esa-sceva/satcom-chunk-collection huggingface dataset:
# Hypoplastic Modeling of Soil-Structure Contact Surface Considering Initial Anisotropy and Roughness
###### Abstract  
The development of a constitutive model for soil-structure contact surfaces remains a pivotal area of research within the field of soil-structure interaction. Drawing from the Gudehus-Bauer sand hypoplasticity model, this paper employs a technique that reduces the stress tensor and strain rate tensor components to formulate a hypoplastic model tailored for sand-structure interfaces. To capture the influence of initial anisotropy, a deposition direction peak stress coefficient is incorporated; meanwhile, a friction parameter is introduced to address the surface roughness of the contact. Consequently, a comprehensive hypoplastic constitutive model is developed that takes into account both initial anisotropy and roughness. Comparative analysis with experimental data from soils on contact surfaces with diverse boundary conditions and levels of roughness indicates that the proposed model accurately forecasts shear test outcomes across various contact surfaces. Utilizing the finite element software ABAQUS 2021, an FRIC subroutine was developed, which, through simulating direct shear tests on sand-structure contact surfaces, has proven its efficacy in predicting the shear behavior of these interfaces.  
hypoplasticity constitutive model; sand-structure contact surface; initial anisotropy; roughness Article
## 2 Data Description and Preprocessing
### Data Source  
The track geometry data utilized are collected from a high-speed railway line in China. The total length of the line is 1318 km, with China Railway Track System II (CRTSII) slab track. CRTSII slab track is one of the standard slab track systems in China, developed from the German Bogl slab track. The system consists of rails, fasteners, track slab, CAM layer, and concrete base slab. The prefabricated track slab of CRTSII slab track system is shown in Figure 1. The length, width, and thickness of the track slab are 6.45 m, 2.55 m, and 0.2 m, respectively. It is formed by the longitudinal connection of prefabricated track slabs, with wide and narrow joints cast-in-place between the slabs. As shown in Figure 2, due to the longitudinal connection of the slabs, the temperature stress is difficult to release, so the CRTSII slab track system is more likely to instability and arching in high-temperature seasons compared to other track systems. Through field investigation, it was found that the arching deformation of the CRTSII slab track is mostly about 5 mm and can reach 20 mm in extreme cases. Therefore, this paper chooses the CRTSII slab track as the research object of arching detection.  
The inspection is performed twice a month, with a spatial sampling interval of 0.25 m. The inspection items include track geometries and vehicle responses, such as left/right longitudinal level (LLL/RLL), left/right alignment (LA/RA), gauge, cross-level, and vertical/lateral acceleration of car body (VAC/LAC), as shown in Figure 3.  
Figure 1: Prefabricated track slab of China Railway Track System II (CRTSII) slab track system.  
Figure 3: Inspection items of the test line. (**a**) LLL; (**b**) LA; (**c**) Gauge; (**d**) Cross-level; (**e**) VAC.  
Figure 2: Diagram of slab arching.
# A Network Device Identification Method Based on Packet Temporal Features and Machine Learning
## 3 Inception-TCN-Attention Model Design
### Packet Temporal Feature Extraction
The structure of the TCN residual block consists of two convolutional layers, the ReLU activation function and the Dropout layer, which use inflated causal convolution to increase the receptive field without increasing the number of parameters. The expansion factor grows exponentially in successive convolutional blocks, which means that the receptive field of each block is larger than the previous one. weight_norm is applied for regularization, which decomposes the weight of the convolutional layer into two parts, size and direction, and normalizes each of these two parts. This technique helps to stabilize the training process, especially for deep networks, and it can help to mitigate the problem of vanishing or exploding gradients, thus speeding up convergence and improving model performance. Hopping layer connections are used to stream gradients during training and allow the model to learn more complex representations. The use of jump-layer connections and ReLU activation functions helps to improve model training and generalization. The number of residual blocks of the sub-model varies according to the number of variables in different datasets and therefore exhibits robustness.
# ADDAEIL: Anomaly Detection with Drift-Aware Ensemble-Based Incremental Learning
## 4 Results
### Evaluation Metrics  
To comprehensively evaluate anomaly detection performance, four widely adopted metrics are used: **Area Under the ROC Curve (AUC), F1-score, Precision**, and **Recall**.  
Figure 3: Two anomaly points from the Yahoo-A3: Benchmark-TS25. The first point shows a pronounced sudden drop, while the second point shows a mild deviation, close to natural data fluctuations.  
These metrics are computed for each test window based on the predicted anomaly scores and ground-truth labels and are averaged over all windows in the test set.  
* **AUC:** The metric measures the ability of a model to rank anomalies higher than normal instances, without assuming a decision threshold.
* **F1-score:** The geometric mean of Precision and Recall, reflecting the trade-off between the two when converting continuous anomaly scores to binary labels.
* **Precision:** The proportion of correctly identified anomalies among all anomalies detected.
* **Recall:** The proportion of true anomalies that have been detected by the model.  
To convert the anomaly scores into binary labels, a threshold optimization strategy is adopted. For each test window, the threshold that maximizes the F1-score based on the ground-truth labels is computed, and the corresponding best F1 performance is reported. This approach enables reporting the best F1-score across different thresholding strategies and base classifiers, facilitating consistent comparisons across methods.
# The Environmental Impact of Inland Empty Container Movements Within Two-Depot Systems
## 3 Problem Description
Trucks are commonly used for short-haul movements due to their flexibility, while rail is preferred for longer distances because of its higher capacity and lower emissions per container-kilometer. Once containers are unloaded at customer locations, they are returned empty to nearby inland depots or back to the port, completing the cycle. This system, however, involves a significant number of empty container movements, particularly across inland routes. These empty trips contribute to operational inefficiencies and increased greenhouse gas (GHG) emissions, especially when road transport is used. Building on the container flow illustrated in the previous figure, further operational details reveal how storage policies, repositioning needs, and customer distribution patterns shape the inland handling of empty containers. To avoid such additional storage costs and customs complexity inside the port, all empty containers are pulled out by any inland transportation modes to the inland freight station. All the empty containers stay idle in the inland station waiting to receive a customer's request. In general, the customers are not located directly near the inland depot; therefore, shipping companies must arrange for the transportation of empty containers directly to the customer's location within the hinterland. This means that repositioning of empty containers should be conducted between depots to restore balance in the distribution of containers across the entire service network, in alignment with transportation demand. Hence, when the shipping line receives a booking request from the customer, it moves empty containers to be transported to their premises by inland transportation. All these additional land movements of empty containers, both within and beyond port areas, contribute significantly to environmental degradation and congestion at local and regional levels. Therefore, shipping companies prioritize the management and optimization of a physical network comprising inland depots, ports, and customer locations. This strategy focuses on minimizing unnecessary empty container movements, thereby enhancing efficiency and mitigating environmental impacts.
# Survey on Power-Aware Optimization Solutions for MANETs
## 1 Introduction
Multipath routing algorithms aim to find novel techniques for power-efficient route setup and reliably, relaying data packets between source-destination pairs in the direction of maximizing the network lifetime. To address the various constraints of MANETs, many power-efficient routing schemes [20] have been developed. Many other studies [46; 47; 48; 49; 50; 51] have proposed energy-efficient cross-layer optimization solutions for MANETs. Some of these studies have reconsidered the impact of energy efficiency, but also other performance metrics, such as scalability, fairness, and delay in the presence of energy flow into the network. Moreover, in some cross-layer studies, the Physical (PHY), MAC, and routing protocols are re-designed using a cross-layer design (CLD) to optimize the rate at which the energy is consumed, rather than just minimizing the total energy expenditure.
# Seasonal Variability and Effect of Sample Storage on Volatile Components in _Calypogeia azurea_
## 2 Results and Discussion
### The Effect of Seasonality on the Content of Volatile Organic Compounds
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{No.} & \multirow{2}{*}{**Compounds**} & \multirow{2}{*}{**RI***} & \multicolumn{5}{c}{**Code***b **} \\ \cline{3-7}  & & & & & & & \\ \hline
1 & 2-methyl-1-propanol & \(<\)700 & - & - & 0.11 (0.03) & - & - \\
2 & pentanal & \(<\)700 & 0.07 (0.02) & 0.03 (0.01) & 0.04 (0.01) & 0.06 (0.01) & 0.10 (0.02) \\
3 & cyclopentanal & 748 & - & - & 0.03 (0.01) & - & - \\
4 & 3-methyl-1-butanol & 768 & 0.05 (0.01) & 0.01 (\") & 0.19 (0.05) & 0.02 (0.01) & 0.05 (0.01) \\
5 & hexanal & 834 & 0.02 (0.01) & 0.05 (0.01) & 0.16 (0.04) & 0.02 (0.01) & 0.03 (0.01) \\
6 & (2)-3-hexen-1-ol & 886 & - & - & 0.13 (0.03) & - & - \\
7 & 1-hexanol & 896 & - & 0.08 (0.02) & 0.15 (0.04) & 0.12 (0.03) & 0.11 (0.03) \\
8 & 10(M+J[59] 91(100) 77(13) & 902 & 0.59 (0.12) & 0.45 (0.10) & 0.57 (10.0) & 0.25 (0.04) & 0.25 (0.04) \\
9 & tricyclene & 939 & - & - & 0.07 (0.02) & - & - \\
10 & a-pinene & 952 & 0.11 (0.03) & 0.07 (0.01) & 0.21 (0.03) & 0.11 (0.02) & 0.23 (0.04) \\
11 & camphene & 971 & - & - & 0.52 (10.10) & - & - \\
12 & g-pinene & 1004 & 0.49 (0.10) & 0.05 (0.01) & 0.16 (0.03) & 0.05 (0.01) & 0.13 (0.03) \\
13 & 3-ocanol & 1018 & - & 0.01 (\") & 0.25 (0.04) & 0.03 (0.01) & 0.01 (\") \\
14 & limione & 1048 & 0.58 (0.11) & 0.20 (0.05) & 0.26 (0.04) & 0.15 (0.04) & 0.15 (0.02) \\
15 & benzene acetaldehyde & 1093 & 0.17 (0.03) & 0.09 (0.02) & 0.07 (0.02) & 0.27 (0.04) & 0.14 (0.02) \\
16 & 1-octen-3-y-acetate & 1123 & 0.06 (0.01) & 0.51 (0.10) & 1.18 (0.12) & 0.31 (0.05) & 0.39 (0.09) \\
17 & benzene ethanol & 1156 & 0.02 (0.01) & 0.03 (0.01) & - & 0.01 (\") & 0.01 (\") \\
18 & 16(M[49] 145(100) 117(34) & 1224 & 4.95 (0.18) & 3.52 (0.14) & 2.26 (0.18) & 3.52 (0.22) & 3.02 (0.22) \\
19 & \(\beta\)-cyclodextrin & 1257 & 1.31 (0.26) & 1.27 (0.18) & 0.67 (0.10) & 0.81 (0.12) & 1.31 (0.18) \\
20 & 1-2-dihydro-free-methylphthalene & 1295 & 0.06 (0.01) & 0.13 (0.03) & - & 0.14 (0.04) & 0.05 (0.01) \\
21 & 20(M+J[18] 121(10) 93(71) & 1332 & 0.11 (0.02) & 0.15 (0.04) & 0.11 (0.03) & 0.17 (0.03) & 0.17 (0.02) \\
22 & 8-elemene & 1344 & 0.38 (0.09) & 2.29 (0.22) & 1.24 (0.14) & 2.12 (0.22) & 2.83 (0.16) \\
23 & methylphthalene & 1359 & 0.11 (0.03) & 0.11 (0.03) & 0.09 (0.02) & 0.06 (0.01) & 0.07 (0.02) \\
24 & 20(M+J[18] 81(100) 96(83) & 1364 & 0.15 (0.04) & 0.21 (0.04) & 0.11 (0.03) & 0.18 (0.04) & 0.15 (0.02) \\
25 & anastreprene & 1379 & 6.79 (0.28) & 12.01 (0.34) & 20.89 (0.56) & 10.57 (0.34) & 11.05 (0.34) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Volatile compounds detected in the samples C-21–C-25.
## 4 Results and Discussion
### Analysis of Static Pressure and Velocity
Generally speaking, the division pier had a great influence on the near-wall velocity of the straight outlet conduit, leading to an overall decrease of the near-wall velocity along the flow direction; however, a large high-speed zone formed on the left side of the initial position of the division pier, which was one of the reasons for the main flow to the left side of the division pier, but there was no obvious difference in the near-wall velocity distribution of the straight outlet conduit with different lengths of the division pier. To further analyze the influence of the division pier on the internal vortex structure of outlet conduit, referring to the identification method of the vortex structure in [33; 34; 35], we used the \(Q\) criterion to analyze the internal vortex structure of the straight outlet conduit in scheme 1; when the threshold value \(Q=300\) s\({}^{-2}\), the internal vortex structures of the straight outlet conduit under three different flow conditions are shown in Figure 15.  
Figure 14: Velocity nephogram of the near–wall area of the straight outlet conduit. When the threshold value \(Q\) = 300 s\({}^{-2}\), along the flow direction, vortex structures with different sizes appeared on the right side of the division pier: under the condition of a low flow rate, there was a large vortex structure on the right side of the division pier; under the optimal flow rate condition, the size of the vortex structure on the right side of the division pier decreased greatly; and under the condition of a large flow rate, the vortex structure on the right side of the division pier became larger, but the range was smaller than that under the condition of a low flow rate condition. Under the three flow rate conditions, there was a small vortex structure on the left side and the end of the division pier, and the range of the vortex structure was little affected by the flow rate condition, indicating that the division pier in the outlet conduit only affected the vortex structure on the right side of the division pier but had little effect on the vortex structure on the left side of the division pier.
## 1 Introduction  
Position-based calculations with binary and decimal numbers require transfers between digits, which have a negative impact on performance or device overhead for calculations with large numbers. In non-position-based number systems, such as the Residue Number System (RNS), transfers between digits are avoided. Interest in RNS as a basis for computing system development emerged as early as the 1950s [1, 2]. In particular, in the USSR, long-range detection radar stations operating on the basis of modular arithmetic and built under the leadership of [PERSON] and [PERSON] were deployed in the 1960s. Currently, various aspects of the residue number system are applied in patents of companies from the USA (Olsen IP Reserve, LLC; the Athena Group, Inc.; Cisco Technology, Inc.), China (Tianjin University; No 30 Inst China Electronic Technology Group Corp.; Univ Xidian), and the UK (Samsung Electronics Co., Ltd.).  
The architecture of computing systems operating in RNS generally consists of forward conversion of positional numbers into RNS [3], computation of the main problem usingmodular addition [4], multiplication [5], number comparison and sign determination [6] operations, reconversion from RNS to positional number system [7; 8; 9; 10; 11; 12; 13; 14]. Furthermore, if addition, subtraction, multiplication in RNS have a parallel structure with numbers of small dimensions, then the operations of sign determination, comparison, conversion from RNS to the positional number system require the calculation of the positional characteristic of the number, which is computationally complex.  
The work is structured as follows. In Section 2, reverse conversion methods for general form moduli, such as Chinese Remainder Theorem (CRT), Mixed-Radix Conversion (MRC), CRT with fractional numbers, New CRT I, and New CRT II are discussed. In Section 3, we consider the application of RNS moduli of the special kind \(2^{n}\pm 1\), \(2^{n}\). Section 4 considers the MRC-based method proposed by the authors. In Section 5, the authors' proposed method based on reference points is considered. However, the result of the work is the method considered in Section 6, which combines the use of modified MRC and the method of reference points. The results of the physical synthesis of the reverse conversion methods are presented in Section 7. Section 8 analyzes the results of the physical synthesis and draws conclusions about the applicability of the proposed approaches.
# TDM-PON PAM Downstream Transmission for 25 Gbit/s and Beyond
## 4 Conclusions  
In this article, we studied the feasibility of downstream PAM TDM-PON with either 50 Gbit/s PAM4 or 37.5 Gbit/s PAM8 to achieve higher bit rates than 25 Gbit/s.  
While PAM8 can reuse the 10 GHz optics designed for previous PON generations, this modulation format appears to be too constraining in terms of noise sensibility and receiver complexity for access network CPEs. A PAM8 decoder should have 3 times less noise than an actual real-time PAM4 decoder, and it would still not be compliant with the ODN classes depicted in the standards of the legacy access network technologies.  
PAM4 is a very good candidate to solve the bit rate growth problem in both fixed and mobile access networks. In O band, where the chromatic dispersion is low, it preserves the IMDD transmission simplicity with no heavy signal processing or equalization requirements. However, bit rate enhancement up to 50 Gbit/s would require higher bandwidth for optoelectronics components up to 25 GHz.  
[PERSON], [PERSON] and [PERSON] conceived of the presented idea. [PERSON] and [PERSON] developed the theory and performed the computations. [PERSON] carried out the experiments. [PERSON], [PERSON], [PERSON] and [PERSON] verified the analytical methods. All authors discussed the results and contributed to the final manuscript.  
This work was supported by the European H2020-ICT-2016-2 project 5G-PHOS.  
The authors declare no conflicts of interest.
# Equalization Methods for Out-of-Band Nonlinearity Mitigation in Fiber-Optic Communications
## 2 The Time-Varying ISI Model of NLIN
### Characterization of ISI Statistics  
In order to design a successful equalization algorithm, one must characterize the statistical behavior of the ISI coefficients, \(\mathbf{R}_{l}^{(n)}\). This behavior is determined by many factors, such as the modulation format, the baud rate of the ICs, the fiber link's structure, and more. To date, most efforts in characterizing these properties have been directed towards second-order moments, namely the variance and covariance of the ISI coefficients. These were obtained analytically by determining the interaction coefficients (\(X_{h,k,l}\) in Equation (1)) [8], or extracted from experiments or split-step simulations [4; 28; 29]. The extraction from experiments and simulations relies on estimating each ISI coefficient from Equation (3) using  
\[\tilde{R}_{l}^{(n)}=-i\frac{s_{n}-a_{n}}{a_{n-l}}=R_{l}^{(n)}+v_{n}, \tag{7}\]  
where \(s_{n}\) and \(a_{n}\) are taken directly from the simulation or experiment, and where \(v_{n}\) is estimation noise, which can be modeled as white noise provided that the transmitted symbols are uncorrelated to each other. Thus, by assessing various statistical properties of \(\tilde{R}_{l}^{(n)}\), we can infer those of \(R_{l}^{(n)}\). For example, the whiteness of \(v_{n}\) implies that \(\langle\tilde{R}_{l}^{(n)}\tilde{R}_{l}^{(n+\Delta n)}\rangle=\langle R_{l}^{ (n)}R_{l}^{(n+\Delta n)}\rangle\) for \(\Delta n\
eq 0\), which means that the autocorrelation functions of \(\tilde{R}_{l}^{(n)}\) and \(R_{l}^{(n)}\) are identical (with the exception of the point \(\Delta n=0\)). Similarly, the cross-correlation between \(\tilde{R}_{l_{1}}^{(n)}\) and \(\tilde{R}_{l_{2}}^{(n)}\) is identical to that of \(R_{l_{1}}^{(n)}\) and \(R_{l_{2}}^{(n)}\). Note that the method of evaluating the amount of nonlinear phase noise [4; 29; 30] is obtained by setting \(l=0\) in Equation (7). For more details, the reader is referred to [28].
# In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks
## 3 Methodology  
Various sensors, configurations, and data analysis methods were developed and explored to achieve optimal accuracy, efficiency, and robustness, ensuring that the system complies with railroad standards. A set of sensors that exhibit promising potential for this study will be discussed, followed by a series of tests for selecting the most suitable sensors and the required data analysis for tie/ballast differentiation.
# A Cross-Layer Media Access Control Protocol for WBANs
###### Abstract  
Wireless body area network (WBAN) is an emerging comprehensive technology that can deeply integrate with e-health and smart sports. As a wearable network, improving the quality of network service and user experience is crucial. Due to the miniaturized design of sensors, their available energy from batteries is limited, making the extension of their lifetime a key research challenge. Existing studies have proposed methods to improve energy efficiency, but there are still limitations in addressing dynamic adaptive aspects of differential energy distribution and channel conditions. In order to further extend the lifetime of sensor nodes and networks while ensuring quality of service, and to provide a reliable transmission mechanism for heterogeneous application data, this paper presents a cross-layer optimized MAC protocol mechanism. The protocol takes into account the transmission requirements of different types of data and redesigns the superframe. To improve the lifetime of nodes, we propose an energy-adaptive adjustment mechanism considering the channel conditions. At the same time, a cooperative transmission mechanism is proposed to further enhance network lifetime. In experiments conducted on two typical networks, compared to IEEE 802.15.6, the power adjustment scheme improves the network lifetime by 2.8 to 3.7 times, and the cooperative mechanism between nodes further increases the network lifetime by 17% to 44%. Our proposed scheme effectively extends the network lifetime while ensuring quality of service, avoiding frequent battery resets for users, and effectively improving the user experience quality.  
wireless body area network; power control; network lifetime; cooperative transmission 20231511381 [[https://doi.org/10.3390/su151411381](https://doi.org/10.3390/su151411381)]([https://doi.org/10.3390/su151411381](https://doi.org/10.3390/su151411381))  
1
# Design of Remote Upgrade System for Data Processing Unit in Marine Engine Room Simulator
## 4 Firmware Remote Upgrade Design
### Reliability Design
#### 4.4.1 Program Validation Mechanism  
The program upgrade file is the code for the work of the DPU. Once a transmission error occurs, the marine engine room simulator will not work properly. Therefore, ensuring the accuracy of the transmission is the key that the upgraded marine engine room simulator can work normally. The cyclic redundancy check (CRC) is a common data confirmation algorithm that can effectively ensure the data integrity of the upgrade file during transmission [33]. In the CRC codes in this paper, the data of the transmission frame is 128 bytes. When the last byte of the transmission frame is less than 128, it can be filled with 0 XFF to ensure that the data check is 128 bytes. When the DPU has received a frame of data transmission, it checks the transferred data and gains the results \"Checksum\". Compared with the check value \"ReadCheckdate\" read from the program upgrade file, If \"Checksum\" equals \"ReadCheckdate\", the data is correct and the received data is written to the Flash.