esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Multi-Agent-Based Approach for Train Rescheduling in Large-Scale Railway Networks
## 2 Literature Review  
Train operations are subject to a variety of disturbances, which can lead to delays of varying severity. Real-time decisions must be made based on the movement status of trains within the network, all within specific time horizons. In the context of the ICONIS decision support system for railway traffic management, developed by Alstom in 2013, train delays are categorized into three levels: small delays (within 15 min), medium delays (between 15 and 30 min), and large delays (up to 60 min). The computational time horizons for resolving these delays are set at 2 s, 5 s, and 10 s, respectively [4]. Despite advancements, generating an optimized solution within these brief timeframes remains a significant challenge for modern MILP techniques, although Alstom's computational criteria are not universal standards.  
To enhance the performance of decision support systems, numerous studies have introduced innovations in railway rescheduling, with approaches varying in terms of their modeling strategies and search algorithms. In this section, building on previous review papers [5,6], we briefly outline the state-of-the-art research in railway traffic management. Additionally, to connect with the approach proposed in this paper, recent applications of the MAS are also discussed.
## 4 Results and Discussion
### Post-adsorption Studies  
#### 4.5.1 FTIR Results  
Since all the adsorbents showed similar FTIR patterns after adsorption, we recorded the spectra for CPW only. To monitor the changes that occurred, the FTIR spectra for CPW before and after adsorption were compared (Figure 8). The changes observed after adsorption confirm the roles of the functional groups in the uptake of Cr(VI) and MB. It was observed that groups such as -C=C, -CO, and -CaO decreased significantly after adsorption, and their positions changed slightly. This confirms their role in the uptake.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{3}{*}{**Adsorbents**} & \multirow{3}{*}{**Temperature (K)**} & \multicolumn{6}{c}{**Thermodynamic Parameters**} \\ \cline{3-8}  & & \multicolumn{2}{c}{**\(\Delta\)H\({}^{\circ}\) (KJ/mol)**} & \multicolumn{2}{c}{**\(\Delta\)S\({}^{\circ}\) (KJ/mol)**} & \multicolumn{2}{c}{**\(\Delta\)G\({}^{\circ}\) (KJ/mol)**} \\ \cline{3-8}  & & **Cr(VI)** & **MB** & **Cr(VI)** & **MB** & **Cr(VI)** & **MB** \\ \hline CPW & 298 & 45.01 & 39.74 & 0.5530 & 0.4831 & \(-\)82.76 & \(-\)75.98 \\  & 303 & & & & & \(-\)80.34 & \(-\)75.06 \\  & 308 & & & & & \(-\)79.11 & \(-\)74.87 \\ \hline APWH & 298 & 41.15 & 36.63 & 0.5404 & 0.4766 & \(-\)55.37 & \(-\)53.75 \\  & 303 & & & & & \(-\)54.30 & \(-\)51.09 \\  & 308 & & & & & \(-\)53.01 & \(-\)49.04 \\ \hline APWN & 298 & 34.07 & 29.44 & 0.5058 & 0.4228 & \(-\)46.80 & \(-\)42.98 \\  & 303 & & & & & \(-\)44.71 & \(-\)42.07 \\  & 308 & & & & & \(-\)43.04 & \(-\)41.15 \\ \hline APWP & 298 & 36.63 & 30.38 & 0.5109 & 0.4317 & \(-\)51.36 & \(-\)45.66 \\  & 303 & & & & & \(-\)50.49 & \(-\)44.37 \\  & 308 & & & & & \(-\)48.80 & \(-\)43.31 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Thermodynamic studies for the removal of Cr(VI) and MB.  
Figure 8: FTIR spectra for CPW before and after adsorption.
## 2 Results
### Bile Salt Hydrolase (Bsh) Levels Correlated with Significantly Enriched Species  
Many Lactobacillus species deconjugate primary conjugated bile acids mediated by bile salt hydrolases (BSH), imparting bile acid tolerance [38]. To further investigate the effect of TCDD on bile acid metabolism, _bsh_ sequences were annotated and quantified within metagenomic samples. Annotations to _bsh_ were increased by TCDD and associated with enriched species, including _L. reuteri_ and _T. sanguinis_ (Figures 1C and 2A, and Table S1).  
Figure 2: TCDD enriched Lactobacillus species possessing bile salt hydrolase (_bsh_). The presence of _bsh_ gene sequences were assessed in metagenomic caecum samples from male C57 BL/6 mice following oral gavage with sesame oil vehicle or 0.3, 3, or 30 \(\mathrm{\SIUnitSymbolMicro g}\)/kg TCDD every 4 days for 28 days using three independent cohorts (\(n=3\)). **(A)** The presence (green boxes) or absence of _bsh_ sequences detected in any of the metagenomic samples (\(n=3\)) are denoted within the respective treatment groups. Significant increases (*) or decreases (@) in normalized _bsh_ abundances (adj. \(p<0.1\)) are denoted. Also denoted is significantly increased species (#) determined by taxonomic analysis that  
corresponded with respective RefSeq species _bsh_ annotations. Significance was determined by Maaslin2 R package. (**B**) Volcano plot displaying log2(fold-changes) in relative abundance of species between vehicle and 30 \(\upmu\)g/kg TCDD treatment groups versus -log(adjusted _p_-values [adj. _p_]). Red dots denotes _bsh_ sequences detected in 30 \(\upmu\)g/kg TCDD treatment group. Significance was determined by the DeSeq2 R package comparing only vehicle and 30 \(\upmu\)g/kg TCDD groups. Red dashed lines are reference to \(-\)log(0.05) value for the _y_-axis and \(-\)1 and 1 for the _x_-axis.  
Conversely, _L. murinus_ associated _bsh_ annotations exhibited a dose-dependent decrease consistent with decreasing trends in taxonomic abundance. Although not reaching significance, many _bsh_ sequences were also associated with unclassified Lachospiraceae species, including _Lachospiraceae bacterium A4_, a community member reaching 5-23% relative abundance in the cecum metagenomic samples (Figure 2A). In contrast, _Lactobacillus gasseri_ was enriched but no _bsh_ sequences were identified (Figure 2B). To summarize, the top enriched species were also associated with increased abundances in _bsh_ levels in the cecum.
## 2 Materials and Methods
### Chemistry
: 180 \({}^{\circ}\)C; yield: 87%; for C\({}_{15}\)H\({}_{13}\)N\({}_{3}\)O\({}_{4}\) (299.28) calculated: C: 60.20%, H: 4.38%, N: 14.04%; found: C: 60.25%, H: 4.39%, N: 14.08%. IR (cm\({}^{-1}\)): 3580 (OH), 3284 (NH), 3056 (CH, arom. ), 2943 (CH, aliph. ), 1661 (C=O), 1622 (C=N); \({}^{\prime}\)H NMR (600 MHz, DMSO-\(d_{6}\)): 3.54 (s, 2H, CH\({}_{2}\)), 7.18-7.27 (m, 2H, ArH), 7.29-7.34 (m, 3H, ArH), 7.87-7.92 (m, 1H, ArH), 7.96 (s, 1H, ArH), 8.15-8.17 (m, 1H, ArH), 8.18 (s, 1H, =CH), 11.42 (s, 1H, OH), 11.66 (s, 1H, NH); \({}^{13}\)C NMR (150 MHz, DMSO-\(d_{6}\)): 41.64 (CH\({}_{2}\)), 120.13, 124.13, 126.84, 128.69, 129.87, 132.92, 136.21, 141.13 (10\(\mathrm{C}\mathrm{a}\mathrm{r}\)), 144.94 (=CH), 153.43, 153.72 (2\(\mathrm{C}\mathrm{a}\mathrm{r}\)), 167.02 (C=O). N\({}^{\prime}\)-[(E)-(2-hydroxy-3,5-dinitrophenyl)methylidene]-2-phenylacetohydrazide **(15)**  
Color: yellow; M.p. : 232-234 \({}^{\circ}\)C; yield: 79%; for C\({}_{15}\)H\({}_{12}\)N\({}_{4}\)O\({}_{6}\) (344.28) calculated: C: 52.33%, H: 3.51%, N: 16.27%; found: C: 52.50%, H: 3.50%, N: 16.31%. IR (cm\({}^{-1}\)): 3487 (OH), 3162 (NH), 3084 (CH, arom. ), 2993 (CH, aliph. ), 1659 (C=O), 1615 (C=N); \({}^{\prime}\)H NMR (600 MHz, DMSO-\(d_{6}\)): 3.64 (s, 2H, CH\({}_{2}\)), 7.22-7.29 (m, 2H, ArH), 7.32-7.36 (m, 3H, ArH), 8.60 (s, 1H, =CH), 8.67-8.69 (m, 1H, ArH), 8.75-8.79 (m, 1H, ArH), 11.64 (s, 1H, OH), 12.39 (s, 1H, NH); \({}^{13}\)C NMR (150 MHz, DMSO-\(d_{6}\)): 41.22 (CH\({}_{2}\)), 122.83, 123.35, 125.13, 127.26, 128.69, 129.62, 129.85, 135.49, 137.79 (11\(\mathrm{C}\mathrm{a}\mathrm{r}\)), 144.95 (=CH), 158.35 (\(\mathrm{C}\mathrm{a}\mathrm{r}\)), 167.49 (C=O). 2-phenyl-\(N^{\prime}\)-[(E)-(2,3,4-trihydroxyphenyl)methylidene]acetohydrazide **(16)**  
CAS Number: 1935707-89-3; Color: white; M.p. : 222-224 \({}^{\circ}\)C; yield: 52%; for C\({}_{15}\)H\({}_{14}\)N\({}_{2}\)O\({}_{4}\) (286.28) calculated: C: 62.93%, H: 4.93%, N: 9.79%; found: C: 62.85%, H: 4.95%, N: 9.78%. \({}^{1}\)H NMR (600 MHz, DMSO-\(d_{6}\)): 3.55 (s, 2H, CH\({}_{2}\)), 6.37 (d, 1H, ArH, \(J\) = 12 Hz), 6.77 (d, 1H, ArH, \(J\) = 6 Hz), 7.22-7.35 (m, 5H, ArH), 8.24 (s, 1H, =CH), 8.48 (s, 1H, OH), 9.46 (s, 1H, OH), 11.32 (s, 1H, OH), 11.70 (s, 1H, NH); \({}^{13}\)C NMR (150 MHz, DMSO-\(d_{6}\)): 41.37 (CH\({}_{2}\)), 108.06, 111.18, 121.50, 127.10, 128.82, 129.58, 133.12, 136.02 (10\(\mathrm{C}\mathrm{a}\mathrm{r}\)), 147.82 (=CH), 149.13, 149.43 (2\(\mathrm{C}\mathrm{a}\mathrm{r}\)), 166.43 (C=O).
# Research on Inertial Force Attenuation Structure and Semi-Active Control of Regenerative Suspension
## 5 The Verification of HVRS Suspension Characteristics
### Quarter-Vehicle Test Setup  
The structure of the suspension vibration test bench is shown in Figure 11.  
The road excitation test bench selects and controls the signals of the road excitation system, and its hydraulic actuator is located below the simulated wheel and generates simulated random road roughness, causing the suspension system to vibrate.  
The quarter-vehicle suspension includes sprung mass, unsprung mass, suspension springs, a shock absorber, and a simulated wheel.  
The displacement sensors, acceleration sensor, and force sensor arranged in the test bench collect state information during the vibration process of the suspension system and transmit it to the suspension control system. One of the displacement sensors is arranged between the unsprung mass and the sprung mass and is used to measure the dynamic deflection of the suspension. Another displacement sensor is arranged between the sprung mass and the test bench to measure the displacement of the sprung mass. The velocity signal of the sprung mass can be obtained by differentiating this signal. The accelerometer is arranged on the sprung mass to measure the acceleration of the sprung mass. The force sensor is arranged under the wheel to measure the dynamic load of the wheel during the test process.  
The suspension controller calculates and processes the collected suspension status information based on the control strategy set by the upper computer, sends out PWM control signals to adjust the value of the dissipation resistance, and achieves semi-active control of the suspension.
# Detection of HTTP DDoS Attacks Using NFStream and TensorFlow
## 3 Methodology
### Dataset Creation  
This section deals with the design and implementation of machine learning models aimed at detecting HTTP DDoS attacks against network infrastructures. This includes the design of an LSTM RNN algorithm based on six correlated and cooperating layers. Using the nfstream [79] and TensorFlow APIs, the measured network parameters can be analyzed from the PCAP input files, where a netflow matrix is generated and subsequently analyzed. Based on the various parameters, it is possible to define the input data size, and the number of epochs, which affects the speed at which a given model can be trained using the experimental input data [80, 81].
## 3 Results and Discussion
### Algorithm Comparison  
The four algorithms all use the corresponding sample dataset after entropy weight feature fusion as the training input, and the experimental results are shown in Table 3. As can be seen, the recognition accuracy values of the optimized SVM algorithms are higher than the conventional SVM algorithm. The result of the traditional SVM is 89.16%, and  
Figure 12: Comparison of data fusion function: (**a**) detection results without data fusion; (**b**) detection results with data fusion.  
after optimization by GS and GA, the accuracy rates are roughly improved to 93.32% and 93.25%. Finally, the FA-ISSA-SVM algorithm adopted performs the best, with an accuracy of 96.21%. The corresponding penalty factor \(C\) is 46.25, and the kernel parameter \(\gamma\) is 8.19.  
In terms of working efficiency, the shortest iteration time of the SVM is 3.56 s. For the optimized SVM mechanisms, the shortest duration is 8.23 s for the FA-ISSA-SVM and is nearly half of the time of the GA-SVM and GS-SVM. So, the proposed method could effectively gain the highest recognition accuracy with a satisfying time cost.
## 2 Review of Cryogenic-Cooling Benefits
### Thermo-Optic Coefficient Measurements  
As with the thermal expansion data of the previous section of this paper, in this section we include only data where nearly complete data sets are available in most of the temperature range of 80-300 K, and ignore single temperature point data. Unlike our previous discussion of the thermal conductivity and thermal expansion coefficient, the theoretical foundation for the thermo-optic coefficient is not nearly as developed, and we refer the reader to previous publications [2; 3; 29] for more background information. In this section, we show in Figures 28-32 the best data sets we were able to find in the literature, mostly generated at a wavelength of 1064 nm. Other data is available at 632.8 nm for example, and where 1064 nm data are unavailable, we will present that data here.  
Figure 28 shows thermo-optic coefficient data for the oxide laser materials YAG, LuAG, and YALO, along their principal axes [29]. In Figure 29 we show the very interesting data for the thermo-optic coefficients of the fluoride laser materials YLF and LuLF [29]. The thermo-optic coefficients for both materials are negative across the entire temperature range of interest, for YLF in a monotonically decreasing way as temperature trends towards zero, and in a much more complicated way for LuLF. The negative value of \(\mathrm{d}n/\mathrm{d}T\) for YLF is often used to minimize thermal aberrations near room temperature by partially compensating for the positive thermal expansion coefficient. This method is obviously still helpful but of less utility as temperature is lowered for cryogenic laser operation. Figure 30 shows the thermo-optic coefficients for YVO\({}_{4}\) and GdVO\({}_{4}\)[3; 30] along their principal axes, all of which remain positive in the temperature range of interest. The YSO \(b\)-axis remains positive as well [30], as shown in Figure 31, but for CaF\({}_{2}\) the thermo-optic coefficient is negative for all temperatures, for both doped and undoped cases. The data for CaF\({}_{2}\) were obtained for a wavelength of 632.8 nm [59], but are included here due to the lack of similar data near 1020 nm. For the sesquioxides, data are available only for Y\({}_{2}\)O\({}_{3}\) and Sc\({}_{2}\)O\({}_{3}\)[3; 30] and shown in Figure 32. While single-point data near 1030 nm and 300 K and data collected at 632.8 nm are available over the temperature range of interest [59], we have not included that data here.  
Figure 28: Thermo-optic coefficient as a function of absolute temperature for YAG (green) [29], LuAG (red) [29], and the three principal axes, \(a\) (blue), \(b\) (black), and \(c\) (pink) of YALO [29], measured at the wavelength of 1064 nm.  
Figure 30: Thermo-optic coefficient as a function of absolute temperature for YYO\({}_{4}\)_a_ (red) and \(c\) (Blue) axes [30] and GdVO\({}_{4}\)_a_ (pink) and \(c\) (green) axes [3], measured at a wavelength of 1064 nm.  
Figure 29: Thermo-optic coefficient as a function of absolute temperature for YLF \(a\) (blue) and \(c\) (red) axes [29] and LuLF \(a\) (green) and \(c\) (pink) axes [29], measured at a wavelength of 1064 nm.
# Lifetime Prediction of Single Crystal Nickel-Based Superalloys
## 2 Experimental Procedure and Methodology
### Lifetime Assessment
#### 2.3.5 Post-Scripting of Modified Chaboche Model  
The stress history at each integration point was obtained from the output database (ODB) of the single crystal plasticity model using Abaqus post-scripting Python 2.7 modules. The process utilized the odbAccess module for accessing ODB data, the abaqusConstants module for specifying data positions such as integration points, and visualization for analyzing the extracted data. Stress tensor components (S, SDV) at all integration points and nodal displacement values (U) were extracted for each frame to obtain the cyclic loading history and nodal positions throughout the analysis.  
Based on the stress histories, the peak-valley algorithm was used to determine the maximum and minimum stress values for each cycle. These values were then used to compute the mean and amplitude stresses, which are required inputs for the modified Chaboche model. By applying the multiaxial anisotropic lifetime assessment theory described in Section 2.3.2, the lifetime at each node is estimated. Lifetime values and the mesh information of the FEA model are stored in the visualization toolkit data format (.vtk) to obtain lifetime contours via Paraview, which is an open-source post-processing visualization engine.
# Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images
## 3 Results
### Efficiency Analysis  
We evaluated the efficiency of our proposed method, EGHNet, by comparing it with several existing methods using parameters (Params.) and floating-point operations per second (FLOPs). All methods used an input size of 256 \(\times\) 256 \(\times\) 3 for consistency.  
As shown in Table 5, FC-EF and FC-Siam-Conc have the lowest Params and FLOPs but also the lowest F1 scores, indicating lower accuracy. FCN-PP and Unet++MSOF have moderate Params and FLOPs with improved F1 scores, yet they still do not achieve the highest performance levels.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \(\lambda\) & **PRE (\%)** & **REC (\%)** & **F1 (\%)** & **OA (\%)** \\ \hline
0 & 92.38 & 84.26 & 88.06 & 98.89 \\
1 & 90.69 & 91.45 & 90.65 & 98.63 \\
5 & 93.08 & 89.65 & 91.14 & 98.74 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The weight balance coefficient affects the results of the ablation experiments on the WHU dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **PRE (\%)** & **REC (\%)** & **F1 (\%)** & **OA (\%)** \\ \hline EGHNet\_backbone & 92.17 & 82.77 & 87.07 & 98.79 \\ EGHNet & 93.08 & 89.65 & 91.14 & 98.74 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of ablation experiments for edge detection assisted change detection on WHU dataset.  
BIT-CD maintains a balance between computational efficiency and performance, with a low parameter count and FLOPs, achieving a high F1 score of 89.35%. ChangeFormer, despite its high computational demands, achieves a high F1 score of 88.03%.  
Our proposed EGHNet demonstrates superior performance with the highest F1 score of 91.14% while maintaining a reasonable parameter count (25.06 M) and FLOPs (112.09 GFLOPs). This highlights EGHNet's effectiveness in delivering top-tier accuracy without the highest computational cost, showcasing a better tradeoff between model size and performance compared to other complex models like ChangeFormer.
## 4 Understanding Centralised Identity Systems
### SAML: Security Assertion Markup Language
#### 4.7.2 SAML: Limitations  
SAML and SSO are commonly used together, but implementing them can be difficult and time-consuming [16]. Configurations must be carefully managed to prevent risks related to the IDP, SP, and cryptographic keys. Furthermore, since SAML relies on a central authority (IDP), it creates a significant vulnerability [37].
# On Alpha-Expansion-Based Graph-Cut Optimization for Decoder-Side Depth Estimation
## 1 Introduction
To provide an initial speed-up of the depth estimation for the immersive video, we have improved IVDE with the use of hierarchical alpha-expansion [13] for faster optimization using graph-cuts. Unfortunately, as presented in the results of experiments shown in Section 4.2, estimation is faster at the expense of a slight decrease of depth map quality. Therefore, using the findings of the previous research on main problems with depth estimation for immersive video [14,15] and evaluations performed by MPEG Video Coding experts [4], we introduce two further novel improvements to the hierarchical alpha-expansion method:  
* The second cycle label offset introduces a change in the starting point for the second iteration cycle, effectively narrowing the search space for depth values. This scheme is designed to leverage the insights gained from the first cycle of graph-cut, effectively optimizing the selection of depth values for subsequent analysis. * By including depth values of adjacent segments into optimization, neighboring segments label examination counteracts the boundary-blurring effects of video compression. These novel proposals are detailed in Section 3.
## 2 Principles of Electrical Microgrid Management
### Hierarchical Management  
Microgrids need management mechanisms in order to ensure the correct operation and coordination of the different controllers, which must supervise several issues such as the power quality, the load sharing between electrical sources, or the connection and disconnection to the main grid. Traditionally, this control has been achieved by means of a three-level hierarchical sche [6].  
* Grid level: This is the outer level of the hierarchical management architecture. The main elements are the distribution network operator (DNO) and the market operator (MO). Their main roles are, respectively, to communicate with different microgrid central controllers (MGCCs), and to manage the MG when it operates on the market (connected to the grid).
* Management level: This stage is controlled by the MGCC, which ensures the synchronization between the MG and the main grid or the quality of the voltage and frequency of the MG, among other functions.  
Figure 2: Basic structure of a microgrid (MG), including a centralized management system.  
* Field level: The inner level of the management scheme is devoted to control the MG at the loads and sources level (balance between generation and consumption, islanding detection, power balance, _etc._).  
This three-level hierarchical scheme is usually implemented in a centralized manner, composed of primary, secondary and tertiary control loops [7; 8; 9], as shown in Figure 3.  
Primary regulation is based on the \"Voltage _vs._ Reactive Power droop control\" and \"Frequency _vs._ Real Power droop control\". These two techniques, largely employed in high power electrical grids, can be also utilized in MGs in grid connected mode and in islanding [10]. The secondary control is necessary in order to restore the voltage frequency and amplitude, values that have been modified by the techniques applied during the primary control. The tertiary control is employed during the grid connected operation of the MG to control the real and reactive power exchange between the main grid and the MG (and _vice-versa_). It is also possible to apply economic criteria to fix the power levels to be exchanged.  
It must be noted that the tertiary control only acts when the MG is connected to the main grid. Secondary control can acts in grid connected mode or in islanding. Secondary control must be centralized, even if the primary control is applied in a decentralized way. The reason is that all the MG power converters must reach the same values in order to correctly restore the system. Thus, to achieve this centralized mode of operation, the application of sensors and actuators that exchange the needed data (measurements and commands) by means of a communication network, is an interesting solution.