esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works  
Various channel models have been introduced in the literature to analyze the performance of relaying systems in 5G scenarios. These generalized fading models can be categorized into small-scale multipath scattering propagation models such as Rayleigh [5], Rican [6], Nakagami-m [7], Weibull [8], Nakagami-q [9], \(\eta-\lambda-\mu\)[10], \(\alpha-\lambda-\mu\)[11], \(\kappa-\mu\)[12], \(\alpha-\mu\), and \(\eta-\mu\) fading; composite multipath shadowing line of sight (LOS) propagation (composite small- and large-scale fading) models such as generalized Gamma [13] and generalized K [14] fading; and composite multipath scattering and shadowing propagation models with LOS, such as the fluctuating two-ray (FTR) fading model [15; 16].  
To date, a number of studies have been performed on the performance analysis of relay-aided wireless communication systems [12; 17; 18; 19; 20; 21; 22; 23]. The authors in [17] derived the expression of the outage probability for a dual-hop DF relaying over a Rayleigh-generalized gamma fading channel. In [18], a dual-hop system was analyzed in relation to mixed \(\eta-\mu\) and gamma-gamma fading. The symbol error rate (SER) performance of an amplify-and-forward (AF) scheme in relation to asymmetric fading channels was examined in [19]. In [12], expressions were derived for the SER, outage probability, and outage capacity of a DF relaying over \(\eta-\mu\) and \(\kappa-\mu\) fading. [PERSON] and [PERSON] in [20] calculated the SER and outage of dual-hop DF relaying over Rice fading.  
The performance of a single-branch single-relay AF relaying system over mixed Rayleigh-Nakagami-m fading was analyzed by [PERSON] in [21]. [PERSON] in [22] analyzed the performance of a dual-branch selection combining scheme over \(\alpha-\kappa-\mu\) fading. The bit error rate (BER) expression for an M-ary phase-shift keying industrial cooperative relaying system was derived over generalized-K composite fading channels by [PERSON] et al. [23]. A summary of related works is presented in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **Fading Channel** & **Coommunication System** \\ \hline
[8] & Weibull & Dual-Hop & OP and Throughput \\
[9] & Hoyt & Generic & CDF \\
[10] & \(\eta\)-\(\lambda\)-\(\mu\) & Generic & PDF \\
[11] & \(\alpha\)- \(\lambda\)- \(\mu\) and \(\kappa-\eta\)- \(\mu\) & Generic & PDF \\
[12] & Dual-Hop Mixed & DF relaying & SER, OP, and OC \\
[13] & Exp.-Gen. Gamma & Dual-Hop Underwater & OP, ABER, and EC \\
[14] & Gen.-K & DF & Secrecy Performance \\
[15] & FTR & mmWave & PDF, CDF and MGF \\
[16] & Rayleigh, Rician & SFBC-OFDM & BER \\
[17] & Rayleigh-Gen. Gamma & Dual-Hop DF & OP \\
[18] & \(\eta\)-\(\mu\)-gamma-gamma & dual hop & OP and EC \\
[19] & Asymmetric Fading & AF & SER \\
[20] & Rice Fading & DF Relay & BER and OP \\
[21] & Rayleigh-Nakagami-m & AF & OP and EC \\
[23] & Gene.-K Composite & Industrial & BER \\ \hline \hline Exp. & Exponential & Gen. & Generalized \\ \hline \hline OP & Outage Probability & OC & Outage Capacity \\ EC & Ergodic Capacity & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Review of related works.
# Towards a Multi-Perspective Time of Flight Laser Ranging Device Based on Mirrors and Prisms
## 2 Materials and Methods
### Measurement Setup
#### 2.4.2 Measurement Software  
The ranging device was set in autonomous ranging mode and was continuously sending measurement data to the master PC through a USB serial data link. The microcontroller, connected to the ranging device, was only used to initiate the measurement, gather time of flight data, calculate the running average, and send data over to the master PC with minimal additional processing. Walk error compensation and time to distance conversions were carried out on the PC.  
The linear actuator waited for commands to go to the home position, detected by a microswitch, or to move to a desired location. Once the command was executed, the PC was informed that linear actuator is finished with moving.  
The master PC was running a python script that took care of setting the linear actuator and saving the ranging data. Data were captured on a span of 800 mm in 10 mm increments. For each set distance, 128 raw samples were taken and saved to a file.
# A New Method for Fatigue Evaluation of Titanium Alloy Welded Structures
## 2 The Principle of Notch Equivalent Stress and the Proposal of a New Method for Life Prediction
### A New Three-Parameter Power Function S-N Curve  
Two prediction models, the traditional three-parameter power function S-N curve and the new three-parameter power function S-N curve, are selected to quantitatively characterize the relationship between the stress range and fatigue life of the above titanium alloy welded structure, and the two fitting results are compared and analyzed.  
The S-N curve models of the above two quantitative characterizations were obtained by fitting, and the fitted curves are shown in Figure 7. According to Figure 7a, it can be seen that the fatigue limit parameter is less than 200 MPa, and with the increase in the stress range, the change rate of the fatigue life also changes from fast to slow. According to Figure 7b, the S-N curve of titanium alloy welded structures fitted by the new three-parameter power function model is closer to the test data and can better characterize the distribution of test data. When the fatigue limit parameter is less than about 210 MPa, the change in fatigue life rate begins to appear at an inflection point. It should be noted that the above S-N curve is applicable to titanium alloy cross joint structure, T-joint structure, lap joint structure, and other similar structures and cannot be used to predict other titanium alloy structures or other material structures with large structural differences.
# Range-Free Localization Approaches Based on Intelligent Swarm Optimization for Internet of Things
## 4 Simulation Results and Discussions
### Impact of the Percentage of Anchor Nodes  
In this section, we present a thorough examination of the proposed approaches to evaluate their performance on the same four distinct networks while changing the percentage of anchor nodes within the network. In this configuration, the number of sensor nodes remained fixed at 200 and the communication range remained constant at 30 m while the percentage of anchor nodes was varied between 15% and 40%. The findings reveal that the accuracy of all the proposed localization methods is impacted by the percentage of anchor nodes.  
Figures 5b-8b depict the results with respect to the percentage of anchor nodes. It can be seen that DV-Hop, W-DV-Hop, MDV-Hop, and HW-DV-Hop exhibit lower localization accuracy compared to HW-DV-HopCSO across the four different network topologies. Furthermore, HW-DV-HopCSO respectively enhances localization accuracy by 51%, 45%, 49%, and 43% compared to DV-Hop and W-DV-Hop and by about 34%, 33%, 25%, and 26% compared to HW-DV-Hop, MDV-Hop, and PSODV-Hop in the random square, H-shaped, O-shaped, and W-shaped networks. In addition, the proposed W-DV-HopCSO and HW-DV-HopCSO algorithms surpass the other approaches in terms of localization accuracy, offering significantly improved results compared to competitive algorithms in all of the different network topologies.
## 3 Methods
### Performance Evaluation  
In multi-class classification, accuracy, macro precision, macro recall, and the macro F1 score are key evaluation metrics.  
The **accuracy** of the model is computed as the ratio of correctly predicted instances to the total number of instances:  
\[\text{Accuracy}=\frac{|\{i\mid y_{i}=\hat{y}_{i}\}|}{|\{i\mid y_{i}=\hat{y}_{ i}\}|+|\{i\mid y_{i}\
eq\hat{y}_{i}\}|} \tag{6}\]  
where  
* \(y_{i}\): true label for the \(i\)-th instance
* \(\hat{y}_{i}\): predicted label for the \(i\)-th instance
* \(|\cdot|\): cardinality (number of elements in the set). **Macro precision*
* (\(P_{macro}\)) is the average of class-wise precision values. The macro precision (\(P_{macro}\)) is computed as the arithmetic mean of precision values for all classes: \[P_{macro}=\frac{1}{N}\sum_{i=1}^{N}\frac{|\{j\mid y_{j}=i\wedge\hat{y}_{j}=i\}|}{| \{j\mid\hat{y}_{j}=i\}|}\] (7) where
* \(y_{j}\): true label for the \(j\)-th instance
* \(\hat{y}_{j}\): predicted label for the \(j\)-th instance
* \(N\): total number of classes
* \(|\cdot|\): cardinality (number of elements in the set). **Macro recall*
* (\(R_{macro}\)) is computed similarly. The macro recall (\(R_{macro}\)) is computed as the arithmetic mean of recall values for all classes: \[R_{macro}=\frac{1}{N}\sum_{i=1}^{N}\frac{|\{j\mid y_{j}=i\wedge\hat{y}_{j}=i\}|}{| \{j\mid y_{j}=i\}|}\] (8) where
* \(y_{j}\): true label for the \(j\)-th instance
* \(\hat{y}_{j}\): predicted label for the \(j\)-th instance
* \(N\): total number of classes
* \(|\cdot|\): Cardinality (number of elements in the set). The **macro F1 score*
* is then calculated as the harmonic mean of macro precision and macro recall: \[F1_{macro}=\frac{2\times P_{macro}\times R_{macro}}{P_{macro}+R_{macro}}\] (9) Since this research focuses on **diverse multi-class datasets**, **the macro precision, recall**, **and F1 score*
* provide a more balanced evaluation than accuracy or micro metrics. By using macro metrics, a **fair comparison across all classes*
* is ensured, making the hybrid feature selection method (HPA-C) more interpretable and applicable to **smart agriculture applications**, where minority-class performance is crucial for reliable decision making.
# Energy Efficient GNSS Signal Acquisition Using Singular Value Decomposition (SVD)
## 4 Simulation and Performance  
This study is conducted using three sets of raw GPS data, which are processed after running the algorithm on SoftGNSS [5], a state of art software-defined Global Positioning System (GPS) receiver whose performance is improved by a dual-frequency approach, which, for this paper, is considered as the ground-truth. When considering the presented results it is important to notice the difference between outputs from the GNSS SVD-C compressed sensing algorithm and the state of the art software, the aim is to have similar performance, but with significantly fewer observations. The algorithm's purpose is to attain the best solution regarding data size, which is a key parameter on standalone battery-operated applications.
## 3 Permanent Deformation Analysis
### Effect of Confining Pressure on the Permanent Deformation  
According to the above test scheme, the relationship between the permanent deformation (\(\epsilon\)) and the numbers of cyclic loading (\(N\)) with different confining pressures is shown in Figure 8. The following can be seen from Figure 8: (1) The relationship curve between \(\epsilon\) and \(N\) can be divided into three stages. The first stage is the rapid growth phase, and the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimen Number** & \(D_{f}\) & \(\sigma_{3}^{\prime}\) (kPa) & \(D_{\epsilon}\) (\%) \\ \hline DT01 & 2.37 & 50 & 93 \\ DT02 & 2.37 & 100 & 93 \\ DT03 & 2.37 & 150 & 93 \\ DT04 & 2.37 & 50 & 90 \\ DT05 & 2.37 & 50 & 96 \\ DT06 & 2.61 & 50 & 93 \\ DT07 & 2.13 & 50 & 93 \\ DT08 & 1.89 & 50 & 93 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test scheme design.  
Figure 7: Schematic diagram of the permanent deformation calculation.  
curve has approximately linear growth. The second stage is the deceleration growth phase; the curve growth rate slows down obviously, and the curve gradient decreases gradually. The third stage is the approaching stability phase, and the permanent deformation closes to stable. (2) As observed in Figure 8, \(\varepsilon\) decreases by 2.79% when the confining pressure increases from 50 kPa to 100 kPa, whereas \(\varepsilon\) decreases by only 0.30% when the confining pressure increases from 100 kPa to 200 kPa. Hence, the effect of confining pressure on \(\varepsilon\) is related to the level of confining pressure. When the confining pressure is smaller, its effect on \(\varepsilon\) is significant, whereas when the confining pressure is larger, its influence on \(\varepsilon\) is smaller. This also reflects that the effect of traffic cyclic loading on subgrade decreases with the increase of subgrade depth.
## 6 Benefits, Challenges, and Limitations of CFD in the Food Industry
### Limitations Associated with Physical Modeling of Complex Food Structures  
The physical modeling of food structures in CFD presents unique limitations due to the heterogeneous nature of food materials. Foods consist of complex geometries, including porous structures, heterogeneous phases (solid, liquid, gas), and varying thermal properties across different components, which complicate the modeling process. For instance, in simulations of heat transfer during cooking or drying, food matrices such as meat, dough, or fruit often exhibit multiphase behavior, making it challenging to model phase transitions like evaporation, melting, or crystallization [53]. Furthermore, the interaction between food structure and fluid dynamics, such as how liquids flow through porous solids or how bubbles behave in frying oil, requires specialized models, such as the VOF or Lattice Boltzmann methods, which can be computationally intensive [14]. Additionally, the difficulty in acquiring accurate, high-resolution 3D scans of food microstructures limits the ability to fully capture these complex interactions [95]. These challenges underscore the need for further research and development in multi-scale modeling approaches to more accurately simulate the diverse physical phenomena in food processing [154; 155].
# Mechanical Incipient Fault Detection and Performance Analysis Using Adaptive Teager-VMD Method
## 2 Proposed Methods
### Optimal VMD Algorithm Based on ICFOA  
The VMD is not a parameter-adaptive signal processing method, so it is necessary to pre-set relevant parameters in practical application. The operation effect of VMD is significantly affected by the choice of presented group \([K,\alpha]\). In this work, the combined value \([K,\alpha]\) of the VMD was optimized by the ICFOA, and an adaptive VMD algorithm was constructed.  
Figure 5: Convergence curves of ICFOA and other optimization algorithms. (**a**) Evaluation function \(f_{1}(x)\), (**b**) Evaluation function \(f_{2}(x)\), and (**c**) Evaluation function \(f_{3}(x)\).  
In order to acquire the optimum VMD parameters, in this study, we applied the ICFOA to automatically select the VMD optimization parameters. Equation (13) is used as the objective optimization function of the ICFOA.  
\[\centering Fitness_{(ICFOA)}=\frac{\sum\limits_{i=1}^{K-1}MI[u(i),u(i+1)]}{MI[S_{ original},S_{reconstructed}]}\@add@centering \tag{13}\]  
where \(MI[u(i),u(i+1)]\) represents the mutual information (MI) between neighboring components. A smaller cumulative sum indicates a weaker correlation between IMF components, that is, better orthogonality. \(MI[S_{original},S_{reconstructed}]\) is the mutual information between the raw signal and the reconstructed one [39]. A larger mutual information value indicates a smaller reconstructed error. In addition, MI can measure the nonlinear relationship between two signals. This approach is not restricted to simple linear relations but also can be used to evaluate the non-linear relationship between variables. Therefore, according to the above analysis, as shown in Equation (13), when \(\sum_{i=1}^{K-1}MI[u(i),u(i+1)]\) is the minimum and \(MI[S_{original},S_{reconstructed}]\) is the maximum, their ratio is the minimum, that is, the fitness value is the minimum. The analysis indicates that the fault signal is correctly divided into the required components. The information coupling between IMF components is minimal, and the signal reconstruction error is the smallest. Thus, the corresponding optimal parameters \([K,\alpha]\) obtained can allow the algorithm to exert the optimal decomposition performance.
# Global IoT Mobility: A Path Based Forwarding Approach
## 5 Simulation and Cost Evaluation  
The cost of the proposed global mobility solution using an IP-over-ICN embodiment is evaluated using a packet level discrete time event simulator built in R language. The built simulation environment has been used to investigate the mobility costs (mainly mobility, signalling and packet delivery costs). We considered a sample IoT network of one TM/RV and 148 nodes across two network domains. Each domain consists of 24 core forwarder nodes and 50 gNBs. Random geometric networks have been used to represent network topologies to ensure spatial homogeneity of the positions of the gNBs. Two network scenarios have been simulated, one with micro mobility only, and another with global (micro and macro) mobility. NAPs have been assumed to be collocated with the gNBs. We assume that NAPs have a circular coverage area of radius between 500 and 1500 m with average connection degree of four neighbors each. A random waypoint mobility model has been used to capture user/node mobility at different speeds ranging from pedestrians moving at 3 mph to vehicles travelling at 70 mph. Initial locations are decided using a uniform random distribution. In our traffic model, we assume that all the IoT MNs in the network are using different protocols such as HTTP, CoAP, TCP, or simply IPv4/v6 to exchange data with an arrival rate of 1 Mbps following a Poisson distribution. We also assume ICN FIDs and scope/ID lengths of 256 bits each. A list of the simulated mobility messages and their corresponding sizes is shown in Table 1.  
Figure 8: Sequence diagram of handover with global mobility support in IP-over-ICN.
## 3 Regulatory Mechanisms of Volatile Terpenoids in Abiotic Stress Responses
### Water  
Water plays a crucial role in plant physiology as both a metabolic solvent and a regulator of growth. Moderate drought stress enhances mechanisms that protect against oxidative stress, thereby modulating terpenoid biosynthesis and emission. As the primary sites of CO\({}_{2}\) exchange, stomata exhibit reduced conductance under elevated CO\({}_{2}\) conditions, primarily due to acidification-induced guard cell closure. This response functions as an adaptive mechanism to enhance water-use efficiency by maintaining photosynthetic rates while minimizing transpiration [200; 201; 202]. Drought effects on net photosynthesis (A\({}_{\text{net}}\)) and BVOC emission rates follow a biphasic pattern: BVOC emissions initially increase, then decline with prolonged drought severity [169]. [PERSON] et al. [203] observed a -50% increase in isoprene emissions in _Ficus septica_ after 4 days of drought, despite a -70% reduction in A\({}_{\text{net}}\) and stomatal conductance (g\({}_{\text{g}}\)), while [PERSON] et al. documented a 3% decline in isoprene emissions in _Quercus virginiana_ after 6 days of drought (with a 65.4% reduction in A\({}_{\text{net}}\) and 72.9% in g\({}_{\text{g}}\)), highlighting species-specific responses. Exogenous application of monoterpenes to tomato leaves has been shown to alleviate drought-induced oxidative stress primarily by directly quenching reactive species and/or enhancing endogenous antioxidant responses. Such treatments significantly reduced the accumulation of H\({}_{2}\)O\({}_{2}\) and mitigated lipid peroxidation, as indicated by decreased MDA levels. Notably, treatment with low concentrations of monoterpenes (1.25 mM) proved most effective in relieving oxidative stress, whereas higher concentrations (5 mM) showed intermediate efficacy compared to both the low-dose and control treatments. Furthermore, high-dose monoterpene application markedly increased the activities of antioxidant enzymes, including SOD and APX. However, the exogenous application of monoterpenes did not improve photosynthetic performance or prevent the decline in photosynthetic efficiency. These find ings suggest that monoterpenes primarily function as oxidative protectants under water deficit by strengthening the antioxidant defense system while offering limited protection to the photosynthetic apparatus [204].  
Reactive terpenoids like \(\alpha\)-pinene, \(\beta\)-pinene [205], and \(\alpha\)-farnesene [206] show elevated emission fluxes under low soil moisture, indicating adaptive defense prioritization under warm, dry conditions. Proteomic analyses reveal isoprene's multifaceted protective roles: drought-modulated chloroplast protein abundance confirms its antioxidant activity, coupled with ABA biosynthesis regulation through hormone signaling crosstalk [207].
## 4 Impact of Network Latency on Teleoperation  
### Teleoperation System Model  
An overview of the CAV teleoperation system architecture is shown in Figure 5. The system architecture for the teleoperation of CAVs includes (1) a teleoperation station with a suitable human-machine interface (HMI) or control interface, (2) a remote CAV, and (3) a wireless communication network [10]. The communication network is responsible for seamless data transmission and operational efficiency between the vehicle and the station. The operated CAV transmits perception data streams obtained from sensors (e.g., video data from the cameras) to the teleoperation station via onboard communication networks for \"uplink\" transmission to a remote operator. The remote operator receives the visual and other perception data, and then, sends back the control data (e.g., steering and braking commands) using a teleoperation controller/interface through the \"downlink\" of the same communication network.
## 4 EBCRP: Energy-Balanced Cluster-Routing Protocol
### Clustering Sensor Nodes Based on Particle Swarm Optimization with Five Mutation Operators
#### 4.1.2 Topology of Particles in Sensor Node Clustering  
In EBCRP, PSO is used to optimize the number of clusters and centroid of each cluster. Figure 4 illustrates the structure of particles utilized in sensor node clustering optimization.  
Figure 3: Main procedure of the proposed energy-balanced cluster-routing protocol (EBCRP).  
A total of \(D=N+1\)-dimensional parameters are optimized, where \(N\) denotes the number of sensor nodes. In this structure of particles, the first dimension represents the number of clusters \(k\), while the \(i\)-th (\(i\in\{2,\ldots,N+1\}\)) dimension represents the probability \(P_{i}\in[0,1]\) that the \((i\!-\!1)\)-th sensor node is selected as the centroid of a cluster.  
During the optimization process, for each particle (solution), \(k\) sensor nodes with the highest probability are selected as cluster centroids of clusters according to the first dimension parameter \(k\) in the particle. Other sensor nodes are assigned to their nearest centroid, which forms the sensor node clustering solution (scheme) of each particle in each generation.
# Landslide Displacement Prediction Method Based on GA-Elman Model
## 2 Method
### GA-Elman Displacement Prediction Model
#### 2.1.2 Construction of GA-Elman Model  
Genetic Algorithm (GA) is a global search algorithm with high parallelism, randomness and adaptability. The global search ability of genetic algorithm is usually used to solve the problems of multi-objective optimization, optimal route and feature selection. GA is widely used in the fields of training connection weights, designing network structure and finding optimal learning rules [29; 30], the first two applications have been extensively studied by many scholars and have achieved good results. At present, most of the current research scholars are focusing on forward neural networks instead of the recurrent neural network[31; 32]. Therefore, this study uses GA to optimize Elman neural network to solve the problem of neural network algorithm trapping in the local optimum and focuses on optimizing the initial weight and threshold of the Elman network.