esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Empirical Studies: Industrial Cases
### Power-Window Switch Module
The AVM result was clearly better than that of FepTotal, and equal to or better than that of FepAddtl. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**(I) Method** & **(J) Method** & **Average difference (I \(-\) J)** & \(p\)**-Value** \\ \hline \multirow{4}{*}{Random} & StTotal & 0.004165355370 & \(<\)0.005 \\  & StAddtl & 0.003424223600 & \(\geq\)0.005 \\  & FepTotal & \(-\)0.069128709500 & \(<\)0.005 \\  & FepAddtl & \(-\)0.082282539710 & \(<\)0.005 \\  & AVM & \(-\)0.08233333370 & \(<\)0.005 \\ \hline \multirow{4}{*}{StTotal} & Random & \(-\)0.004165355370 & \(<\)0.005 \\  & StAddtl & \(-\)0.000741131770 & \(\geq\)0.005 \\  & FepTotal & \(-\)0.073294064870 & \(<\)0.005 \\  & FepAddtl & \(-\)0.086447895080 & \(<\)0.005 \\  & AVM & \(-\)0.086498688740 & \(<\)0.005 \\ \hline \multirow{4}{*}{StAddtl} & Random & \(-\)0.003424223600 & \(\geq\)0.005 \\  & StTotal & 0.000741131770 & \(\geq\)0.005 \\  & FepTotal & \(-\)0.072552933100 & \(<\)0.005 \\  & FepAddtl & \(-\)0.085706763310 & \(<\)0.005 \\  & AVM & \(-\)0.085757556970 & \(<\)0.005 \\ \hline \multirow{4}{*}{FepTotal} & Random & 0.069128709500 & \(<\)0.05 \\  & StTotal & 0.073294064870 & \(<\)0.005 \\  & StAddtl & 0.072552933100 & \(<\)0.005 \\  & FepAddtl & \(-\)0.013153830210 & \(<\)0.005 \\  & AVM & \(-\)0.013204623870 & \(<\)0.005 \\ \hline \multirow{4}{*}{FepAddtl} & Random & 0.082282539710 & \(<\)0.05 \\  & StTotal & 0.086447895080 & \(<\)0.005 \\  & StAddtl & 0.085706763310 & \(<\)0.005 \\  & FepTotal & 0.013153830210 & \(<\)0.005 \\  & AVM & \(-\)0.000050793660 & \(\geq\)0.005 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Scheffe post-hoc test results for the PWS Puddle Lamp. Figure 4: Test case prioritization results of the power-window switch module.
## 3 Data and Methods
### Color Fusion of Frequency Decomposition Data  
#### 3.4.1 Frequency Decomposition  
Spectral decomposition technology uses mathematical transformation to convert seismic signals from the time domain to the frequency domain. It characterizes sedimentary bodies in the frequency domain to avoid interference between frequencies in the time domain. This method allows the identification of sedimentary bodies with varying thicknesses [61; 62]. The spectral decomposition method consists of three steps: short-time Fourier transform, continuous wavelet transform, and generalized S transform. These steps ultimately yield two types of data bodies: tuning bodies and discrete frequency energy bodies. The discrete frequency volume represents time in the vertical direction and contains only a single frequency component, allowing for the study of vertical distribution characteristics of sedimentary bodies by analyzing the change in different frequency energies. The tuning data volume is generated through a short-time window discrete Fourier transform along the layer or between two layers, resulting in an amplitude data volume with continuous frequency change along the vertical direction. In comparison to conventional data volumes, more information can be obtained by analyzing the response of the sedimentary body at different frequencies on the tuning body [63; 64].  
Combined with the seismic data of the study area, three frequency bodies of 20 Hz, 45 Hz, and 70 Hz are selected based on the corresponding tuning thickness that can realize the description of the thickness envelope of the target sand body. The comparison of the original seismic profile and the 45 Hz frequency division profile are compared in Figure 4. The 45 Hz section illustrates that after 45 Hz frequency division processing, the corresponding relationship between the seismic event and the medium-thickness sand body is enhanced.
# A Balanced Symmetrical Branch-Line Microstrip Coupler for 5G Applications
## 2 Design of Resonator, Filters and Coupler
On the other hand, the upper and lower shunt stubs have higher current densities when simulating Port 2 and Port 3, respectively. Figure 3: The proposed coupler. Figure 2: Layout of the proposed LPFs with their frequency responses. To improve the performance, the frequency response of the proposed coupler was obtained as functions of \(\mathrm{l_{1}}\), \(\mathrm{l_{2}}\), \(\mathrm{w_{1}}\), \(\mathrm{w_{2}}\), \(\mathrm{w_{3}}\) and \(\mathrm{w_{4}}\), and the results are presented in Figures 5 and 6. As can be seen, by increasing \(\mathrm{l_{1}}\) and \(\mathrm{l_{2}}\), the operating frequency moves to the left which verifies Equation (8). However, increasing \(\mathrm{l_{1}}\) and \(\mathrm{l_{2}}\) can improve the common port return loss (\(\mathrm{S_{11}}\)) and isolation factor (\(\mathrm{S_{41}}\)), which is shown in Figure 6. Decreasing the width \(\mathrm{w_{1}}\) reduces the magnitude balance of \(\mathrm{S_{21}}\) and \(\mathrm{S_{31}}\) but improves the return loss and isolation. On the other hand, by tuning \(\mathrm{w_{3}}\), we can increase the magnitude balance. By changing \(\mathrm{w_{3}}\), the operating frequency changes slightly. But by decreasing \(\mathrm{w_{3}}\), the isolation will be increased. By increasing \(\mathrm{w_{4}}\), the insertion loss, coupling factor (\(\mathrm{S_{31}}\)) and isolation will be improved. However, the best value of return loss is obtained for \(\mathrm{w_{4}}\) = 0.4 mm. Figure 7 depicts the steps of our coupler design. A phase shift of 270\({}^{\circ}\) will be created according to the position of the output ports in the branch line layout as well as the overall structure of the designed coupler. Thus, by tuning the physical lengths and widths of our LPFs and by changing their positions in the branch-line structure we were able to obtain a phase shift of 270\({}^{\circ}\) between \(\mathrm{S_{31}}\) and \(\mathrm{S_{21}}\). For example, the relatively symmetrical branch line coupler with an even number of shunt stubs can create a 270\({}^{\circ}\) (or 90\({}^{\circ}\)) phase difference between the output ports. On the other hand, where the best isolation and return loss values are close to the operating frequency, the phase is balanced easily. Figure 4: Current density distribution of the proposed coupler at 5.2 GHz. Figure 5: S\({}_{21}\) and S\({}_{31}\) as functions of the significant lengths and widths. Figure 6: \(S_{11}\) and \(S_{41}\) as functions of the significant lengths and widths.
## 4 Evaluation of Performance and Accuracy of Different Implementations
### Results and Discussion
As with [PERSON]'s rule, the approaches using a desired accuracy lead to faster calculations compared to the 10-step approach. This influence is greater in the implementation in Fortran by a factor of 2.5 than in MATLAB (Factor 2). Using Figure 7b,c, the two approaches for the derivation can also be compared with regard to the computing time. Notably, in the case of the implementation in MATLAB,  
Figure 7: Calculation time of the various Newton termination criteria when using the extended notch root approximation according to [PERSON] (**a**), according to [PERSON] and [PERSON] using the analytical derivative (**b**) and according to [PERSON] and [PERSON] using the numerical approximation of the derivative (**c**). the approximate method for the derivative via the difference quotient leads to higher calculation times with the same termination criterion, while in Fortran, no significant influence is visible. One attempt at an explanation is based on Fortran's high degree of optimization. The recorded calculation times in Fortran are so short that other effects, such as background tasks in the operating system, have a greater influence than the actual calculations. Considering that the numerical derivative from Equation (26) is significantly easier to handle than the analytical derivation from Equations (24) and (25), it can be concluded that for the notch root approximations according to [PERSON] and [PERSON], the numerical derivative in combination with the termination criterion 2a or 2b is a practical choice. The deviations of the estimated stresses of all variants in comparison to each other are on the order of \(10^{-7}\) MPa. Deviations of this order of magnitude have a negligible influence on the calculated service life estimation. It can therefore be concluded that for both notch root approximations in question, termination criteria using a specific accuracy significantly reduces the number of necessary Newton iterations and improves the computational performance compared to the 10-step termination criterion proposed in the FKM Guideline nonlinear [25]. At the same time, the resulting stresses and strains are not significantly influenced.
## 2 Materials and Methods  
### Description of Case Study Location and WDN  
Noja is a seaside village in the north of Spain, in the Cantabria community (Figure 1). It is located 43 km east of Santander and 75 west of Bilbao. The total population is 2650 habitants [48]. However, mainly during the summer period, bank holidays and Easter,the population grows dramatically to up to 80,000 habitants. Indeed, Noja is identified as the Spanish town with the highest percentage, 91%, of second residences over the total of residences in the village [49].  
The population fluctuation between the summer period/bank holidays and the rest of the year generates big differences in the total amount of water supplied. Figure 2 depicts the total daily flow (TDF) delivered into the network from February 2021 to April 2022. During the months of July and August, the TDF reaches its maximum value, an average of 5080 m\({}^{3}\)/day with a maximum of 6110 m\({}^{3}\)/day during the Spanish long weekend in the middle of August 2021. On the other hand, the minimum TDF values occur during the winter months of January/February, with an average of 1262 m\({}^{3}\)/day. During the Easter period, the TDF also increases as a result of more people traveling to the area (week of April 2022). In Figure 2, the effect of Easter is evident in 2022 but less so in 2021. This is because, in 2022, the COVID pandemic restrictions had already been lifted.  
The water operator must deal with a very seasonal demand pattern, where the maximum demand is 539% higher than the minimum demand period. Figure 3 shows the hydraulic performance between a typical winter week of low consumption compared to a summer week of high demand. The pressure at the inlet of the PRV, \(P1\), is represented by the red line. The pressure at the outlet of the PRV, \(P2\), is the blue line. The pressure at the critical point, \(P3\), is represented by the green line and the _flow_ at the inlet of Noja is the black line.  
The situation described above with a very variable demand pattern creates a challenging scenario, which requires the employment of advanced pressure management to assure the right level of service to the customer, while keeping pressure as low as possible to reduce leakage rates and the appearance of new bursts. Since the demand is much higher during the summer period, the operator decides to keep a higher value of pressure, assuring the level of service. On other hand, during the winter, with lower demand, the pressure is much lower.  
Figure 1: Study area location.  
Noja town supply is fully gravitational from a distribution tank. The length of the WDN amounts to a total of 48.33 km. The main pipe materials are ductile iron (DI) and polyethylene (PE), which represent around 90% of the total length. The remaining 10% consists of asbestos (AB) and polyvinyl chloride (PVC) pipes. Large sizes, from DN150 to DN300, are made up of metal material. For sizes below DN150, plastic materials are used.  
Figure 3: Hydraulic performance of WDN. Left: low-consumption winter week; Right: high-consumption summer week.  
Figure 2: TDF from February 2021 to March 2022 in Noja village.
## 3 Results
### Experimental Results
In the dataset derived from online sources, the top-1/top-3 performance improves to 0.83/0.92, showing a 6-point increase in top-1 accuracy. The transformation is even more pronounced in the clinical vignette-based dataset. Here, the accuracy at three iterations reaches 0.72 for top-1 and 0.81 for top-3, reflecting an impressive, nearly 20 percentage point enhancement in top-1 accuracy, even surpassing the performance of professional psychologists. The accuracy trended upward with additional iterations of confirmation but tends to plateau after eight iterations. This pattern suggests that while iterative confirmation significantly enhances diagnostic accuracy initially, there is a threshold beyond which further iterations yield diminishing returns. Interestingly, while the two approaches are similar, it appears more beneficial to distribute the questioning across more iterations rather than asking more questions in each iteration. For example, when the model performs five iterations, each querying four symptoms, it collects a total of 20 symptoms and achieves a top-1 accuracy of 0.75. In contrast, three iterations that request eight symptoms each gather 24 symptoms but yield a slightly lower performance of 0.73. Despite the smaller amount of evidence, the performance is better in the first scenario, suggesting that distributing the questions across more iterations improves the diagnostic refinement capacities of the model, demonstrating the reasoning abilities that our proposed model shows. Figure 5: Improvement over iterations and for different number of questions asked per iteration for the vignettes dataset and the validation set.
# A Predictive Model to Correlate Amino Acids and Aromatic Compounds in Calabrian Honeys
## 2 Materials and Methods
### HS-SPME-GC-MS Analysis  
The composition of the headspace was examined using gas chromatography mass spectrometry (GC-MS) with a QP2010 Ultra instrument from Shimadzu, Italy. Volatile compounds were separated using a capillary column MEGA SE52 (5% phenyl, 95% methyl polysiloxane), with dimensions of 30 m \(\times\) 0.25 mm i.d. and a film thickness of 0.25 \(\upmu\)m. The temperature of the oven was initially maintained at 60 \({}^{\circ}\)C for 1 min, followed by a gradual increase at a rate of 3 \({}^{\circ}\)C/min to reach 180 \({}^{\circ}\)C. Subsequently, the temperature was ramped up at a rate of 10 \({}^{\circ}\)C/min to achieve 220 \({}^{\circ}\)C and held for 10 min. The total runtime for the analysis was set at 55 min. Helium gas (purity \(>\) 99.999%) was employed as the carrier gas. The injection temperature was set at 250 \({}^{\circ}\)C, and the injection mode was set to split with a split ratio of 50. Mass scan spectra were recorded in the range of 35-500 amu using the electron ionization (El) source at 70 eV. The MS ion source and interface temperature were both maintained at 250 \({}^{\circ}\)C, and a solvent cut time of 0.5 min was selected. The control of equipment and data acquisition was managed through the GC-MS SOLUTION software. The identification of volatile compounds was conducted on a tentative basis. This involved comparing the mass spectra of unknown peaks with those stored in databases such as the National Institute of Standards and Technology (NIST Vs. 2011) library, with a similarity threshold of over 75%. Additional resources like WILEY5 and a specialized library for the analysis of essential oils were also utilized. For each peak, the retention index was determined and compared with values reported in the literature. The relative concentration of each isolated compound was calculated as a normalized % abundance, which provides a measurement of the relative ratios of components within each sample.
# Algorithm for Determining the Position of a Ship Hull-Cleaning Robot
## 4 Methods
### Kalman Filter  
The Kalman filter is a method developed by [PERSON] and [PERSON] in the 1960s to estimate the value of an observed variable based on its previous values and assuming that they may be subject to error [25]. The model consists of three matrices (input, state, and output), as well as two quantities denoting process and measurement noise, respectively.  
In the case of discrete process modeling, the use of the Kalman filter consists of two phases: the first involves taking into account the passage of time (making a prediction of the forecasted variable as well as updating the covariance between the forecasted and actual values), while the second concerns updating the values of the remaining matrices constituting the model.  
Due to its smoothing and auto-tuning properties, the Kalman filter is used in many fields related to measurements, including in issues related to determining the position of devices in space or on a plane [26, 27, 28].
# Systematic Review: Anti-Forensic Computer Techniques
## 4 Results
### Exposition of Anti-Forensic Techniques
#### 4.2.2 Exposition of Anti-Forensic Techniques: Countermeasures  
Some authors have focused on explaining countermeasures to mitigate the effects of anti-forensics in a more general scope, such as[PERSON] et al. [203; 204; 205; 16; 205]. Others, however, detail, in depth, the techniques used to mitigate specific attacks.  
Figure 13: Exposition of anti-forensic techniques in number of articles.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Exposition of Anti-Forensic Techniques** & **Number of Studies** & **References** \\ \hline Classification and general review & 14 & [1,8; 10; 11; 15; 82; 195; 202; 196; 201; 202] \\ \hline Countermeasures & 7 & [16; 203; 204; 205; 206; 207; 208] \\ \hline Explanation of specific anti-forensic tools & 1 & [209] \\ \hline Detection of traces left by anti-forensic tools & 7 & [2; 14; 201; 202; 203; 204; 205; 206; 207; 208; 209; 210; 211; 212; 213; 214] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Studies classified by category in Section 4.2 (exposition of anti-forensic techniques).  
[PERSON] et al. [206] focus on avoiding information being hidden in an NTFS file system and elaborate guidelines to prevent it.  
[PERSON] et al. [207] explore anti-forensic agents in databases, present a taxonomy of the impact they cause, and focus their study on providing a list of mitigation recommendations.  
[PERSON] et al. [208] use reverse engineering on the \"Steg\" software (steganography software using the IDA tool) to propose countermeasures to detect and defeat this type of software.  
Further articles explore different aspects of the anti-forensic field, including the definition and classification of anti-forensic techniques [204], their impact on the investigation of cybercrime [16], their application in blogs to maintain anonymity [205], methods by which to hide data in NTFS partitions [206], the analysis of anti-forensic techniques in databases [207], and the reverse engineering of steganography tools [208], all of them focusing on the proposal of countermeasures to solve these problems.  
Among the contributions of these studies are the definition and classification of anti-forensic techniques [204], the proposal of countermeasures for law enforcement [16], the analysis of anti-forensic techniques in blogs to maintain anonymity [205], methods by which to hide data in NTFS partitions [206], the analysis of techniques with which to prevent anti-forensic techniques in databases [207], and countermeasures for \"Steg\" software (steganography software using the IDA tool) [208].
## 3 Results

Table 5: Results for medium Gaussian SVM. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline  & \multicolumn{6}{c}{**TRAINING SET**} \\ \hline  & \multicolumn{2}{c}{**ACCURACY**} & \multicolumn{2}{c}{**SENSITIVITY**} & \multicolumn{2}{c}{**SPECIFICITY**} & \multicolumn{2}{c}{**PRECISION**} \\
**LESION** & \multicolumn{2}{c}{**MEAN**} & \(\pm\)**1 SD** & \multicolumn{2}{c}{**MEAN**} & \(\pm\)**1 SD** & \multicolumn{2}{c}{**MEAN**} & \(\pm\)**1 SD** & \multicolumn{2}{c}{**MEAN**} & \(\pm\)**1 SD** \\ \hline BCC & 0.97899 & 0.002878 & 0.946323 & 0.014022 & 0.982366 & 0.003188 & 0.884054 & 0.017584 \\ SCC & 0.975928 & 0.003423 & 0.906904 & 0.021586 & 0.985793 & 0.00251 & 0.901248 & 0.016761 \\ MEL & 0.965308 & 0.004586 & 0.879849 & 0.023875 & 0.977466 & 0.003912 & 0.847469 & 0.023878 \\ AK & 0.97474 & 0.003065 & 0.891863 & 0.016402 & 0.986582 & 0.002767 & 0.904245 & 0.020062 \\ BKL & 0.971558 & 0.00339 & 0.897429 & 0.017104 & 0.982317 & 0.002985 & 0.879933 & 0.018989 \\ DF & 0.977219 & 0.002996 & 0.877022 & 0.024321 & 0.991761 & 0.002146 & 0.939128 & 0.015253 \\ NV & 0.960462 & 0.004176 & 0.85456 & 0.025912 & 0.975699 & 0.004218 & 0.83514 & 0.02571 \\ VASC & 0.988055 & 0.00234 & 0.912879 & 0.018859 & 0.998694 & 0.000701 & 0.990039 & 0.005171 \\ MEAN \(\pm\) 1 SD & 0.973896 & 0.008384 & 0.895854 & 0.027499 & 0.985085 & 0.0075 & 0.897657 & 0.049623 \\ \hline \end{tabular}
\end{table} 
Table 8: Results for cosine k-NN. \begin{table}
\begin{tabular}{l c c c c c c c} \hline  & \multicolumn{6}{c}{**TRAINING SET**} \\ \hline  & \multicolumn{2}{c}{**ACCURACY**} & \multicolumn{2}{c}{**SENSITIVITY**} & \multicolumn{2}{c}{**SPECIFICITY**} & \multicolumn{2}{c}{**PRECISION**} \\
**LESION** & \multicolumn{2}{c}{**MEAN**} & \(\pm\)**1 SD** & \multicolumn{2}{c}{**MEAN**} & \(\pm\)**1 SD** & \multicolumn{2}{c}{**MEAN**} & \(\pm\)**1 SD** \\ \hline BCC & 0.97899 & 0.002878 & 0.946323 & 0.014022 & 0.982366 & 0.003188 & 0.884054 & 0.017584 \\ SCC & 0.975928 & 0.003423 & 0.906904 & 0.021586 & 0.985793 & 0.00251 & 0.901248 & 0.016761 \\ MEL & 0.965308 & 0.004586 & 0.879849 & 0.023875 & 0.977466 & 0.003912 & 0.847469 & 0.023878 \\ AK & 0.97474 & 0.003065 & 0.891863 & 0.016402 & 0.986582 & 0.002767 & 0.904245 & 0.020062 \\ BKL & 0.971558 & 0.00339 & 0.897429 & 0.017104 & 0.982317 & 0.002985 & 0.879933 & 0.018989 \\ DF & 0.977219 & 0.002996 & 0.877022 & 0.024321 & 0.991761 & 0.002146 & 0.939128 & 0.015253 \\ NV & 0.960462 & 0.004176 & 0.85456 & 0.025912 & 0.975699 & 0.004218 & 0.83514 & 0.02571 \\ VASC & 0.988055 & 0.00234 & 0.912879 & 0.018859 & 0.998694 & 0.000701 & 0.990039 & 0.005171 \\ MEAN \(\pm\) 1 SD & 0.973896 & 0.008384 & 0.895854 & 0.027499 & 0.985085 & 0.0075 & 0.897657 & 0.049623 \\ \hline \end{tabular}
\end{table} 
Table 7: Results for medium Gaussian k-NN.
# Thermoelastic Investigation of Carbon-Fiber-Reinforced Composites Using a Drop-Weight Impact Test
## 1 Introduction
### Thermoelastic and Thermoplastic Effects  
The thermoelastic effect is a small reversible change in the temperature of an object when it undergoes elastic deformation. The thermoelastic effect depends upon the type of strain, which can be elastic or non-elastic. As described by [PERSON] in 1853 [13], a purely elastic tensile strain in an adiabatic configuration generates a thermoelastic effect that results in the cooling of isotropic specimens with positive thermal expansion coefficients. However, if inelastic deformation is also present, then the thermoplastic phenomenon occurs, resulting in heating due to the dissipation of the applied mechanical energy; this will normally overcome the thermoelastic contribution and result in a net specimen temperature increase. Mechanical deformation causes temperature changes, but they go unnoticed if the loading is slow, resulting in an isothermal process [14]. At the other extreme, under high-speed loading conditions, considerable viscous heating can occur, possibly leading to localized melting [15].  
The thermoelastic response is produced by dilational strain. Tensile stress produces a drop in temperature, whereas compressive stress results in a temperature increase. [PERSON] [16] developed the theory of thermoelastic diffusion. In this theory, the coupled thermoelastic model that is used implies infinite speeds for the propagation of thermoelastic waves. The overall effects arising from the coupling of fields of temperature, mass diffusion, and strain in an elastic cylinder have been discussed by [PERSON] and [PERSON] [17]. [PERSON] et al. [18; 19; 20; 21] introduced the theory of generalized thermoelastic diffusion, which predicts finite speeds of propagation for thermoelastic and diffusive waves. The present study was motivated by the importance of studying the thermoelastic effect in CFRP.  
When composites are subjected to dynamic loading under impact events, the zero or near-zero plasticity of these structures past their yielding points results in a susceptibility to delamination, matrix cracking, and fiber breakage [22].