esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Simulation of Lightning Current Discharge on Rocks
###### Abstract  
Lightning is a transient, high-current discharge occurring within a thundercloud, between clouds, or between a cloud and the ground. Cloud-to-ground (CG) lightning is the most studied because of its impact on human life. The aim of this study is to elucidate the effects of lightning in Earth materials by simulating the lightning current discharges in a laboratory setting. Technical applications of this work include the study or development of customized materials used to prevent accidents, limit damage, or reduce interruptions in electrical power system owing to lightning strikes, such as lightning arresters or high-voltage fuses. High-voltage electrical arcs were discharged through rock specimens, and power, energy, and duration of discharge were estimated to provide a better understanding of the origin of naturally occurring fulgurites (shock-impact glasses) and the lightning/rock interaction. X-ray powder diffraction showed that the samples used for the experiment represent basalt (samples A0, A1-A4) and granite (samples B1, B2). Optical microscopy provides direct evidence that materials can be physically altered due to the heat generated by an arcing event. Optical microscopy observations showed that arcs passed through the target rocks and mimicked the effect of lightning strikes hitting the surface of the rock, melting the target rock, and passing to ground. Fulgurite glass observed on basalt samples shows the impact origin lining the surface of millimeter-size craters and a slash-like coating, whereas in the granite sample, the fulgurite was not observed because the arc passed directly to the laboratory ground. Significant differences in the duration of the experimental electrical arcs that passed through dry and wet samples (A1 and A3; A2 and A4, respectively) were observed. This discrepancy can be ascribed to the variation of the electrical properties related to the distribution of the water layer on the rock sample and to the occurrence of magnetite grains, which may increase the local conductivity of the sample owing to its electromagnetic properties.  
electrical arc; lightning/rock interaction; rock fulgurite 2018
## 8 Reduced-Order Vibroacoustic Computational Model
### Computation of the Projection Basis for the Internal Acoustic Fluid
#### 8.2.2 Case of a Liquid with a Free Surface  
In such a case, there is a free surface \(\Gamma_{0}\) in the internal acoustic cavity (see Section 4.3) for which the boundary condition defined by Equation (10) is added, and consequently the corresponding matrix \(\left[\mathbb{K}\right]\) is positive definite. The undamped acoustic modes of the acoustic cavity \(\Omega\) are computed by solving the \((n\times n)\) generalized symmetric real eigenvalue problem  
\[\left[\mathbb{K}\right]\mathbb{P}=\lambda\left[\mathbb{M}\right]\mathbb{P}\,, \tag{50}\]  
that admits an increasing sequence of \(n\) strictly positive eigenvalues (corresponding to the acoustic modes), \(0<\lambda_{1}\leq\ldots\leq\lambda_{n}\), associated with the eigenvectors \(\mathbb{P}_{1},\ldots,\mathbb{P}_{n}\) (the acoustic modes). Let \(0<N\leq n\). We introduce the \((n\times N)\) real matrix of the \(N\) acoustic modes associated with the first \(N\) positive eigenvalues,  
\[\left[\,\mathcal{P}\,\right]=\left[\,\mathbb{P}_{1}\,\ldots\,\mathbb{P}_{N}\, \right]. \tag{51}\]  
Matrix \(\left[\,\mathcal{P}\,\right]\) satisfies the orthogonality properties defined by Equations (48) and (49).
# Finite Element Analysis and Optimization of Hydrogen Fuel Cell City Bus Body Frame Structure
###### Abstract  
Hydrogen fuel cell city bus is a type of new energy public transportation. In this paper, in order to evaluate the safety performance of a newly developed hydrogen fuel cell city bus body frame designed by the collaborating enterprise, finite element analysis is conducted to investigate its structural mechanics and dynamic characteristics under four typical operating conditions, including horizontal bending, ultimate torsion, emergency cornering, and emergency braking. Based on the simulation results, although the body frame of the bus meets the stiffness design requirements and avoids body resonance, it exhibits maximum stresses of 328.9 MPa and 348.6 MPa under emergency cornering and ultimate torsion conditions, respectively, exceeding the material yield strength and failing to satisfy the strength design requirements. Therefore, the size optimization method is employed to optimize the thickness of the body frame components. After optimization, the maximum stresses are reduced to 262.7 MPa and 300.6 MPa, respectively, representing a reduction of up to 20.13%. The optimization significantly improves performance and meets the strength design requirements. Furthermore, the body frame is lightened by 106 kg, achieving the goal of weight reduction.  
hydrogen fuel cell city bus; body frame; finite element; size optimization Article
## 4 Measurement Methods and Data Analysis
### Data Analysis
#### 4.2.3 Data Analysis at the Same Measurement Distance and Different Rotational Velocities
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Equal Spacing Measurement**} & \multicolumn{3}{c}{**Equal Spacing Measurement**} & \multicolumn{3}{c}{**Equal Spacing Measurement**} & \multicolumn{3}{c}{**Equal Spacing Measurement**} \\
**Rotational** & & **(5 mm)** & & & **(10 mm)** & & & **(5 mm)** & \\ \cline{2-10}
**Velocity** & **Dynamic** & **Actual** & **Number** & **Dynamic** & **Actual** & **Number** & **Dynamic** & **Actual** & **Number** \\
**(r/s)** & **Indication Error** & **Spacing** & **of Mean-surement** & **Indication Error** & **Spacing** & **of Mean-surement** & **Medication Error** & **Spacing** & **of Mean-surement** \\
**(mm)** & **(mm)** & **Points** & **(mm)** & **(mm)** & **Points** & **(mm)** & **(mm)** & **(mm)** & **Points** \\ \hline
0.2 & \(-\)0.0081 & 5.21 & 600 & \(-\)0.0067 & 10.47 & 300 & \(-\)0.0076 & 20.27 & 155 \\
0.6 & \(-\)0.0066 & 5.75 & 549 & \(-\)0.0142 & 11.51 & 274 & \(-\)0.0064 & 21.05 & 150 \\
1.0 & \(-\)0.0033 & 6.35 & 497 & \(-\)0.0121 & 12.61 & 249 & \(-\)0.0065 & 22.12 & 143 \\
1.4 & \(-\)0.0260 & 8.87 & 356 & \(-\)0.0179 & 13.28 & 237 & \(-\)0.0037 & 22.12 & 143 \\
1.8 & \(-\)0.0310 & 11.33 & 278 & \(-\)0.0436 & 11.39 & 278 & \(-\)0.0126 & 22.66 & 139 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Dynamic performance of laser tracker at evenly spaced and different speeds (D = 3 m). Figure 12: Relationship between speed and number of measurement points—(equally spaced). (2) Whether at low speed or high speed, the sampling frequency is fixed proportionally to the number of measurement points at the fixed speed. Conclusion (3) in Section 4.2.2 indicates that the sampling frequency has a fixed relationship with the number of measurement points at the fixed measurement distance and rotational velocity. The data analysis in Section 4.2.2 explores the experiment of the tracker at the dynamic limit speed, and this part examines the experiment at different rotational velocities. When the rotational velocity is set to 0.2 r/s, and the measurement frequency is set to 10 Hz, 20 Hz, and 50 Hz, the measurement points are 48, 96, and 240, respectively. Figure 13 shows the relationship between the rotation speed of the circular trajectory generator and the number of measuring points when the tracker is set at equal sampling frequency.
## 3 Methodology
### Retrieval-Augmented Generation
#### 3.2.1 Vector Embeddings  
Vector embeddings are a fundamental concept in RAG, representing objects such as control panel components as vectors in a continuous vector space. This method captures functional relationships and similarities between parts, supporting intelligent applications like automated part classification, predictive maintenance, and inventory optimization. The embedding process transforms discrete part information into numerical vector representations that reflect both semantic and functional characteristics.  
Embedding models play a crucial role in this process by converting words or terms into vectors based on their meanings and usage within a specific context. Trained on large collections of text, these models learn to position semantically related terms closer together in a high-dimensional space [21]. In the context of control panel manufacturing, for example, embedding models can identify that'relay' and 'contactor' are functionally similar and frequently used together, mapping them to nearby points in the vector space. This numerical encoding preserves meaningful relationships, enabling systems to perform tasks such as clustering, classification, and analogy detection more effectively.  
Figures 3 and 4 illustrate how this works. Terms like'relay' and 'contactor' appear close to each other due to their similar roles, while components like 'timer' are positioned further apart, reflecting their distinct functions. The diagrams also highlight analogical patterns such as the relationship between'switch:block' and 'button:light', demonstrating how embeddings capture structure and meaning within technical vocabularies.  
Figure 3: An example structure of word embeddings and their relationships.  
We utilize an open-source embedding model, BAAI/bge-small-en-v1.5 [22]. The bge-small-en-v1.5 model, developed by the Beijing Academy of Artificial Intelligence (BAAI) as part of the FlagEmbedding project, is a compact English text embedding model designed for efficient performance in resource-constrained environments. As a smaller variant of the larger bge-base and bge-large models, it utilizes 384-dimensional embeddings.
# Field-Based Optimal Placement of Antennas for Body-Worn Wireless Sensors
## 1 Introduction
In our investigation, half wave dipole antennas in a vertical position were considered. Figure 1: Structure of the wireless body-worn sensor system.
# Efficient V2V Communications by Clustering-Based Collaborative Caching +
###### Abstract  
Vehicle-to-vehicle (V2V) communication plays an important role in enabling autonomous driving. However, when multiple vehicles request the same content, like road conditions, delivering it individually by V2V communication can significantly increase traffic volume, potentially causing congestion in the wireless channel. To address this issue, Content-Centric Network (CCN) technology is applied to V2V communication, which improves communication efficiency by exploiting content cached at vehicles. However, previous methods faced the following challenges: (i) vehicles could not use content stored in nearby vehicles outside the communication path, and (ii) redundant caching of the same content occurred at nearby vehicles. To tackle these challenges, this paper proposes a collaborative caching method in which vehicles are grouped into clusters and each cluster has a designated head responsible for managing caches across all vehicles within the cluster. In this way, this method enables vehicles to use the content cached at adjacent vehicles that are not directly on a communication path. In addition, it eliminates redundant caches, allowing a more diverse range of content storage. Extensive simulation results demonstrate that the proposed approach effectively reduces content delivery latency by 33% compared to the method using clusters without cooperative caching and by 19% compared to the ECV+ method.  
V2V communication; Content-Centric Network; collaborative caching; clustering +
Footnote †: This paper is an extended version of our paper published in [PERSON]; [PERSON] Efficient V2V communications by clustering-based collaborative caching. In Proceedings of the 2022 IEEE International Conference on Vehicular Electronics and Safety (ICVES), Bogota, Colombia, 14-16 November 2022; pp. 1-6, doi: 10.1109/ICVES6941.2022.9986927.
## 4 Experiment  
### Experiment Setup  
**Datasets**. There are currently three main datasets for camouflaged object detection: CHAMELEON [38], CAMO [39], and COD10K [40]. The method proposed in this article needs to demonstrate good generalization ability and robustness in different camouflage scenarios. Therefore, we used three mainstream public datasets for evaluation criteria, which included multiple categories of camouflage objects, such as animals, plants, and artificial objects, with different camouflage difficulties, such as background matching, destructive colors, and edge blurring, to fully evaluate the advantages and disadvantages of our method. This article follows a previous work [40] and uses the training set of CAMO [39] and COD10K [40] as the training set (4040 images). The remaining images will be used as the test set.  
**Evaluation Metrics**. In order to evaluate the performance of the method proposed in this article, four widely used standard metrics were used, namely, a structural metric \(S_{\pi}\uparrow\)[41], which takes into account the structural similarity between saliency maps and truth values; an adaptive E-metric \(E_{\phi}^{d}\uparrow\)[42], which takes into account the degree of edge alignment between saliency maps and true values; a weighted F-measure \(F_{\beta}^{w}\uparrow\)[3], which considers the balance of accuracy and recall between saliency maps and true values; and the mean absolute error \(M\downarrow\), which takes into account pixel-level differences between saliency maps and true values.  
**Experimental Process**. We used PyTorch [43] to implement our proposed model. We trained and tested our model on an Ubuntu 20.04 system using two NVIDIA GeForce RTX 3070 GPUs. During the model training phase, we set the size of the input image to \(320\times 320\) and performed data augmentation with random horizontal flipping and color jitter. We used the pre-trained ENet-B7 [24] model on ImageNet to initialize the parameters of the encoder network, and adopted a poly [44] strategy to adjust the learning rate, where the initial learning rate was 0.001 and the exponent was 0.9. In the testing phase, we first performed network inference on the image and then restored the size of the output image to the original size of the input image. We used bilinear interpolation in both size transformations. We did not use any post-processing methods to improve the final output performance.
## 2 Materials and Methods
### Selecting Satellite-Derived Indicators for CLQ Evaluation  
Since the four typical periods remote sensing images selected through the GEE random forest classification model (Table 1) had already undergone atmospheric and radiometric correction within the GEE platform, the Python 3.4.3 rasterio package was used to read the remote sensing image files, and geopandas package was used to read the vector files of Shuanghe Farm. Subsequently, the reflectance data for all bands of the remote sensing images were extracted using each plot within the farm area as the basic unit. The specific extraction method involved extracting the reflectance data for all pixels within a vector plot unit. The quartile method was then used to remove any outlier pixel band data from these values. The quartile method is a statistical technique used to describe the distribution of a dataset. When the reflectance dataset within a plot unit is ordered from smallest to largest, it is divided into four equal parts, each containing 25% of the data. These four segments are the first quartile (Q1), median (Q2), third quartile (Q3), and fourth quartile (Q4), respectively. The first quartile is the 25 th percentile of the dataset, separating the lowest 25% from the remaining 75%. The third quartile is the 75 th percentile of the dataset, separating the lowest 75% from the remaining 25% [38]. This study uses the difference between the third quartile (Q3) and the first quartile (Q1), with the purpose of excluding the top and bottom 25% of the data, retaining only the middle 50% of the dataset. Afterward, the average reflectance value of all remaining pixels is calculated, providing the complete band reflectance data for the vector plot unit. This method is applied to all plot units within the farm area, resulting in a total of 1413 Landsat 9 full-band reflectance datasets based on the individual plot units.  
In this study, the reflectance of the seven spectral bands from Landsat 9 remote sensing, extracted using the method described above, is directly used as input for the cultivated land quality assessment model at the farm scale. Additionally, to facilitate comparison with other studies, based on the extracted spectral reflectance of the seven bands from Landsat 9, the study also calculates five commonly used vegetation indices in cultivated land quality evaluation: NDVI, EVI, SAVI, RVI, and DVI [17; 19; 39]. This allows for a subsequent comparison between the farm-scale cultivated land quality assessment model based directly on spectral band data and the model based on vegetation indices. All the indicators used in the farm-scale cultivated land quality assessment based on Landsat 9 satellite remote sensing in this study are shown in Table 2.
## 3 Proposed Methods
### Enhancing Shape Bias Through Architectural Modifications  
In prior work, approaches such as replacing the texture of images in a training dataset with those from other images or removing textures altogether have been employed to enhance CNNs' shape bias. These methods encourage the model to ignore local textures and prioritize global shape information during learning. Additionally, texture bias, an inductive bias inherent to CNNs, can be attributed to the convolution operation, which correlates with surrounding pixels. Due to their repeated convolutional operations, CNNs tend to emphasize local textures.  
To address this, we propose strategies such as increasing the kernel size of convolutional layers or expanding the dilation rate. These strategies broaden the pixel range used in each convolutional operation, thereby expanding the CNN's receptive field and enabling the network to consider a wider array of pixel arrangements. Moreover, when the dilation rate is increased, pixels between the convolved pixels remain unprocessed, which may allow the network to ignore local continuous pixel textures. For example, a convolution operation with a kernel size of five and a dilation rate of two covers the same \(5\times 5\) area in a single operation. However, with a dilation rate of two, gaps are introduced between the pixels used for computation, allowing the model to disregard continuous local textures.
## 2 System Model
### Position Estimation
#### 2.2.2 Receiver Mobility Model  
The following describes a model representing the movement of a ship. It is assumed that the ship moves at a constant velocity \(V_{r}\) in the horizontal plane. The vertical movement represents the up-and-down fluctuations caused by ocean waves, which are expressed as a superposition of three sinusoidal waves. This approach enables a more realistic simulation of wave undulations. Accordingly, the ship's position \(x_{r}(t)\), \(y_{r}(t)\), and \(z_{r}(t)\) can be expressed by the following equations.  
\[x_{r}(t)=x_{r0}+V_{rx}t, \tag{4}\]  
\[y_{r}(t)=y_{r0}+V_{ry}t, \tag{5}\]  
\[z_{r}(t)=A_{1}\sin(\omega_{1}t+\phi_{1})+A_{2}\sin(\omega_{2}t+\phi_{2})+A_{3} \sin(\omega_{3}t+\phi_{3}). \tag{6}\]  
In this context, the initial position of the ship is given as \((x_{r0},y_{r0},z_{r0})\), where \(V_{rx}\) and \(V_{ry}\) represent the velocities in the \(x\) and \(y\) directions, respectively. The parameters \(A_{i}\), \(\omega_{i}\), and \(\phi_{i}\) denote the amplitude, angular frequency, and phase of the sinusoidal components, respectively.
## 2 Proposed System Schematic Model
Further, the P/S module converts the \(M\) parallel time domain signal \(S_{m}^{j}(t)\) to a serial signal \(S_{OFDM}^{j}(t)\), which is expressed as  
\[S_{OFDM}^{j}(t)=\sum_{m=0}^{M-1}X_{m}^{j}e^{2\pi ifmt} \tag{3}\]  
The transmitted OAM-OFDM-SMM multiplexed signal after adding a cyclic prefix (CP) into a \(S_{OFDM}^{j}\) signal and converting it into an analog signal in the DAC module corresponding to the \(u_{th}\) mode states for \(1\leq\ u_{th}\leq\) U is given in matrix form by Equation (4):  
\[S_{OAM-OFDM}(t)=\sum_{j=1}^{L}\sum_{u=0}^{U-1}\sum_{m=0}^{M-1}X_{m}^{j}e^{2\pi if _{m}t}e^{\frac{2\pi ini}{U}} \tag{4}\]  
\[S=S_{OAM-OFDM}(t)=[S_{1}(n),S_{2}(n)\ldots\ldots\ldots,S_{N}(n)]^{T} \tag{5}\]  
When this signal shown by Equation (5) is transmitted through the turbulent FSO channel, a phase distorted signal \(\hat{S}\) is received at the optical receiver end and given by  
\[\hat{S}= \begin{bmatrix}\hat{S}_{1}(n),\hat{S}_{21}(n)\ldots\ldots\ldots\ldots \ldots,\hat{S}_{N}(n)\end{bmatrix} ^{T}=HS \tag{6}\]  
The plane wave from the output of the OAM state de-muv subsystem is demodulated in the \(j_{th}\) OFDM demodulator subsystem using the inverse process used in the OFDM modulator subsystem. Thus, the output signal matrix from the de-multiplexer and OAM state converter is expressed by [16]. \[Y=[Y_{1}(n),Y_{2}(n),\ldots\ldots\ldots\ldots Y_{N}(n)]=G\hat{S} \tag{7}\]  
where \(H\) and \(G\) are the \(N\times N\) atmospheric turbulence channel and OAM-OFDM-SMM MIMO matrix respectively with \(h_{ij}\in H,S_{if}\in G\) and \(i,j\in N\). The estimation SNR for the tap gain coefficient \(w_{ij}^{l}\) of length \(L+1\) for the spatial mode multiplexed equalization is expressed as  
\[\overset{\wedge}{y}_{j}(n)=\sum_{i=1}^{N}\sum_{l=0}^{L}w_{ij}^{l}\overset{ \wedge}{s}_{i}(n-l) \tag{8}\]
# Enhanced Efficiency and Reduced Side Lobe Level Convex Conformal Reflectarray
## 1 Introduction
There are few exceptions to this, such as the solution proposed in [23], based on the design of an ad hoc feed, consisting of a coupled tapered slot-line antenna to obtain a uniform illumination of the reflectarray aperture in one plane, or that in [24], where the use of non-symmetric feeds uniformly illuminating the antenna aperture for both centered or off-set positions is suggested. The solutions proposed in [24] enhance the planar RA performance, but at the cost of an increase in the feed complexity and size. Both in [23] and in [24], the reflectarray is planar; according to the authors' knowledge, the design of a convex RA whose features are enhanced by playing with the design of a _ad hoc_ has never been proposed in the literature. According to the procedure here introduced, first the features of the feed are determined, considering the entire antenna system: the results, summarized in Section 5, prove that the proposed solution not only maximizes the antenna efficiency, but allows also to control the SLL, despite what is possible with other commonly adopted feed horns. Then, a proper architecture for its manufacturing is selected: here, it was decided to adopt a smooth-wall configuration, where the desired radiation pattern is obtained by exploiting a series of slope variations along the horn profile since it is an effective, easy-to-realize geometry that presents the further advantage of being particularly suitable for 3D-printing manufacturing, leading to fast and low-cost prototyping [25; 26].
## 3 Diagnostic Model and Diagnostic Process
### Diagnostic Process  
The fault diagnosis process based on GAF-ResNet-MA is as follows: Firstly, the vibration signal is divided into frames of a certain length, encoded by GAF, and a two-dimensional image is generated. Secondly, the image dataset is divided into a training set and a testing set in a certain proportion. Thirdly, the training set is input into ResNet for training to obtain a fault diagnosis model, and then the test set data are input to analyze the diagnostic performance. The specific diagnostic process is shown in Figure 4.  
Figure 4: The GAF–ResNet-MA diagnostic process.
## 3 Power Allocation of SCO\(-\)AN  
The transmission power of the wireless communication system is limited. It is essential to allocate secrecy capacity under limited transmission power.