and challenges associated with the uncertainty of the results due to manual handling of the test samples, the accuracy of the methods, inability to concurrently diagnose existing transformer faults, and identifying transformer incipient faults [5; 6; 7; 8; 9; 10; 11; 12]. The combination of Ratio methods (IEC, Rogers, and Doernenburg) is limited in classifying the measured gas ratios into fault types in different interpretation techniques, often, inconsistent results are obtained when using different methods [5]. Also, it is noted that methods like the Rogers ratio are not capable of diagnosing borderline incipient faults.
## 1 Introduction: The Development of the Mining Industry and the CBTC System
In the three systems, the ATS system is the core of the CBTC system; it is a monitoring system based on a series of programs such as big data processing and computing, computer network sharing and real-time signal data conversion, it cooperates with the ATP system, the ATO system, the CI (Computer Interlocking) system, and other systems to realize the real-time monitoring, information collection, resource allocation, and utilization of the Urban Rail Transit System. Figure 2 shows the structure of the ATS system, and Figure 3 shows the specific functions of the ATS system. Specially, the CBTC system not only has an excellent application effect above ground, but it also has a great application effect in the operation and monitoring of the underground subway [27]. As a kind of underground rail transport, subway is also not susceptible to the artificial reconnaissance and surveillance, so in 2016, through the improvements of the CBTC system, [PERSON], [PERSON], and [PERSON] [27] tried to applied the monitoring and tracking functions of the CBTC system to the subway transport and made good progress. In fact, as early as in 2006, [PERSON] et al. [28] put forward the concept of creating an underground city and suggested that the CBTC system should be the main traffic control and monitoring system under the ground. In 2013, [PERSON] et al. [29; 25] published their research on the underground simulation of the CBTC system in the 2013 IEEE International Conference on Communications (ICC), and in 2014, [PERSON] [30] explored whether the CBTC system could be used in underground traffic in London. However, by 2015, underground traffic control and communications based on the CBTC system seemed to have been achieved according to the research of [PERSON], [PERSON], and [PERSON] [31]. Through the news reports [32], by 2019, London had fully automated subway operations based on the CBTC system. Although the running environment of underground mine transportation is similar to that of subway, the running of subway is mainly for the service of passengers, similar to Urban Rail Transit. Therefore, as the CBTC system can be used in the running of underground subway to some extent, with this precedent, it is believed that the CBTC system can be used in the intelligent underground mine after apposite improvements. In a nutshell, this concept paper briefly introduces the progress in the present stage of the research to apply the CBTC system, especially the three systems (ATO system, ATP system, and ATS system) to the underground transportation in order to solve the practical problems in the engineering of transportation in underground mines.
## 3 Proposed Method
### Path Planning  
After the total potential map is obtained, the path searching will start. Figure 8 shows the overall algorithm to obtain the feasible path, \(S\). However, the generated path will typically oscillate, especially during the planning of a straight corridor in an indoor-constrained space [19]. Consequently, a low pass filter is used to smoothen the path.  
Figure 8: Flowchart algorithm 1: Obtaining feasible trajectory.
# Robust Image Embedded Watermarking Using DCT and Listless SPIHT
## 4 Simulation Results and Analysis
### Robustness after Different Attacks  
Table 4 shows the quality of the extracted watermark after some conventional attacks such as cropping, addition of noise, sharpening, inversion, histogram equalization and auto contrast operations. It observed that the proposed method could able to extract a good quality watermark in all cases. Image inversion and auto contrast operation give better quality watermark with an NC value close to 1. Acceptable quality watermark is also obtained in cropping attack, masking face with a black box, cropping a quarter part of the image on any direction, noise and sharpen operations. Similar acceptable quality watermark can also be obtained by masking the image with a white box. The PSNR values indicate the extracted quality of the watermarked images. PSNR values of 35-36 dB indicate good quality watermarked images are also obtained in most of the attacks.  
Figure 7: PSNR vs. Compression ratio on (**a**) Mandrill; (**b**) Lena; (**c**) Boat images.  
A comparison of quality of the watermark (NC values) for different attacks is shown in Table 5. The comparison is made with recent CRT-based algorithms by [PERSON] et al. in [25; 26]. It can be seen that there is a significant improvement in the quality of the watermark in invert, sharpen, histogram equalization and cropping attacks using the proposed scheme. There is also a noticeable improvement in cropping, sharpening, invert, histogram equalization attacks compared to [PERSON] et al. [26]. Further, the proposed scheme shows some improvement for all the mentioned attacks compared to the scheme by [PERSON] et al. [25].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Attacks** & **Crop** & **Noise** & **Sharpen** & **Invert** & **Histogram** \\ \hline
**Attacked Images** & & & & & \\ \hline
**PSNR (dB)** & 35.8 & 35.9 & 37.9 & 35.9 & 34.9 \\ \hline
**Extracted Watermark** & & & & & \\ \hline
**NC** & 0.7158 & 0.7208 & 0.5034 & 0.9771 & 0.5415 \\ \hline
**Attacks** & Crop (top left) & Crop (top right) & Crop (bottom left) & Crop (bottom right) & Auto contrast \\ \hline
**Attacked Images** & & & & \\ \hline
**PSNR (dB)** & 35.8 & 35.8 & 35.84 & 35.86 & 35.82 \\ \hline
**Extracted Watermark** & & & & & \\ \hline
**ATIRGO** & & & & & \\ \hline
**NC** & 0.7316 & 0.7269 & 0.7411 & 0.7316 & 0.9838 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation results of common image processing attacks.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scheme** & **Crop** & **Noise** & **Sharpen** & **Invert** & **Histogram** & **Crop** \\ \hline [PERSON] et al. & & & & & & \\ \hline
**[PERSON] et al.** & & & & & & \\ \hline
**Net
## 3 Results and Discussion
### Swelling Behaviour  
Over a one-hour period, the swelling capacity (W) of the polymers was studied using the swelling experimental data explained earlier in Section 2.6.2. Figure 8 depicts the polymers inflating rapidly after 10 min, then decreasing as they approached equilibrium and reaching a plateau between 50 and 60 min. The polymers reached equilibrium at 0.48 and 1.25 g\(\cdot\)g\({}^{-1}\) NIP and MIP, respectively. The swelling might be caused by the N-group of 2-vinyl pyridine, which was employed in the polymerization. These findings suggest that water penetrates the polymer network, reducing the osmotic pressure differential between the solution and the polymer and thereby slowing water diffusion [33]. Furthermore, the swelling of the MIP allows water to infiltrate into cavities, increasing interaction with the target chemicals. The MIP's higher swelling capacity also implies the MIP is more prone to swelling in water than the NIP due to the specific imprints and the chemical nature of the imprints within the MIP, which allows greater interaction with the solvent. This is one of the desired outcomes of molecular imprinting since it gives the polymer selectivity and recognition capabilities.
# Automated Modal Analysis Using Stochastic Subspace Identification and Field Monitoring Data
## 4 Examples of Field Applications
### Description of First Dataset  
The first dataset collected from a seven-story RC frame at National Chung Hsing University in Taichung, Taiwan, is analyzed using the proposed procedure. The frame is approximately 94 m, 37.5 m, and 30.2 m in length, width, and height, respectively. It was built in 1992 and was accidentally damaged by the Chi-Chi earthquake in 1999. The reconnaissance report of the destructive earthquake, carried out by senior engineers, identified moderate damage. Fortunately, this RC frame is deployed by a SHM system, and the accelerometers measured all the structural responses before and after the strong excitation. The SHM system, installed on the RC frame, consisted of 29 accelerometers, which were distributed on the basement, first, third, and sixth floors, measuring responses in different directions. Figure 4a shows the side view and the locations of all sensors, marked with individual channel numbers. Hence, a simplified structural system, related to the limited location of sensors, can be constructed. A ten-year dataset from 1994 to 2005 was collected, and 16 sets of measurements were identified using the proposed procedure.
## 3 Results and Discussion
### Binding Efficacy of Natural Compounds against Selected Target Enzymes
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Legands** & **AChE** & **BACE1** & **TPK-I** & **CDK5** & **GSK-3\(\beta\)** & **15-LOX-2** & **M\_iNOS** & **H\_iNOS** & **COX-1** & **COX-2** \\ \hline Ligand\_1 & \(-9.1\) & \(-7.8\) & \(-6.9\) & \(-8.2\) & \(-6.4\) & \(-10.6\) & \(-8.6\) & \(-8.0\) & \(-8.4\) & \(-8.8\) \\ Ligand\_2 & \(-8.8\) & \(-9.3\) & \(-7.7\) & \(-7.4\) & \(-8.3\) & \(-9.2\) & \(-9.6\) & \(-8.8\) & \(-8.7\) & \(-8.7\) \\ Ligand\_3 & \(-9.4\) & \(-10.1\) & \(-8.2\) & \(-10.4\) & \(-8.7\) & \(-9.6\) & \(-11.4\) & \(-10.9\) & \(-9.5\) & \(-8.5\) \\ Ligand\_4 & \(-9.0\) & \(-7.7\) & \(-7.5\) & \(-8.2\) & \(-7.9\) & \(-9.8\) & \(-8.4\) & \(-8.8\) & \(-7.4\) & \(-6.8\) \\ Ligand\_5 & \(-8.6\) & \(-10.0\) & \(-9.9\) & \(-8.0\) & \(-8.6\) & \(-10.7\) & \(-8.5\) & \(-8.6\) & \(-8.1\) & \(-8.3\) \\ Ligand\_6 & \(-9.3\) & \(-7.8\) & \(-6.9\) & \(-7.5\) & \(-7.0\) & \(-10.4\) & \(-9.2\) & \(-9.4\) & \(-8.4\) & \(-8.0\) \\ Ligand\_7 & \(-9.1\) & \(-8.4\) & \(-7.9\) & \(-8.1\) & \(-8.1\) & \(-10.4\) & \(-10.2\) & \(-9.4\) & \(-8.8\) & \(-8.6\) \\ Ligand\_8 & \(-9.1\) & \(-9.0\) & \(-9.3\) & \(-7.5\) & \(-8.5\) & \(-10.8\) & \(-10.6\) & \(-9.2\) & \(-9.1\) & \(-8.7\) \\ Ligand\_9 & \(-8.7\) & \(-9.9\) & \(-8.5\) & \(-8.7\) & \(-8.8\) & \(-9.2\) & \(-9.7\) & \(-9.3\) & \(-8.9\) & \(-8.1\) \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & & & & & & & \\ \end{tabular}
\end{table} 
Table 2: Therapeutic efficacy of nine natural products against ten target enzymes based on protein-ligand binding or the molecular docking score (kcal/mol). study and the experimental data obtained with globimetulin B (**5**) and bangwavnsene B (**8**). By contrast, 3\(\beta\)-acetoxy-11,12-epoxytaraxerol, which had the highest inhibitory effect against AChE (IC\({}_{50}\) of 13.89 uM), showed a binding score of \(-\)8.7 kcal/mol compared to \(-\)9.1 kcal/mol for 7\(\beta\),15\(\alpha\)-dihydroxy-3\(\beta\)-palmitatelp-20(29)-ene, which was inactive up to 143.67 uM. This indicates the importance of combining an _in silico_ study with _in vitro_ experiments for an efficient selection of potential lead compounds against any disease. In the current drug discovery process, bioinformatics tools, mostly molecular docking simulation, are the most widely used platform for both academic and industrial researchers to select the best potential leads targeting any desired target enzymes associated with any disease development and proliferation [50; 53]. However, proper hypotheses and computational knowledge are required to minimize the error and produce reliable outputs.
# A High-Gain CNTFET-Based LNA Developed Using a Compact Design-Oriented Device Model
## 4 LNA Design  
The above model was applied to the design of an LNA. The architecture we chose is based on the widely-adopted cascode configuration since it can provide a good noise figure, modified by replacing the common-gate stage with a gm-boosting architecture. The schematic is shown in Figure 5.  
The gm-boosting effect is provided by the secondary amplifier composed of \(M_{2}\) and \(M_{3}\). Together, they actively provide the gate voltage to the common-gate stage \(M_{1}\) so  
Figure 5: LNA schematic.  
that its source is kept at a nearly constant voltage. This architecture indeed provides a much lower input impedance than a simple transistor, and that essentially cancels the Miller effect on the drain-gate capacitance of \(M_{5}\), enlarging the operational frequency of the amplifier.  
By simple circuit inspection, it is possible to compute that the resistance seen from the drain of \(M_{5}\) is indeed (neglecting the output conductance of \(M_{1}\)):  
\[R_{\text{GB}}\simeq\frac{1}{g_{\text{m1}}}\,\frac{1}{1+|A_{\text{v2}}|} \tag{10}\]  
where \(A_{\text{v2}}\) is the gain of the secondary amplifier.  
\[A_{\text{v2}}=\frac{-g_{\text{m2}}}{g_{\text{o2}}+g_{\text{o3}}}\simeq\frac{-1 }{2\,\lambda\,(V_{\text{G2}}-V_{\text{TH}})} \tag{11}\]  
so that the voltage gain of the first stage (\(M_{5}\)) is very low:  
\[A_{\text{v1}}=-g_{\text{m5}}\,R_{\text{GB}}\simeq-\frac{g_{\text{m5}}}{g_{ \text{m1}}}\,\frac{1}{1+|A_{\text{v2}}|} \tag{12}\]  
and \(|A_{\text{v1}}|\ll 1\) provided that \(g_{\text{m5}}\) and \(g_{\text{m1}}\) are comparable (as they should be) being the two transistors \(M_{5}\) and \(M_{1}\) biased with the same current. This way, the input capacitance of the amplifier reduces to:  
\[C_{\text{in}}=C_{\text{gs5}}+C_{\text{gd5}}\,(1-A_{\text{v1}})\simeq C_{\text {gs5}}+C_{\text{gd5}} \tag{13}\]  
instead of \(C_{\text{gs5}}+C_{\text{gd5}}(1+g_{\text{m5}}/g_{\text{m1}})\) we would have had without the gm-boosting stage.  
Back to the complete amplifier, its gain can be computed with reference to its small-signal equivalent circuit shown in Figure 6. There, for notational simplicity, we imply that \(g_{\text{o4}}\) also includes the output load conductance (and so will be much higher than the output conductances of the other transistors).  
The overall voltage gain is then:  
\[A_{\text{v}}=-\frac{g_{\text{m5}}\cdot[g_{\text{o1}}(g_{\text{o2}}+g_{\text{ o3}})+g_{\text{m1}}(g_{\text{m2}}+g_{\text{o2}}+g_{\text{o3}})]}{(g_{\text{o1}} \cdot g_{\text{o5}}+g_{\text{o4}}\cdot g_{\text{o5}}+g_{\text{o1}}\cdot g_{ \text{o4}}\cdot(g_{\text{o2}}+g_{\text{o3}})+g_{\text{m1}}\cdot g_{\text{o4}} \cdot(g_{\text{m2}}+g_{\text{o2}}+g_{\text{o3}})} \tag{14}\]  
Figure 6: LNA equivalent circuit.  
and, considering the output conductances to be negligible with respect to transductances, the voltage gain simplifies as follows:  
\[A_{\mathrm{v}}\simeq-\frac{g_{\mathrm{m5}}}{g_{\mathrm{o4}}} \tag{15}\]
## 3 Results and Discussion
### Microhardness  
Plastic deformation of the weld made by the FSW method under the influence of the burnishing tool can be considered a process of generating new dislocations and moving them in the crystallites. At obstacles (grain boundaries, inclusions, slip bands), dislocations stop or bend, forming loops or half-loops lying in one crystallographic plane. As a result, the surface layer of the weld is strengthened, which is manifested by the formation of compressive stresses in the surface layer and an increase in its microhardness. The analysis of Figure 11 shows that regardless of the assumed feed rate, the microhardness of the surface layer increases with the increase in the tool burnishing force, reaching the maximum value at \(P=50\) N. For the feed rate \(f=500\) mm/min, this corresponds to an increase in microhardness by 7.98% (to the value of \(H_{v}=405.15\) N/mm\({}^{2}\)), for the feed rate of \(f=1000\) mm/min 24.01% (to the value of \(H_{v}=387.4\) N/mm\({}^{2}\)) and for the feed rate of \(f=1500\) mm/min 9.11% (to the value of \(H_{v}=352\) N/mm\({}^{2}\)). The reason for the decrease in the microhardness value may be a decrease in the value of the maximum stress in the area under the burnishing tip due to the increase in the contact surface. The stress distribution under the burnishing tip is uneven, and with some combination of burnishing force and contact area, it may happen that the stress is lower. This is particularly visible in the case of stresses in the transverse direction \(\sigma_{x}\) for variants no. 7  
Based on the ANOVA analysis of variance, an adequate regression model of the surface layer hardness value (Figure 12) was developed at the significance level \(\alpha=0.05\) in the form (10):  
\[H_{V}=-1015.5-0.019833f+58.9167P-0.00075 fP-0.596667P^{2} \tag{10}\]  
Results of ANOVA analysis show that \(f\) and \(P\) are not statistically significant for \(H_{V}\) (\(p\)-value 0.359 and 0.106). Table 10 presents the R\({}^{2}\) value and significance analysis (\(p\)-value) for individual structural elements of regression models for \(H_{V}\). The R\({}^{2}\) value shows the high accuracy of the models. The results indicate that not all structural elements of the model are statistically significant. However, attempts to omit them again in the model resulted in a decrease in the quality of the model--a decrease in the value of R\({}^{2}\).
## 2 Derivation of Amplification Factors (_A_Fs)
### Database  
#### 2.4.1 Descriptions of Soil Profiles Studied  
In this study, a monolayer soil profile is characterized by one of six different shear wave velocity values, 100, 150, 200, 300, 400, and 600 m/s, which overcomes a semi-infinite bedrock. The latter, for its part, is characterized by a shear wave velocity equal to 750, 800, 900, 1000, 1200, or 1500 m/s. The depth of the soil layer varies from 5 to 10, 20, 30, 50, 75, 100, 150, and 200 m. In total, three hundred twenty-four (324) soil profiles are generated and considered for this study. Furthermore, all 324 soil profiles are considered in two sets of profiles for a total of 648 different soil conditions: 1--clay/cohesive soil; 2--sand/granular soil. Although certain rare exceptions may exist, the considered variants of simplistic soil profiles thus generated by the aforementioned combinations cover virtually all practical cases encountered in nature and in engineering practice.
## 3 Test Results
### Hardened Properties of RPC  
#### 3.2.1 Compressive Strength Development of RPC  
The compressive strength development of all of the RPC mixtures is presented in Figure 6. The compressive strength of RPC varies and depends on the combination of SF content, WB, and SP dosage to adjust flowability. In general, the compressive strength of the RPC mixture was increased with the increase in curing time. However, for mixtures 0.19 WB-23.54 SF, 0.22 WB-25 SF, and 0.22 WB-15 SF, the drop in the compressive strength value at the age of 56 days was observed. It should be noted that  
Figure 4: Setting time of key mixtures: (**a**) effect of changing WB for mixtures containing 20% SF; (**b**) effect of changing SF for the mixtures with WB = 0.22.  
Figure 5: Relationship between setting time and flowability.  
the permeable porosities of these 56-day samples were higher than those of the 28-day samples, as shown in Table 3. These results indicate that the number of parameters, including WB and SF content, influence the compressive strength development of RPC. The influence of these parameters together with the effect of curing condition will be addressed in the following sections.
# Evolution of Hybrid LiFi-WiFi Networks: A Survey
## 5 Handover and Load Balancing in HWLNets
### Summary  
Load balancing and handover are closely linked in any network system.  
Frequent handovers in HWLNets are due to the channel quality and resource availability in the LiFi part of the network and impose a significant problem concerning QoS. High channel quality does not equal high data rate account of resource engagement. For the handover policy, we must consider the time user spends on a single AP without being handed over to another or disconnected all together due to user movement and blockages.  
Optimization can be achieved via decision-making algorithms, taking into account that a user can be connected with multiple APs for the HHO process with an emphasis on QoS and user fairness. When HHO is no longer an option and the user would benefit more with a different network technology, VHO is employed and the user is switched.  
In HWLNets, user mobility is a major factor in channel variance, thus imposing load balancing. When discussing throughput, an arrangement has to be made to consider data rate and handover rate with regard to delay, jitter, packet loss, and others. This optimization problem can be solved in many ways but with high computational complexity. Another research approach to this problem is the use of decision-making methods that result in optimization degradation.  
To make load balancing effective in multi objective scenarios, we can employ intelligent control methods which rely on user speed and queue length data or we can use predefined parameters to compose a decision scheme. These methods must have low complexity and adaptiveness in order to work in real-life scenarios.
## 2 Materials and Methods
### Methodology Framework
#### 2.3.3 Network Architecture and Hyperparameter Setting  
The deep neural network in this study is constructed using the PyTorch [24] framework. To determine the optimal performance configuration for the TF-BiLSTM-attention model, experimental investigations are conducted with the assistance of NNI. The final parameter settings that yielded the best results are identified as follows: a hidden size of 11, a sequence length of 7, a batch size of 16, an epoch size of 20, a learning rate of 0.0001, and the adoption of the \"adam\" optimization function, a widely-used technique in deep neural networks. Moreover, to measure the model's predictive efficacy, the Mean Squared Error (MSE) loss function is employed, effectively guiding the training process towards attaining accurate predictions.
### Observational Data
#### 2.1.1 Remote Sensing Data
In addition to electromagnetic waves, acoustic remote sensing is another vital method for ocean data acquisition [45]. Acoustic remote sensing utilizes sound waves to investigate situations below the ocean surface, offering significant advantages over electromagnetic waves due to their efficient propagation through water. Common instruments in acoustic remote sensing include single-beam and multibeam echo sounders, side-scan sonar,and acoustic Doppler current profilers. These tools can generate high-resolution ocean floor maps, detect fisheries, and offer valuable insights into underwater environments [46; 47].