esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Clothing Framework for Health Monitoring Applications
## 3 Proposed Framework for Smart Clothing (Leggings)
### Development of Textile Materials
#### 3.3.3 Self-Healing Textile Materials  
Embedded devices in smart clothing should have the capability to maintain their basic function after mechanical damage. Self-healing is a way to promote clothes durability to avoid damage. Research is being continued to recover the essential functions of sensors and self-repair capability of clothing [96]. For healing mechanical damage, excessive electrode adhesion and wire bonding are occurred in sensors during operation. Self-healing polymers are prepared as an example through performing a validation of embedding external healing agents and catalysts in a capsule [97]. Large strains break the capsule and healing agents are released into the crack areas to trigger polymerization and the release of molecules [98]. Researchers have found that self-healing magnetic ink can repair itself automatically by healing multiple cuts in milliseconds. Self-healing magnetic ink is used in smart clothing components including batteries, electrochemical sensors, and circuitry. Microscopic particles are inserted into e-ink by crushed up neodymium magnets which are used for creating printed electronic components. In addition, carbon powder is mixed and placed in a magnetic field to raise the ink's conductivity. A design of self-healing fabric is presented in Figure 14.
# Editorial for the Special Issue: Assessment of Renewable Energy Resources with Remote Sensing
## 3 Brief Discussion of the Published Articles in the Special Issue
[9] used a 27 year long multi-mission scatterometer dataset to determine the global and near-coastal wind resource. The authors identified the seasonal patterns of the global wind climate and discussed the differences between the two hemispheres. They also investigated extreme winds, including the speeds for 50 and 100 year return periods. Unfortunately, we received only one contribution on the geothermal energy resource. [PERSON] et al. [5] evaluated the geothermal conditions of an area in the central region of Spain. The evaluation was based on geological and geophysical studies and, in particular, the time domain electromagnetic method and electrical resistivity tomography. The authors demonstrated that both geophysical prospecting methods constitute a useful tool to define the underground geological characterization and analyze the potential areas for geothermal exploitation.
## 5 Finite Element Model  
The present vibration analysis assumes harmonic motion; therefore, the generalized displacements \(\underline{\mathbf{u}}(\xi^{\alpha},t)\), \(\boldsymbol{\varphi}(\xi^{\alpha},t)\) and the seventh parameter \(\Psi\) can be expressed as [53]  
\[\underline{\mathbf{u}}(\xi^{\alpha},t) = \underline{\mathbf{u}}(\xi^{\alpha})e^{-i\omega t}, \tag{20}\] \[\boldsymbol{\varphi}(\xi^{\alpha},t) = \boldsymbol{\varphi}(\xi^{\alpha})e^{-i\omega t},\] (21) \[\Psi(\xi^{\alpha},t) = \Psi(\xi^{\alpha})e^{-i\omega t}, \tag{22}\]  
Note that \(\omega\) represents the frequency of natural vibration and \(i=\sqrt{-1}\). For the finite element approximation, the displacements and the seventh parameter are approximated as  
\[\underline{\mathbf{u}}(\xi^{\alpha})=\sum_{j=1}^{q}\psi_{j}(\xi^{\alpha}) \underline{\mathbf{u}},\qquad\boldsymbol{\varphi}(\xi^{\alpha})=\sum_{j=1}^{ q}\psi_{j}(\xi^{\alpha})\boldsymbol{\varphi},\qquad\Psi(\xi^{\alpha})=\sum_{j=1}^{q} \psi_{j}(\xi^{\alpha})\Psi. \tag{23}\]  
To obtain the mass matrix \([\mathbf{M}^{e}]\) and the stiffness matrix \([\mathbf{K}^{e}]\) of the element \(e\), the generalized displacements and the seventh parameter must be replaced by their approximations in the virtual kinetic energy of Equation (18) and in the virtual strain energy of Equation (19), respectively.  
Therefore, the finite element model (i.e., the eigenvalue problem) governing the free vibration analysis can be written as  
\[\left([\mathbf{K}]-\omega^{2}[\mathbf{M}]\right)\{\boldsymbol{\Delta}\}=\{0\}, \tag{24}\]  
where \(\{\boldsymbol{\Delta}\}\) is the vibration mode vector associated with \(\omega\), and \([\mathbf{K}]\) and \([\mathbf{M}]\) are the global stiffness and mass matrices, respectively, which result from assembling the local stiffness and mass matrices of each element. Note that, if it is the case, boundary conditions must be applied to the global matrices. In this work, the eigenvalue problem is solved using a C++ template library for linear algebra called Eigen [54].  
Finally, to illustrate the general process for the present finite element analysis, a flowchart is depicted in Figure 2. The dashed rectangle represents the processes executed for each seven-parameter element in the model mesh.
## 3 Numerical Simulation of Tunnel Face Reinforcement Scheme
### Analysis of Simulation Results for Tunnel Face Reinforcement Schemes
#### 3.2.1 Transverse Soil Deformation at the Tunnel Face  
The transverse deformation patterns of the soil for Schemes 1 and 2 at the three positions are shown in Figure 3.  
Figure 3: Stratigraphic deformation of shield tunnel cross-section under different reinforcement schemes.  
Figure 3a,b indicate that when the shield is excavating within the reinforcement zone, Scheme 1 results in minor settlement concentrated around the tunnel, with a \"cat head\" shaped deformation pattern. Scheme 2 produces a similarly shaped but wider deformation area, with settlement mainly occurring at the tunnel crown. The maximum settlements for Schemes 1 and 2 are \(-\)29.73 mm and \(-\)15.80 mm, respectively, and the maximum uplifts at the tunnel invert are 18.73 mm and 9.52 mm, respectively.  
Figure 3c,d illustrate that at the interface between the reinforced and unreinforced zones, the deformation area in Scheme 1 expands into a \"V\" shape. Scheme 2 exhibits a similar shape but with a slightly smaller affected area. Maximum settlements for Schemes 1 and 2 are \(-\)59.53 mm and \(-\)26.94 mm, respectively, and the maximum uplift is 35.49 mm and 26.55 mm, respectively.  
Figure 3e,f illustrate that in the unreinforced zone, Scheme 1 shows a \"funnel\"-shaped settlement area, while Scheme 2 presents the same shape but with a wider range. Maximum settlements for Schemes 1 and 2 are \(-\)31.62 mm and \(-\)7.24 mm, respectively, and maximum uplift is 37.28 mm and 29.60 mm, respectively.  
In summary, as shield tunneling progresses, the transverse deformation of the ground primarily manifests as settlement at the crown and uplift at the invert. The deformation pattern evolves from \"cat head\" to \"V\" to \"funnel\" shape, with increasingly sharp contours and broader impact zones. Although Scheme 2 results in a wider affected zone during excavation in both reinforced and unreinforced areas compared to Scheme 1, it consistently shows lower maximum settlement and uplift values. At the reinforcement interface, Scheme 2 also exhibits a smaller disturbed area and lower deformation values than Scheme 1. Therefore, Scheme 2 yields more uniform and reduced ground deformation.
# MVCG-SPS: A Multi-View Contrastive Graph Neural Network for Smart Ponzi Scheme Detection
## 2 Related Work
### Graph Neural Networks for Fraud Detection  
GNNs have demonstrated exceptional capabilities in modeling complex relationships in financial fraud detection. Relational Graph Neural Networks (RGCNs) [29] were among the early models to process heterogeneous graphs, capturing interactions between accounts, transactions, and entities. [PERSON] et al. [30] advanced the field by introducing reinforcement learning into fraud detection systems, where dynamic decision-making enhanced the adaptability of fraud detection models. [PERSON] et al. [31] proposed a hybrid approach that integrates graph convolutional networks with reinforcement learning to identify fraudulent patterns in transaction networks, achieving higher accuracy and better adaptability to changing fraud tactics. Additionally, SAMCL [32] proposed subgraph-aligned contrastive learning for anomaly detection, utilizing subgraph similarity to identify fraud in large-scale transaction networks. Despite these advances, most GNN-based fraud detection methods focus on static or single-view graph representations. Static graph methods struggle to capture temporal dynamics and cross-view relationships, which are critical in detecting sophisticated schemes like Ponzi fraud. MVCG-SPS addresses this limitation by integrating temporal, relational, and frequency-aware features across multiple views.
# Theoretical Study on a Cable-Bracing Inerter System for Seismic Mitigation
## 2 Theoretical Analysis of a Cable-Bracing Inerter System
### Layout of SDOF System with CBIS  
The mechanical system of a CBIS consists of an additional damping element, an inerter element and a spring element. The damping element is set in parallel with the inerter element. The spring element is then connected with the paralleled inerter and damping element in a series. Figure 3 shows the layout and mechanical model of an SDOF structure equipped with a CBIS when the structure is deformed in a horizontal direction.  
In Figure 3, \(m\), \(c\), and \(k\) are the mass, damping coefficient, and stiffness of the SDOF system, the primary structure, respectively. \(\theta\) is the inclined angle of the diagonal cable; \(m_{\mathrm{d}}\) is the inertance of the CBIS; \(c_{\mathrm{e}}=c_{\mathrm{d}}\cos^{2}\theta\) is the equivalent damping coefficient considering the inclined angle of the cables, where \(c_{\mathrm{d}}\) is the damping coefficient of the damping element, and \(k_{\mathrm{b}}\) is the stiffness of the supporting spring element. The output force of this inerter system is the resultant force of the inerter element and the eddy current damping element.
# DANet: Temporal Action Localization with Double Attention
## 3 Double Attention Network
### Loss Function  
To train the network, we adopt a supervised learning paradigm. Specifically, the loss function is defined as follows:  
\[\mathcal{L}_{\text{total}}\ =\sum_{t}\frac{\left(\mathcal{L}_{cls}+\mathds{1}_{ \alpha}\mathcal{L}_{\text{reg}}\right)}{T_{+}}, \tag{9}\]  
where \(\mathcal{L}_{cls}\) denotes the classification loss, \(\mathcal{L}_{\text{reg}}\) denotes the regression loss, \(\mathds{1}_{\alpha_{\text{G}}}\) denotes whether the current time step is within an action instance, and \(T_{+}\) denotes the total number of positive samples. To supervise the classification training process, we utilize the widely adopted focal loss [38] as \(L_{\text{cls}}\), which is as follows:  
\[FL(p_{t})=-a_{t}(1-p_{t})^{\gamma}\log(p_{t}), \tag{10}\]  
where \(p_{t}\) is the probability that time step, \(t\), belongs to an action instance, \(\gamma\) denotes a predefined parameter, and \(\alpha_{t}\) is used to adjust the ratio between the positive and negative sample losses. Given that TAL differs from other video comprehension tasks, primarily in localization, we conduct experimentation on \(L_{\text{reg}}\). To supervise the training of regression loss better, our model achieves optimal regression performance through Intersection over Union (IoU)-based DIoU regression loss (see Section 4.3.6), which is as follows:  
\[DIoU=IoU-\frac{d^{2}}{c^{2}}, \tag{11}\]  
where \(IoU\) denotes the degree of overlap between predicted results and ground truth.  
The variable \(v_{t}=\{v_{\text{bs}},v_{\text{te}}\}\) denotes the predicted intervals between the current time step, \(t\), and the action boundaries, then let the variable \(g_{t}=\{g_{\text{ts}},g_{\text{te}}\}\) denote the intervals of ground truth between the current time step, \(t\), and the action boundaries. \(d\) is given by:  
\[d=\lambda(v_{\text{te}}-v_{\text{ts}}-g_{\text{te}}+g_{\text{ts}}), \tag{12}\]  
where \(\lambda\) is a hyper-parameter, and \(c\) is given by:  
\[c=max(v_{\text{bs}},v_{\text{te}})+max(g_{\text{ts}},g_{\text{te}}). \tag{13}\]
###### Abstract  
In the engineering field, severe vibration of the pipeline system occurs under random excitation, which leads to vibration failure of the pipeline system due to overload. The traditional method is to increase the rigidity of the pipeline system, and to avoid low-frequency resonance by using clamps or damping materials. However, due to structural limitations, it is difficult to apply clamps and damping materials. Particle damping technology has been applied in many fields, and the vibrational energy in the broadband frequency domain could be dissipated based on nonlinear particle collision damping. In this paper, a particle impact damper is designed for vibration reduction of the pipeline system. The damping capability is identified to investigate the effects of particle material, filling rate, particle size, damper structure, and boundary conditions. The results indicate that the ideal damping performance can be obtained by properly selecting particle parameters. Based on applying particle damping on the pipeline system, the proposed particle impact damper showed excellent damping capability under random excitation.  
pipeline system; particle impact damper; vibration reduction; random excitation Article
## 2 Materials and Methods
### Dose Conversion and Ventilation Models
#### 2.4.5 Elemental Composition and Mineral Mapping  
Elemental composition and mineral mapping were conducted using a Tescan TIGER MIRA3 Field Emission Gun Scanning Electron Microscope (FEG-SEM) (Tescan, Brno, Czech Republic) equipped with backscattered electron (BSE) imaging and energy-dispersive X-ray spectroscopy (EDS). Samples were prepared by mounting polished thin sections in epoxy resin and coating them with a conductive layer of carbon to optimize imaging and analysis.  
BSE imaging was used to investigate the microstructure and mineralogical matrix, with emphasis on identifying accessory minerals associated with uranium and thorium, such as uraninite (UO\({}_{2}\)), thorianite (ThO\({}_{2}\)), monazite [(Ce, La, Nd, Th)PO\({}_{4}\)], and zircon (ZrSiO\({}_{4}\)). Energy-dispersive X-ray spectroscopy was utilized to generate high-resolution elemental maps, providing spatial distributions of uranium and thorium across the sample surfaces. Regions of interest were identified based on preliminary gamma spectrometry and radon/thoron exhalation results. Analytical parameters, including accelerating voltage (15-20 kV), beam current, and working distance, were optimized to ensure the precise quantification of elemental concentrations and the accurate identification of mineral phases. This methodology provided detailed spatial and compositional data on uranium- and thorium-bearing minerals, facilitating the understanding of their contribution to radon and thoron emissions from the tested building materials.
# QoS Management and Flexible Traffic Detection Architecture for 5G Mobile Networks
## 5 Quantitative and Qualitative Analysis
### High Controller Response Time and Network Overload Problem Simulation
This is due to the fact that the specific QoS packets do not have to wait for the controller response to be routed; they are routed using the default OF-Bearer preconfigured during the attachment process (see Figure 7). On the other hand, if the standard OpenFlow architecture is used, the round-trip time between the hosts is linearly affected, the worst case being when a delay is added in both directions of controller communication (two-way delay). Another important point is that even without the additional delay, the standard OpenFlow architecture presents a round-trip time 19 ms bigger than that in the proposed architecture. This delay  
Figure 9: Round-trip times in both architectures and for both additional delay types. represents two waiting times for the controller responses in the standard OpenFlow architecture, one in the OF-NB 1\(\longrightarrow\)application server direction and the other in the application server\(\longrightarrow\)OF-NB 1 direction. The value of 19 ms is lower than that of a real case, because in the prototype, the controller is implemented very close to the nodes. If a real network is considered, the delay curves for the standard OpenFlow architecture significantly move up in the graph due to the propagation delay.
## 4 Case Study
### Performance of Proposed Model
#### 4.3.2 Ribbon Width  
The total error for ribbon width is 9.42% (accuracy 90.58%), with the error for each type detailed in Table 3. Figure 9 compares the ribbon width values to the ground truth, where the x-axis represents the spread insulation edges, and the y-axis indicates the ribbon width, showing both ground truth and predicted values for each type. Type 8 has the highest error at 22.58%, as shown in Figure 6, where the graph highlights that significant errors occur in the thinner parts of the ribbon width.
###### Abstract
In some countries, the handling of these products requires the use of protective gear and specialized ventilation [19]. Thus, for the sake of simplicity, the interest in this study will be focused on liquid, homogeneous, water solvent and sugar-based phantoms as they are cost effective, simple to fabricate and do not need any special protective protocol. Their 900 MHz frequency limit [10] will be reviewed. In order to provide a comprehensive solution, simplicity of corresponding homogeneous numerical phantoms should be consistently provided. It is known that numerical computation of body phantoms demands a high number of resources. A key factor in this respect is the phantom electrical size. Distinguing non-representative regions of the phantom, where the actual field is negligible, would alleviate these required resources. This is particularly meaningful for implants inserted into the trunk (e.g., pacemakers [20], spinal cord stimulators [21], gastro-intestinal stimulators [22] and central venous catheters [23], among others) where there are no clear criteria to define the minimum representative size at commercial RF frequencies. Extracting the minimum homogeneous phantom size representative of the whole body would build on the simplicity of sugar-based homogeneous phantoms. This will be done in the present paper by studying the convergence of the electric field attenuation inside a phantom with increasing size. The analysis will be done in a simulation model, corroborated by anechoic chamber measurements for a specific case. The bands to be analyzed are set to Industrial Scientific Medical (ISM) bands f = 868 MHz, f = 2.45 GHz and 5.8 GHz; and MedRadio f = 402 MHz for AMI [24]. The paper is divided as follows: after the introduction, the second section deals with the employed materials and methods. In the third section, the computational analysis to determine the minimum required size of the phantom, the corresponding phantom characterization and the measurements at the anechoic chamber that will validate the computer model are shown. The last section describes the conclusions.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 4 Proposed Elliptic Curve Signcryption Based Security Protocol for Smart Card (ECSSP-SC)
### Registration Phase
#### 4.2.2 Registration of the \(Ust\) with \(Rgc\)  
Similarly, the computation performed in the registration of the user \(USR\) with \(RGC\) is given below and the pictorial representation of this process has been demonstrated in Figure 5.  
* The user \(USR\) chooses its identity as \(I_{U}\) and sets the password as \(PWD\). The \(USR\) computes: \[S_{U}=I_{U}G\] (17) and saves \(S_{U}\).
* \(USR\) randomly selects \(x\in Z_{n}\) and computes its public key as: \[P_{U}=xG\] (18) The user also computes: \[R_{U}=(PWD\oplus P_{U})\] (19) and directs the message \(\{I_{U},R_{U}\}\) to the \(RGC\).
* On getting the message \(\{I_{U},R_{U}\}\), the \(RGC\) generates the key: \[K_{RS}{}^{\prime}=(v_{R}\oplus I_{R})\] (20) and computes: \[A_{RU}=(K_{RS}{}^{\prime}\oplus R_{U})\] (21) \(RGC\) issues a smart card with \(\{A_{RU},Hash(),G\}\) to the \(USR\).
* On getting the smart card, the \(USR\) saves \(x\) on the smart card.  
Figure 4: Registration of \(SVR\) with \(RGC\) in ECSSP-SC.
# Seismic Performance Evaluation of Steel Buildings with Oil Dampers Using Capacity Spectrum Method
## 3 Proposed CSM Procedure for Buildings Equipped with Oil Dampers
Then, the maximum story velocities of the MDOF, \(V_{max}\), were estimated as following; \[V_{max,i}(\mu)=\omega_{eq}\cdot\delta_{i}\] (13)
* Using Equations (8), (9) and (16) the effective damping of the system, \(h_{eff,SRSS}\), the equivalent damping coefficient, \(C_{eq}\), and the viscous damping of oil dampers, \(h_{V}\), were estimated, respectively;
* Demand spectra were updated for each ductility factor as shown in Figure 4b (dashed-line response spectra);
* The next performance points were considered, and the same process was repeated until convergence (the difference between successive effective damping ratios becomes negligible), as shown in Figure 4c (dashed-dotted-line response spectra);
* The final performance points were connected and the point of intersection of the formed curve (blue solid line in Figure 4d) with the capacity curve was defined as the ultimate performance point of the ESDOF;
* Maximum seismic performance of the corresponding MDOF could be deducted from the ultimate performance point of its ESDOF. For the ESDOF system presented in this section, the maximum displacement was estimated as 6.15 cm. In Figure 4d, the average maximum displacements from the THA of six selected Level-2 earthquakes (details are given in Section 4.4) are marked by a vertical broken line. A difference of 20% was observed from the proposed CSM result.
# Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
### Survey Methodology
#### 1.4.4 Keywords  
The keywords used to collect the papers were combinations of the following: \"Arabic OCR\", \"Arabic Deep Learning\", \"Arabic Text Correction\", \"Arabic Error Correction\", \"Arabic Diacritization\", \"Arabic Large Language Models\", \"Arabic OCR Transformers\", \"Arabic OCR GANs\", \"Arabic OCR LSTM\", \"Arabic OCR RNN\", \"Arabic OCR CNN\", \"Arabic OCR CTC\", \"Arabic Handwritten Text Recognition\", \"Deep Learning Arabic Handwritten Text Recognition\".
## 4 Implementation
### Implementation Results
#### 4.4.1 Oversampling  
When a camera's frame rate exceeds the transmitter's packet rate by at least two times, the data packet is sampled multiple times, resulting in the oversampling effect. Additionally, packet merging problems occur at the receiver end. The SN was introduced to the DS to address this issue because it advanced the receiver's ability to reduce the effect of the camera's frame rate variation. When the receiver detects an identical SN value in the DS of different packets, the redundant data are deleted. As shown in Figure 13, the receiver discards consecutive packets with the same SN and merge packets with successive SNs (n \(-\) 1, n, n + 1). The DSs are then added to the data packet and repeated N-times in our suggested algorithms to decrease the packet loss rate when the camera cannot capture imaged in the time occurring between sub-packets. To improve the OCC system's reliability by preventing missing packets as a result of a change in the camera's frame rate, the \(N\) value must be calculated using Equation (7) as follows:  
\[N\geq\frac{\left(T_{cam}\right)_{\text{max}}}{DS\_length} \tag{7}\]  
where \(T_{cam}\) is the gap-time interval of a camera and \(DS\_length\) is the interval of a Data Sub-packet frame. As shown in Equation (7), if the \(N\) value is more than \(\frac{T_{cam}}{DS\_length}\), we can obtain at least one Data sub-packet twice, in which case oversampling occurs.