esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Handcrafted Fractal Features
#### 2.1.6 Metrics Obtained from the Descriptor Curves  
Fractal descriptors based on the probabilistic approach, lacunarity, and percolation were calculated with \(L\) scale variations, according to the _gliding box_ method. In these cases, the value \(L_{max}=41\) was considered in this investigation [29]. The multidimensional and multiscale Higuchi fractal dimension was determined with \(l\) scale variations of the sliding vector, also with a maximum value of 41, and a granularity factor \(d=8\)[28]. The values from each approach were used to define feature curves for each image under analysis. In the Higuchi fractal dimension and lacunarity approaches, the curves were composed of their respective local values concerning each size of the sliding vector/hypercube. Regarding the percolation approach, the obtained curves were \(C\), \(Q\), and \(M\), referring to the percolating regions.  
In addition, each curve was represented by scalar values to compose the descriptor vectors [38; 75]. We used the following extracted metrics: area under the curve (\(A\)), skewness (\(S\)), area ratio (\(\Gamma\)), maximum point (\(MP\)), and maximum point scale (\(MPS\)). The area under the curve (\(A\)) was calculated by the trapezoidal numerical integration method given by Equation (19).  
\[A=\int_{a}^{b}f(x)dx\approx\frac{b-a}{2N}\sum_{n=a}^{b-1}(f(x_{n})+f(x_{n+1})), \tag{19}\]where \(a\) and \(b\) denote the minimum and maximum \(L\) values, respectively, and \(N\) denotes the number of samples.  
The skewness (\(S\)) metric defines the asymmetry of a sample concerning its mean value. A negative skewness corresponds to a sample where the more frequent values occur on the lower part of the interval range, and the opposite applies if the skewness is positive. A completely symmetric sample yields a skewness of 0. Given a sample with \(N\) values, skewness was defined by Equation (20), where \(\overline{x}\) denotes the mean value of the sample, and \(x_{i}\) represents the \(i\)-th value of \(x\).  
\[S=\frac{\frac{\frac{1}{N}\sum_{i=a}^{b}(x_{i}-\overline{x})^{3}}{\sqrt{\left[ \frac{1}{N-1}\sum_{i=a}^{b}(x_{i}-\overline{x})^{2}\right]^{3}}}}. \tag{20}\]  
The area ratio (\(\Gamma\)) was calculated as the ratio of the area of the right half of the curve to the area of the left half of the curve. This was calculated by considering the area under the curve (\(Area_{a,b}\)) between two points \(a\) and \(b\) on the \(x\) axis, as represented by Equation (21).  
\[Area=\int_{a}^{b}f(x)dx. \tag{21}\]  
From this definition, the area ratio \(\Gamma\) was calculated according to Equation (22).  
\[\Gamma=\frac{A_{\left(b/2+1,b\right)}}{A_{\left(a/2\right)}} \tag{22}\]  
The two last measures were obtained from the maximum point (\(MP\)) and its respective observation scale \(L\) (\(MPS\)).
_Algorithms_**2015**, \(8\), 100-127; doi:10.3390/a8020100  
_algorithms_  
**ISSN 1999-4893**  
www.mdpi.com/journal/algorithms  
_Article_  
Appendix A Study on the Fuzzy-Logic-Based Solar Power MPPT Algorithms Using Different Fuzzy Input Variables  
**[PERSON] *, [PERSON] and [PERSON]**  
Department of Aerospace Engineering, Tamkang University, Tamsui, New Taipei City 25137, Taiwan;  
E-Mails: [EMAIL_ADDRESS] (Y.-C.W.); [EMAIL_ADDRESS] (B.-C.C.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +886-226-215-656 (ext. 3318); Fax: +886-226-209-746.  
Academic Editor: [PERSON]  
_Received: 04 March 2015 / Accepted: 02 April 2015/ Published: 8 April 2015_  
**Abstract:** Maximum power point tracking (MPPT) is one of the key functions of the solar power management system in solar energy deployment. This paper investigates the design of fuzzy-logic-based solar power MPPT algorithms using different fuzzy input variables. Six fuzzy MPPT algorithms, based on different input variables, were considered in this study, namely (i) slope (of solar power-_versus_-solar voltage) and changes of the slope; (ii) slope and variation of the power; (iii) variation of power and variation of voltage; (iv) variation of power and variation of current; (v) sum of conductance and increment of the conductance; and (vi) sum of angles of arctangent of the conductance and arctangent of increment of the conductance. Algorithms (i)-(iv) have two input variables each while algorithms (v) and (vi) use a single input variable. The fuzzy logic MPPT function is deployed using a buck-boost power converter. This paper presents the details of the determinations, considerations of the fuzzy rules, as well as advantages and disadvantages of each MPPT algorithm based upon photovoltaic (PV) cell properties. The range of the input variable of Algorithm (vi) is finite and the maximum power point condition is well defined in steady condition and, therefore, it can be used for multipurpose controller design. Computer simulations are conducted to verify the design.  
**Keywords:** maximum power point tracking, fuzzy rule determination, fuzzy MPPT algorithms
## 4 Methods of Estimation and Test
### Methods of LS and WLS  
As the explicit forms of the maximum likelihood estimators cannot be obtained every time, some other methods are developed to overcome this problem. The LS and the WLS are well-known methods used for estimating unknown parameters [22]. Here, we consider these two approaches for estimating the unknown parameters of the \(PZD\). Let \(x_{1}\),\(x_{2}\),..., \(x_{n}\) be the ordered observations obtained from a sample of size \(n\) from the \(PZD\). By calculating the minimum of the function  
\[S(\Psi)=\sum_{i=1}^{n}\eta_{i}\left\{1-\left(1+\frac{\theta x_{i}^{\beta} \Bigl{(}\theta+2+\theta x_{i}^{\beta}\Bigr{)}}{2+\theta}\right)e^{-\theta x_{ i}^{\beta}}-\frac{i}{n+1}\right\}^{2} \tag{23}\]with respect to \(\theta\) and \(\beta\), respectively, the LS estimates \(\hat{\theta}_{LSE}\) and \(\hat{\beta}_{LSE}\) can be obtained by setting \(\eta_{i}=1\), while we can obtain the WLS estimates \(\hat{\theta}_{WLS}\) and \(\hat{\beta}_{WLS}\) by setting \(\eta_{i}=\frac{(n+1)^{2}(n+2)}{i(n-i+1)}\). The following equations can be solved to yield these estimates as well as  
\[\frac{\partial S(\Psi)}{\partial\theta}=\sum\limits_{i=1}^{n}\eta_{i}\left\{1- \left(1+\frac{\theta x_{i}^{\beta}\left(\theta+2+\theta x_{i}^{\beta}\right)} {2+\theta}\right)e^{-\theta x_{i}^{\beta}}-\frac{i}{n+1}\right\}\varphi_{1}(x _{i},\Psi)=0,\]  
\[\frac{\partial S(\Psi)}{\partial\beta}=\sum\limits_{i=1}^{n}\eta_{i}\left\{1- \left(1+\frac{\theta x_{i}^{\beta}\left(\theta+2+\theta x_{i}^{\beta}\right)} {2+\theta}\right)e^{-\theta x_{i}^{\beta}}-\frac{i}{n+1}\right\}\varphi_{2}(x _{i},\Psi)=0,\]  
where  
\[\varphi_{1}(x_{i},\Psi)=\frac{\left(\theta x_{i}^{\beta}+2x_{i}^{\beta}+\theta +3\right)\theta^{2}x_{i}^{2\beta}e^{-\theta x_{i}^{\beta}}}{2+\theta},\text{ and }\varphi_{2}(x_{i},\Psi)=\frac{\left(1+x_{i}^{\beta}\right)\theta^{3}x_{i}^{2 \beta}\ln(x_{i})e^{-\theta x_{i}^{\beta}}}{2+\theta}.\]
# Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG-VSC Algorithm
## 4 Flight Validation and Performance Analysis
### Flight Validation and Performance Analysis
#### 4.2.2 Hardware Platform Ground Testing and Analysis
Optimizations in task parallelization and data handling are being investigated to ensure that the hardware can manage higher task loads without sacrificing responsiveness. (4) Fault tolerance and reliability  
In complex flight environments, these two aspects are essential for maintaining system stability, as they assess different dimensions of platform performance. Fault tolerance focuses on how each platform manages system faults, communication errors, and unexpected failures during multi-UAV tasks, ensuring continuous operation with minimal disruption. Reliability, by contrast, evaluates the platform's consistency in performing without failures over extended periods, particularly under challenging mission conditions. As shown in Figure 16, the ZCU104 platform exhibits superior fault tolerance due to its combination of ARM processing and the FPGA's flexibility. This allows the system to handle unexpected faults with minimal disruption, contributing to more stable UAV operations in dynamic conditions. In terms of reliability, ZCU104 also performs well, maintaining consistent operation under continuous workloads. While the E5-2630v4 demonstrates strong fault tolerance, its reliance on server-grade processing limits its adaptability to sudden failures in real-time operations. The ARM-A53 platform, with its limited processing power, shows lower fault tolerance, which can lead to system instability under high-load or fault-prone conditions. Its reliability also suffers under extended operational scenarios, making it less suitable for complex, high-demand missions. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Hardware Platforms** & **Model** & **Throughput (Tasks/s)** \\ \hline ARM & ARM-A53 & 100 \\ ARM+FFOA & ZCU104 & 150 \\ CPU & E5-2630v4 & 200 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Throughput comparison of hardware platforms in default training mode. Figure 15: Energy consumption comparison across hardware platforms. Based on the evaluation of latency, energy consumption, throughput, and fault tolerance and reliability, the ZCU104 platform demonstrates a balanced performance across all metrics, making it the optimal choice for multi-UAV coordination and control tasks. Its competitive latency, efficient energy consumption, and stability in dynamic conditions make it well-suited for flight control missions. While the E5-2630v4 excels in computational power and throughput, its high energy consumption and relatively higher latency limit its suitability for real-time UAV control, rendering it more appropriate for ground-based simulations. The ARM-A53, despite its low energy consumption, lacks the necessary processing power, throughput, and fault tolerance to meet the demands of complex multi-UAV tasks, leading to its exclusion from further consideration.
## 3 Proposed Solution
### Gini Index-Based Trust Model
The use of the Gini index improves CPS security's overall resiliency and efficacy, protecting and preserving the integrity of these intricate and linked systems. Despite being a relatively recent concept, the application of the Gini index in CPS security has shown promise in related disciplines. The Gini index, for instance, has been used to examine how energy consumption is distributed among sensor nodes in WSNs. The index assists in identifying nodes that consume excessive amounts of energy, highlighting potential weaknesses that attackers could exploit or places where energy-saving techniques could be used [76]. The Gini index has been used in cloud computing systems to evaluate the equity of resource distribution among virtual machines, improving load balancing and system performance [77]. There are a number of possible advantages to using the Gini index in CPS security. Firstly, it gives security professionals a quantifiable measure of resource allocation so that they can identify and prioritize areas that need attention and changes in resource allocation [78]. Secondly, it assists in the identification of crucial parts or subsystems that would require stronger security precautions because of resource concentration. Thirdly, the Gini index helps to spot potential attack vectors and weak spots that bad actors may exploit. [79]. Security solutions can be adapted to safeguard the most important and vulnerable parts of the CPS by evaluating resource allocation patterns. However, there are restrictions to take into account when using the Gini index for CPS attack detection. The Gini index does not offer information about particular attack kinds or methodologies because it primarily concentrates on resource distribution [80]. Furthermore, the accuracy and dependability of resource allocation data have a direct impact on the Gini index's validity and effectiveness as an attack-detection tool [81].
## 4 Numerical Modeling
### Pile Capacities and Horizontal Subgrade Modulus of Piles
\begin{table}
\begin{tabular}{c c c} \hline  & \multicolumn{2}{c}{**Allowable Pile Bearing Capacity Values (kN)**} \\ \cline{2-3}
**Pile No.** & **Service Condition** & **Extreme Condition** \\ \hline A-Axis; D = 0.65 m, L = 18 m & 1018 & 1711 \\ B-Axis; D = 0.65 m, L = 18 m & 1373 & 2308 \\ C-Axis; D = 0.65 m, L = 18 m & 1147 & 1932 \\ Unereinforced Pile; D = 0.65 m, L = 8.50 & 420 & 711 \\ Unereinforced Pile; D = 0.65 m, L = 5.50 & 247 & 418 \\ Une reinforced Pile; D = 0.65 m, L = 3.00 & 78 & 134 \\ \hline \end{tabular}
\end{table} 
Table 3: Summary of the pile bearing capacity. For the foundation soils under the cantilever slab, the depth where the bearing capacity will be considered is a depth range from the bottom of the footing to 1.5 B below the bottom of the footing (AASHTO [22]). To calculate the bearing capacity appropriately, the depth of influence due to the applied foundation loads was calculated. The minimum depth below the foundation where the applied stress due to foundation load decreases to 20 percent of the effective overburden pressure for coarse-grained materials (NAVFAC, [23]) is calculated as the depth of influence. The Boussinesq [24] stress distribution solution has been selected to compute the stress distribution under the loaded areas. The depth of influence is obtained as 7.5 m. The average SPT N\({}_{1,60}\) value within this depth has been calculated conservatively as 10. The bearing capacity after the application of the soil improvement by considering the area replacement ratios is calculated as follows: the average pile bearing capacity of the 4 rows (3.00, 5.50, 8.50, and 8.50 m in length) of 65 cm in diameter unreinforced bored piles is calculated as 304 kN. The calculated average unreinforced pile bearing capacity is given in Table 5.  
The unreinforced bored piles are planned with different center-to-center spacing. The average center-to-center spacing is taken as 1.1 m (in X) to 2.625 m (in Y). The bearing capacity of the cantilever slab is sufficient under the foundation loads. Additionally, the vertical subgrade modulus to be used in the 3D analysis will be considered as 20,000 kN/m\({}^{3}\). After improving the soil with unreinforced bored piles, a higher vertical subgrade modulus is calculated but the value was limited to 20,000 kN/m\({}^{3}\) to be on the safe side.
# Multi-Sensor Fusion Positioning Method Based on Batch Inverse Covariance Intersection and IMM
###### Abstract  
For mass application positioning demands, the current single positioning sensor cannot provide reliable and accurate positioning. Herein, we present batch inverse covariance intersection (BICI) and BICI with interacting multiple model (BICI-IMM) multi-sensor fusion positioning methods, which are based on the batch form of the sequential inverse covariance intersection (SICI) fusion method. Meanwhile, it is proved that the BICI is robust. Compared with SICI, BICI-IMM reduces estimation error variance of the motion model and has less conservativeness. The BICI-IMM algorithm improves the accuracy of local filtering by interacting with multiple models and realizes global fusion estimation based on BICI. The validity of the BICI and BICI-IMM algorithm are demonstrated by two simulations and experiments in the open and semi-open scenes, and its positioning accuracy relations are shown. In addition, it is demonstrated that the BICI-IMM algorithm can improve the positioning accuracy in the actual scenes.  
multi-sensor fusion; batch inverse covariance intersection; interacting multiple model; robust filter +
Footnote †: journal: Applied  
1
## 1 Introduction
Due to the difference in rock strengths, when sudden lithology changes occur, the MSE values may also significantly change [40]; these are linked to other parameters such as the drilling speed (or ROP) [39]. The MSE must be numerically close to the ultimate confined compressive strength (UCCS) of the formation; in other words, the lowest value of MSE corresponds to the UCCS [36; 37]. The performance of the drilling process can be estimated through a pre-operational test, which is a simple and practical procedure to determine the relation between the ROP and the parameters WOB and RPM [41]. The test consists in continuously increasing the WOB while keeping the RPM fixed at a short depth interval to obtain the ROP values, which is repeated for different RPMs [42]. It has a new focus in the oil and gas industry to relate the drilling performance to the MSE obtained during the drilling operations. The achievement of the highest ROP, as it has been applied in the industry, may not be applicable anymore. In this sense, a new concept of pre-operational tests based on the MSE was presented by [43], comparing the interpreted relation of the WOB vs. ROP with the WOB vs. MSE for different RPMs. Another important aspect is the use of these technologies in the exploration of other natural resources. Nowadays, the exploration of petroleum, natural gas, and geothermal sources is gaining importance due to the increase in the demand for these energy sources [16]. The advantages of using geothermal energy to produce heat and/or power include the large number of geothermal resources available, low operating costs, and the expected contribution to the decarbonization of the energy sector [44]. Drilling costs can account for more than 60% of the total cost of an enhanced geothermal system (EGS) [45]. Most of the research performed on the topic proposes a single-objective optimization approach. Thus, this research aims to analyze the field data from pre-salt operations, using a multi-objective optimization based on the recreation of pre-operational drilling tests. The multi-objective optimization of the drilling process will be performed using the ideal combination of the highest ROP and the lowest MSE, allowing for the discovery of the best set of drilling mechanics parameters for the specific lithology studied.
# Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
## 4 Results
### Crop Classification Results
#### 4.1.4 Crop Classification Results Based on Genetic Algorithm's Results (GA Model)  
The results above indicate that the Sentinel-1 multi-temporal polarimetric features have the ability to distinguish specific crop types. To further investigate the efficient combination of those features with the corresponding Sentinel-2, we used our custom GA to identify the most useful ones. At first, we executed a single run of the GA, which resulted in a list of 80 selected features: 47 optical and 33 polarimetric. This particular model outperforms the previous ones in terms of both OA achieved (93.60%), as well as the weighted average f1-score (93.59%), which is higher by more than 1%. Apart from this, it showcases improvements (1-2%) in most of the individual crop type metrics (Table S7).  
Using as input training data the optimal 80-feature subset derived from the GA significantly enhanced the discriminant ability of the SVM classifier. However, one single run might not always result in the best feature subset, considering the inherent probabilistic nature of GAs. In order to overcome this randomness, we carried out a statistical study of the GA, by running 100 separate experiments and, therefore, extracting 100 different optimal feature subsets. In an attempt to identify the most informative features among all 818 features, we counted the number of times \(t\) that each feature appeared in these different subsets. This number \(t\) can be interpreted as a measure of each feature's relative importance. Figure 7 presents the performance of the SVM model when trained with feature appeared more than \(t\) times (GA\({}_{t}\) model). Specifically, the red line describes the number of features (right axis) for each different \(t\), whereas the blue and green lines illustrate the OA and the macro averaged f1-score, respectively (left axis). The thresholds that generate optimal solutions, i.e., OA above 93.9% and f1-score above 91.5%, are 13, 14, 15, 16, and 17, which corresponds to 142, 126, 111, 96, and 82 selected features, respectively. This behavior is expected because, as seen in Figure 5, feature sets of more than 80 features can achieve the optimum results, when used to train an SVM model.  
Figure 6: Classification metrics for the different crop type of the S1 model.  
Table 4 presents the classification metrics of the GA\({}_{15}\) model (i.e., the model trained with the set of features derived by using \(t=15\)). The scores in each metric and for each crop type are higher compared to the S2 model, up to 3.15%. The macro f1-score has increased significantly from 89.93% to 91.75% that can be translated to an increase of 1.82% average f1-score improvement, in each single crop type, which is a remarkable improvement.
# Edge-Computing Meshed Wireless Acoustic Sensor Network for Indoor Sound Monitoring
## 6 Discussion
### Computing Unit  
As can be observed in the table, the work that differs more in terms of computing unit is the MESSAGE approach, which uses a light PIC microcontroller instead of a single-board computer as the Raspberry Pi or the Tronsmart. In this sense, although the MESSAGE computer platform is the one that offers the cheapest computing unit, it would be unable of performing expensive (in terms of hardware) computations in the edge such as acoustic event classification using machine learning or deep learning approaches using its 64 KBytes of program memory and 3968 Bytes of data memory. Considering that the scope of the MESSAGE project was to measure noise levels instead of performing acoustic event classification in the edge, the usage of such a light computing unit was justified for their approach, but is not sufficient for the scope of the current paper.  
Raspberry Pi model 3B, chosen by the FIWARE-based platform [9], enables the performance of acoustic signal processing and classification in the edge. Actually, in previous works (i.e., [16]), the authors of the current paper also chose to use a Raspberry Pi model 3B+, which is a very similar on-board computer. The main difference is that while the processor of the first one operates at 1.2 GHz, the processor of the latter one operates at 1.4 GHz. However, after performing several classification tests, we realized that upgrading the computing unit to a newer model (Raspberry Pi model 4B with 4 GB of RAM) allowed us to obtain 50% faster classification results when using state-of-the-art deep neural networks (i.e., MobileNet V2 [32], which has a size of 8.8 MB) as classification algorithms, which enabled us to reduce the acoustic window. Actually, it took 1.3 s to perform the classification of a 4 s fragment in a Raspberry Pi Model 3B+ and 0.7 s to do the same action using the same algorithm in a Raspberry Pi Model 4B. These timings correspond to an algorithm that, from a 4 s raw audio fragment, calculates the spectrogram of the audio file,normalizes it and passes it through a MobileNet V2 convolutional neural network stacked with a decision tree. More details regarding the algorithms can be found in [25].  
Compared to the computing unit used in SONYC [23], the Tronsmart MK908 ii device has 2 GB of RAM, compared to the 4 GB of the Raspberry Model 4B used in this project. For this reason, authors believe that the computing unit presented in this paper (Raspberry Pi Model 4B with 4 GB of RAM) is the most suitable for a real-time acoustic event classification application.