esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Setup and Test Conditions  
Erosion-corrosion tests were performed using a recirculating submerged impingement jet apparatus (SIJ), as shown in Figure 3. The recirculating system enables the sand to mix with the water which flows through the nozzle, with a diameter of 4 mm, and impinges on the specimen at an angle of 90\({}^{\circ}\). This specific angle of attack was chosen to determine material degradation of elbows and tees. The specimen was positioned at a distance of 5 mm from the nozzle. A cooling coil was immersed in the reservoir which compensates for temperature increases due to pump friction. All the experiments were conducted at a temperature of 29-33 \({}^{\circ}\)C. The impingement tests were carried out at different slurry velocities (10, 13, 16, and 20 m/s) and different sand loadings (300, 400, 500 and 600 mg/L). The flow loop was rinsed with clean water after each experiment, and the slurry solution was refilled. Therefore, the interaction between silica particles and steel particles left in the solution was ignored as it is beyond scope of this study. The specimens were weighed before and after jet impingement tests using a digital balance (Precisa XB 220A) with a sensitivity of \(\pm\)0.1 mg.  
The static corrosion tests were conducted using the linear polarization method. A potentiostat (Gamry Ref 600) consisting of three electrodes was used for the static corrosion measurements. The linear polarization resistance (\(R_{p}\)) was measured using a three-electrode potensiostat in which an Ag/AgCl electrode was used as the reference electrode, with platinum as the reference electrode and the specimen as the working/test electrode. The test was conducted at 0.25 mV/s scan-rate over a potential range from \(-\)20 mV to +20 mV.  
Figure 3: Experimental setup (SIJ) used in this study.  
The corrosion current-density (\(i_{corr}\)) was calculated from the polarization resistance using Equation (3), where \(\beta_{a}\) and \(\beta_{c}\) are Tafel constants.  
\[i_{corr}=\frac{\beta_{a}\beta_{c}}{2.3R_{p}(\beta_{a}+\beta_{c})} \tag{3}\]  
The calculated \(i_{corr}\) was then converted to the weight loss using [PERSON]'s law. All the experiments were repeated three times to check the validity and repeatability.
# An Efficient Approach for LBS Privacy Preservation in Mobile Social Networks
## 5 Algorithm Design
### The Algorithm Scheme  
For the security parameters \(th_{k}\) and \(th_{l}\) in a region, the service category server bootstraps the LBS system and initializes it. If a user wants to obtain service from the LBS system, he must register himself to the LBS system. Before sending a request to the LBSP, he should firstly unite with other \(k-1\) users to become a clustering group by using the request aggregation protocol. Then the kinds of service category \(l\) in the clustering group can be calculated by the variety judgment protocol. The representative user of the clustering group firstly should judge the relationship between \(l\) and \(th_{l}\). If \(l\geq th_{l}\), the representative user will judge the relationship between \(k\) and \(th_{k}\). Then there will be two results: If \(k\geq th_{k}\), which means the clustering group has met the conditions of clustering success, the clustering group will wait for new users to join to meet the condition (i.e., \(k\geq th_{k}\)) until timeout. If \(l<th_{l}\), which means the clustering group has not met the condition of clustering success, the representative user will choose filtering aggregation protocol or representative aggregation protocol by judging the relationship between \(k\) and \(th_{k}\) for improving the success rate of clustering. Note that, if a clustering group does not meet the conditions (i.e., \(k\geq th_{k}\) and \(l\geq th_{l}\)) and runs into a timeout, the clustering process of this group is a failure, and no service request will be sent to LBSP. The scheme of our SCTB algorithm is shown as Figure 10.
# Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks
## 2 System Dynamic Models
### Linear Model  
The horizontal dynamic equation of a mass-spring-damper system sliding on a surface with no friction is:  
\[m\ddot{x}+d\dot{x}+kx=F \tag{1}\]  
where \(m\) is the value of the sliding mass, \(d\) the damping coefficient, \(k\) the stiffness coefficient, \(F\) the external force applied and \(x\) the mass position. The involved quantities and sign convention are reported in Figure 1.  
To compute the poles of the system, Equation (1) can be rewritten in state-space form, defining the state-space vector \(\mathbf{x}=[x_{1},x_{2}]^{T}\), with \(x_{1}\) the velocity of the mass and \(x_{2}\) its position.  
\[\begin{cases}x_{1}=-\dfrac{d}{m}x_{1}-\dfrac{k}{m}x_{2}+\dfrac{F}{m}\\ x_{2}=x_{1}\end{cases} \tag{2}\]  
The system dynamic matrix of (2), obtained by computing the partial derivatives of \(\dot{x}_{1}\) and \(x_{2}\) with respect to \(x_{1}\) and \(x_{2}\), is therefore:  
\[\mathbf{A}=\begin{bmatrix}-\frac{d}{m}&-\frac{k}{m}\\ -\overline{1}&0\end{bmatrix} \tag{3}\]  
It is important to notice that, as expected, the equilibria of the system (2) do not depend on the choice of the state space variables, as the system is linear. In order to obtain the analytical formulation of the system poles, it is possible to solve \(det(\lambda I-A)=0\). The resulting poles, \(\lambda_{1}\) and \(\lambda_{2}\), are:  
\[\lambda_{1,2}=-\dfrac{d}{2m}\pm\sqrt{\dfrac{d^{2}}{4m^{2}}-\dfrac{4}{m}} \tag{4}\]
# Review of CMOS Integrated Circuit Technologies for High-Speed Photo-Detection
## 1 Introduction
In practice, optical fibers have successfully replaced the legacy copper cables over long distances and extension of their usage into shorter distances appears promising. Furthermore, with advanced silicon photonic technologies, it is anticipated that optics can be used even for micro-scale interconnections. Although several problems remain to be solved, many research groups are working toward the same goal, i.e., the realization of high-performance computing and communication systems on a single chip. Silicon photonics with mature complementary metal-oxide-semiconductor (CMOS) IC technologies will provide solutions to fulfill the aforementioned industrial demands. In this paper, we summarize the current status and attempt to forecast the future of optical interconnection in a wide perspective by reviewing papers in as many fields as possible. We also provide a review of key circuit techniques for high-speed photo-detection. This paper is organized as follows. In Section 2, various aspects of optical links are discussed. Subsequently, we focus on the subject of an optical receiver by providing the basis of a photodetector (PD) in Section 3 and the basic topologies of a transimpedance amplifier (TIA) in Section 4. In Sections 5 and 6, the detailed circuit techniques for the optical receiver are introduced. Finally, we conclude this review work.
## 3 Dataset Description
### HFAVD Dataset Structure
#### 3.3.1 Respiratory Rate and Breathing Characteristics  
To estimate the respiratory rate, the approach proposed by [39] was employed, encompassing the following steps: (a) Chest keypoint detection using OpenPose: The OpenPose framework was utilized to detect the keypoint corresponding to the chest region in the video frames. (b) Displacement detection using SelFlow: An Optical Flow-based neural network called SelFlow was employed to detect the displacement of the chest keypoint between consecutive frames. (c) Projection of displacement along the x- and y-axes: The displacement values obtained were projected onto the x- and y-axes, separating the movement into vertical (up/down) and horizontal (left/right) directions. (d) Signal processing techniques: Signal processing methods, including filtering and detrending, were applied to the displacement data to enhance the accuracy of respiratory rate estimation. (e) Calculation of true peak count and scaling: The true peak count was calculated based on the processed displacement data. This count was then scaled to estimate the number of breaths per minute. Furthermore, in addition to estimating the respiratory rate, various breathing characteristics, such as stability and rhythmicity, were assessed based on the amplitude and wavelength of the respiratory wave. The mean absolute error (MAE) for respiratory rate estimation was 1.5 breaths per minute (bpm). Regarding the limitations of this model, its effectiveness is restricted by its inability to handle body movement efficiently and its reliance on the subject maintaining a stationary position during recording. This requirement poses the risk of inaccurate estimations. Consequently, the model is unsuitable for use when the subject is walking, engaged in motion, or even driving on the road, due to the presence of vehicle vibrations.
# Development and Biomechanical Evaluation of a Back Exoskeleton to Support Lifting Tasks
## 5 Discussion
### Influence on Kinematics and Muscle Activity
In addition to increasing the number of test participants, future research should incorporate additional biomechanical and physiological measures, such as muscle fatigue analysis (via EMG median frequency), metabolic cost measurements, and subjective workload assessments (e.g., Borg scale). These metrics would provide a more comprehensive understanding of how exoskeletons affect long-term usability, user comfort, and fatigue resistance. Especially during repetitive lifting tasks, monitoring changes in EMG signal characteristics or oxygen consumption over time could help identify whether the system reduces physical strain or delays fatigue onset. Furthermore, metabolic data, such as heart rate or respiratory exchange ratio, could serve as valuable indicators of overall energy efficiency during assisted vs. unassisted conditions. Kinematic analyses beyond the hip joint--such as lumbar spine motion--could further reveal how the exoskeleton interacts with whole-body movement patterns. Furthermore, future studies should evaluate long-term user acceptance in real-world healthcare environments and explore the impact of exoskeleton-assisted lifting on musculoskeletal health.
## 2 Methodology for Estimation of Damping on Bridges Based on Dynamic Tests under Railway Traffic
### Prony Method Analysis for Damping Estimation
The amplitude \(A_{i}\), the frequency \(f_{i}\), the damping coefficient \(\xi_{i}\) and the phase angle \(\phi_{i}\) from each sinusoid are given by Equations (5)-(8), respectively:  
\[A_{i}=magnitude\ of\ r_{i}=\sqrt{{a_{i}}^{2}+{b_{i}}^{2}} \tag{5}\]\[f_{i}=\frac{1}{2\pi\Delta t}(imag(\ln(p_{i}))) \tag{6}\] \[\tilde{\varsigma}_{i}=-\frac{1}{2\pi f_{i}\Delta t}(real(\ln(p_{i})))\] (7) \[\varnothing_{i}=\textit{phase angle of }r_{i}=\arctan\biggl{(}\frac{b}{a} \biggr{)} \tag{8}\]  
After calculating the different parameters for each sinusoid, it is possible to construct the estimated data \(\hat{y}(t)\) given by Equation (1). To compare the estimated data \(\hat{y}(t)\) with the original free vibration data \(y(t)\), the Mean Squared Error (\(MSE\)) statistical estimator is used. This estimator measures the average squared difference between the original and predicted values, and is given by  
\[MSE(n)=\frac{\sum_{i=0}^{L-1}(y(t_{i})_{n}-\hat{y}(t_{i})_{n})^{2}}{L} \tag{9}\]  
where \(n\) is the order number of the model which comes from Equation (4) and indicates the degree of the denominator polynomial of the rational transfer function \(b(z)/a(z)\), and \(L\) is the length of the signals. The ideal order number of the model is the one with the lowest \(MSE\) value. Despite the Prony method's capabilities for damping estimation, due to the high number of frequencies estimated, the accuracy of the method can be compromised, since it is prone to false detect frequency modes that are not related to the structure. Typically, most of these frequencies present very low or negative damping and may wrongly be interpreted as dominant modes. This problem could be overpassed with the energy sorting technique [48] presented in Section 2.4, where the modes are sorted according to their energy content.
## 6 Resulting Graph Visualizations
### Star Motif
#### 6.1.2 Visualizing the Reconnaissance Tactic by Average Duration  
Figure 3 presents the visualization of the Reconnaissance Tactic by average duration. The average duration of the connections in the star motif did not identify areas of interest, as green (bin = 3) and blue (bin = 4) are average behaviors in this graph. The blue connections in Figure 3 correspond to the high connections found in Figure 2, although the duration per connection is considerably higher, ranging from 300 to 1700 times longer than the other connections in green. The connections in green transferred 0 bytes, whereas the connections in blue transferred data from between 2 bytes and 1.5 MB of data per connection. Sample data points for Reconnaissance points of interest based on average duration are presented in Table 4. The maximum duration was 972,063.54, the minimum duration was 0.04, and the average duration was 12,947.3263.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ID** & **From** & **To** & **Total\_Dur** & **Avg\_Dur** & **Total\_Bytes** & **Avg\_Bytes** & **Count** & **CountBin** \\ \hline edge\_3 & 143.88.2.10 & 143.88.7.12 & 778,386.2988 & 0.6913 & 925,758,636,800 & 822,247.5387 & 1,125,888 & 4 \\ edge\_4 & 143.88.210 & 143.88.7.10 & 1792.93927 & 1.4007 & 798,720 & 624 & 1280 & 4 \\ edge\_42 & 143.88.2.10 & 143.88.7.14 & 3080.24 & 3.0080 & 0 & 0 & 1024 & 4 \\ edge\_43 & 143.88.2.10 & 143.88.7.13 & 3080.264 & 3.0080 & 0 & 0 & 1024 & 4 \\ edge\_257 & 143.88.5.12 & 143.88.5.1 & 943,576.7 & 1.8776 & 36,458,752 & 72.55068772 & 502,528 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Reconnaissance points of interest (average duration).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**ID** & **From** & **To** & **Total\_Dur** & **Avg\_Dur** & **Total\_Bytes** & **Avg\_Bytes** & **Count** & **CountBin** \\ \hline edge\_0 & 143.88.2.10 & 143.88.7.15 & 353,248.5154 & 0.2126 & 2,654,582,328,320 & 1,597,759.9722 & 1,661,440 & 6 \\ edge\_1 & 143.88.2.10 & 143.88.7.11 & 972,063.5371 & 0.3123 & 5,579,520 & 1,7928 & 3,112,192 & 6 \\ edge\_2 & 143.88.2.10 & 143.88.7.1 & 279,987.9888 & 0.1338 & 8,567,808 & 4,0934 & 2,093,056 & 6 \\ edge\_3 & 143.88.7.12 & 143.88.7.12 & 778,386.2988 & 0.6914 & 925,758,636,800 & 822,247.5387 & 1,125,888 & 6 \\ edge\_257 & 143.88.5.12 & 143.88.5.1 & 943,576.7243 & 1.8777 & 36,458,752 & 72.5507 & 502,528 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Reconnaissance points of interest (connection count).
# Thermal and Mechanical Behavior of Elastomers Incorporated with Thermoregulating Microcapsules
## 2 Materials and Methods
### Characterization Techniques
#### 2.2.2 Scanning Electron Microscopy (SEM)  
The elastomers' morphologies and filler distributions were depicted by means of scanning electron microscopy (SEM) with a FEI QUANTA 250, working between 5 and 30 kV.
## 4 Description of the SAVOA  
### Population Initialization Strategy Based on Sobol Sequence  
The initial population with good distribution is the basis of the metaheuristic algorithms [38]. The standard African vulture algorithm uses a random approach to population initialization, which can lead to an uneven distribution of generated individuals and affect the performance of the algorithm. This paper introduces the Sobol sequence as a mechanism for population initialization, which is a low-discrepancy random sequence that possesses high uniform coverage in multidimensional space.  
Define the range of the search space as \([lb_{j},ub_{j}]\) and the initial position of the population can be defined as:  
\[X_{i,j}=lb_{j}+S_{i,j}\times(ub_{j}-lb_{j}),\;i=1,\;2,\ldots,pop;\;j=1,\;2, \ldots,d \tag{49}\]  
where \(lb_{j}\) and \(ub_{j}\) represent the lower and upper boundaries of the \(j\) dimension, respectively; \(S_{i,j}\) is the value of the \(i\) sample in the \(j\) dimension generated by the Sobol sequence, which ranges from \([0,1]\); \(pop\) and \(d\) are population size and spatial dimension, respectively. Figure 4 shows the distribution comparison between random initialization and Sobol sequence initialization when generating a population of 500 individuals in two-dimensional space. It can be seen that the randomized approach is prone to uneven distribution and aggregation, whereas the individuals generated by the Sobol sequence cover the entire search space more evenly.
# Detecting the Structural Hole for Social Communities Based on Conductance-Degree
###### Abstract  
It has been shown that identifying the structural holes in social networks may help people analyze complex networks, which is crucial in community detection, diffusion control, viral marketing, and academic activities. Structural holes bridge different communities and gain access to multiple sources of information flow. In this paper, we devised a structural hole detection algorithm, known as the Conductance-Degree structural hole detection algorithm (CD-SHA), which computes the conductance and degree score of a vertex to identify the structural hole spanners in social networks. Next, we proposed an improved label propagation algorithm based on conductance (C-LPA) to filter the jamming nodes, which have a high conductance and degree score but are not structural holes. Finally, we evaluated the performance of the algorithm on different real-world networks, and we calculated several metrics for both structural holes and communities. The experimental results show that the algorithm can detect the structural holes and communities accurately and efficiently.  
structural hole; social networks; conductance; label propagation algorithm; minimal cut
# A Multidimensional Analysis of Released COVID-19 Location-Based Mobile Applications
## 4 The Framework of Mapping and Evaluating COVID-19 LBMAs
### Technology Aspects of COVID-19 LBMAs
#### 4.2.2 Proximity Tracing Technologies  
Bluetooth consists of proximity technology that is a simplified term for Bluetooth Low Energy and is useful for indoor and outdoor proximity services. It usually functions in conjunction with other smart devices where they can estimate their differential distance, usually with the utilization of an LBMA [29]. The accuracy of proximity information is usually around 5 m [30]. Mainly the received signal strength identification (RSSI) is a measurement of the power present in a received radio signal and utilized to estimate the relative distance between different smart devices. As a complementary technology to Bluetooth, contact-tracing tokens have been released that identify automatically close contacts of the user without the use of any mobile application. The advantage of this method is increased privacy for two main reasons. Data is available only to approved authorities of each country, and it is impossible for a software update to surreptitiously turn on location data or other sensors without notifying the user [31].  
Another proximity tracing technology is the Wi-Fi antenna that utilizes the signal for Internet access on smart devices. The Wi-Fi positioning system can be implemented utilizing multiple points of access and by using trilateration upon an end-user that is utilizing the received Wi-Fi signal on a smartphone. The research in this area is focused on increasing the emitting or the receiving signal on smart devices and finding the optimal solutions for accurate positioning when it comes to indoor environments [32].
# A Comparative Evaluation of Cement and By-Product Petrit T in Soil Stabilization
## 1 Introduction  
Soil improvement by adding stabilizing agents is the most common, effective and economical technique to enhance the strength characteristics of soft soil [1]. A wide range of soils can be improved by adding different types of hydraulic binders, but the cement has been the most popular and successful stabilizer used [2]. Nowadays, the use of waste materials from the various industrial process as a cementitious binder has increased to stabilize different types of soils. These by-product materials are considered to be easily available and cheap compared to the traditional binders [3; 4; 5]. In addition, there is an environmental benefit from the reuse of these types of by-products. Its contribution is to decrease environmental impact posted by producing these material [6].  
Generally, cement improves the physical and mechanical properties of soils by produce primary and secondary cementitious materials [7]. After adding cement to wet soil, three types of primary cementing components are produced as a result of the hydration reaction of cement: calcium-silicate hydrate (CSH), calcium-aluminate-hydrate (CAH), and hydrated lime (Ca(OH)\({}_{2}\)). Here, C (CaO), S (SiO\({}_{2}\)), A (Al\({}_{2}\)O\({}_{3}\)), and H (H\({}_{2}\)O) are employed. A pozzolanic reaction produces a secondary cementitious material due to the reaction between the hydrated lime and silica and alumina from clay minerals and provides further cementing components of CSH and CAH [8; 9; 10].  
The benefit of using cement in the soil stabilization field has been extensively investigated [11; 12; 13; 14; 15; 16; 17; 18]. In addition, the possibility of using industrial by-product materials to improve soft soils has beenencouraged by many studies [6; 19; 20; 21; 22; 23]. Enhanced soil strengths, reduced soil plasticity and swelling potential are the most desirable outcomes from the treatment. For certain soil stabilization applications, the cost of the binder itself could result in a considerable saving in the overall costs of the project [24]. Petrit T is a waste product from sponge iron production [18]. It is considered a cheap by-product material [25]. Therefore, in order to decrease the environmental impact and costs, finding new binders as alternatives to Portland cement is important. Thus, there is a need to compare the effectiveness of using cement and by-product Petrit T as stabilizing agents.  
This paper presents a comparative evaluation between using Portland cement and by product Petrit T to enhance the physical and mechanical properties of sandy clayey silt soil. An extensive experimental program was carried out in this study, including tests of unconfined compressive strength, laser particle size, consistency limits, and pH tests, using various binder amounts and curing periods.
# An Improved Q-Learning-Based Sensor-Scheduling Algorithm for Multi-Target Tracking
## 4 Detailed Description of the Proposed Algorithm
### Target Prioritizing
#### (1) Target Type  
For different types of targets, the system pays different attention to them. For example, in military applications, systems have different importance for monitoring soldiers and armored vehicles because of their differences in speed and combat effectiveness. Therefore, it is necessary to distinguish the type of target and give an evaluation metric about its importance, which can be used as a reference for sensor scheduling. We use \(\eta\) to measure the importance of the targets. The value of \(\eta\) for different targets is evaluated and normalized according to specific applications requirements, so we have \(\eta\in(0,1)\).
## 5 Discussion  
This investigation introduces an innovative methodology designed to optimize the satellite handover process within Low Earth Orbit (LEO) constellations through the integration of graph neural network (GNN) and deep reinforcement learning (DRL) technologies, thereby ensuring uninterrupted handover and enhancing the quality of service. The outcomes section underscores the model's proficiency in mitigating handover failures and curtailing transmission latency, factors that are paramount to ensuring the reliability and efficiency of satellite communications. Prior investigations in this domain have predominantly concentrated on the deployment of conventional handover mechanisms. The method presented herein is advanced by the amalgamation of graph neural network (GNN) and deep reinforcement learning (DRL) algorithms, leveraging GNN's robust representational capabilities to adapt to evolving satellite network conditions, and employing DRL to effectuate real-time adaptive handover decisions.