esa-sceva/satcom-chunk-collection huggingface dataset:
# Sentence Compression Using BERT and Graph Convolutional Networks
## 3 Sentence Compression Using BERT and GCNs
### Sentence Compression Using GCNs  
Figure 2 depicts a basic architecture representing sentence compression using a GNN to process syntactic information. An input sequence \(s=\{w_{1},w_{2},...,w_{n}\}\) is re-calculated by the GNN and its syntactic parse tree. Node \(T_{k}\), the output of the GNN layer for the \(k\)-th word, becomes an input for the score layer.  
In a graph, each node is naturally defined by its features and related nodes. The objective of GNNs is to learn a node embedding that contains the neighbors' information of the node. From a parsing perspective, the node embedding of a syntactic dependency tree is represented by aggregating with its parent and child nodes.  
A GCN is one of the most commonly used GNNs. A basic aggregator in GCNs is a convolution operation that shares all the weights within the same layer. GCNs, similar to GNNs, can consider multi-hop neighboring nodes when they have multiple layers. In this study, we introduce two types of GCNs to obtain a node's embeddings: (1) GCNs with undirected edges and (2) directed GCNs where all edges have directions.  
In GCNs, a node can be propagated to the next layer by aggregating the neighbors' information using a convolutional operation. Equation (4) is a layer-wise propagation rule in a multi-layer GCN [4]:  
\[T^{h}=\sigma(\bar{D}^{-\frac{1}{2}}\bar{A}\bar{D}^{-\frac{1}{2}}T^{h-1}W_{g} ^{h}), \tag{4}\]  
Figure 1: Baseline model representing sentence compression.  
where \(\bar{A}=A+I_{N}\) is an adjacency matrix of an undirected graph with self-connections added. \(I_{N}\) is the identity matrix, \(\tilde{D}_{ii}=\sum_{j}\bar{A}_{ij}\), and \(W_{\bar{g}}^{h}\) is a layer-specific learnable weight matrix. \(\sigma\) is an activation function, and the rectified linear unit (ReLU) was used in this study. The adjacency matrix was normalized by \(\tilde{D}^{-\frac{1}{2}}\bar{A}\tilde{D}^{-\frac{1}{2}}\). \(T_{k}^{h-1}\) is an input matrix to the \(h\)-th layer of a GCN that is propagated to \(T^{h}\) by Equation (4) and \(T^{0}=[w_{1},w_{2},...,w_{n}]^{T}\).  
Figure 3 illustrates an example of how to propagate a node \(T_{k}^{h-1}\) with two child nodes and a parent node (indicated by arrows at the bottom) to \(T_{k}^{h}\) in the \(h\)-th layer. As all edges in a GCN have no direction, the adjacency matrix for this example is symmetric. In the \(h\)-th GCN layer, the node \(T_{k}^{h-1}\) itself, its parent, and two child nodes are all aggregated by a convolutional operation and applied by the ReLU function for propagation to a new \(T_{k}^{h}\) in the next layer.
## 2 Materials and Methods
### Statistical Analyses  
Data are presented as mean \(\pm\) SD values and CV% (coefficient of variations). Before any statistical analysis, the normal distribution of the data (Shapiro-Wilk test) was confirmed (\(p>0.05\)). For data analysis, G*Power software (version 3.1, Universitat Dusseldorf, Dusseldorf, Germany) was used to determine the sample size, with the desired significance level (\(\alpha=0.05\)), effect size (d = 0.40), number of groups (5), and power (0.95). According to the G*Power analysis, the recommended sample size for achieving the desired statistical power is 125, while the actual sample size in this study is 114. Although a smaller sample size may seem like a limitation, additional statistical indicators such as the intraclass correlation coefficient, coefficient of variation, and confidence intervals confirm the reliability of the results. These factors suggest that the smaller sample size does not significantly undermine the power and validity of this study, and the results remain statistically valid. A one-way analysis of variance (ANOVA) was used to determine differences in anthropological characteristics between playing positions among the entire player group. When a significant F value was achieved, pairwise differences between mean values were identified using with Bonferroni post-hoc procedures. For the calculation of effect size, we used a one-way analysis of variance (ANOVA), which automatically provides the relevant effect size values (e.g., partial eta squared). This statistical method allowed for us to assess the magnitude of differences between the groups in relation to the dependent variable. For the reliability of the measurements, we used confidence intervals (CI), coefficient of variation, and the intraclass correlation coefficient (ICC). All statistical tests were performed using the software package SPSS version 20.0 (SPSS, Inc., Chicago, IL, USA). Significance was set a \(p\leq 0.05\).
# Recent Advances on Surface Modification of Halloysite Nanotubes for Multifunctional Applications
## 3 Application of Surface Modification of HNTs  
### As the Filler Nanocomposites  
Composite materials are vital for the development of modern science and technology. They are widely used in magnetic materials, magnetic facility, flame retardant, optics, scaffolds for tissue engineering, and electronics. Meanwhile, the nanocomposites exhibit a complex template and tedious preparation process. It is imperative to find effective modules and efficient production processes. Due to a high specific surface area and unique surface chemical properties, HNTs are widely used to improve a polymer's property. In the meanwhile, the low surface charge and weak interfacial interaction could be problematic [33]. Surface modified HNTs not only demand well dispersed and strong interfacial interactions [34], but also to provide abundant bond formation [35]. HNTs showed better interactions among clay-polymer nanocomposites by chemical or physical pretreatment [36]. Functionalization of nanotubes composite polymer will achieve a win-win situation.  
HNTs have been used extensively for enhancing properties of polymers. [PERSON] et al. [5] had successfully modified the HNTs by graft \(N\)-(\(b\)-aminoethyl)-\(c\)-aminopropyltri-methoxysilane, the modified and unmodified HNTs mingle with nonpolar polypropylene (PP) and polar polyoxymethylene (POM) by utilizing immiscible blend system, respectively. The results indicated that pure polymer blend and B-HNT nanocomposites always form obvious agglomeration due to the weak interface interaction between the polymer and HNTs. However, they present different phenomena to the B-MHNT nanocomposites that disperse well in the polymer blend. This suggests that modified B-MHNTs obtained a better dispersion when compared to the unmodified (B-HNTs) in blend matrix. Meanwhile, the functionalized HNTs are used to enhance the chemical interactions as natural rubber (NR) filler [37]. The bis(triethoxysillypropyl)-tetrasulphide was used to modify the HNTs by way of silane coupling agent. In general, the natural rubber composites with modified HNTs (NR-HNTs-Si) showed excellent physical properties and thermal stability when compared with the unmodified HNTs nanocomposite (NR-HNTs) and natural rubber-silica (NR-Si). The HNTs were modified with polyhydonanine (PRD) by the way of oxidative polymerization to prepared styrene butadiene rubber (SBR) [38]. The results indicated that the tensile strength of SBR/PRD-HNTs composites have significant reinforcement when compared with unmodified HNTs that increased by 117% and 87%, respectively. HNTs also can be treated with \(\gamma\)-irradiation [39] to enhance the strength of epoxy nanocomposites. When compared with untreatments, the treatments have significant effect on tensile strength and Young's modulus, which rose by 46% and 38%, respectively, because of uniform dispersion and abundant hydroxy.
# A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices
## 1 Introduction
### Research Gap  
Based on Section 1.1, it appears that the research in utilizing FPGA technology to enhance the performance of point multiplication on BEC has made significant progress in terms of optimizing latency, area utilization, and throughput. However, there are still some potential research opportunities for further exploration in this field.  
* **Efficiency improvement for larger curve parameters:** The existing studies mention specific curve parameters (e.g., \(d=59\)) for evaluating the performance of FPGA implementations. However, as the size of curve parameters increases, the efficiency of point multiplication significantly decreases. There is a need for further research to develop FPGA architectures that can handle larger curve parameters while maintaining high performance and resource efficiency.
* **Exploration of energy-efficient FPGA implementations:** While the existing studies focus on performance and resource efficiency, energy efficiency is also a critical factor, particularly in resource-constrained environments or battery-powered devices. Future research can focus on developing FPGA architectures and techniques that optimize energy consumption during point multiplication operations on elliptic curves.
* **Exploration of security aspects:** The existing studies primarily focus on performance and resource optimization. However, security is a crucial aspect when implementing cryptographic algorithms. There is a research gap in exploring FPGA implementations that not only provide high performance but also address security concerns.  
Therefore, further advancements can be made in ECC implementations, leading to improved performance, resource efficiency, energy efficiency, security, and adaptability.
# Use of Wireless Sensor Networks for Area-Based Speed Control and Traffic Monitoring
## 4 LoRaWAN Area-Based Speed Control
### Selecting the System Operation Area  
Given the design and nature of LoRaWAN systems, this solution will be scalable, meaning it can be deployed in specific, small areas with the option to scale up to a nation-wide solution, in line with road traffic improvement policy. Therefore, it was decided that the minimum operation area for the system should be the basic local government unit, i.e., communes. This choice naturally aligns with the administrative division of Poland and enables an easy upscaling of the system following the district-province-country sequence. Communes have a number of responsibilities, including the broadly understood public order and citizen safety (Art. 7 (1) cl. 14) [60], which covers road traffic safety in addition to explicitly specified fire and flood safety. However, after being deprived of the right to conduct speed checks, communes have largely lost the ability to act directly in this regard. For this reason, they should support any new solutions aimed at improving road traffic safety and increasing operational effectiveness.  
The following part of the paper focuses on the area of the Stare Babice commune, which is part of the Warsaw West (PL--Warszawa Zachodnia) district. The Stare Babice commune is located in the central part of the Mazowieckie province. It is situated in close proximity to Warsaw, at its western border, adjacent to the Kampinos Forest (PL--Puszcza Kampinoska), between two routes of international importance (Poznan and Gdansk). Expressway S8 passes through the eastern part of the commune and is part of the Warsaw ring road junction. The commune borders the O'azow Mazowiecki, Leszno, and Izabelin communes, as well as the Warsaw districts of Bemowo and Bielany. The commune covers an area of approximately 63 km\({}^{2}\). Excluding the section without roads and the portion within the Kampinos Forest, it can be fitted into a 14 km x 6 km rectangle (Figure 3).  
A transport route consisting of provincial road No. 580 (leading from Warsaw towards Zelazowa Wola and Sochacew) crosses the area of the commune as illustrated in Figure 3. Provincial roads No. 718 and 898 also connect with road No. 580. The proximity to Warsaw, high and constantly growing population density, extensive road network, and the vicinity of other communication routes (92, S7, S8) contribute to very high traffic volumes, which do not typically favor road traffic safety.
# Corrosion Susceptibility and Microhardness of Al-Ni Alloys with Different Grain Structures
###### Abstract  
The development of Al-Ni alloys with a controlled microstructure has had a great impact on the field of study of aluminum-based alloys. In the present research, the influence of thermal parameters on the grain structures resulting from the directional solidification process of Al-Ni alloys with different alloy content has been studied. It has also been evaluated how different structures and the distribution of second phases influence the corrosion behavior and microhardness of alloys. The columnar-to-equiaxed transition (CET) was observed to occur when the temperature gradient in the melt decreased to values between 1.3 and 2.9 \({}^{\circ}\)C/cm. In addition, a small increase in the microhardness values was observed as a function of the Ni content. When the Ni content increases, the resistance to polarization decreases for samples with equiaxed grain structure throughout the range of compositions studied. Furthermore, the equiaxed grain structure presents higher resistance to polarization values than the columnar grain zone for alloys with a composition equal to or lower than the eutectic composition.  
Al-Ni alloys; directional solidification; microstructures; thermal parameters; microhardness; corrosion +
Footnote †: journal: Applied
## 5 Shaking Table Test
### Test Results
#### 5.4.2 Acceleration Response  
As shown in Figure 17, sensors were installed at key locations (A1 to A4) to measure the transmitted acceleration and analyze the response behavior of the test specimen to the input seismic load. The measured accelerations were compared with the results of analytical verification to confirm the dynamic behavior pattern. The shaking table test involved conducting seismic simulations in three axes for the test specimen. However, in the analytical verification, only the X-axis was considered. Therefore, the comparison results were limited to the X-axis. The measured acceleration response in the vibration table test showed a pattern where vibration started at the support column and was amplified as it was transmitted to the main body. Then, after approximately 28 s, the protrusion concrete was damaged, leading to the termination of the test. The impact signal due to the damage of the protrusion concrete was measured at the moment when the holding time of the input seismic motion ended, making an accurate comparison challenging. However, the acceleration patterns before 28 s in the shaking table test results and the analytical verification results appear similar, indicating a reasonable outcome. Consequently, if no damage occurs at the support, structural integrity can be assured. However, in the event of damage to the support, the intrinsic impact load of the structure could be added, potentially altering the seismic behavioral characteristics.  
Figure 16: Failure mode of the test specimen.  
Figure 17: _Cont._
# Enhancing Recognition of Human-Object Interaction from Visual Data Using Egocentric Wearable Camera
## 3 Proposed System
### Segmentation and Hand Joint Extraction  
Before recognizing HOI from the video sequences, we first performed the required preprocessing. In this regard, we first applied window-based segmentation over each video sequence and divided it into non-overlapping and fixed-size (i.e., 5 s) segments. This division was based on the findings that interactions are typically captured well within such a short span, allowing for the precise and efficient analysis of dynamic hand movements. We called each window segment a video snippet and labeled it as per the original video sequence. We then converted each video snippet into individual frames, where a frame was represented as a 2D RGB matrix. Each segment consisted of a series of 75 frames, which were processed individually one-by-one to capture frame-based hand joints. The hand pose information obtained from the set of 75 frames was then concatenated to form a complete representation of the window. This method ensured that interactions were consistently captured and facilitated accurate hand pose information extraction from each segment.  
Against each frame, we obtained a set of 21 key points, i.e, standard hand joints [34; 35], from the user's hand as 2D landmarks. These keypoints were further transformed into a 3D representation using the MANO parametric hand model [35]. The process started with 2D joint landmarks from hand images, which a pre-trained regressor used to estimate the initial shape and pose parameters. These parameters were optimized to minimize the reprojection error, and the refined parameters were applied to the MANO model to obtain consistent 3D coordinates of the hand joints. Thus, we finally obtained a 3D hand joint representation, which was recorded as a \(21\times 3\) matrix known as the hand joint matrix (HJM), against each frame. This matrix represented the hand movement information in the form of hand joints, where each hand joint was represented as a 3D point, i.e., spatial coordinates (x, y, z). Figure 3 provides a sample representation of the 3D hand joint model illustrated by MANO.
## 4 Numerical Results
### Nomenclature  
(1) Indices and Sets  
\(i,j\) Index for nodes. \(s\) Index for scenarios.  
\(ij\) Index for branch from node \(i\) to  
\(t\) Index for time periods.  
\(k\) Index for branches. \(\Omega_{PV}\) Node set of PV generation.  
\(\Omega_{WF}\) Node set of WF generation.  
\(\Omega_{\textit{made}}\) Batch set of distribution network. \(\Omega_{G}\) Set of power nodes.  
(2) Parameters  
\(S\) Number of WF and PV output  
scenarios.  
\(T\) Number of periods in the daily  
scheduling period.  
\(U_{i}^{N}\) Rated voltage of node \(i\).  
\(I_{k,\max}\) Maximum transmission current of  
branch \(k\).  
\(U_{i,\min}U_{i,\max}\) The lowest and highest allowed  
voltage magnitudes of node \(i\).  
Maximum abandonment rate of WF  
and PV in an allowable daily  
dispatching period of the  
distribution system.  
\(\lambda\) Maximum loss of load rate.  
\(u_{i}\) Upper limit of the expected value of  
objective function \(i\).  
Normalized function value of  
\(f_{ij}\) frontier solution \(j\) for objective  
function \(i\).  
\(S_{\alpha}\) Number of Pareto frontier solution  
set in external archives.  
(3) Variables  
\(P_{i,l,s}^{PV}\) Actual power of PC in dispatching  
\(P_{i,l,s}^{\textit{WF}}\) Period \(t\) under scenario \(s\).  
\(P_{L,i,s}\) Active load of node \(i\) at dispatching  
period \(t\) under scenario \(s\).  
\(F_{\text{LMC},k}\) Capacity margin of branch \(k\).  
\(U_{i,l,s}\) dispatching period \(t\) under  
scenario \(s\).  
\(P_{i,l,s}^{\textit{net}}\), \(Q_{i,l,s}^{\textit{net}}\), \(Q_{i,l,s}\)  
scenario \(s\).  
Net active and reactive power of  
node \(i\) at dispatching period \(t\) under  
scenario \(s\).  
Active and reactive power injected  
from the superior grid to node \(i\) at  
dispatching period \(t\) under  
scenario \(s\).  
Energy not supply at node \(i\) at  
\(P_{i,l,s}^{\textit{ENS}}\) dispatching period \(t\) under  
scenario \(s\).  
\(P_{i,l,s}^{\textit{load}}\) Load power at node \(i\) at dispatching  
period \(t\) under scenario \(s\).  
\(P_{i,l,s}^{\textit{BS}}\), \(Q_{i,l,s}^{\textit{BS}}\) dispatching period \(t\) under  
scenario \(s\).  
Energy not supply at node \(i\) at  
dispatching period \(t\) under  
scenario \(s\).  
Load of power at node \(i\) at dispatching  
period \(t\) under scenario \(s\).  
\(P_{i,l,s}^{\textit{load}}\) Load power at node \(i\) at dispatching  
period \(t\) under scenario \(s\).  
\(P_{
## 3 Wavelet Packet Transform-Based Feature Extraction
### Signal Features Dimensionality Reduction  
Due to the high real-time requirements of radio fuze signal processing, it is necessary to reduce the dimensionality of the extracted signal features. In addition, it can be seen from Table 2 that the \(p\)-values of different wavelet packet nodes feature differently. For example, wavelet packet node (3,1) corresponds to a \(p\)-value of 0.3883, with minimal difference significance, and wavelet packet node (3,7) has a \(p\)-value of \(1.4187\times 10^{-9}\), which is also less significant compared to node (3,6) with a \(p\)-value of \(1.6436\times 10^{-22}\). Therefore, the feature selection of the wavelet packet nodes needs to be optimized. In this manuscript, the signal features dimensionality is reduced for two purposes. First, signal feature dimensionality reduction enables the computational complexity of signal classification recognition to be computed and improves the computational real-time performance of the algorithm. Secondly, signal feature dimensionality reduction can maximize the feature differences between different signals and improve the classification recognition accuracy of the classification algorithm.  
In this manuscript, we use the principal component analysis (PCA) method for dimensionality reduction to reduce the features of a total of 15 nodes from wavelet packets (0,0) to (3,7) to 8 principal components. The schematic of variance explained by the principal components and the cumulative variance after PCA are shown in Figure 5. In the figure, the red bars indicate the degree of variance explained by the different principal components, and the blue curves indicate the degree of variance explained by the cumulative variables.  
To visualize the distribution of the principal component values of the target and jamming signals more easily, statistical boxplots of the different principal components are drawn, as shown in Figure 6. Where 'target' in the horizontal coordinate represents the distribution of values of the corresponding principal components of the target signal, 'jamming 1', 'jamming 2', and 'jamming 3', represent the noise AM frequency sweeping jamming signals, sine AM frequency sweeping jamming signals, and square AM frequency sweeping jamming signals, respectively.  
Figure 5: Schematic representation of the variance explained of principal component analysis.
## 3 Results and Discussion
### Sample Characterization
#### 3.1.3 Texture Parameters  
The nitrogen adsorption-desorption isotherms and pore size distribution of the four sorbents exhibit features that more closely align with Type II isotherms, according to IUPAC classification (see Figure 3). Although the materials are mesoporous, the isotherms do not display the capillary condensation characteristic of Type IV. Instead, the observed gradual increase and lack of a pronounced plateau suggest multilayer adsorption typical of Type II behavior. The hysteresis loops (H3 type) suggest slit-like pores or plate-like particle aggregates [27]. The computed textural properties of the examined biosorbents are outlined in Table 1. Despite slight variations in the value, the biosorbents generally exhibit relatively low surface areas and pore volumes. The average pore diameters differ across the biosorbents, but all suggest a mesoporous structure.  
Figure 3: Adsorption-desorption isotherms and pore size distribution of MO (**a**) and M (**b**), LA (**c**) and L (**d**).  
The pH of biosorbents is another critical factor influencing adsorption. The studied biosorbents have pH values ranging from 5.8 to 6.3 (see Table 1). This near-neutral pH range makes the biosorbents suitable for use in environments where extreme pH conditions might otherwise limit their effectiveness.