esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Materials  
This study utilised Oxford clay sourced from Eynsham, Oxfordshire, UK, with approximate coordinates of 51\({}^{\circ}\)46/55\({}^{\prime\prime}\) N and 1\({}^{\circ}\)23/05\({}^{\prime\prime}\) W. Oxford clay is a Jurassic-aged, highly plastic clay known for its significant shrink-well behaviour. Samples were collected from depths ranging from 0.25 to 3.0 m below ground level, within a clay stratum beneath superficial sand and gravel deposits. Groundwater was found at depths between 0.9 and 2.93 m, indicating that the clay typically remains relatively moist. In its natural condition, the clay is grey and silty, with occasional shell fragments and a slight organic odour. Initial laboratory characterisation of the air-dried and pulverised soil confirmed its high plasticity. The particle size distribution, determined through combined sieve and hydrometer analysis, was approximately 60% clay, 37% silt, and 3% sand. The Atterberg limit tests yielded a liquid limit (LL) of 53% and a plastic limit (PL) of 22%, resulting in a plasticity index (PI) of 31. Based on these indices, the soil is classified as a high-plasticity clay (CH) under the Unified Soil Classification System (USCS) and as group A-7-6 according to the AASHTO classification. The natural pH of the clay ranged from 7.4 to 8.4. Modified Proctor compaction tests determined an optimum moisture content (OMC) of approximately 12% and a maximum dry density (MDD) of 1.80 g/cm\({}^{3}\). The high plasticity index (PI) and the moderate OMC indicate a medium-to-high swell potential. Given these properties, Oxford clay is considered a suitable candidate for investigating the effectiveness of stabilisation under moisture cycling.  
Two stabilising agents were used: ordinary Portland cement (OPC) and guar gum (GG). OPC was added at 6% by dry weight of soil, a dosage commonly used for subgrade stabilisation to enhance strength and reduce plasticity. This percentage aligns with field applications, such as in the stabilisation of the Oklahoma US-62 Highway [19], and falls within the recommended range of 3-8% [27]. The effectiveness of cement arises from the hydration of tricalcium silicate (C\({}_{3}\)S) and dicalcium silicate (C\({}_{2}\)S), which produce calcium silicate hydrate (C-S-H) gel and calcium hydroxide [Ca(OH)\({}_{2}\)]--the primary agents for binding and soil improvement. Guar gum, a natural biopolymer, was used at 1% by dry weight of soil. The guar gum used was a commercially available food-grade powder (greater than 99% purity), with a manufacturer-reported average molecular weight of 1.0 million Da. It is derived from the endosperm of guar beans (Cyamopsis tetragonoloba) and forms a highly viscous gel when hydrated [28]. This dosage is within the optimal range (0.5-2%) reported in the literature for clayey soils, beyond which strength improvements tend to plateau [28]. Both stabilisers were applied in dry-powder form and thoroughly mixed with the pulverised soil to ensure uniform distribution before adding water.
# Experimental Investigations of a Tunnel Lining Segment Strengthened by In Situ Spraying Mortar
## 3 Experimental Results  
### Properties of Spraying Mortar Materials  
#### 3.1.1 Compressive and Flexural Strength  
The experiment results of H-70 compressive and flexural strength are shown in Figure 12. Under standard curing conditions, 7- and 14-day compressive strengths of  
Figure 11: Monitoring-point arrangement scheme.  
Figure 10: Strain-gauge arrangement scheme.  
H-70 reached 61% and 90% of the 28-day compressive strength, respectively. The 7- and 14-day flexural strengths of H-70 reached 65% and 86% of the 28-day flexural strength, respectively. This indicates that H-70 has high early strength, which is beneficial for rapid repair. Comparing the compressive and flexural strength of the two curing conditions at 28 days, it can be seen that the curing conditions (mainly the difference in humidity) have no obvious effect on the strength of H-70. The reason for this phenomenon is that H-70 has sand with a special particle size as the main aggregate, and the gaps between the sand particles are filled with finer powder particles, such as cement and silica fume, so that the mortar test block can reach the maximum compact state. The microstructure of the H-70 test block is denser than that of the ordinary concrete test block, so the external humidity environment has no obvious effect on the internal hydration process of H-70.
## 2 Optimization Model for Network Reconfiguration Problem of Ship Power System
### Constraints on Network Reconfiguration of Ship Power Systems
#### 2.3.2 Power System Topology Constraints  
The essence of ship power grid reconfiguration lies in achieving safe system operation through modifications to the grid's topological structure [23]. To ensure the connectivity of the ship's power system post-reconfiguration, a radiative constraint must be established within its topology. This stipulates that, for any energized load, it is not permissible for both normal and backup power supply lines to be operational simultaneously; only one line may be activated at a time. This approach aims to prevent the emergence of ring or island topologies within the reconfigured grid. The constraint formula is formulated as Equation (16):  
\[z_{im}+z_{in}=1\quad\left(i\in\Omega\right), \tag{16}\]  
where \(\Omega\) is the set of critical and important loads; \(z_{in}\) is the normal supply path switching attribute of a load, denoted by 0; and \(z_{in}\) is the alternate supply path switching attribute of a load, denoted by 1.
## 3 Problem Formulation
### Induction of Zero-Phase Voltage of GPT  
A second problem to consider is an induction of a zero-phase voltage of GPT even in the absence of a ground fault. The phenomenon can be attributed to both the harmonic current due to the switching of the transformerless inverter and the current flowing to the primary side of the GPT due to the square wave injection of the IMD.  
Figure 5: Leakage current loop in ungrounded system connected to transformerless solar photovoltaic (PV) generation facility with IMD.  
Figure 6: Leakage current loop in ungrounded system connected to transformerless PV generation facilities with IMD and GPT.  
First, the harmonic current effect due to switching of the transformerless inverter is described. Corresponding harmonic currents circulate as current loops are formed through elements such as the GPT primary ground and parasitic capacitance components of solar modules or energy storage devices. A harmonic current is generated due to switching, which consequently flows through the neutral line on the primary side of the GPT and induces zero-phase voltage on the tertiary side of the GPT regardless of the ground fault occurrence. This problem may cause burnouts of the CLR and GPT due to exceeding the rated load of CLR. Moreover, malfunctions may occur if the operating reference voltage of OVGR or SGR linked to GPT is surpassed, as depicted in Figure 7.  
While capable of inducing a zero-phase voltage of GPT, IMD may be disregarded as a major factor for the proposed scenario for the reasons as follows. The IMD square wave voltage source utilizes a voltage of several tens of Vs. On the other hand, the resistance of the measuring circuit and the coupling resistance corresponding to the internal configuration of the IMD are connected in series; hence, the size being several hundreds of \(\left[\mathrm{k}\Omega\right]\). When a current loop is formed through the IMD and the primary side of the GPT, even when line impedance or other impedances are not considered, the resulting current flowing to the primary side of the GPT is minute because the magnitude of resistance, due to the IMD configuration being substantial compared to the size of the IMD square wave voltage. For this reason, the zero-phase voltage induced on the tertiary side of the GPT by the IMD can be considered negligible.
## 4 Vibration Control Analysis of Track-Bridge Structure Based on TIDs
### Adverse Effects of TID on Track Slab Vibration  
To further investigate the impact of TID installation on the vibration acceleration of the underlying structures, the vibration acceleration amplitude of the track slab \(a_{sm}\) is calculated based on the optimal TID control parameters corresponding to each inertance  
Figure 5: TID control results. (**a**) Optimal value of \(a_{rm}\). (**b**) \(a_{rRMS}\) corresponding to the minimum \(a_{rm}\). (**c**) \(a_{rst}\) corresponding to the minimum \(a_{rm}\).  
value, as provided in Table 2. Additionally, using the TID parameter ranges defined in Section 4.2, the minimum \(a_{sm}\) for each inertance value is computed. The results are shown in Figure 6; the bold italic numbers in the figure represent the difference between the acceleration amplitude of the track slab corresponding to the minimum rail acceleration amplitude and that in the uncontrolled case.  
The results indicate that the minimum value of \(a_{sm}\) for each inertance coefficient is lower than the uncontrolled case. However, the value of \(a_{sm}\) corresponding to the minimum \(a_{rm}\) is not always the smallest and may even exceed the uncontrolled result. In particular, when \(a_{rm}\) reaches its global minimum (\(b_{t}=5\)), \(a_{sm}\) is also greater than the uncontrolled case, indicating a clear adverse effect. In particular, the maximum increase in the vibration acceleration amplitude of the track slab is 4.93 m/s\({}^{2}\), accounting for an 11.22% rise. This demonstrates that excessive suppression of rail vibration may lead to amplified vibration in the lower structural layers. Therefore, when designing TID control parameters in practical engineering applications, it is essential to not only target vibration reduction in the upper (rail) structure but also to consider the influence of TID on the lower layers. The goal should be to minimize the downward transfer of vibrational energy.
# Interference Mitigation for GNSS Receivers Using FFT Excision Filtering Implemented on an FPGA
###### Abstract
Section 2 reviews the recent related research on interference mitigation techniques for GNSS receivers. Section 3 illustrates the stages of developing the proposed FFT excision mitigation technique by discussing the MATLAB simulations. Section 4 demonstrates the VHDL hardware implementation stages. Section 5 discusses and evaluates the simulation results of the hardware implementation using VHDL and the validation stages. Finally, Section 6 concludes the paper.
# The Numerical Study for the Effect of Stiffness Matching on Wheel-Rail Curve Squeal Noise
## 4 Discussion
### Setting Scheme of Wheel-Rail Stiffness Match  
When an unstable mode occurs, the modal frequency closely approximates the resonant frequency of the wheel and rail. This proximity of resonant frequencies between the wheel and rail can readily lead to the instability of the wheel-rail system when subjected to single-frequency excitation. Theoretically, it is possible to manipulate the stiffness of the system by considering factors such as material properties, constraints, and structural features. This manipulation can shift the wheel and rail away from their resonant frequencies, thereby achieving noise suppression.  
If a significant disparity exists in the resonance frequencies of the wheel and rail, the likelihood of system instability under single-frequency excitation diminishes. This insight offers an optimization strategy for controlling curve squeal noise. In this paper, the issue of stiffness matching will be explored from the perspectives of materials and constraints, with the aim of addressing curve squeal noise more effectively.  
Through the change of the elastic modulus of the rail, the influence of the stiffness matching on the system stability is explored from the material perspective. To discuss the cases of rail stiffness less than wheel stiffness, equal to wheel stiffness, and too much relative to wheel stiffness, the elastic modulus of rail is taken as 1.5 \(\times\) 10\({}^{5}\) MPa, 1.8 \(\times\) 10\({}^{5}\) MPa and 2.4 \(\times\) 10\({}^{5}\) MPa, respectively, and then compared with the standard elastic modulus of rail is 2.1 \(\times\) 10\({}^{5}\) MPa.  
Through the change in rail support stiffness, the influence of the stiffness matching on the system stability is explored from the constraint perspective. To discuss the effects of different support stiffnesses, the complex modal analysis of the wheel-rail system is carried out when the rail support stiffness is 0.5 \(\mathrm{K_{eq}}\), 0.75 \(\mathrm{K_{eq}}\), 1.25 \(\mathrm{K_{eq}}\), and 1.5 \(\mathrm{K_{eq}}\), respectively, where the vertical spring stiffness \(\mathrm{K_{Veq}}\)\(=\) 35 MN\(/\)m and the lateral spring stiffness \(\mathrm{K_{Leq}}\)\(=\) 50 MN\(/\)m are set as reference rail support stiffness \(\mathrm{K_{eq}}\).
# Signal Design and Compatibility Assessment for LEO Navigation Augmentation System +
## 2 Signal Design Requirements
### Compatibility Requirement  
LEO navigation augmentation signals are usually broadcasted in the GNSS L frequency band, such as signals broadcasted in the L1 and L5 frequency bands in CENTISPACE. Reference [18] also analyzed broadcasting LEO navigation augmentation signals in the satellite communication frequency band of 1518-1525 MHz. As a service signal added later, LEO navigation augmentation signals need to consider compatibility with existing signals to avoid unacceptable interference with existing services [19].
## 2 Problem Formulation  
Assume that there are \(R\) FH signals impinging on a single antenna, the \(r\)th FH signal can be expressed as  
\[x_{r}(t)=\alpha_{r}(t)e^{j(2\pi f_{r}(t)t+\phi_{r}(t))},\ \ \ \ \ 0<t\leq T \tag{1}\]  
where \(\alpha_{r}(t)\) is the complex envelope, \(f_{r}(t)\) is the FH instantaneous frequency, \(\phi_{r}(t)\) is the phase of the signal, and \(T\) is the observation time. The received multiple FH signals by a single channel can be expressed as  
\[x(t)=\sum_{r=1}^{R}x_{r}(t)+n_{r}(t),\ \ \ \ \ 0<t\leq T \tag{2}\]where \(n_{r}(t)\) is additive white Gaussian noise. According to actual situations, FH signals are assumed to be relatively power stable, which means that all the hops of the same source have similar power, and the signals do not collide.  
As shown in Figure 1, the task of the algorithm is to track and separate FH signals under sub-Nyquist sampling. The tracking results and comparative power are the basis of the separation.
## 4 Discussion
### Tracking  
This last point is the overall objective of our contribution. In the previous work [2], it was impossible to track the fire despite of sensing and detecting capabilities of the sensors because the motes were destroyed by the fire. Our solution is to protect the mote using a thermal shield called Firesensorsock. The goal of this protection is to dampen the thermal impact from the fire on the motes during the fire spread and contact. This shield must also allow both a continuous emission of data and the temperature and the hygrometry inside the sock to vary on short time scales for locating the fire's position. In this case, the tracking of the fire spread during its spatial and temporal evolution is possible with a spatial accuracy governed by the number of nodes and a temporal accuracy due to the WSN response time. As a summary, one can consider that:  
1. the protected WSN goes on transmitting the data during the fire and allows an intrusive vision of the phenomenon; the flow of data is not interrupted and it is a reliable upgrade for WSNs.
2. the protected WSN allows one to calculate the fire spread, even if different rates of fire spread exist along the same fire line, by scanning the effect of fire heterogeneity.  
It is important to insist on the following: the thermal data, i.e., temperature and hygrometry, detected by the protected mote are not the ones of the surrounding external environment. We rather demonstrate in this work that the role of our protection enhances the performance of a WSN when exposed to a large scale natural fire. The efficiency of the network facing to another kind of combustible generating others external conditions by combustion should also be investigated.  
Finally, another feature of the network that should be studied concerns transmission. In multihop networks, the time of fire detection by a mode and the time of arrival on the base station is an important parameter which can modify the phenomenon perception in the time [7]. Indeed, we have not analyzed the radio transmission delay by varying the number of sensors under repeatable fire conditions. In the present work, the number of nodes is not important enough to present any effect on the data transmission delay, but this parameter must be taken into account if the number of nodes increases.
# Sports Risk Prediction Model Based on Automatic Encoder and Convolutional Neural Network
## 2 Overview of Relevant Theories
### Introduction to Relevant Algorithms and Technologies  
#### 2.2.1 Resampling Method  
The problem of unbalanced datasets is extremely common in machine learning and data mining, especially for problems such as classification and clustering [14]. An unbalanced dataset is one in which the number of samples in one or some categories far exceeds the number of samples in other categories, which can lead to models that are better at prediction for the more numerous categories but perform poorly for the less numerous categories. Therefore, it is particularly important to solve the problem of unbalanced data sets. The resampling technique is one of the most effective ways to solve the unbalanced dataset problem by increasing the sample size of the minority class and its proportion, thus making it easier for the classifier to identify the minority class. When the number of majority class samples is too large, the resampling technique can reduce its effect on the classifier by reducing the number of majority class samples, thus improving the problem of unbalanced datasets, improving the performance of the model, and making the classifier more accurate in learning the minority classes, which is very important for solving practical problems. According to the methods of balancing class distribution, resampling techniques can be divided into three categories: undersampling, oversampling, and hybrid sampling.  
Undersampling methods are methods that reduce the degree of sample imbalance by reducing the number of majority-class samples or increasing the number of minority-class samples. Common undersampling methods include random undersampling, undersampling with put-back, and prototype selection methods. In contrast to undersampling methods, oversampling methods generate new training data by performing certain transformations on the original data (e.g., rotating, flipping, scaling, adding noise, etc.). The two main types of oversampling methods are synthetic oversampling and random oversampling [15]. Hybrid sampling methods combine the advantages of both undersampling and oversampling to balance the number and proportion of data by adding samples from a few classes and removing samples from most classes while retaining as much of the original information as possible. Hybrid sampling methods usually include random hybrid sampling methods, resampling hybrid sampling methods, and data augmentation hybrid sampling methods.
# Prediction of the Moisture Content in Corn Straw Compost Based on Their Dielectric Properties
## 2 Materials and Methods  
### Sample Preparations  
In this experiment, fresh sheep manure and corn straw compost produced in June 2021 in Xinkou Industrial Park, Baodi District, Tianjin, were used as the test material. The test raw materials were dried in a far-infrared drying oven (Model: HY-1B, manufacturer: China Tianjin Tongli Xinda Instrument Factory, Tianjin, China), crushed using a high-speed grinder (Model: LD-Y400A, manufacturer: Shanghai Topsun Electrical Co., Ltd., Shanghai, China), and then sieved with a standard inspection sieve with an aperture (Shaoxing Shangyu Huafeng Hardware Instrument Co., Ltd., Shaoxing City, Zhejiang Province, China) size of 1.2 mm to remove the fine straw and other debris. The sieved samples were placed into sealed bags for storage. Seven samples of 160 g were removed from the sealed bags and placed in a 5 \(\times\) 5 \(\times\) 5 cm self-made acrylic capacitor on an electronic analytical balance (Model: LQ-CZ10002, Manufacturer: Kunshan Ucovite Electronic Technology Co., Ltd., Suzhou, Jiangsu Province, China). The amount of deionized water to be added was calculated to prepare the samples with various moisture contents, provided that the initial sample mass was known. Deionized water was sprayed on the samples with a high-pressure ultrafine mist spray bottle under constant stirring. The prepared compost samples were placed in a sealed bag and placed in a cool and dry place for 2 d; during this period, the samples were shaken 3-5 times a day to ensure uniform diffusion of moisture. Subsequently, 10 g of each sample was dried in a far-infrared drying oven at 75 \({}^{\circ}\)C for 24 h until the mass was constant, and the actual moisture content of the wet base of each sample was calculated based on the difference between the mass of the samples before and after drying. Finally, samples with moisture contents of 5%, 10%, 15%, 20%, 25%, 30%, and 35% (all wet basis) were obtained for the determination of dielectric parameters. The preparation process is illustrated in Figure 1.
## 4 Results and Discussion
### Crack Initiation and Propagation Concerning Frictional Contact  
#### 4.2.1 Crack Initiation  
The effects of friction force, relative crack length and loading angle on crack initiation and propagation of CCBD specimens are studied in this section. For some of the reasons illustrated in Section 2.2, the generalized MTS criterion, considering the influence of T-stress, would be used to predict the initiation and propagation of the crack. It can be seen from the conclusion in Section 4.1 that a large friction coefficient will cause the crack to be in the adhesive zone earlier [50], which makes the normal propagation of the crack gradually difficult [16]. Meanwhile, the phenomenon is more obvious under larger relative length of crack \(\alpha\) and loading angle \(\beta\). However, many literatures have also shown some experimental results of the instability of compression-shear cracks, e.g., (1) the shifting phenomenon of crack initiation point [4; 22], which indicates that the crack initiation point would shift to the center of the crack at a larger loading angle; (2) the multi-branching phenomenon of the crack [51], which generally occurs in compression-shear fracture under a certain loading angle. Therefore, in order to explore the law of crack initiation and propagation stably, the friction coefficient \(\mu\) is equal to 0, 0.3 and 0.6 respectively and the relative crack length \(\alpha\) = 0.3 and 0.5 with the loading angle \(\beta\)\(\leq\)\(60^{\circ}\).  
The crack propagation angle \(\theta_{0}\), which is defined as positive in the counterclockwise direction, versus the loading angle \(\beta\) is compiled in Figure 7. It can be seen that there is a certain amount of the deviation between the crack propagation angle predicted by GMTS criterion and that predicted by MTS criterion in  
increases with the increase of friction. Further, a larger relative crack length magnifies the above effect.