esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigating Shape Variation Using Generalized Procrustes Analysis and Machine Learning
## 2 Materials and Methods
### Investigation of Morphological Diversity
#### 2.4.1 Visible Features Clustering and Population Structure Investigation  
The proposed clustering method is based on the Gaussian mixture model (GMM) [49]. The GMM is based on a set of \(k\) multivariate Gaussian distributions. Each specimen \(j\) represented by its features \(\vec{f}_{j}\) is assigned to one of the \(k\) clusters. The model is based on  
\[p(\mathbf{F}|\vec{\pi},\mu,\Sigma)=\prod_{n=1}^{N}\sum_{j=1}^{k}\pi_{j}\mathcal{ N}(\vec{f}_{n}|\vec{\mu}_{j},\Sigma_{j}). \tag{2}\]  
The affiliation of a specimen feature \(\vec{f}_{j}\) to the cluster \(i\) is calculated by the variable \(\vec{z}=\big{(}z_{1},\dots,z_{k}\big{)}\), which is a 1-of-K coded vector, and the conditional probability  
\[p(z_{i}=1|\vec{f}_{j})=\frac{\pi_{i}\mathcal{N}(\vec{f}_{j}|\vec{\mu}_{i}, \Sigma_{i})}{\sum_{m=1}^{k}\pi_{m}\mathcal{N}(\vec{f}_{j}|\vec{\mu}_{m}, \Sigma_{m})}. \tag{3}\]  
During optimization, the parameters \(\{\Sigma_{1},\dots,\Sigma_{k}\}\), \(\{\mu_{1},\dots,\mu_{k}\}\), \(\vec{z}\) and \(\vec{\pi}\) are optimized. After optimization, we use the class probability \(p(z_{i}=m|\vec{f}_{j})\) as an estimate for specimen \(j\) belongs to the cluster \(m\in\{1,\dots,k\}\).  
We use the _mclust_[65] package relying on expectation maximization [49] for optimization. For the investigation of the morphological structure, \(k\) must be defined by the user. However, several strategies for \(k\) optimization exist, e.g., the Bayesian information criterion [66].  
The optimization of the \(k\) parameter is a fundamental problem in genetics as well. In genetics the marginal log-likelihood [63], evidence lower bound [48] or biological motivated criterion [62] are used. Nevertheless, these optimization criteria are mainly focusing on technical parameters. Motivated by the facts,  
1. That the used models may not be a good approximation for the unknown probability density functions,
2. The data are typically restricted as well as incomplete, and
3. Different \(k\)'s may capture biological significant information on different scales [67].  
We hypothesize that by finding consensus in a set of reasonable cluster models relevant pattern representative for the overall population can be obtained.
## 2 Materials and Methods
### MicroCT Scanning Procedures  
To our knowledge, three suggested methodologies of analysis for evaluating the cleanliness and the residual debris after root canal treatment can be used: confocal laser scanning microscopy (CLSM), field emission scanning microscopy (FESEM) and micro computed tomography (microCT). In the present study, microCT was chosen because FESEM requires preparation of the specimens (sputter coating and critical point drying), causing macroscopic artifacts and consequential bias. At the same time, SEM analysis allows a two-dimensional analysis of a restricted area of root canal surface, limiting the statistical analysis. CLSM is a useful technique in endodontics that avoids the drawbacks of FESEM, but it is not in accordance with the aim of our research. MicroCT imaging is a nondestructive and reproducible technology leading to very thin sections and a true three-dimensional reconstruction of an object. In particular, microCT imaging has been increasingly used to investigate endodontic anatomical features such as apical transportation, centering ratio and the volumetric changes of instrumented root canals. As a nondestructive technique, microCT enhanced our experimental protocol because it allowed us to analyze samples before and after the endodontic activations. After root canal shaping, all specimens of the three groups were scanned by a SkyScan 1072 microCT scanner (Bruker micro-CT, Kontich, Belgium) at 100 kV, 98 mA, 7.32 \(\mu\)m isotropic resolution, 180\({}^{\circ}\) rotation around vertical assets, total amount of 2 frames with 1 mm thick aluminum filter. The two-dimensional images were reconstructed using the CT-Analyser (V1.16; [PERSON] micro-CT, Kontich, Belgium) software used for image processing and analysis. The volume of AHTD (mm\({}^{3}\)) for the entire root canal system as well as from the different root thirds (coronal, middle and apical) were obtained by using the three-dimensional analysis tool. Specimens of EA group and EB group were scanned after the activation of the irrigating solution using the previously described parameters. Control negative specimens were not scanned because they were not subjected to irrigant activation, and thus the endodontic volume was not modified [9].
# Sound Localization Framework for Construction Site Monitoring
## 5 Experiments
### Microphone Array Shape for 3D Localization
The arrangement of each microphone was tested 10 times using sample sounds from a specific test point (x, y, z). During each test, the system performed an approximate localization of the recorded sound. The differences in the x, y, and z distances and the absolute distance from the sound source were calculated. The experimental results obtained using the tetrahedral array showed the average x-, y-, and z-axis errors of 0.32, 0.26, and 0.29 m, respectively, and 0.26 m in the absolute distance. This arrangement showed a relatively high accuracy compared to the right-angled pyramidal array.
# A Secure and Efficient Authentication Scheme for Fog-Based Vehicular Ad Hoc Networks
## 5 Security Analysis
### Formal Analysis
#### 5.1.8 AVISPA Simulation  
In this section, we presents formal security verification of our proposed protocol using AVISPA [37]. AVISPA has been widely used to verify the security of authentication protocols, primarily to assess their resilience against man-in-the-middle and replay attacks. Moreover, AVISPA is a formal analysis tool that implements an authentication protocol using the High-Level Protocol Specification Language (HLPSL). In addition, AVISPA uses four back-end models: \"on-the-fly model-checker (OFMC)\", \"constraint logic-based attack searcher (CL-AtSe)\", \"SAT-based model-checker (SATMC)\", and \"tree automata based on automatic approximations for the analysis of security protocols (TA4 SP)\". The back-end models evaluate the security features of an authentication protocol and generate the output format as a result. Since XOR operation is used in our proposed protocol, we only use OFMC and CL-AtSe back-end models. Figure 5 depicts the simulation results, and the proposed protocol is considered safe. Therefore, we can demonstrate that the proposed protocol resists replay and man-in-the-middle (MITM) attacks.
# Output Power Smoothing Control for a Wind Farm Based on the Allocation of Wind Turbines
## 3 Power Smoothing Control Strategy
### Evaluation of the Power Efficiency  
The efficiency of the power smoothing control method can be evaluated through the power smoothing function \(P^{*}_{level}\) and maximum energy function \(P^{*}_{max}\), which are expressed as [5]:  
\[P^{*}_{level}=\int\limits_{0}^{t}\left|\frac{dP_{o}(t)}{dt}\right|dt; \tag{20}\]  
\[P^{*}_{\max}=\int\limits_{0}^{t}P_{o}(t)dt, \tag{21}\]  
where \(P_{o}(t)\) is the output power at the time \(t\).  
If the power smoothing function \(P^{*}_{level}\) is small, the fluctuation of the output power is small. In other words, the performance of the power smoothing is great. The output power efficiency is also an important factor to power smoothing control method. The efficiency cannot be sacrificed too much for realizing the power smoothing. The larger the maximum energy function \(P^{*}_{max}\) is, the higher the output power efficiency is. Thus, the two factors will be calculated in the simulation to evaluate the effectiveness of proposed power smoothing control strategy.
# Exploration of Swarm Dynamics Emerging from Asymmetry
## 4 Measures to Quantify the Swarm Dynamics
### Fitness Function Candidates
#### 4.3.2 Fitness Function B  
Another possibility of characterizing the swarm pattern from the asymmetry of static structure can be based on the variation of the pair-wise distance between two agents. We define the fitness function as:  
\[\phi = \frac{1}{T}\sum_{t=1}^{T}\sigma\left[D_{ij}(t)\right], \tag{9}\]  
where \(D_{ij}(t)=\left\|\vec{x}_{i}(t)-\vec{x}_{j}(t)\right\|\) is the distance between agent \(i\) and agent \(j\) at the time step \(t\). This function is the standard deviation \(\sigma\) of the pair-wise distances, measured by the snapshots of the swarm, too. If the structure is a simple ball-like pattern or just dispersing, the pair-wise distances will be uniform. As it is suggested that the distance between two agents at the equilibrium state depends on the ratio of kinetic parameters \(c_{1}/c_{3}\) (\(c_{1}\): cohesion coefficient, \(c_{3}\): separating coefficient) of these agents [7; 10].
# Broadband Dual-Polarized 2 \(\times\) 2 MIMO Antenna for a 5G Wireless Communication System
###### Abstract  
In this study, we proposed an indoor broadband dual-polarized 2 \(\times\) 2 MIMO (multiple-input and multiple-output) antenna having dimensions of 240 mm \(\times\) 200 mm \(\times\) 40 mm, for application in 5G wireless communication systems. The proposed antenna comprised two vertically polarized circular monopole antennas (CMAs), two horizontally polarized modified rectangular dipole antennas (MRDAs), and a ground plane. The distance between the two MRDAs (MRDA1 and MRDA2) was 70.5 mm and 109.5 mm in the horizontal (\(x\)-direction) and 109.5 mm vertical (\(y\)-direction) directions, respectively. Conversely, the distance between the two CMAs (CMA1 and CMA2) was 109.5 mm and 70.5 mm in the horizontal (\(x\)-direction) and vertical (\(y\)-direction) directions, respectively. While the CMAs achieved broadband characteristics owing to the optimal gap between the dielectric and the driven radiator using a parasitic element, the MRDAs achieved broadband owing to the optimal distance between the dipole antennas. The observations in this experiment confirmed that the proposed could operate in the 5G NR n46 (5.15-5.925 GHz), n47 (5.855-5.925 GHz), n77 (3.3-4.2 GHz), n78 (3.3-3.8 GHz), and the n79 (4.4-5 GHz) bands. Moreover, it exhibited a wide impedance bandwidth (dB magnitude of \(S_{\rm 11}\)) of 101% in the 2.3-7 GHz frequency range, high isolation (dB magnitude of \(S_{\rm 21}\)), low envelope coefficient correlation (ECC), gain of over 5 dB, and average radiation efficiency of 87.19%, which verified its suitability for application in sub-6 GHz 5G wireless communication systems.  
5G; MIMO; dual polarization; broadband Article  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON] broadband Dual-Polarized 2 \(\times\) 2 MIMO Antenna for a 5G Wireless Communication System
## 2 Literature Review
Overall, in comparative studies, Convolutional codes and the Viterbi algorithm consistently outperform other coding methodologies like LDPC codes, turbo codes, and BCH codes in terms of complexity, efficiency, resistance to burst errors, and stream decoding capability. These advantages make them suitable for GPS communication systems with high data rates and relatively low error probability. This study aims to provide a comprehensive assessment of the advantages and disadvantages of each coding method employed in satellite communication systems. By doing so, it offers valuable insights into why convolutional codes and the Viterbi algorithm are suitable for GPS systems.
# Harmonized Autonomous-Human Vehicles via Simulation for Emissions Reduction in Riyadh City
## 5 Experiment and Results
### Statistical Validation  
To assess the statistical significance of the observed improvements in traffic performance and emission metrics across the different AV penetration scenarios, we employed paired \(t\)-tests comparing each scenario to the baseline (100% HV). This method is appropriate for evaluating whether the mean differences in paired observations (e.g., trip time, stop frequency, emissions per vehicle) are significantly different from zero. The tests were conducted on trip-level outputs sampled across the 15-hour simulation duration, ensuring temporal representativeness during both peak and off-peak periods. Each metric--average travel time, stop frequency, average speed, and CO\({}_{2}\) emissions--showed statistically significant differences at the 95% confidence level (\(p<0.05\)) for all AV scenarios when compared to the baseline. This indicates that the observed improvements are not due to random  
Figure 11: Cumulative CO\({}_{2}\) emissions by scenario over 15-hour simulation.  
Figure 10: Average stop frequency comparison across AV scenarios.  
simulation variation but rather to structural behavioral differences introduced by AV integration, namely, reduced reaction times, smoother acceleration/deceleration profiles, and better coordination. These findings reinforce the reliability of the simulation results and the validity of the proposed HARMONY-AV framework as a tool for sustainable mobility planning in mixed traffic environments. To enhance transparency, Table 7 provides the specific \(p\)-values for each scenario and performance metric comparison.
# Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks
## 6 Network Parameters Adjustment and Performance Enhancement
### Study of the Methods of Weight Calculation of CHs  
It is a good choice to select sensors with high residual energy as CHs. However, those nodes with high residual energy tend to be very far away from the mobile sink. Consequently, if we only considered the residual energy of nodes, long distance communication will occur between CHs and the sink. Thus, it was necessary to achieve a balance between the residual energy and distance from CHs to mobile sink to prolong the lifetime of the system. We first set the radius of the mobile sink to be 0.25R, and then we calculated the weight of each node by using Equations (7)-(9). Simulation results are provided in Figure 12, and it illustrates that it is more reasonable for us to use Equation (9) to calculate the weight of nodes.  
Figure 11: Different radius of mobile sink under different network size.  
Figure 12: Different methods of weight calculation.  
A great deal of research defines the network lifetime as the time when the first node dies. However, using Equation (8) may make the node's death premature because the distance accounts for a large proportion in the weight calculation. In order to highlight the importance of the residual energy, we adopted Equation (9) as the method of weight calculation under the circumstances that the initial energy of each node was less than 1 J. Simulation results demonstrate that it performed better in terms of the time when the first node dies.
# Analysis of Coastal Zone Data of Northern Yantai Collected by Remote Sensing from 1990 to 2018
## 2 Materials and Methods
### Extraction of Coastal Zone Coverage Information  
Based on the 2018 coastline, this study defines the coastal zone as the zonal area formed by extending \(2\) km from coastline to inland and uses the same visual interpretation method as the extraction of the coastline to extract the surface coverage information of the coastal zone. Referring to the content and indicators of China's geographic survey, this study highlights the types and elements of coastal zone development and utilization and forms a classification system of land cover for coastal zone development and utilization change monitoring. The surface coverage of the coastal zone in the study area includes eight first-class classifications: cultivated land, forests/grasses coverage, building areas, roads, structures, manually excavated land, desert/bare land, water areas [31]. Due to the difference in image resolution and subjective error of visual interpretation in different years, this study only considers the change of the first-class coastal zone in the study area and does not analyze the second-class land cover types. In this paper, UAV is used to verify the correctness of information extraction results, and it can also reach places that are difficult for human beings to reach for monitoring (Figure 3). The classification system of first-class land cover is consistent with the classification standard of Shandong Province's geographic condition monitoring (Table 2).
# DRL-Based Dynamic SFC Orchestration Algorithm for LEO Satellite Networks
## 3 System Model
### Markov Decision Process
\begin{cases}-\theta\cdot\delta_{i}\cdot hop_{i},&\text{if readjustment succeeds}\\ -\theta,&\text{if readjustment fails}\end{cases} \]  
where \(\delta_{i}\) represents the input traffic demand of \(SFC_{i}\), \(lifecycle_{i}\) represents the lifecycle of \(SFC_{i}\), \(hop_{i}\) represents the number of hops during readjustment, and \(\zeta,\eta,\theta,\vartheta\) are adjustment parameters. The cumulative reward \(R(\tau)=R_{recvenue}+R_{cost}=R+C\), which is consistent with the formula in problem modeling. The process evaluates the quality of the current orchestration policy \(\pi\) through the state value function:  
\[V_{\pi}(s,a)=E_{\pi}[\sum_{k=0}^{T-1}\Gamma^{k}r(s_{t+k},a_{t+k})+R(\tau)|s_{t} =s] \tag{24}\]  
where \(T\) represents the episode length; \(\Gamma\in[0,1]\) is the discount factor, indicating the importance of future rewards relative to current rewards. The optimization objective of the process, which is also the optimal policy for dynamic SFC orchestration, can be expressed as follows:  
\[\pi^{*}=\arg\max_{\pi}V_{\pi}(s,a),\,\forall s,a \tag{25}\]This formulation aims to find the policy \(\pi^{*}\) that maximizes the expected cumulative discounted reward across all possible state-action pairs, considering both immediate rewards and long-term benefits.
# Current Trends in Fully Automated On-Line Analytical Techniques for Beverage Analysis
## 2 On-Line Solid-Phase Extraction (On-Line SPE)
### SPE Applications  
In addition to the beverages field--the primary purpose of the present report--SPE has been applied to the analysis of environmental, clinical, food, and botanical samples. Table 1 summarizes the most commonly studied compounds and the most frequently analyzed types of beverages.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
Figure 1: Schematic set up for on-line solid-phase extraction in a back-flush mode.  
[PERSON] et al. [38] developed a method for determining Pb(II) in water (tap and mineral water) and beverage samples (apple juice, orange juice, sea-buckthorn juice and liqueur). In this method, a hybrid monolithic column based on layered double hydroxides (LDHs) nanosheets-alginate hydrogel was used as an SPE device. Traces of Pb(II) could be preconcentrated quantitatively in pH 6.0 with high recoveries (\(>\)97%). The preconcentration factor and the precision expressed as the percent relative standard deviation (% RSD) were found in 0.39 \(\mu\)g/L, 53.7 and 2.65%, respectively. This SPE method demonstrated a high absorption capacity and preference for the extraction of Pb\({}^{2+}\) over other heavy metals studied as Cu\({}^{2+}\), Zn\({}^{2+}\), Cd\({}^{2+}\), and Mn\({}^{2+}\). The column was free of matrix interferences and can be potentially applied to the separation, preconcentration and determination of trace amounts of Pb\({}^{2+}\) in beverages.  
In another reported work, [PERSON] et al. [20], used three different approaches to obtain nano/microfibers, examining their diversity and extraction properties. For that purpose, the authors investigated five types of nano/microfiber fiber polymers as sorbents: (1) Polyamide 6 nanofibers, (2) polyvinyl dil fluoride nanofibers, (3) polyethylene microfibers and (4-5) two new polycaprolactone nanofiber/microfiber and polycaprolactone microfibers/polyvinylidene dil fluoride nanofiber composite polymers. Extraction conditions were optimized for each polymer. The nano/micro compound consisting of polycaprolactone microfibers/polyvinylidene dil fluoride nanofibers was chosen as the most suitable sorbent for the on-line extraction of achratoxin A (OTA) in beer, detecting a concentration between 2.5 and 5.0 mg/L and having high recovery percentages between 99.1 and 103.9%.  
Finally, we have to highlight the research of [PERSON] et al. [41], which determined protocatechuic acid (PCA) in fruit juices from a local market in Changsha, China, being necessary to perform a selective sample pretreatment to avoid matrix effects, before analysis by HPLC. PCA was extracted with a magnetic molecular imprinted polymer (MMIP), which facilitated its extraction and detection in grape juice, at a concentration of 0.45 \(\mu\)g/mL (PCA was not found in apple, pineapple, orange or peach juices), with satisfactory recoveries (92-107%). The results indicated that the synthesized MMIP could be used for the efficient and selective extraction of PCA in complex matrices.
# Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models
###### Abstract  
Data reliability is of paramount importance for decision-making processes in the industry, and for this, having quality links for wireless sensor networks plays a vital role. Process and machine monitoring can be carried out through ANDON towers with wireless transmission and machine learning algorithms that predict link quality (LQE) to save time, hence reducing expenses by early failure detection and problem prevention. Indeed, alarm signals used in conjunction with LQE classification models represent a novel paradigm for ANDON towers, allowing low-cost remote sensing within industrial environments. In this research, we propose a deep learning model, suitable for implementation in small workshops with limited computational resources. As part of our work, we collected a novel dataset from a realistic experimental scenario with actual industrial machinery, similar to that commonly found in industrial applications. Then, we carried out extensive data analyses using a variety of machine learning models, each with a methodical search process to adjust hyper-parameters, achieving results from common features such as payload, distance, power, and bit error rate not previously reported in the state of the art. We achieved an accuracy of 99.3% on the test dataset with very little use of computational resources.  
wireless sensor network; link quality estimation; deep learning; failure detection 20
###### Abstract  
Wind speed and wind power are two important indexes for wind farms. Accurate wind speed and power forecasting can help to improve wind farm management and increase the contribution of wind power to the grid. However, nonlinear and non-stationary wind speed and wind power can influence the forecasting performance of different models. To improve forecasting accuracy and overcome the influence of the original time series on the model, a forecasting system that can effectively forecast wind speed and wind power based on a data pre-processing strategy, a modified multi-objective optimization algorithm, a multiple single forecasting model, and a combined model is developed in this study. A data pre-processing strategy was implemented to determine the wind speed and wind power time series trends and to reduce interference from noise. Multiple artificial neural network forecasting models were used to forecast wind speed and wind power and construct a combined model. To obtain accurate and stable forecasting results, the multi-objective optimization algorithm was employed to optimize the weight of the combined model. As a case study, the developed forecasting system was used to forecast the wind speed and wind power over 10 min from four different sites. The point forecasting and interval forecasting results revealed that the developed forecasting system exceeds all other models with respect to forecasting precision and stability. Thus, the developed system is extremely useful for enhancing forecasting precision and is a reasonable and valid tool for use in intelligent grid programming.  
data pre-processing strategy; artificial neural network; multi-objective optimization algorithm; combined model +
Footnote †: journal: Applied Sciences