esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A  
Usually, \(r_{1}=0.001\) is used for shot noise estimation, \(r_{2}=1\) is used for quadrature measurement. The variance of \(\widehat{y}\) under the two attenuation rates can be expressed as:  
\[\begin{split}\langle\widehat{y}^{2}\rangle_{1}&=V_{s 1}=r_{1}\eta T_{ch}(V_{A}+\epsilon)N_{0}+N_{0}+V_{el},\\ \langle\widehat{y}^{2}\rangle_{2}&=V_{s2}=r_{2} \eta T_{ch}(V_{A}+\epsilon)N_{0}+N_{0}+V_{el},\end{split} \tag{10}\]  
The real-time measured shot noise variance value and the excess noise variance value using this value as a unit are estimated by the following:  
\[\begin{split}\widetilde{N_{0}}&=\frac{r_{2}V_{s 1}-r_{1}V_{s2}}{r_{2}-r_{1}}-V_{el},\\ \widetilde{\epsilon}&=\left[\frac{V_{s2}-V_{s1}}{( r_{2}-r_{1})\eta T_{ch}}-V_{A}\widetilde{N_{0}}\right]/\widetilde{N_{0}}. \end{split} \tag{11}\]  
The total variance is given by [23]:  
\[V_{s,i}=V_{part1,i}+V_{part2,i}. \tag{12}\]  
where  
\[V_{part1,i}=\gamma\left[r_{i}\eta T_{ch}(V_{A}+2+\epsilon)+1\right]N_{0}+V_{ el}, \tag{13}\]  
\[V_{part2,i}=(1-r_{i})^{2}D^{2}+\eta\langle I_{j}^{lo}\rangle+\eta r_{i}^{2} \langle I_{j}^{s}\rangle. \tag{14}\]  
According to Equations (17)-(20), Equations (21) and (22) can be rewritten as:  
\[\begin{split}\eta\langle I_{j}^{lo}\rangle&=\frac{ \eta}{2}I_{1}^{lo}+\frac{\eta}{2}I_{2}^{lo}\\ &=\frac{\eta}{2}\frac{D_{1}^{lo}}{\eta(2T_{1}^{lo}-1)}+\frac{ \eta}{2}\frac{D_{2}^{lo}}{\eta(2T_{2}^{lo}-1)}\\ &=571.9D,\end{split} \tag{15}\]  
\[\begin{split}\eta r_{i}^{2}\langle I_{j}^{s}\rangle& =r_{i}^{2}(\frac{\eta}{2}I_{1}^{s}+\frac{\eta}{2}I_{2}^{s})\\ &=r_{i}^{2}(\frac{\eta}{2}\frac{D_{1}^{s}}{\eta(1-2T_{1}^{s})}+ \frac{\eta}{2}\frac{D_{2}^{s}}{\eta(1-2T_{2}^{s})})\\ &=571.9r_{i}^{2}D.\end{split} \tag{16}\]
# Multilayer Model in Soil Moisture Content Retrieval Using GNSS Interferometric Reflectometry
## 5 Discussion and Conclusions  
It has taken decades for GNSS-IR to evolve into a fairly developed approach for retrieving SMC. The goal of the current study is to lessen the impact of vegetation, snow cover, and soil texture. Moreover, the majority of research locations are farms. Moreover, progressively switching from the ground scene to the space scene was the platform.  
Another crucial component in the study of meteorology is the study of tidal flat SMC. The purpose of this study is to investigate how soil moisture is distributed unevenly due to groundwater penetration and rainfall. The model developed in this work can accurately quantify SMC at various depths using simulation analysis, and it has some value for researching how SMC changes during rainfall and groundwater infiltration.  
In this study, the retrieval of SMC was carried out by using the total transmission of electromagnetic waves with vertical polarization characteristics in GNSS signals on the soil surface. Moreover, a multi-layer model for soil moisture measurements using GNSS-IR was established, including sink and reverse osmosis modes. We used the Hilbert transform to extract the envelope of the interference signal to reduce the effect of the SNR waveform burr. Then EMD algorithm was used to separate the low and high-frequency components of the raw interference signal to reduce the influence of high-frequency components in spectrum estimation. There is a very good connection between the soil moisture retrieval based on the two models and the predetermined soil moisture. The multi-layer SMC retrieval model is more versatile than the conventional GNSS-IR single-layer SMC retrieval model in that it can assess the SMC at various soil depths. In order to understand the vertical distribution of soil water content and to direct the agricultural output, this study's theoretical model serves as a guide. The lowest soil often has low measurement precision due to signal attenuation during soil propagation. Data indicate a correlation of 0.92 between the measured results and the specified soil moisture level.  
The soil model used in this study is based on true SMC data using a simulation platform in order to calibrate the GNSS-IR multi-layer SMC measurement model proposed in this study and enhance the measurement's accuracy. We will conduct experimental verification in subsequent work.  
Methodology, J.L. and D.Y.; Software, J.L.; Writing--original draft, J.L.; Writing--review & editing, X.H. and F.W.; Funding acquisition, F.W. and L.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Multi-Parameter Integrated Inversion of Terrestrial GNSS reflection signals in the hydrology river (NSFC), grant number 42104031.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 12: The scatter plot of the set SMC and measurement results for the two modes.
## 2 System Model  
This paper aims to jointly design the transmitters of the source and relay and the receivers of the source in the FD BWSN with SWIPT. We adopt a three-node sensor system consisting of two sources and a relay and assume that the sources are equipped with the PS receiver and that the relay applies an AF scheme. Without a loss of generality, we suppose that the energy conversion efficiency at the PS receiver is 100 percent and that the PS ratio is fixed.  
The considered three-node MIMO BWSN with SWIPT consists of two sources: \(S_{1}\) and \(S_{2}\) both equipped with \(M_{T}>1\) transmit antennas and \(M_{R}>1\) receive antennas. \(S_{1}\) and \(S_{2}\) decode the information, harvest the energy by PS, and exchange information with the help of the single AF relay node \(R\) with \(N_{T}>1\) transmit antennas and \(N_{R}>1\) receive antennas, as shown in Figure 1. All nodes are assumed to operate in FD mode, which means they transmit and receive data at the same time and frequency.  
Figure 1: The system model of the multi-input multi-output (MIMO) full-duplex (FD) bidirectional wireless sensor network (BWSN) with energy harvesting (EH).  
Let \(\mathbf{H}_{S_{i}R}\in\mathbb{C}^{N_{R}\times M_{T}}\) and \(\mathbf{H}_{RS_{i}}\in\mathbb{C}^{M_{R}\times N_{T}}\) denote channel matrices from \(S_{i}\)'s transmit antennas to \(R\)'s receive antennas and that from \(R\)'s transmit antennas to \(S_{i}\)'s receive antennas, respectively. We assume that all channels are statistically independent, reciprocal in the incoming and outgoing directions, and slowly time-varying quasi-static flat Rayleigh fading. Moreover, the self-interference channels at the corresponding nodes are represented as \(\mathbf{H}_{S_{1}S_{1}}\in\mathbb{C}^{M_{R}\times M_{T}}\), \(\mathbf{H}_{S_{2}S_{2}}\in\mathbb{C}^{M_{R}\times M_{T}}\), and \(\mathbf{H}_{RR}\in\mathbb{C}^{N_{R}\times N_{T}}\).  
Meanwhile, in our system, the two source nodes \(S_{1}\) and \(S_{2}\) are set far apart so that the direct link between them is assumed to be ignored. Moreover, we suppose that the perfect channel state information (CSI) is available at each node [38; 39; 40] and that the transmit power of the two sources are equal.  
For a further analysis, the node deployment and optimization models are presented as follows.
## 2 Finite Element Simulation
### Small Box Girder  
One prestressed concrete bridge with a cross-section consisting of seven small box girders in Handan, China was chosen as the case study. A Portland cement concrete overlay with a thickness of 12 cm and an asphalt concrete overlay with a thickness of 10 cm were used. The dimensions of the small box girder cross-sections are illustrated in Figure 4. Six temperature sensors were installed in the top flanges (1-T and 7-T), webs (1-W and 7-W), and bottom flanges (1-B and 7-B) of the small box girders #1 and #7, as shown in Figure 4. Due to space limitations, the data from 17 July 2015 in summer, which is a sunny day with the highest ambient air temperature during the monitoring period, as well as high solar radiation and low wind speed, were selected for analysis. The field test was carried out after bridge opening. Therefore, there was no temperature gauge placed inside the cells of the small box girders. The Measured Temperature Method was not considered in the analyses of the temperature distributions on concrete small box girder cross-sections. The meteorological data, including ambient air temperature, hourly global solar radiation, hourly diffuse solar radiation, and wind speed, were measured using a movable automatic meteorological station at the bridge site with a measurement time interval of one hour.  
The midas FEA software was chosen to establish a FEM of the small box girder cross-sections by using two-dimensional plane strain elements, as shown in Figure 5. The three simulation methods for the internal thermal boundary conditions summarized in Section 1, except the Measured Temperature Method, were considered in the FEMs of small box girders. For the Ambient Temperature Method, the measured hourly ambient air temperature curve (17 July 2015) was used, as shown by the solid line in Figure 6. For the Mean Temperature Method, the mean temperature (27.1 \({}^{\circ}\)C) over the previous  
Figure 4: Layout of the small box girder cross-sections (unit: cm).  
Figure 3: Comparison of different simulation methods for the temperature curves inside the cavities.  
two days (16 and 17 July 2015) was calculated, as shown by the dashed line in Figure 6. The mesh size of the FEMs using the Ambient Temperature Method or Mean Temperature Method was set as 20 mm. The total numbers of nodes and elements were 34,020 and 32,462, respectively, as illustrated in Figure 5a. For the Air Element Method, the total numbers of nodes and elements for the FEM were 45,632 and 44,786, respectively, as illustrated in Figure 5b. Other detailed information was the same as the FEM of the box girder, as reported in Section 2.1.
# Research on High-Performance Fourier Transform Algorithms Based on the NPU
## 1 Introduction
In benchmarking against the GPU OpenCL-based Cooley-Tukey algorithm running on NVIDIA Tegra X2, our MM-2 DFT algorithm outperforms on small inputs, while the MV-2 FFT algorithm shows superior performance on larger inputs. Based on these algorithms, we then discussed the energy efficiency of various computing units within the NPU. In summary, our contributions are as follows:  
* We analyzed the Discrete Fourier Transform (DFT) and several typical Fast Fourier Transform (FFT) algorithms. Then, we analyzed the adaptability of those algorithms and designed corresponding acceleration strategies based on the distinctive hardware architecture of NPU;
* We have presented the direct matrix multiplication algorithm (MM-2 DFT), which can fully utilize the computing power of the cube unit for accelerating the DFT algorithm. The MM-2 DFT is highly suitable for scenarios with smaller inputs;
* We have presented the matrix-vector iterative operation-based FFT algorithm (MV-2 FFT), which uses both the cube and vector unit to perform the FFT algorithm based on the matrix divide-and-conquer method. Building on this algorithm, we developed two implementations to evaluate the performance difference between matrix and vector multiplication on various computation units of NPUs. The results revealed the fact that (1) the MV-2 FFT is better suited for scenarios with larger inputs, and (2) vector multiplication is quicker and more energy-efficient than matrix multiplication on NPUs;
* We deployed the NPU algorithm implementations on a real hardware platform and evaluated their performance and power consumption in a typical image preprocessing task. Based on that evaluation, we comprehensively analyzed the energy efficiency of different algorithms and the typical NPU's internal hardware units.
# A New Constructive Heuristic Driven by Machine Learning for the Traveling Salesman Problem
## 1 Introduction
### The Traveling Salesman Problem  
Given a complete graph \(G(V,E)\) with \(n\) vertices belonging to the set \(V=\{0,\ldots,n-1\}\), and edges \(e_{ij}\in E\) for each vertex \(i,j\in V\) with \(i\
eq j\), let \(c_{ij}\) be the cost for the directed edge \(e_{ij}\) starting from vertex \(i\) and reaching vertex \(j\). The objective of the Traveling Salesman Problem is to find the shortest possible route that visits each vertex in \(V\) exactly once and creates a loop returning to the starting vertex [1].  
The [21] formulation of the TSP is an integer linear program describing the requirements that must be met to find an optimal solution to the problem. The variable \(x_{ij}\) defines if the optimal route found picks the edge that goes from vertex \(i\) to vertex \(j\) with \(x_{ij}=1\), if the route does not pick such edge then \(x_{ij}=0\). A solution is defined as a matrix \(X\) with entries \(x_{ij}\) and dimension \(n\times n\). The objective function is to minimize the route cost, as shown in Equation (1).  
\[\min\sum_{i=0}^{n-1}\ \sum_{j=0,j\
eq i}^{n-1}\ \ \ c_{ij}\ x_{ij} \tag{1}\]  
\[\sum_{i=0,i\
eq j}^{n-1}x_{ij}=1,\ \ \ \ \ \ \ \ \ j=0,\ldots,n-1 \tag{2}\]  
\[\sum_{j=0,j\
eq i}^{n-1}x_{ij}=1,\ \ \ \ \ \ \ \ \ \ i=0,\ldots,n-1 \tag{3}\]  
\[\sum_{i\in Q}\sum_{j\in Q,j\
eq i}x_{ij}\leq|Q|-1,\ \ \ \ \ \ \forall Q\subset\{0,\ldots,n-1\},\,n>|Q|\geq 2 \tag{4}\]  
\[x_{ij}\in\{0,1\},\ \ \ \ \ \ \ \ \ \ i,j=0,\ldots,n-1,\ \ \ i\
eq j \tag{5}\]  
There are the following constraints: each vertex is arrived at from exactly one other vertex (Equation (2)), each vertex is a departure to exactly one other vertex (Equation (3)) and no inner-loop between vertices for any proper subset \(Q\) is created (Equation (4)). The constraints in Equation (4) prevent that the solution \(X\) is the union of smaller tours. To conclude, each edge \(x_{ij}\) in solution must be not fractional (Equation (5)). We point out that the graphs used in this work are symmetric and placed in a two-dimension space.  
A CL with cardinality \(k\) for vertex \(i\) is defined as the set of edges \(e_{ij}\), with \(j\in CL[i]\), such that the vertices \(j\) are the closest \(k\) vertices to vertex \(i\). Note that for each vertex there is a CL, and for each CL there are at most two optimal edges.
# Signature Split Method for a PQC-DSA Compliant with V2V Communication Standards
## 2 Preliminaries
### V2 Verifier
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **WAVE** & **C-V2X** & **eC-V2X** & **5G-V2X** \\  & **(IEEE 802.11p)** & **(3 GPP Rel.14)** & **(3 GPP Rel.15)** & **5G-V2X** \\ \hline Processing rate & & & 10\(\sim\)100 & \\ (Mbps) & & & (Maximum 1000) & Maximum 2000 \\  & & & & \\ \hline  & & & 400 & Platooning: 6000 \\ BSM packet size & 2000 & & (advanced: 12,000 ) & Advanced: 12,000 \\  & & & & Autonomous: 41,700 \\ \hline Maximum & & & & Platooning: 350 m \\ communication radius & 1 km & 320 m & 1 km & Advanced : 700 m \\  & & & & Autonomous: 1 km \\ \hline End-to-end & 100 & 100 & 100 & Platooning: 25 \\ latency (ms) & & & (platooning: 10) & Advanced: 100 \\  & & & & Autonomous: 5 \\ \hline Vehicle maximum & 200 & & 280 & 500 \\ relative speed (km/h) & & & & \\ \hline Correctness & & 80\(\sim\)95\% & 90\(\sim\)100\% & 90\(\sim\)100\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Attribute parameters required for V2X communication. The detailed structure of \"run_remote\" and \"run_local\" API is as follows:  
* _run_remote_ \"run_remote\" API simultaneously generates as many payloads as the number of remote cars using a multiple processes and broadcasts the payloads to local car. Using \"yml\" module, 300 pieces of location information are sent in a text file unique to each remote car to the local car at 0.1 s intervals. Additionally, based on the time function of the \"time\" module, the payload generation time for 300 pieces of location information is measured. In this process, each specific payload of remote cars is generated by calling the internal \"_Get_wsm_payload_\" API. The payload has three components, Bytestring (communication flag), Wsm header (Wsm header), and IEEE 1609.data (IEEE standard protocol), and consists of a total of 16 pieces of information. It requires about 500 bytes, excluding signature length information and signature. Therefore, if the sum of the signature length information and the signature of PQC-DSA is less than 1500 bytes, then the WAVE protocol requirement of less than 2000 bytes is satisfied, and it is applicable to the WAVE protocol. This process maintains the IDs and locations of n cars in context. The payload generation process includes a signature to the current location. Moreover, when the payload is generated, it uses multiprocessing to send the payload to \"run_local\" API. The initial communication of between remote cars and local car involves the process of generating a key used for signing. * _run_local_ Local car receives a payload containing the current position of remote cars. \"_run_local_\" API is always active in receive standby mode. It then goes through a signature verification process and deconstructs the payload when the verification is successful. If the signature verification succeeds, API disassembles payload, and the local car obtains the location information. If the signature verification fails, one waits for the next payload reception.
## 3 Results
### Analysis of Interactive Factors
#### 3.3.1 Analysis of Interactive Factors on Cohesion  
When the soil wet bulk density was 2.3 g cm\({}^{-3}\), the cohesion increased with the increase in clay content and moisture content (Figure 3a). The maximum soil cohesion value was obtained when clay content was within 36-40% (the coded value within 1-1.682) and moisture content within 30-34% (the coded value within \(-\)1.682-1). The contour map showed that the variation rate of cohesion along the direction of clay content was higher than that of moisture content, which indicated a greater influence of clay content on soil cohesion than moisture content. In Figure 3b, when the moisture content was 40%, cohesion increased firstly and then decreased with the increase in clay content and density. The maximum cohesion was reached when clay content was between 36 and 40% (coded value 1-1.682) and density was between 2.6 and 2.8 g\(\cdot\)cm\({}^{-3}\) (coded value 1-1.682). The contour map suggests a greater impact of clay content on soil cohesion than moisture content, as the variation rate of cohesion along the direction of clay content was greater than density. Figure 3c shows that cohesion decreased with the increase in moisture content and density when the clay content was 31%. The highest cohesion was estimated when the moisture content and density were within 30-34% (coded value \(-\)1.682\(-\)\(-\)1) and 2.6-2.8 g\(\cdot\)cm\({}^{-3}\) (coded value 1-1.682), respectively. Moreover, as shown from the various rates of cohesion in different directions in Figure 3c, the soil moisture content has a greater impact on cohesion than wet bulk density. However, the interactive effect of these two factors on cohesion was negligible (\(p>0.1\)).  
To summarize, the contribution rates of clay content (\(X_{1}\)), moisture content (\(X_{2}\)), and density (\(X_{3}\)) to cohesion from high to low followed the order \(X_{1}>X_{2}>X_{3}\) (Table 5). Considering the interactive influences of two factors on soil cohesion, the contribution rates from high to low were ordered as \(X_{1}X_{2}>X_{1}X_{3}>X_{2}X_{3}\).
## 3 Proposed Superpixel-Based MSGATN
### Multi-Scale Siamese Graph Attention Network  
In the proposed MSGATN, a GATN is employed to better reveal the relationships between multi-scale unstructured superpixels, and the graph attention mechanism is the linchpin of GATN. To further facilitate the understanding for the proposed MSGATN, the mathematical style of graph attention is given as follows: Let \(f_{i}\in\mathbb{R}^{C_{I}}\) and \(f_{j}\in\mathbb{R}^{C_{I}}\) be the feature vectors of current node \(i\) and its neighbor node \(j\), respectively. Then, the edge score \(e_{ij}\) can be obtained by  
\[e_{ij}=\ \big{(}concat\big{(}\ f_{i}W,f_{j}W\big{)}\big{)}A \tag{9}\]  
where \(W\in\mathbb{R}^{C_{I}\times C_{O}}\) and \(A\in\mathbb{R}^{2C_{O}\times 1}\) are the learnable supervised parameters, \(concat(\cdot)\) represents a feature-wise integration, and \(C_{I}\) and \(C_{O}\) denote the input and output feature lengths, respectively. With each \(e_{ij}\) acquired, the attention score \(a_{ij}\) can be given as follows:  
\[a_{ij}=\frac{exp\big{(}LeakyReLU\big{(}e_{ij}\big{)}\big{)}}{\sum_{k}exp(Leaky ReLU\big{(}e_{ik}\big{)})} \tag{10}\]  
where \(LeakyReLU(\cdot)\) represents a nonlinear activation, and \(k\) denotes all the neighbor nodes of \(i\). In the proposed MSGATN, the graph attention mechanism is widely used to refine the graph feature representation. To improve the recognition for multi-scale objects, the proposed network is trained over multi-scale graphs from superpixel segmentation of different superpixel numbers. In our method, the \(N\_seg\) is set to 2000, 4000, and 6000 to obtain input graphs of different scales. Basically, GATN can tackle inductive tasks with graphs of varied scales. Based on this fact, the proposed MSGATN can learn multi-scale feature representation through training over several multi-scale constructed graphs in a Siamese framework.
# Enhancing Discoverability: A Metadata Framework for Empirical Research in Theses
## 7 Discussion
### Adaptation of the DDI Standard  
In this work, an attempt is made to combine the DDI (specifically DDI Lite) and Dublin Core standards to form a new metadata framework that can include basic information about theses containing empirical research. DDI was chosen as the reference model for designing the initial metadata entry form because it offers a wide array of fields capable of thoroughly describing empirical studies. However, the aim here is not to implement DDI in its entirety but to selectively use only those fields that can adequately describe the empirical nature of the work while maintaining the simplicity and interoperability of Dublin Core.  
Particular attention was given to fields under the stdInfo and method subcategories of the Study Description section of the DDI standard. These contain critical metadata elements that can highlight the empirical character of a thesis, such as the research method, data collection techniques, and study scope. These fields were evaluated for their compatibility with both quantitative and qualitative research documentation.  
Moreover, this initiative does not seek to achieve full metadata curation or exhaustive documentation of research projects. Most theses do not include supporting materials such as datasets, codebooks, or variable files, which are essential for proper DDI documentation. Therefore, comprehensive documentation must remain the responsibility of the research team itself and should be conducted during the research lifecycle--not retrospectively.