esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The safe management of traffic on rail networks requires that trains can be reliably located at all times. In many countries, this is achieved by electrically identifying their presence using 'track circuits' at regular intervals along each track: these devices detect when the wheels and axles of a train short-circuit the two rails (they are said to'shunt' them) and the information is passed on to the signaling system. The quality of the electrical contacts between the rails and the wheels obviously plays a decisive role in the correct operation of this detection principle. The occurrence of high wheel-rail contact resistances can lead to malfunctions, known as 'deshunting', in which the system is unable for a certain period of time to conclude whether a train is present or absent on a section of track. This type of potentially risky event must obviously be avoided at all costs. In this article, we present the second part of a study devoted to the degradation of the wheel-rail contact resulting from steel oxidation, using a scaled-down home-made test bench that reproduces the real contact in the laboratory under controlled conditions. Following on from the first part, which dealt with the _static_ characterization of the contact studied in the direct current, we are now tackling its _dynamic_ characterization (rolling contact) under AC electrical conditions close to those of a track circuit. These new investigations again involve different degrees of rail oxidation, and analyze the influence of parameters such as contact force, speed, and current intensity.  
wheel/rail electrical contact; health monitoring; deshunting; track circuit; oxidation 2025155760  
1
# Design of a Baseband Signal for the 406 MHz Satellite Emergency Radio Transmitter Based on STM32
## 3 Baseband Signal Generation METHOD
### Data Bits Design  
#### 3.3.1 System Bit Design  
The system bits design includes synchronization bit, frame synchronization bit and fixed bit. The synchronization bit is a 15-bit code of \"1\", and the frame synchronization bit is a 9-bits code of \"00010111\". In summary, the system can be expressed as \"0 xffe2f\" in 32 bits.
## 8 Conclusions and Future Work  
This work presents the EdgeDewSim Extended Simulator, which supports the connection and disconnection of devices into the cluster and its network. With this capability, we were able to experiment with two simple mobility models that tend to simulate a human's movements and incorporate a new reliability score into two very well-studied algorithms, BPA and E-SEAS [7; 22]. The reliability score offers improvements in job completeness and operations executed, which vary depending on the scenario. For future work, it is necessary to extend the mobility models used to generate the connection and disconnection events to reflect real-world human movements more accurately and explore the usage of human-generated movement data to validate the heuristics' effectiveness. Also, as mentioned in Ref. [9], using RL techniques makes it possible to surpass the human-designed heuristics used in this type of environment. However, it is necessary to explore new RL techniques to handle a broader range of dynamic jobs and explore generalization techniques to improve the RL model's adaptability to diverse environments.  
A significant branch of future work involves the practical implementation and testing of the proposed scheduling algorithms in real use-case scenarios. This includes deploying the algorithms in dew computing environments, such as smart city infrastructure or industrial IoT setups, to assess their performance and robustness in real-world conditions. By evaluating the algorithms in diverse applications, we can identify potential challenges, refine them based on real-world feedback, and ensure their readiness for deployment in practical settings. These efforts will bridge the gap between theoretical advancements and practical applicability, paving the way for more reliable and effective device management solutions in dynamic dew computing environments.  
Conceptualization, P.S., S.M., A.N., R.T.I., M.H. and C.M.; methodology, P.S., S.M., A.N., R.T.I, M.H. and C.M.; software, P.S., S.M., A.N., R.T.I, M.H. and C.M.; validation, P.S., S.M., A.N., R.T.I, M.H. and C.M.; formal analysis, P.S., S.M., A.N., R.T.I, M.H. and C.M.; investigation, P.S., S.M., A.N., R.T.I, M.H. and C.M.; resources, P.S., S.M., A.N., R.T.I, M.H. and C.M.; data curation, P.S., S.M., A.N., R.T.I., M.H. and C.M.; writing--original draft preparation, P.S., S.M., A.N., R.T.I., M.H. and C.M.; writing--review and editing, P.S., S.M., A.N., R.T.I, M.H. and C.M.; visualization, P.S., S.M., A.N., R.T.I, M.H. and C.M.; supervision, P.S., S.M., A.N., R.T.I, M.H. and C.M.; project administration, P.S., S.M., A.N., R.T.I, M.H. and C.M.; funding acquisition, P.S., S.M., A.N., R.T.I., M.H. and C.M. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Agency for Research and Development (ANID)/ Scholarship Program/DOCTORADO NACIONAL/2020-21200979. We also gratefully acknowledge funding from the National Center for Artificial Intelligence CENIA FB210017, Basal ANID. Additionally, we thank the funding provided by CONICET grant number PIBAA-28720210101298 CO and grant number PIP-11220210100138 CO.
[35] developed a prediction model based on the Escaping Bird Search (EBS) optimization method and Multivariate Adaptive Regression Splines (MARS). For the training set, test set, and full dataset, respectively, the coefficients of determination (R\({}^{2}\)) scores of their prediction model were 0.997, 0.993, and 0.995, respectively. This study mainly focuses on providing accurate estimations for the bearing capacity of foundation systems composed of GRSB and VESC. The input variables considered in this research were the ratio of the sandy bed thickness to the foundation diameter (\(t/D\)), the ratio of the diameter of the geogrid-reinforced layer to the diameter of the footing (\(d/D\)), the undrained shear strength of unreinforced clay (\(q_{u}\)), the ratio of the length of the encasement to the diameter of the stone column (\(h_{t}/d_{sc}\)), and the ratio of the length to the diameter of the stone column (\(L/d_{sc}\)). For this purpose, a databank related to 245 experimental tests is gathered from the open-source literature [12]. Then, a comprehensive and reliable model is developed by using Multivariate Polynomial Regression (MPR), which is a white-box supervised intelligence system. To the best of the authors' knowledge, this is the first study in which MPR is used to produce mathematical formulas for determining the bearing capacity of this type of composite foundation system. In contrast to the majority of the earlier uses of ML techniques in such fields, which were black-box models and required specialized software to compute the predictions, the explicitly developed model in this study is user-friendly and can be used with a basic calculator. Several statistical and graphical criteria are used to assess the accuracy and reliability of the developed model. Finally, the effect of each input variable on the bearing capacity is quantitatively evaluated using a parametric analysis.
# Experimental Study of Material Proportioning for Similar Modeling of Brittle Rocks
## 3 Experimental Results and Analysis  
### Physical and Mechanical Parameters of the Materials  
Each group of specimens was weighed and their dimensions measured. Next, uniaxial compression and Brazilian splitting tests were conducted to obtain the stress-strain curves of the specimens. The density (\(\rho\)), compressive strength (\(\sigma_{\text{c}}\)), tensile strength (\(\sigma_{\text{t}}\)), and elastic modulus (\(E\)) of the specimens were calculated. Additionally, the rockburst tendency index (\(W_{\text{cf}}\)) and the brittleness index (\(B_{\text{j}}\)) were determined using Equations (1) and (2), presented in Section 2.4. The test results are presented in Table 3. The density range of the specimens produced with different proportioning schemes was between 2.10 and 2.40 g/cm\({}^{3}\), while the compressive strength ranged from 0.12 to 1.94 MPa. The tensile strength varied between 0.01 and 0.20 MPa, and the elastic modulus was in the range of 0.120 to 0.772 GPa. The rockburst tendency index exhibited a range of 0.59 to 3.29, and the brittleness index varied from 0.141 to 1.578. Note that the variation ranges in the aforementioned parameters represent only the results derived from the 25 mix design schemes.
# A Neural Network Approach to a Grayscale Image-Based Multi-File Type Malware Detection System
## 4 Results  
### Comparison of Single-File Type Models  
As previously mentioned, this work focuses on the detection of malicious files in three main formats: PDFs, MS Documents, and PE files. Thus, in order to prepare a solid ground for comparison, the pre-trained model AlexNet was trained to detect benign and malicious data for individual file types using the grayscale-based image transform, as per Section 3.2. Accordingly, to represent compact neural network models, GoogleNet was also trained on these data. The findings for these are seen in Table 3.  
As presented, slightly higher accuracies are reported for the AlexNet model as opposed to GoogleNet. Nonetheless, both models return adequate results considering that the files contain imbalanced data. Figure 6 displays the relevant confusion matrices for the AlexNet model trainings, which justifies the model's performance against imbalanced data.  
It is also important to highlight that, since the datasets for each file format were generated from different sources, the amount of underlying data is different for each. Hence, the accuracies vary across different file types. Notably, the highest accuracy was achieved with PDF files, which also had the largest pool of training and testing files. The accuracy rankings for PE and MS documents also remained consistent with the amount of data available. This suggests a direct correlation between the number of files and enhanced model performance. However, due to the limited availability of data in other file type categories, efforts to equalize the amount of data across the file types were constrained.
# Small Satellite Tools for High-Resolution Infrared Fire Monitoring
## 3 The FireBIRD Main Payload
### Spatial Sampling of the FireBIRD Main Payloads  
Figure 8 illustrates schematically the projection and forward movement direction of the TET-1 and BIROS main payload detector line arrays onto and over the ground, respectively.  
Figure 9 shows five TET-1 image stripes with the Baltic Sea and the north of eastern Germany. The shift of the left and right image stripes along the flight direction is due to the 6.4\({}^{\circ}\) forward and 6.4 \({}^{\circ}\) backward viewing direction of the NIR and green band detector lines, respectively, as illustrated in Figure 8. The white horizontal \"bottom lines\" in all five image stripes indicate that the image stripes were registered at the same time, where the time is increasing from the bottom to the top along the image stripe.  
Figure 8: Illustration of the detector line array’s projection onto the ground and the forward movement direction of the TET-1 and BIROS multi-spectral camera systems: left for the VIS-NIR camera with its three CCD lines for the green, red, and near-infrared bands; right for one spectral band of their MIR and TIR bands, each equipped with two (cold-redundant) staggered arrays.  
Figure 9: Five image stripes of the TET-1 camera systems, with the Baltic Sea and Ruegen Island in the middle of the forward-looking NIR band on the outer left side. In the center are shown the three nadir-looking bands (from left to right): the mid-wave (MW or MIR) band, the long-wave (LW or TIR) band, and the VIS red band. On the outer right side is shown the backward-looking VIS green band.
# Moving Ad Hoc Networks--A Comparative Study
## 3 Vehicular Ad-Hoc Networks (VANET)
Like the GSR protocol, the SAR protocol uses road map information obtained by external devices to construct a \"spatial model\" for path finding, which can predict and avoid routing recovery problems caused by holes in the network topology. However, the disadvantage of the SAR protocol is that it does not know whether there are vehicles on the selected road to complete the forwarding task. The STAR protocol solves this problem by choosing to find a route along the road with vehicles. The A-STAR protocol uses urban bus lines as a strategy to find an efficient path for data transmission. The protocol is suitable for scenarios with dense urban traffic, and it assumes that bus vehicles cover major urban roads. Based on GSR and A-STAR, the GyTAR protocol fully considers factors such as vehicle driving directions, vehicle density, road multidirectional, and traffic environment changes, making the routing protocol more in line with actual traffic scenarios. Because the road conditions in real life are complicated and the driving conditions of vehicles are also ever-changing, the design of the routing protocol in VANET must be more in line with the actual scene. At present, designing an efficient routing protocol that can effectively find transmission paths in various scenarios is still a major challenge for network layer research. * Other Challenges: These include the lack of centralized management entities and link-layer synchronization, the ideal connectivity to maintain high portability, the seamless switching of channels between Layer 2 and Layer 3, equal insurance on wires based on user acceptance of the appropriate safety level and personal cost, balanced vehicle safety standards, and parcels being lost when driving at high speed. However, a vehicle self-organizing network is a special type of mobile self-organizing network which has broad application prospects. The high-speed mobility of vehicle nodes in VANET and the rapid change of network topology have brought severe challenges to the design of network architecture and the design of the related protocols. Table 2 shows the summary of the existing surveys in VANET.
## 3 Quality of Service
### Space Diversity
#### Frequency Diversity  
In addition to space diversity, frequency diversity reduces the effects of the environment on the wireless communication by limiting the interference. Two frequency diversity schemes are possible: channel hopping and channel blacklisting. The channel hopping allows avoiding interference by changing the transmission frequency, while the devices maintain lists of frequencies to avoid due to their significant interference with the channel blacklisting scheme. The WirelessHART and the ISA100.11a standards use both of them and possess therefore an efficient response against interference. The WISA specification uses channel hopping, but does not blacklist faulty channels. In comparison, the ZigBee and 802.15.4e FA MAC standards do not provide any mechanism to avoid potential interference, which may lead to erroneous transmissions or even worse to a total transmission break down. However, the ZigBee PRO standard proposes an enhancement of the ZigBee standard by offering frequency agility. The available channels are scanned during the network setup phase to select a frequency without interference. Then, the frequency is shared by all ZigBee compliant devices and remains unchanged until the next network setup period. Even if the frequency agility may limit the effects of potential interference during the network formation, it can rapidly become inefficient in case of additional sources of interference.
## 2 Project Description  
### General Description  
The NJHEP, undertaken by the Water and Power Development Authority, Pakistan, is a recent hydroelectric project located in northeastern Pakistan, in the Muzaffarabad district of Kashmir, as shown in Figure 1. Its construction commenced in late 2008, and the first turbine generated power in April 2018. The designed capacity of the project is 969 MW of electric power generated from 283 m\({}^{3}\)/s of water; this water is diverted from the Neelum river to the Jhelum river through tunnels, creating a gross hydraulic head of 420 m. The main structures in this project include a diversion dam and an intake system, headrace tunnels, an underground powerhouse complex, and a tailrace tunnel. Underground excavation work accounted for more than 80% of the construction that was involved in the project.  
The 28.6-km-long headrace tunnels comprise single (34%) and twin (66%) circular and horseshoe-shaped tunnels. The tunnels are oriented along the northeast-southwest direction, and the cross-section of the excavation spans ranges of 10.7-11.8 m and 7.75-8.53 m for the single and twin headrace tunnels, respectively. A single tunnel with the modified horseshoe-shaped cross-sectional area and a hydraulic span of 9.6 m is split into twin headrace tunnels (0.9 km from the intake portal). Thereafter, these twin headrace tunnels, with a hydraulic diameter of 6.8 m, rejoin at approximately 20.5 km downstream from the intake portal. After converging into a single tunnel, the headrace tunnel continues for a distance of 8 km downstream and it reaches the underground power station comprising four generating units. After generating 969 MW of electric power, a 3.5-km tailrace tunnel discharges the water into the Jhelum river. The project also consists of 10 access adits, with a total length of approximately 11 km.  
Commonly unreinforced concrete lining (Type A) with a minimum thickness of 350 mm was used in this project. Type B linings also have the same minimum thickness; however, they are singly reinforced. In the single tunnel, heavily reinforced Type C structural lining with a thickness of 600 mm is used. The same type of lining, with a thickness of 500 mm, was used in the twin headrace tunnels. Approximately 44% of the length of the headrace tunnels is lined with the Type A concrete liner, whereas 42% (the section mined using the TBM-based method) is lined with shotcrete.  
For construction, management, and documentation purposes, the project was divided into three sectors or sites, referred to as Lot C1, Lot C2, and Lot C3 (Figures 1 and 2a). Table 1 lists details of the underground excavation in the project for all three lots.  
Figure 1: Project location.  
Figure 2: _Cont._
# Effect of Multiple Reclosing Time Intervals on Axial Vibration of Winding  
[PERSON]  
1 Hbei Electric Power Research Institute, Shijiazhuang 050021, China; [EMAIL_ADDRESS] (S.G.); [EMAIL_ADDRESS] (Y.T.)2 State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050057, China; [EMAIL_ADDRESS]  
3 Hbei Provincial Key Laboratory of Power Transmission Equipment Security Defense, North China Electric Power University, Bostolag 071003, China; 22022212082@[PERSON] proposed a laminated reinforcement cylindrical-shell model to study the deformation impulses of multiple short circuits [10]. [PERSON] found the elastoplastic tangent stiffness matrix of transformer winding elements according to the large displacement elastoplastic theory [11]. [PERSON] established a refined model for the middle disk of the transformer's medium-voltage winding [12]. [PERSON] proposed a method for transformer model creation suitable for direct coupling calculation of the electromagnetic field and structure field [13]. Then, a numerical calculation model incorporating multiple short circuits of transformer winding was used to analyze the effect of different residual stresses on the residual deformation [10]. The distribution and variation trends in winding-shape variables, equivalent stress, and equivalent plastic strain of loading and unloading short-circuit force in a single short-circuit impulse were obtained using Workbench. The accumulation mechanism of winding axial vibration in multiple short-circuit impulses was obtained [14]. When the transformer is subjected to the accumulation of short-circuit impulses, the winding undergoes cumulative deformation [12].  
These studies established the transformer-winding vibration model and calculated the vibration response of the winding. Based on this work, the cumulative effect of winding vibration was analyzed and the vibration mechanism of the winding multiple short circuit was proposed. However, the established model does not consider the influence of disk displacement and acceleration of the unattenuated short circuit on the winding structure and other parameters, nor does it account for the superimposed influence of other parameters on the vibration response of the secondary short circuit.  
In this paper, the mathematical model of transformer axial vibration is established based on an actual 110 kV transformer. Furthermore, the characteristics of variation in vibration amplitude, vibration displacement, and acceleration are obtained. Considering the continuous influence of winding vibration, the reclosing time interval is changed, the winding's vibration response at the corresponding time interval is calculated, the behavior of the winding's secondary vibration at different time intervals is analyzed, and the shortest reclosing time is judged.
# Shear Wave Velocity Estimation Using the Real-Time Curve Tracing Method in Ultrasound Elastography
## 4 Results
### Result of Velocity Reconstruction Image  
In the homogeneous region, the experimental data of velocity image reconstruction in the intercepted homogeneous area shown in Figure 5b are quantitatively analyzed. The estimated velocity of the shear wave in the homogeneous area with the real-time curve tracing method is 2.9026 m/s, the standard deviation is 0.1032 m/s and the relative error is 8.21%.  
In the stiff spherical inclusion regions with varying elastic values, the experimental data of velocity image reconstruction in the black rectangle intercepted area and the background area shown in Figure 6b are quantitatively analyzed. The comparison results are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Background** & **Theoretical** & **Estimated Method** & **Estimated Velocity (m/s)** & **Relative Error (\%)** \\ \hline \multirow{4}{*}{30} & & The time-to-peak & \multirow{2}{*}{2.8078 \(\pm\) 0.1944} & \multirow{2}{*}{11.21\%} \\  & & method & & \\ \cline{1-1}  & & The lateral peak & \multirow{2}{*}{2.8622 \(\pm\) 0.2114} & \multirow{2}{*}{9.49\%} \\ \cline{1-1}  & & method & & \\ \cline{1-1}  & & The real-time curve & \multirow{2}{*}{2.9382 \(\pm\) 0.1221} & \multirow{2}{*}{7.09\%} \\ \cline{1-1}  & & tracing method & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of shear wave velocity estimation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Modulus of Elasticity (kPa)**} & \multirow{2}{*}{
\begin{tabular}{c} **Theoretical** \\ **Velocity (m/s)** \\ \end{tabular} } & **Estimated Method** & **Estimated Velocity (m/s)** & **Relative Error (\%)** \\ \hline \multirow{4}{*}{1} & Inclusion: 10 & 1.8257 & 1.9544 \(\pm\) 0.3914 & 7.05\% \\  & Background: 30 & 3.1623 & 2.9179 \(\pm\) 0.2236 & 7.73\% \\ \hline \multirow{2}{*}{2} & Inclusion: 40 & 3.6514 & 3.3501 \(\pm\) 0.5583 & 8.25\% \\  & Background: 30 & 3.1623 & 2.9797 \(\pm\) 0.2179 & 5.77\% \\ \hline \multirow{2}{*}{3} & Inclusion: 60 & 4.4721 & 4.1692 \(\pm\) 0.6952 & 6.77\% \\  & Background: 30 & 3.1623 & 2.9754 \(\pm\) 0.2328 & 5.91\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of shear wave velocity estimation. Analysis of velocity image reconstruction in the intercepted area and the background area with the real-time curve tracing method.  
Figure 9: Velocity estimation in homogeneous region. (**a**) The time-to-peak method. (**b**) The lateral peak method. (**c**) The real-time curve tracing method.