esa-sceva/satcom-chunk-collection huggingface dataset:
# Mobile Robot Path Planning Considering Obstacle Gap Features
## 2 Path Planning Method with Clearance Characteristics
### Global Path Planning Based on Gap Map
#### 2.1.1 The Improvement of Heuristic Function
The specific calculation is shown in Equation (6), as follows:  
\[w=e^{-(i-1)^{2}/(G-g+1)^{2}} \tag{6}\]  
where, \(i\) represents the number of currently searched grids, \(G\) represents the total number of gap regions, and \(g\) represents the number of gap regions that have been searched. It can be obtained by the Gaussian function property that, the value of \(w\) is low when the more gap regions exist in the remaining search region, and the heuristic is weak.
## 3 Results and Discussion
### Effect of the pH  
An aqueous solution's pH is important in adsorption processes, because pH affects the type of charge of the adsorbent surface, and thus the adsorption ratio of Cu\({}^{2+}\) from it. The experiments on the pH effect were conducted at various values of pH between 2 and 12, to evaluate the Cu\({}^{2+}\) adsorption from aqueous solutions by the AgNPs. The percentage of the Cu\({}^{2+}\) adsorption increased rapidly with the increase in the pH from 2 to 7; subsequently, with the pH increased from 7 to 12, the Cu\({}^{2+}\) adsorption increased slightly, as displayed in Figure 6b. The reason for the increase in the percentage of Cu\({}^{2+}\) adsorption is due to the increase in the concentration of the OH\({}^{-}\) ions on the AgNPs' surface, with an increased pH from 2 to12, thus charging the surface with a negative charge, which increased the potential of the Cu\({}^{2+}\) adsorption on the AgNPs' surface. [27]. Moreover, the presence of functional groups in the _C. arreensis_ leaf extract, such as C=O and C-O, enhanced the ionic bonding with Cu\({}^{2+}\) ions. Thus, adsorbed amount of the Cu\({}^{2+}\) on the AgNPs' surface increased [15].
# Research on High-Performance Fourier Transform Algorithms Based on the NPU
## 2 Related Works
### The Applications and Acceleration Research of the Fourier Transform
#### 2.1.2 The Research on Accelerating the Fourier Transform  
Due to the inherent computational complexity of the Fourier Transform algorithm, specialized computational acceleration is traditionally required, particularly through the utilization of heterogeneous computing architectures in various engineering applications. Heterogeneous acceleration of the Fourier Transform and its fast algorithms have been extensively studied, but most of these studies are based on specialized hardware for the Fourier Transform, such as GPU processors or FPGA. For instance, [PERSON] et al. [20] proposed a new FFT hardware that enhanced throughput by simplifying computation and reducing memory usage. Their architecture also reduces energy consumption by improving input scheduling algorithms. [PERSON] et al. [21] proposed a new large-scale FFT framework that improves parallelism efficiency by reducing communication overhead using advanced floating-point compression techniques. [PERSON] et al. [22] studied multidimensional FFT on large-scale GPU systems. They evaluated the features of parallel FFTs, optimized performance with tuning methodology, and achieved linear scalability on these GPU systems.
# Word Sense Disambiguation Using Cosine Similarity Collaborates with Word2 vec and WordNet
## 4 Experiments and Results
### Experiment on Sense Relations and Sense Distribution  
In this experiment, we wanted to know whether sense relationships/sense distribution affected the results. We used Google word-embedding vectors to construct a sentence vector using the average of the word-embedding vectors as a baseline. We selected the context size of ten, which is in the middle of the range of 2-14. When not combining with sense distribution, the similarity threshold (\(\alpha\)) is 0; otherwise, the similarity threshold (\(\alpha\)) is 0.37 and \(n\_best=1\).  
From Table 3, the results show that sense relations significantly affect the results and, when combined with the probability of sense distribution, the results have been improved a little bit.
# Mechanical and Dielectric Properties of Aligned Electrospun Fibers
## 4 Applications of Aligned Fibers  
The authors of this paper, [PERSON] et al. (2017), observed improvements in the mechanical strength and dielectric strength with increase in degree of alignment of fibers [171]. Aligned fibers are greatly beneficial when they are used in applications including field  
\begin{table}
\begin{tabular}{c c c} \hline
**Pure Polymers** & **Dielectric Constant** & **Property** \\ \hline PAN & \(\sim\)3.5 & Physical properties can increase with graphene [161] \\ \hline PMMA & \(\sim\)3.5 & Physical properties can increase with graphene [161] \\ \hline PVDF & \(\sim\)11 & Physical properties can increase with addition of AgNP [4] \\ \hline PVP & - & EMI increases with addition of FeCo (\(\epsilon^{\prime}\)\(\sim\)EMI) [167] \\ \hline PVA & - & Uniform distribution of ZnO increases EMI (\(\epsilon^{\prime}\)\(\sim\)EMI) [169] \\ \hline PU & - & EMI increases with PEDOT (\(\epsilon^{\prime}\)\(\sim\)EMI) [170] \\ \hline PCL & \(\sim\)10 & Dielectric increases with DMF concentration [164] \\ \hline \end{tabular} PMMA, polymethyl methacrylate; PVDF, polyvinylidene fluoride; PEDOT, conductive poly(3_A_-ethylenedioxythiophene); DMF, dimethyl-formamide; PU, polyurethane.  
\end{table}
Table 4: Dielectric properties of different nanoparticles.  
effect transistors, gas and optical sensors, fiber reinforced composite materials, and tissue engineering [172; 173]. [PERSON] (2009) discussed the application of aligned fibers in the field of tissue engineering [174]. Moreover, [PERSON] and [PERSON] (2006) and [PERSON] et al. (2004) stated that there are other applications found in the variety of areas if the fibers are in aligned form [175; 176]. This section discusses mechanical and dielectric applications of aligned electrospun fibers.
# Visual-Inertial Method for Localizing Aerial Vehicles in GNSS-Denied Environments
## 3 Results
To integrate the spatial integrity of points and georeferencing accuracy of LiDAR point cloud data, unstructured LiDAR point cloud data was converted to structured point cloud data by creating virtual scans in the FARO Scene 2024. These virtual scans were imported into RealityCapture as depth images with an additional intensity channel. While depth pixels of these images were used to project LiDAR scanned points in 3D coordinate space, their intensity values were used to extract features as if they were monochromatic images. These extracted features from LiDAR depth images were matched and aligned against those from UAV camera images, while georeferenced and spatially accurate projected points from LiDAR depth maps served as tie points for triangulated point clouds. In this way, both spatial integrity and georeferencing accuracy of LiDAR point cloud data are combined with point cloud data derived from UAV camera images. However, in some areas, the software may hardly detect features in both LiDAR points and images, resulting in a degradation of the images' pose accuracy. After the Bundle Adjustment stage, camera poses of all images involved in this workflow are finally estimated and further used for the test against camera localization. Table 2 reports, per each image, the coordinates obtained by image pose estimation and GNSS (only X and Y), the Pitch and Roll angles estimated by the IMU and available in the EXIF tags, and the indication of the two reference points used for the calculation. Table 2 also provides the localization of the camera obtained as a result of the calculation using the approach presented. Finally, the table reports the Euclidean distance between pose estimation and GNSS, the Euclidean distance between the localization obtained with the method and image pose estimation (3D), and the localization and the GNSS position (2D). Firstly, the difference (Euclidean distance) between the pose estimation calculated with RealityCapture and the GNSS measurements is always less than 3 m, except for image 6, where the distance is 3.24 m. Unfortunately, the photogrammetric software does not provide the overall accuracy of the pose estimation (position) after the bundle adjustment. It is important to highlight that the Euclidean distance between the localization obtained with our method and GNSS ranges from 0.45 m to 4.50 m, which falls within the uncertainty of the DJI Inspire 1. Additionally, the Euclidean distances between the localization and the camera pose estimation range from 1 m to nearly 2 m (with one instance measuring 2.30 m). Assuming an overall uncertainty of \(\pm\)1 m, almost all camera localizations derived from the model would be within this error margin.
# PrECast: An Efficient Crypto-Free Solution for Broadcast-Based Attacks in IPv4 Networks
## 4 The PrECast Solution Architecture  
In this section, we provide a detailed specification of our proposed PrECast architecture to eliminate IPv4 broadcasting and attacks resulting from IPv4 broadcasting. Similar to the DAI architecture, our solution is hardware-based and thus requires no configuration at the client-side. We focus on important network and system performance metrics while designing our solution. They are efficiency, scalability, flexibility and zero-configuration at the client-side. Broadcasting consumes considerable network bandwidth, CPU and buffer resources at every single network device such as switches and hosts on the network. However, several network protocols like ARP, DHCP and the Routing Information Protocol (RIPv1) depend heavily on broadcasting services. In our university network, we estimated that almost 70% of the broadcast traffic is due to the ARP protocol. Before we describe the PrECast architecture, we explain how LAN switches are modified to run our proposed solution.
## 4 Systematic Review of the Research Pillars
### Digital Twins in the Built Environment
#### 4.1.4 Zero-Energy Building and Energy Optimization by IoT  
IoT-based energy optimization systems, which use data analytics and cutting-edge technologies, are essential for lowering energy consumption and carbon footprints in zero-energy buildings (ZEBs). By integrating numerous elements, these systems allow for real-time energy resource monitoring and management, which significantly boosts sustainability and efficiency. IoT devices gather real-time data on environmental conditions and energy use, enabling dynamic adjustments to optimize energy consumption and distribution [42]. This approach enhances energy management by responding to changing conditions in real-time [57]. Advanced Algorithms such as metaheuristic algorithms and deep reinforcement learning maximize the performance of HVAC systems and renewable energy sources, resulting in an energy consumption decrease of up to 25% [58; 59]. These systems ensure flexibility by balancing energy supply and demand through demand-side resources such as air cooling and battery storage. This approach reduces energy consumption while maintaining user comfort [60]. To achieve zero-energy targets, buildings must integrate renewable energy sources such as solar and wind into their energy systems. IoT enables this integration by facilitating real-time energy monitoring and control through hybrid systems [57; 58]. Predictive models optimize the use of renewable energy by efficiently scheduling energy demands and reducing reliance on fossil fuels [59].
# Performance Analysis of On-Demand Scheduling with and without Network Coding in Wireless Broadcast
## 3 System Model
### Graph Model
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Notation** & **Description** & **Notes** \\ \hline \(c_{i}\) & A client & \(c_{i}\in C;\,C~{}=~{}\{c_{1},\,c_{2},\,\cdots,\,c_{n}\}\) \\ \(d_{j}\) & A data item & \(d_{j}\in D;\,D~{}=~{}\{d_{1},\,d_{2},\,\cdots,\,d_{m}\}\) \\ \(R(c_{i})\) & Requested data items by \(c_{i}\) & \\ \(CH(c_{i})\) & Cached data items by \(c_{i}\) & \(R(c_{i})\cap CH(c_{i})=\emptyset\) \\ \(G(V,E)\) & A graph & \\ \(v_{ij}\) & A vertex representing client \(c_{i}\) requests data item \(d_{j}\) & \(v_{ij}\in V(G),\,1\leq i\leq n,\ 1\leq j\leq m\) \\ \(N(d_{j})\) & popularity of \(d_{j}\) & \\ \(d_{j(i)}\) & Client \(c_{i}\) is pending for \(d_{j}\) & \\ \(t_{i}\) & Waiting time of \(c_{i}\) & \\ \(\sum t(d_{j})\) & Summed waiting time of clients pending for \(d_{j}\) & \\ \(T_{i}\) & Slack time of \(c_{i}\) & \\ \(l_{i}\) & Size of the requested data item of \(c_{i}\) & \\ \(\delta\) & A clique & \\ \(|\delta|\) & Number of vertices in \(\delta\) & \\ C\({}_{\delta}\) & Set of clients covered in \(\delta\) & \\ \(\beta(\delta)\) & Encoded packet for the clique \(\delta\) & \\ \(K\) & Number of different data items encoded in \(\beta(\delta)\) & \\ \(\delta_{max}\) & Maximum clique & \\ \(\delta_{max}^{v_{ps}}\) & Maximal clique covering vertex \(v_{ps}\) & \(|\delta_{max}^{v_{ps}}|\leq|\delta_{max}|\) \\ \(B\) & Channel broadcast bandwidth & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of notations. **Definition 2**. : _Let an arbitrary clique \(\delta\) contains \(|\delta|\) number of vertices, i.e., \(\delta=\{v_{i_{1}i_{1}},v_{i_{2}i_{2}},\cdots,v_{i_{|\delta|j|_{\delta}}}\}\). The corresponding clients are \(C_{\delta}=\{c_{1},c_{2},\cdots,c_{|\delta|}\}\). The encoded packet for clique \(\delta\) will be \(\beta(\delta)=\{d_{1}\oplus d_{2}\oplus\cdots\oplus d_{K}\}\), where \(K\) different data items are requested by clients in \(C_{\delta}\). Please note that \(K\leq|\delta|\)._  
By encoding the set of data items in \(\delta\) for broadcasting, scheduling with coding can satisfy multiple requests for different data items in a broadcast tick. In contrast, scheduling without coding can only serve the requests for the same data item in a broadcast tick.
# Stability Analysis in Multi-VSC (Voltage Source Converter) Systems of Wind Turbines
## 3 Equilibrium Points Computation and Eigenvalue Analysis in a Multi-VSC System
### Methodology for Equilibrium Points Computation  
The equilibrium states for each converter consist of the common voltage at the PCC, \(V_{PCC,0}\), and the inductor current \(I_{LLi,0}\)--both in the dq reference frame and as already used in (3)-(6)--where \(i=1\dots n\) and corresponds to the converter under study, \(VSC_{i}\). The equilibrium states of the wind farm's output current \(I_{o,0}\) should also be estimated, as this is a state variable that is utilized in the grid impedance submodel of each VSC.  
When \(V_{PCC,q}\) is maintained at zero by the PLL its corresponding equilibrium state, denoted as \(V_{PCC,q,0}\), also equals zero. In line with this and following the definition of \(V_{M}\) as detailed in [23] it follows that \(V_{PCC,0}\) is equivalent to \(V_{PCC,r\!f}\). Similarly, the equilibrium state of the d-axis inductor current, \(I_{Ld,i0}\), matches \(I_{Ldref}\). The equilibrium state for the q-axis inductor current, \(I_{Ld,i0}\), is deduced by applying Kirchoff's Voltage Law (KVL) to the grid side:  
\[V_{PCC,d,0}=V_{Sd,0}+R_{S}I_{od,0}-\omega_{n}L_{S}I_{oq,0} \tag{8}\]  
The correlation between each converter's inductor current and the total system's output current along the d- and q-axes is characterized as follows:  
\[I_{od,0}=nI_{Ld,i0}=nI_{Ldref} \tag{9}\]  
\[I_{\alpha q,0}=\Big{(}\frac{K_{I,a1}+...+K_{I,an}}{K_{I,ai}}\Big{)}I_{Lq,i0}- n\omega_{n}V_{PCC,d,0}C_{Fi} \tag{10}\]  
where \(n\) is the number of VSCs corresponding to the wind turbines.  
The d-axis grid voltage, represented as \(V_{Sd,0}\), is determined by the following formula:  
\[V_{Sd,0}=|V_{Sdbc}|cos\delta \tag{11}\]  
in which \(\delta\) symbolizes the grid angle relative to each converter's capacitor voltage. This angle can be estimated by considering the active power each converter injects into the grid in the \(abc\) frame.  
\[P_{i}=\frac{3}{2}\frac{|V_{PCC,abc}||V_{Sdbc}|sin(\delta)}{\omega_{n}L_{S}} \tag{12}\]  
As previously noted, the control strategy is focused on the d-axis, which means the active power in the dq frame is computed using only the d-axis variables:  
\[P_{i}=\frac{3}{2}V_{PCC,d}I_{od}=\frac{3}{2}|V_{PCC,abc}|I_{od} \tag{13}\]Consequently, the angle \(\delta\) can be estimated through (12) and (13), leading to the following formula:  
\[cos\delta=\sqrt{1-\left(\frac{\omega_{n}L_{S}I_{old}}{|V_{Sabc}|}\right)^{2}} \tag{14}\]  
By incorporating (9), (10), (11) and (14) into (8), we can derive the equilibrium state of the inductor current for each VSC on the q-axis as shown in (15):  
\[\begin{split} I_{Lqi,0}=\left(\frac{K_{L,ai}}{K_{L,a1}+\ldots+K_{ L,an}}\right)\times\\ \frac{\sqrt{|V_{Sabc}|^{2}-\left(n\omega_{n}L_{S}I_{Ldref} \right)^{2}}+nR_{S}I_{Ldref}-V_{PCCref}\left(1-n(\omega_{n})^{2}L_{S}C_{Fi} \right)}{\omega_{n}L_{S}}\end{split} \tag{15}\]  
These equilibrium points are estimated for all the VSCs that are connected to the grid; in that way, a holistic nonlinear model can be linearized around them.
## 5 Machine Learning Model Designs
### Regression and Classification Problems
#### 5.1.5 Support Vector  
Support Vector Machines, or SVMs for short, are a model used in machine learning characterized by their robustness and adaptability. It can conduct linear or nonlinear classification and regression and even identify outliers with decent accuracy.  
The core idea of SVM is to put each feature vector in high-dimensional space and draw an imaginary high-dimensional line [33], known as a hyperplane. The expression of hyperplane is given in Equation (14)  
\[w.x-b=0 \tag{14}\]  
\[wx=\sum_{i=1}^{n}w_{i}x_{i} \tag{15}\]  
where \(w\) represents the real valued vector, \(x\) is the input feature vector, \(b\) is a real number, and \(n\) is the number of dimensions of the feature vector. For example, when we consider binary classification with labels \(-1\) and \(1\), the predicted label can be represented as  
\[y=\pm(wx+b) \tag{16}\]  
Therefore, the primary objective is to find the optimal value of w and b. Thus, Equation (16) can be translated as  
\[f(x)=\pm(w^{*}x-b^{*}) \tag{17}\]  
where \((w^{*}x-b^{*})>=1\) when the output is \(1\) and \((w^{*}x-b^{*})<=-1\) when the output is \(-1\). The difference between these parallel hyperplanes is known as the margin. The main idea is to maximize the margin. The distance between two parallel hyperplanes can be represented as \(\frac{2}{|w|}\). Therefore, to maximize the distance between the hyperplanes, \(w\) has to be minimized. The vectors that support making the hyperplane are known as support vectors.  
However, there are very rare instances where data is linearly separable, so for that SVM uses the kernel to classify non-linearly separable data. There are mainly three types of kernels.  
1. Linear Kernel
2. Polynomial Kernel
3. Radial Basis Functions Kernels  
The linear kernel works well for linearly separable data, as shown in Figure 5, whereas the polynomial kernel represents kernels with more than one degree and is represented as \(f(x)=(w_{i}x_{i})^{k}\), where \(k\) denotes the degree of the polynomial. Radial Basis Function (RBF) kernels are one of the most commonly used kernels in SVM problems. They don't require any prior information about the data and perform well for linearly separable data as well as non-linearly separable data. RBFs are represented as \(k(x,y)=exp(-\gamma(|x-y|)^{2})\), where \(|x-y|\) is the euclidean distance between the vectors and \(\gamma\) is a hyper-parameter equivalent to \(\frac{1}{2\sigma^{2}}\), where \(\sigma\) is a free parameter. \(\gamma\) is a parameter of the RBF kernel that controls the decision region. When gamma is small, the 'curve' of the decision boundary has a very low slope, which results in the decision zone having a relatively wide breadth. There are big regions of decision boundaries surrounding data points when the gamma is big because the 'curve' of the border is likewise high, as shown in Figures 6 and 7.
## 2 Theoretical Derivations
### Calculation of Prestress Loss, \(\sigma_{st,l}\)
The stress at the end section of the retraction length can be written as follows:  
\[\sigma_{re}=\sigma_{st}e^{-\mu\theta_{re}}. \tag{4}\]  
The length reduction of the strand caused by the anchor deformation and the clip retraction, \(\Delta l_{re}\), can be expressed by Equation (5):  
\[\Delta l_{re}=\int_{0}^{\theta_{re}}\frac{\sigma_{l2}r_{st}}{E_{st}}d\theta= \frac{2r_{st}\sigma_{st}\left[1-e^{-\mu\theta_{re}}(1+\mu\theta_{re})\right]} {\mu E_{st}} \tag{5}\]  
where \(E_{st}\) is the elastic modulus of the adopted steel strand (N/mm\({}^{2}\)) and \(e^{-\mu\theta_{re}}\) is expanded into a power series according to the Taylor formula. Only the first three terms of the formula have sufficient precision, since \(\mu\theta_{re}\) is adequately small, which is given by Equation (6):  
\[e^{-\mu\theta_{re}}=1-\mu\theta_{re}+\frac{(\mu\theta_{re})^{2}}{2}. \tag{6}\]  
Figure 2.0 Stress of a micro-segment of a strand. Equation (7) can be derived by incorporating Equation (6) into Equation (5) and omitting the high micro \((\mu\theta_{re})^{3}\):  
Figure 2.1  
The correspondence between the retraction length, \(l_{re}\), and the retraction angle, \(\theta_{re}\), is represented as follows:  
Figure 2.2  
Therefore, the retraction length, \(l_{re}\), and its corresponding angle, \(\theta_{re}\), can be given by Equations (8) and (9). Figure 2.3  
The various types of anchorage used with steel strands were classified as plug and cone, straight sleeve, contoured sleeve, metal overlay, and split wedge anchorages. The value of \(\Delta l_{re}\) varies with the type of anchor.
# MBPPE: A Modular Batch Processing Platform for Electroencephalography
## 2 Materials and Methods
### Data Formatting  
Given the diverse data types and structures from various acquisition devices, we apply a specific formatting process to the input data to standardize its structure, optimizing subsequent EEG data processing and analysis. MBPPE supports direct reading of standard format mat files and NumPy [11] array files. Upon analyzing the data file, the time series for each channel is independently extracted and stacked, thereby generating a two-dimensional matrix (channels, samples). In this structure, each matrix row represents an EEG signal channel, and each column corresponds to a sample point. For instance, a \(62\times 200\) matrix indicates data comprising 62 channels and 200 sample points. During data acquisition, the duration of samples may vary across channels, potentially resulting in EEG time series of unequal lengths that are unfit for matrix formation. To resolve this complication, we introduce two adjustment methods for users to standardize the time series length: one involves trimming the data based on the shortest sampling duration; the other uses the longest sampling duration as a reference to interpolate data at the ends of shorter sequences. MBPPE offers filling approaches such as zero padding, specified value padding, or mean value padding. Figure 3 depicts the data formatting process using zero padding. Although this change may modify the original data structure, it improves the efficiency of subsequent matrix operations.  
Figure 2: Operational modes: (**a**) Offline mode; (**b**) Online mode.  
Moreover, all the data are temporarily stored in memory, enabling users to save the data locally in a standardized mat or NumPy array format according to their requirements. This strategy minimizes unnecessary input/output (I/O) overhead, increases system efficiency, and reduces storage requirements.