esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial Pattern Simulation of Antenna Base Station Positions Using Point Process Techniques
## 1 Introduction  
Spatial statistics refers to the study of spatially combined data using appropriate statistical methodologies. In spatial statistics, point pattern process is a powerful tool to analyze the spatial association between different positions of a particular event (locations of the antenna base stations) and is considered an advanced statistical technique based on the homogeneity of the point process space. Homogeneity of the point process is explained by the dynamic effects under the spatial association leading us to simulate the appropriate spatial patterns [1, 2, 3].  
The main goal of point pattern analysis is to investigate the process and especially to examine if the pattern is independent, regular or clustered. Based on these patterns, simulations is considered, and analysis of the final patterns takes place, so as to investigate their closeness to the real data [4, 5]. For that reason, appropriate spatial models must be introduced to simulate the real data; the most common models are the Poisson point process and modifications such as inhibition point process and determinantal Poisson point process. The first model considers the distance between two positions using a proposed thresholding variable \(\delta\) which is used to delete the positions close to \(\delta\). The second model considers the diversity between two positions, in the sense that the probability of observing two points close to or similar to each other is lower than the Poisson point process [6].  
Many times in point process analysis, the prediction of the positions of the appropriate event is considered, and appropriate simulations are applied to overcome inaccuracies. In our case, this approach is applied, where our goal is to propose point process models close to the real data and simulate them for prediction of the positions. A variety of methods for the visualization of point data are provided based on the spatial patterns. These methods allow us to view the point patterns, explore structure in data by estimations from appropriate models, and test hypotheses relating to the process by considering the observed event distribution.  
Finally the investigation of the spatial association for the proposed point process patterns could be considered, with the motive of explaining the regularity or in-regularity of the process, especially the existence, or degree of intensity, of the clustering patterns. In this work, investigation of spatial locations simulation is proposed based on Ripley's K-function and L-function.
# Fuzzy Logic and Bio-Inspired Firefly Algorithm Based Routing Scheme in Intrabody Nanonetworks
## 2 Related Work
### Fuzzy Logic-Based Decision Systems (FLBDS)  
After FLBDS was proposed in 1965 [20], researchers adapted them in various directions, including expert systems and artificial intelligence. FLBDS are based on simple concepts, consisting of an input stage, a processing stage and an output stage. The input stage maps inputs to the suitable membership functions. The processing stage invokes a set of rules, which generates a result for each input and combines the results of the rules. Finally, the output stage converts the combined result back into a specific control output value [21]. The most common membership functions used in the literature are triangular, trapezoidal and bell curves [22]. FLBDSs attributes such as low complexity, high flexibility and applicability for an uncertain environment raise its support in Wireless Sensor Networks (WSNs). In WSNs, FLBDSs have been used to improve forwarding decisions and quality of service. In the state of the art literature, the use of FLDBS in developing a clustering-based routing algorithm handles more effectively the challenge of routing associated with an uncertain environment. Besides, it also optimizes the selection of routing metric used by enabling the combination and evaluation of diverse parameters in an efficient manner [23; 24; 25].  
In Table 1, we further shed more light on the contribution of the FLBDS on strengthening the performance of WSNs. From Table 1, we comprehend that the low complexity decision process of FLBDS advocates it as a perfect choice for improving the decision process in IBNNs.
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 2 Classification of Whole-Genome Alignment Algorithms
### Anchor-Based Methods
#### 2.2.5 Diallgn  
DIALLGN (Differential Alignment of Gene Neighborhoods) is a powerful tool for comparative genomics research, enabling the identification and analysis of conserved gene neighborhoods across multiple genomes. It was designed for multiple sequence alignment and is particularly useful to detect local homologies in sequences with low overall similarities [40]. Researchers can use DIALLGN to gain insights into gene function, genome evolution, and regulatory mechanisms underlying the organization of genes in the genome.  
Various versions of this algorithm have been implemented and their main innovation consists of the insertion of user-specified external information. The user can insert the two sequences' anchors' details, including the length of the anchor and its starting position in the sequences. DIALLGN's performance may be affected by the quality of the input genomic sequences, the presence of genomic rearrangements or structural variations, and the parameters chosen for the analysis. Users should carefully interpret the results and validate the identified gene neighborhoods using additional experimental or computational methods.  
The DIALLGN algorithm leverages sequence similarity and genomic context to identify conserved gene neighborhoods across multiple genomic sequences, through aligning and clustering genes with similar sequences and genomic positions [41].  
1. The algorithm starts by identifying anchor genes that are conserved across the genomic sequences being compared. These anchor genes serve as reference points for aligning and extending the gene neighborhoods;
2. Then, it extends the gene neighborhoods by searching for additional genes that are located nearby in the genomic sequences and exhibit sequence similarity to the anchor genes;
3. It uses a scoring system to evaluate the similarity between genes and determine the quality of the alignments. The scoring criteria may include sequence identity, gap penalties, and other parameters that reflect the degree of conservation between genes;
4. As the algorithm extends the gene neighborhoods and identifies conserved gene pairs, it clusters these genes together based on their proximity and sequence similarity.  
The complexity of DIALLGN depends on the chosen parameters and the size of the input sequences. It typically has a worst-case time complexity of \(O(n^{3})\), where \(n\) is the length of the input sequences.
# Large-Area Coverage Path Planning Method Based on Vehicle-UAV Collaboration
## 3 Coverage Path Planning Method Based on Vehicle-UAV Collaboration
### Coverage Area Segmentation Method Based on Dynamic Adjustment
Figure 6: The modification of center positions for incomplete hexagons. edges are generated. By virtue of the abovementioned construction of the VG, the Voronoi edge divides the coverage area into a series of Voronoi units, and each Voronoi unit is regarded as a sub-area, as shown in Figure 7.  
Through the above steps, all supply points will be adjusted multiple times to ensure the points are located in the most reasonable position, and all sub-areas are adjusted again to ensure that the points within a sub-area have a shorter distance to the corresponding supply point. The contours of these sub-areas and these supply points are the key factors for the generation of vehicle-UAV cooperative coverage paths.
## 4 Intravalley Relaxation and Fermi Degeneracy Formation
### Model and Method  
The aforementioned scenario has been examined theoretically by [PERSON] et al. [11]. They have considered the model, in which many electrons are coupled with acoustic (ac) and optical (op) phonons, and the time evolution of carriers in the \(\Gamma\) valley has been investigated by using the second order perturbation theory [11]. In this section, we review their model and method.  
They start from the Hamiltonian (\(\equiv H\)), which is given as follows:  
\[H=H_{0}+H_{\rm I},\ \ \ \ \ H_{\rm I}=H_{\rm ep}^{\rm(ac)}+H_{\rm ep}^{\rm(op)} \tag{1}\]  
\[H_{0}=H_{\rm e}+H_{\rm P}^{\rm(ac)}+H_{\rm P}^{\rm(op)}\equiv\sum_{\mathbf{k}, \sigma}\varepsilon_{\mathbf{k}}a_{\mathbf{k},\sigma}^{\dagger}a_{\mathbf{k},\sigma}+\sum_{ \mathbf{q}}\omega_{\mathbf{q}}b_{\mathbf{q}}^{\dagger}b_{\mathbf{q}}+\omega\;{\rm op}\sum_\mathbf{{q}}f_{\mathbf{q}}^{\dagger}f_{\mathbf{q}}, \tag{2}\]  
\[H_{\rm ep}^{\rm(ac)}=\frac{S}{\sqrt{2N}}\sum_{\mathbf{q},\mathbf{k},\sigma}\left(b_{ \mathbf{q}}^{\dagger}+b_{-\mathbf{q}}\right)a_{\mathbf{k}-\mathbf{q},\sigma}^{\dagger}a_{\mathbf{ k},\sigma} \tag{3}\]  
\[H_{\rm ep}^{\rm(op)}=\frac{1}{\sqrt{N}}\sum_{\mathbf{q},\mathbf{k},\sigma}\frac{iV}{ \mathbf{q}}\left(f_{\mathbf{q}}^{\dagger}a_{\mathbf{k}-\mathbf{q},\sigma}^{\dagger}a_{\mathbf{k}, \sigma}-f_{\mathbf{q}}a_{\mathbf{k},\sigma}^{\dagger}a_{\mathbf{k}-\mathbf{q},\sigma}\right). \tag{4}\]  
Here, \(a_{\mathbf{k},\sigma}^{\dagger}(a_{\mathbf{k},\sigma})\) is an electron creation (annihilation) operator, having a wavevector \(\mathbf{k}\) and spin \(\sigma\). The conduction band dispersion is assumed to be parabolic: \(\varepsilon_{\mathbf{k}}=B\cdot(\mathbf{k}\cdot\mathbf{k}/\pi^{2})\) where \(B\) represents a bandwidth. \(b_{\mathbf{q}}^{\dagger}(b_{\mathbf{q}})\) and \(f_{\mathbf{q}}^{\dagger}(f_{\mathbf{q}})\) are phonon creation (annihilation) operators of acoustic (ac) and op (optical) phonons, respectively, having a wavevector \(\mathbf{q}\). The acoustic phonon band dispersion is given by \(\omega_{\mathbf{q}}=\omega_{\;\rm M}\cdot(\mathbf{q}/\pi)\). \(S\) and \(V\) are electron-ac and -op phonon coupling constants, respectively.  
The time evolution of electron population, \(n_{\mathbf{k},\sigma}(t)\), is evaluated in terms of the second order perturbation theory. For this purpose, we expand:  
\[\langle n_{\mathbf{k},\sigma}(t)\rangle\equiv\frac{{\rm Tr}(n_{\mathbf{k},\sigma} \tilde{p}(t))}{{\rm Tr}(\tilde{\rho}(t))}, \tag{5}\]  
by \(H_{\rm I}\). The density matrix of the system is given by:  
\[\tilde{\rho}(t+\Delta t)={\rm exp}_{+}\left\{i\int_{0}^{\Delta t}d\tau\tilde{ H}_{with \(C_{i}=\pi S^{2}N^{-1}\) or \(2\pi V^{2}N^{-1}/\mathbf{q}^{2}\) for \(i\) = ac or op, respectively.  
In [11], the parameters were set to \(B=5\) eV, \(\omega_{\rm M}=24\) meV, \(\omega_{\rm op}=38\) meV, \(S=0.5\) eV and \(V=0.13\) eV, for GaAs and also InP.
## 2 Materials and Methods
### Measures and Procedures  
Four anthropometric tests were conducted: body height (BH), seated body height (SBH), body mass (BM), and body fat (BFat). The ability to accelerate was tested with a 10 and 20 m sprint (S10 and S20). Agility was tested by conducting three tests: the 20 yard shuttle agility test (20Y) and triangle test of change of direction speed (TCODS) were used to test generic agility, and the \"triangle\" RAG test (TRAG) to test reactive agility [19]. To evaluate muscular performance, the Opto Jump system (Microgate, Bolzano, Italy)--an optical measurement system for assessing jump performance and timing--was used for three tests: squat jump (S), countermovement jump (CMJ), and drop jump (DHJ). As a measure of explosive strength, the reactive strength index (RSI) was calculated; it is calculated by dividing the jump height by the ground contact time during the DHJ. The Encephal App Stroop application was used to assess the cognitive functioning of the participants [20]. The Stroop application was downloaded from the Google Play app store (Encephal App Stroop, version 2.0.7). Height measurements (BH and SH) were obtained using a Seca Instruments stationmeter. Body mass (BM) and body fat percentage (BFat) were evaluated using a Tanita Pro MC-780U body composition analyser (Tanita Corp., Tokyo, Japan). This device provides a print-out of the measured body mass and calculates body fat. Participants' gender, age, and body height were inputted into the device, and participants stood barefoot in an upright, stable position. The device utilised impedance, age, and height to estimate the percentage of total body fat. For the assessment of S10 and S20, a Brower timing system (Salt Lake City, UT, USA), a widely utilised and previously validated system, was employed [21]. Two electronic timing gates were positioned at intervals of 1, 11, and 21 m from the starting line. These photocells were installed 1 m above floor level, in accordance with the maximum height of the manufacturer's standard tripods. Participants were instructed to sprint as swiftly as possible for the specified distance, with their preferred leg positioned on the starting marking. The BlazePod reactive light training system (Play Coyotta Ltd., Tel Aviv, Israel) was utilised for the 20Y, TCODS, and TRAG agility assessments. For the 20Y test, three 50 cm cones with lighting pods mounted on top were positioned along a line 4.57 m (5 yards) apart. Participants initiated the test from a two-point stance, beginning after touching the middle pod, and then sprinted as quickly as possible 4.57 m to the left. They subsequently ran 9.14 m to touch the illuminated cone on the right before concluding by returning and touching the middle pod. To conduct the TCODS and TRAG tests, three lighting pods were affixed to 50 cm cones arranged in an equilateral triangle formation, with equal sides and angles of 60\({}^{\circ}\) (refer to Figure 1).
# Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time
## 2 Study Area and Data Sets
### Additional Datasets  
The 2-m air temperature data acquired from the Modern-ERA Retrospective Analysis for Research and Applications Version 2 (MERRA-2, [PERSON] et al. (2018)) are used in this study to force the API model. Temperature is the main driver of evapotranspiration, which influences the rate of soil moisture depletion. Higher air temperature causes a more rapid decline of surface soil moisture.  
The leaf area index (LAI) data developed by [PERSON], [PERSON] et al. (2019) are used to investigate how the performance of correction algorithms depends on vegetation density. These data were generated by a two-step integrated method based on the MODIS LAI data. First, a temporal spatial filter was used to fill the gaps of the MODIS LAI data, and the lower quality data were excluded according to the quality control (QC) and the corresponding values were filled by making the best use of the high quality data. The TIMESAT Savitzky-Golay (SG) filter ([PERSON] et al., 2019) was then applied to generate the final improved MODIS LAI products. We use the 2016 LAI data to represent our study period. The LAI data can be obtained from [[http://globalchange.bnu.edu.cn/research/lai](http://globalchange.bnu.edu.cn/research/lai)]([http://globalchange.bnu.edu.cn/research/lai](http://globalchange.bnu.edu.cn/research/lai)).  
An improved comprehensive 30 \(\times\) 30 arc-second resolution gridded soil characteristics data set of China ([PERSON] et al., 2019) is used to determine model initial parameters. The data set includes 28 attributes for eight vertical layers from surface to the depth of 2.3 m. The porosity attribute in the top surface layer (i.e., 0-0.045 m) is used in this study to estimate saturation soil moisture. The data set can be downloaded from [[http://globalchange.bnu.edu.cn/research/soil2#cite](http://globalchange.bnu.edu.cn/research/soil2#cite)]([http://globalchange.bnu.edu.cn/research/soil2#cite](http://globalchange.bnu.edu.cn/research/soil2#cite)).
# Principles and Methods of Servomotor Control: Comparative Analysis and Applications
## 3 Basic Principles of Servomotor Control
### Vector Control Principle in Servomotors  
Vector control is a control method that allows the optimization of the control of alternating current (AC) motors and synchronous motors with permanent magnets. This control method uses an approach where the torque and current flux of the motor are separately considered. The main idea is to transform the three-phase current and voltage into a rotating coordinate system, aligning the magnetic flux with the rotor's magnetic field. This enables independent control of the torque, leading to better dynamic response of the control system, reduced additional noise, and increased efficiency. Based on [68; 69; 70; 71; 72], it is possible to identify the key aspects in the construction of this principle and draw conclusions about its advantages and disadvantages.  
The main components of the field-oriented control system are:  
* Coordinate transformation. Vector control relies on transforming currents into a coordinate system using Park and Clarke transformations, simplifying the control task and optimizing the motor's operation.
* Current control. Precise control of currents is crucial. Independent torque control allows for minimizing losses and improving efficiency.
* Use of PI controllers. Using controllers of this type helps reduce control errors, enhance responsiveness, and provide continuous support for the desired motor performance. The key advantages of the vector control principle include:  
1. Improved dynamic response. Fast and accurate motor control enables instant dynamic response.
2. Reduction of torque disturbances. This significantly reduces torque fluctuations, ensuring smooth motor operation.
3. Increased efficiency. Optimization of motor currents and minimization of losses lead to improved overall motor efficiency.
4. Increased power density. The design of more compact and lightweight servomotors with higher power can be achieved, making them suitable for use in limited spaces.
# Explainable Machine Learning for Mapping Rainfall-Induced Landslide Thresholds in Italy
## 2 Study Area, Data, and Methodology  
### Study Area  
This study focused on the entire territory of Italy, which is geographically defined by the Alps in the north and the Apennine Mountains running through the central and southern regions (Figure 1a). The study area is predominantly composed of sedimentary, metamorphic, and volcanic rocks, with cambisols, luvisols, and regosols as the primary soil types, collectively covering 63% of the total land area [26]. Italy experiences a typical Mediterranean climate, characterized by warm, dry summers and mild, humid winters. The northern Alpine region, in contrast, features an alpine climate with frequent snowfall during winter. Between 1981 and 2020, the mean annual precipitation across Italy ranged from 350 mm to 1895 mm (Figure 1b), exhibiting a highly uneven spatial distribution. The northern Alpine region received the highest precipitation, while a distinct high-rainfall belt extended along the Apennine Mountains. Conversely, lower precipitation levels were observed along the western coastal areas and the eastern Adriatic coast. Due to the combined effects of topography and climate, rainfall-induced landslides are highly frequent in Italy. Research conducted by [PERSON] et al. [27] identified 6312 temporally precise rainfall-triggered landslides between January 1996 and December 2021. Landslide activity peaked during autumn and winter, particularly in October and November, with November alone accounting for 17.3% of all recorded events. In contrast, the lowest landslide occurrences were observed in July and August, contributing only 3.8% and 3.9% of total events, respectively.
## 4 Experiments and Results
### Power Consumption  
Figure 16a shows mean power in the network (see Equation (6)). Mean power is calculated using only living nodes. As commented, nodes in non-hierarchical simulations transmit at a fixed power, but mean power in the network is reduced as the average number of hops necessary to reach the destination decreases. In hierarchical networks, power depends also on transmission distance. Hence, mean power decreases in time, mostly because living nodes tend to move closer to avoid loss of connectivity. In general, mean power after deployment is higher in simulations with obstacles. However, when nodes start to move closer to each other, their relative locations depend on the obstacle layout, and mean power may grow or decrease punctually depending on the number of living nodes.  
Figure 15: Node distribution evolution in time: (**a**) mean distance per (living) node; (**b**) network uniformity.  
Power on the network obviously depends on the number of transmitted packets. This number depends on routing. In closest neighbor routing, a node transmits a packet to all of its neighbors. Even though some of these neighbors, those farther from the sink node than the transmitting node, do not retransmit the packet and a node never transmits the same packet twice, in the worst case scenario, a node close to the sink in an N node MWSN might need to route N-1 packets. We can observe in Figures 7 and 8 that geographic routing networks are highly interconnected. Hence, the mean number of packets transmitted over these networks is typically very high, but decreases in time with the number of living nodes (Figure 16b). In hierarchical networks, the mean is very stable because most L1 nodes have approximately the same number of attached L0 nodes, and there is only one retransmission per packet. Transmitted packets' standard deviation in non-hierarchical networks grows in time, because nodes tend to move closer to each other, and hence, interconnectivity grows; but outside nodes route much less traffic than those closer to the sink node (see Figures 7d,e and 8d,e). In a hierarchical network, this deviation is low and stable until nodes start to die. At this point, the ratio between L1 and L0 nodes grows, and L1 nodes are the ones that contribute to the deviation in these simulations. There are no significant differences in scenarios with and without obstacles in terms of power in the network.
## 4 Experiments and Results
### The Kolmogorov-Smirnov test  
Once the executions were carried out, the Kolmogorov-Smirnov test was applied to define whether there was a significant difference in the results obtained by the best two algorithms (MAIS and PSO), see Table 4. For this test, a positive hypothesis and a negative hypothesis are defined, with \(\alpha=0.05\).  
* H0: The results of the fitness function in both algorithms are the same.
* H1: The results of the fitness function in both algorithms are unequal.  
Because the asymptotic significance is less than 0.05, H1 is proven, meaning that there are significant differences between the results of both algorithms. Since the H1 hypothesis is accepted, the Z-value is 2.711 concluding that the differences are large, see Table 5, so it is concluded that both algorithms have significantly different behaviors.
## 3 Results and Discussion
### Method Validation
#### 3.2.3 Linearity  
The linearity is evaluated based on the correlation diagram of concentrations (standard solutions) versus UV absorbance values (Table 3). Standard calibration curves were calculated by the least-squares method. The good linearity of the method was also estimated by the % y-intercept values (intercept value \(\times\) 100/100% response) for each analyte, which should be <2%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Substances**} & **Retention** & **Tailing** & **Capacity** & **Resolution** & **Theoretical** & **HETP** \\  & **Time** & **Factor** & **Factor** & **Resolution** & **(N)** & **mm \(\times\) 10\({}^{-3}\)** \\ \cline{2-7}  & **(min)** & **(Tf)** & **(k\({}^{\prime}\))** & **(Rs)** & **(N)** & **mm \(\times\) 10\({}^{-3}\)** \\ \hline FERA & 4.4 & 0.9 & 1.1 & & 1109 & 225.4 \\ RSV & 7.2 & 0.9 & 2.6 & 7.5 & 13,902 & 18.0 \\ QR & 7.2 & 1.6 & 2.9 & 2.5 & 43,334 & 5.8 \\ RTN & 11.4 & 1.8 & 4.7 & 25.0 & 99,790 & 2.5 \\ \(\alpha\)-TOC & 15.0 & 1.7 & 6.5 & 20.0 & 71,317 & 3.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: System suitability parameters.  
Figure 2: Chromatogram at 290 nm of (1) standard solution, (2) sample, (3) blank of diluent, and (4) substrate blank.