esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Estimation of Load Weight
Figure (a)a presents a comparison between the actual transported mass and the estimated mass for each lifting  
Figure 11: Load weight estimation using linear approximation (**a**) real and estimated mass, (**b**) relative mass estimation error. event indexed from 0 to 53, indicating that the estimated values closely follow the real data across various load conditions. Figure 12b displays the percentage error for each test case, revealing a generally consistent performance. This multidimensional approach significantly improved estimation performance, providing a more robust and accurate characterization of the lifting efficiency under diverse operating conditions. The maximum error reached nearly +24.43%, indicating overestimation for a load weight of 301.5 kg (index 6), while the minimum estimation error observed was approximately +0.0055% (underestimation) for a real load of 404.5 kg (index 12). The mean absolute percentage error (MAPE) was 6.4%, demonstrating that the method provides improved accuracy for practical applications, without requiring physical load sensors. To assess potential overfitting of the multivariate regression model and evaluate key assumptions such as unbiasedness, homoscedasticity, and independence of errors, a residual analysis is conducted. Figure 13 presents the plot of residuals versus fitted mass values. The results show no discernible patterns or funnel-shaped dispersion, indicating that the variance in the residuals remains close to constant across the prediction range. Additionally, the mean of the residuals is 0.7 kg, which is close to zero, further supporting the assumption of an unbiased model and suggesting that overfitting is not a concern in this case. Figure 12: Load weight estimation using multivariable regression: (a) real and estimated mass, (b) relative mass estimation error. Figure 13: Residual analysis for multivariable regression.
# RIS-Assisted High-Speed Communications with Time-Varying Distance-Dependent Rician Channels
## 3 Received Power Maximization and Doppler Shift Alignment  
In this section, we first obtain the optimal phase shift set of the RIS, based on the proposed channel model. Then, we demonstrate how the RIS can be used to align the DS. The results reveal that when the transceiver and RIS are close, the LoS channel dominates and vice versa; and optimizing the phase shift set cannot remove the DS unless the direct link vanishes.
# High-Performance Computing and Artificial Intelligence for Geosciences
###### Abstract  
Geoscience, as an interdisciplinary field, is dedicated to revealing the operational mechanisms and evolutionary patterns of the Earth system. Geoscience is very important to enhancing our understanding of the Earth, including its life, resources, and environment. In recent years, the rapid development of high-performance computing and artificial intelligence technologies has presented unparalleled opportunities and challenges to geoscience research. This book aims to explore the significance of high-performance computing and artificial intelligence research in advancing our understanding of geosciences.  
High-performance computing provides powerful computational capabilities for geoscience research. The Earth's system encompasses vast amounts of data and intricate interrelationships, including atmospheric dynamics, seismic activities, and ocean circulation, among others. Traditional data processing and modeling methods often struggle to meet the demands of these complex systems. However, the development of high-performance computing technologies has enabled scientists to leverage tools such as parallel computing and supercomputers to process large-scale datasets and conduct more precise simulations and predictions. Through high-performance computing, scientists can enhance their understanding of the dynamical processes within the Earth system, resulting in the improved accuracy and timeliness of weather forecasting, climate simulations, earthquake early warning systems, and more.  
Artificial intelligence technology has introduced novel ideas and methodologies into geoscience research. The investigation of geosciences often necessitates the extraction of valuable information from extensive datasets to show the inherent patterns within the Earth system. Machine learning and deep learning algorithms in artificial intelligence are able to process intricate data and discern patterns. They autonomously learn and extract features from massive datasets, aiding scientists in uncovering hidden patterns and trends embedded within the data. For instance, in climate change research, artificial intelligence can analyze climate models and observational data to offer more precise predictions and evaluations. Moreover, artificial intelligence has practical applications in geological exploration and resource surveys, thereby enhancing the efficiency and accuracy of resource exploration.  
2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Support Vector Machine for Malicious Primary User Emulation Detection
### Entropy Calculation  
The selected entropy detector is in the frequency domain as it is not sensitive to noise. This system is considered to improve the detection of PU presence in the network and operates with low SNR. It is considered a system without a priori knowledge of the signal. A binary hypothesis test is used to find out the presence of the PU/PUE signal, where \(H_{0}\) is the noise in the absence of a signal and \(H_{1}\) indicates the presence of a signal, as described below [27].  
\[y(n)=\left\{\begin{array}{cc}u(n)&H_{0}\\ s(n)+u(n)&H_{1}\end{array}\right. \tag{9}\]  
A discrete Fourier transform (DFT) is applied to (9) and (10) is obtained [28].  
\[\overline{Y}(k)=\left\{\begin{array}{cc}\overline{U}(k)&H_{0}\\ \overline{S}(k)+\overline{U}(k)&H_{1}\end{array}\right. \tag{10}\]  
where \(\overline{Y}(k)\), \(\overline{S}(k)\) and \(\overline{U}(k)\) are the complex spectrum of the received signal, PU signal, and noise, respectively. The DFT size \(K=N\)[28]. We use the Renyi entropy described below in Equation (11) [29].  
\[H_{\alpha}(Y)=\frac{1}{1-\alpha}\times log\left(\sum_{i=1}^{n}p_{i}^{\alpha}\right) \tag{11}\]  
Given \(Y\) discrete events, \(P_{i}\) is the probability of occurrences in the _ith_ bin, \(\alpha\) is the Renyi entropy order, where \(\alpha\geq 0\) and \(\alpha\
eq 1\). Using the histogram method to estimate the received signal probability of detection, for a given number of bins \(L\), Renyi entropy is estimated by Equation (12) for a statistic test [29; 30].  
\[T(Y)=\frac{1}{1-\alpha}\times log\left(\sum_{i=1}^{L}\frac{k_{i}}{N}^{\alpha}\right) \tag{12}\]  
In the entropy detector, we compare the test statistics with a threshold for calculating the probability of detection. We will use the entropy value as an input to the SVM directly without making any decision regarding the detection by itself, as it requires the SVM classification results.
## 2 Biochemistry and Genetic Regulation of Aroma and Flavor Compounds in _C. sativa_ L.
### Terpenes
Ccinene, featuring a sweet, herbal, and sometimes fruity fragrance, is thought to have antiviral and antifungal properties [62; 63]. Nerolidol, recognized for its woody, citrusy scent is being investigated for neuroprotective, anti-parasitic, and antimicrobial potential [64; 65; 66]. Terpinolene has a complex blend of floral, herbal, and citrus notes. This terpene is less common and is still under study; it may have sedative effects [67; 68]. Finally, \(\alpha\)-Bisabolol has a sweet floral aroma and is being studied for possible pharmacological benefits [69; 70]. Table 1 provides a concise summary of key cannabis terpenes, including their chemical classification and characteristic aroma and flavor attributes. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Terpene** & **Class** & **Aroma Description 1** & **Flavor Impression 1** \\ \hline Myrcene & Monoterpene & Musky, earthy, fruity & Mildly sweet, herbal \\ \(\alpha\)-Pinene & Monoterpene & Piney, resinous & Sharp, woody \\ \(\beta\)-Pinene & Monoterpene & Woody, turpentine-like & Resinous, slightly bitter \\ Limonene & Monoterpene & Citrus (lemon, orange) & Sweet, citrusy \\ Linalool & Monoterpene & Floral, lavelander & Lightly spicy, floral \\ Terpinolene & Monoterpene & Floral, herbal, citrus & Mild, slightly bitter \\ Ocimene & Monoterpene & Sweet, herbal, woody & Subtle, slightly fruity \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of key aroma and flavor terpen in _Cannabis sativa_ L., including chemical class, characteristic aroma descriptors, and flavor impressions.
# Pixel-Level Analysis for Enhancing Threat Detection in Large-Scale X-ray Security Images
###### Abstract  
Threat detection in X-ray security images is critical for preserving public safety. Recently, deep learning algorithms have begun to be adopted for threat detection tasks in X-ray security images. However, most of the prior works in this field have largely focused on using image-level classification and object-level detection approaches. Adopting object separation as a pixel-level approach to analyze X-ray security images can significantly improve automatic threat detection. In this paper, we investigated the effects of incorporating segmentation deep learning models in the threat detection pipeline of a large-scale imbalanced X-ray dataset. We trained a Faster R-CNN (region-based convolutional neural network) model to localize possible threat regions in the X-ray security images on a balanced dataset to maximize detection of true positives. Then, we trained a DeepLabV3+ model to verify the preliminary detections by classifying each pixel in the threat regions, which resulted in the suppression of false positives. The two models were combined in one detection pipeline to produce the final detections. Experiment results demonstrate that the proposed method significantly outperformed previous baseline methods and end-to-end instance segmentation methods, achieving mean average precisions (m_APs_) of 94.88%, 91.40%, and 89.42% across increasing scales of imbalance in the practical dataset.  
X-ray; threat detection; semantic segmentation; instance segmentation; deep learning; security 10261. [[https://doi.org/10.3390/app112110261](https://doi.org/10.3390/app112110261)]([https://doi.org/10.3390/app112110261](https://doi.org/10.3390/app112110261)) +
Footnote †: journal: Applied Sciences  
14 October 2021  
1
## 4 Results and Analysis
(**b–i**) show results of the algorithm proposed in this study, Kernel IF, VABS, SFBA, SSFAD, Global RX, SRX_LOCAL and LRASR, respectively. (**b’-i’**) show a partial 3D view of the surrounding region of the underwater target detected by the presented method, Kernel IF, VABS, SFBA, SSFAD, Global RX, SRX_LOCAL and LRASR, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Image** & **Indicators** &
\begin{tabular}{c} **Proposed** \\ **Algorithm** \\ \end{tabular} & **Kernel IF** & **VABS** & **SFBA** & **SSFAD** & **Global RX** & **SRX_LOCAL** & **LRASR** \\ \hline \multirow{2}{*}{**\#1**} & FNR & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 \\  & FPR & 0 & 0.95 & 0.99 & 0.93 & 0.94 & 0.94 & 0.92 \\  & SCR & 0.39 & 5.23 & \(\backslash\) & 0.82 & 4.06 & 228.71 & 26.82 & 112.52 \\ \hline \multirow{2}{*}{**\#2**} & FNR & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 \\  & FPR & 0 & 0.96 & 0.95 & 0.94 & 0.94 & 0.92 & 0.83 & 0.99 \\ \cline{1-1}  & SCR & 4.95 & 4.34 & 12.12 & 1.13 & 0.80 & 2.71 & \(\backslash\) & 9.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison results of the precision in different algorithms.
###### Abstract  
The proportional integral derivative (PID) regulator is the most often utilized controller in the industry due to its benefits. It permits linear systems to operate well, but it causes non-linear behavior when the system is subjected to physical variable circumstances, such as temperature and saturation. A PID controller is insufficient in this case. The proportional integral (PI) controller inside the direct torque control (DTC) regulates the speed of the doubly fed induction motor (DFIM). However, the system consisting of DTC and a DFIM is non-linear due to its multivariable parameters, resulting in undesirable overshoots and torque ripples. As a result, several approaches are used to improve the DTC's robustness. The integration of optimization methods was discovered. These algorithms are used to provide gains that are near-optimal, bringing the system closer to its ideal state in order to accomplish effective torque and speed control. This article focuses on a comparative study of the different objective functions, in order to have very effective DFIM behaviors, by using a genetic algorithm. Agenetic algorithm (GA) is presented in this study for adjusting the optimal PID parameters in DTC to control the DFIM, utilizing objective functions such as integral square error (ISE), integral time absolute error (ITAE), and integral absolute error (IAE), employed independently and in a weighted combination. This article offers a comparison of several objective functions inside the DTC and DFIM, which will be utilized in future research into another optimization technique for this control type. Matlab/Simulink was used to construct the novel hybrid structure based on the GA-DTC intelligent control. The simulation results demonstrated the efficiency of the GA-DTC intelligent control with a weighted combination, providing acceptable performance with respect to rapidity, precision, and stability, as well as an improvement of 14.53% in the rejection time reduction, fewer torque ripples and flux ripples on the stator and rotor by 27.88%, 15.13%, and 4.375%, respectively, and respective increases of 32.45% and 71% in the THDs of the stator and rotor currents, which are acceptable.  
D 2022 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Human Pose Estimation Using MediaPipe Pose and Optimization Method Based on a Humanoid Model
## 4 Experimental Setup and Results  
For the proposed human pose estimation system, Intel NUC 11 th i7 (NUC11 TNKi7) was used as the SBC for camera image acquisition and joint estimation, and Intel RealSense is installed as a commercial RGB-D sensor. In the SBC, the CPU is a quad-core i7-1165G7 (4.7 GHz) and the memory is 16 GB DDR4. The HDD is SSD NVMe 480 GB, and the OS is Ubuntu 18.04. For a high-mobility pose estimation capability, these modules are installed on a mobile robot base, as shown in Figure 8.  
The basic configuration of uDEAS is determined as follows:  
* Number of optimization variables: 19.
* Initial row length: 3.
* Maximum row length: 12.
* Number of maximum restarts: 20.  
For pose estimation with a single image or an initial image of a video, the search ranges of each variable need to be determined appropriately, i.e., not too wide for search efficiency and not too narrow for inclusion of global minima. Table 1 summarizes the upper and lower bounds of the optimization variables in which all the joint variables can generate six ADL poses. In pose estimation with simulated poses where the ground-truth joint angles are known, the size factor is set as 1.0. Among the joint variables, the knee and elbow joints must have only positive angles, and, thus, their lower bounds are all set as zero.  
For pose estimation with a sequence of images, the search ranges are adjusted around the optimal variables found by uDEAS in the previous image frame. In this paper, the upper limit of the optimization variable was determined by adding 10 degrees to the optimal variable of the previous frame, and the lower limit was set by subtracting 10 degrees from this. In this case, when the minimum rotation angle must be 0 degrees, such as the elbow  
Figure 8: Human pose recognition system.  
Figure 7: Penalty functions for suitable joint angles. (**a**) single-sided negative function, (**b**) single-sided positive function, and (**c**) double-sided function.  
or knee joint, care must be taken to ensure that the adjusted search's lower limit does not fall below 0 degrees.
# Gap Analysis of Ambient Electromagnetic Noise Measurements Stored in the ITU Data Banks
## 3 Gap Analysis
### Geographical Distribution of Measurements  
The geographical distribution of the measurements is shown in Figure 2. This map shows the first major deficiency of this data set. Only measurements in Europe, Japan and the USA are available. These are all rich and highly industrialized countries, data from Lower and Middel Income Countries (LMIC) is missing. Approximately 80% of the measurements have been done in Japan, see Table 1. The impact of these observations will be discussed in Section 4.  
\begin{table}
\begin{tabular}{c c c} \hline
**Country** & **Nr of Meas.** & **Rel. nr. of Meas.** \\ \hline Germany & 15,347 & 17\% \\ \hline Japan & 71,796 & 80\% \\ \hline Netherlands & 750 & 1\% \\ \hline \end{tabular}
\end{table}
Table 1: Number of outdoor measurements per country. The dominant contribution is highlighted in yellow.  
Figure 1: Man-made noise levels as a function of frequency for 4 different environments, according to P.372-17 [15]. The median value of the AWGN component is shown. The Galactic noise level shown will be dominant at frequencies where the ionosphere is (partly) transparent.  
In Europe, the measurement locations do not evenly cover the continent. South Europe, Eastern Europe and Scandinavia are not represented. It can be seen in Figures 4 and 7b that the geographical spread of the measurement locations is especially poor in Spain and in the USA. The number of measurement locations in each country is given in Table 2. Approximately 79% of the measurement locations are in Germany, see Table 1. The impact of these observations will be discussed in Section 4.  
Figure 4: Geographical distribution of man-made noise measurements in the United States of America (USA). The measurements were done on two locations near Boulder, CO, USA.  
Figure 5: An overview of the geographical distribution of the man-made noise measurements in Europe. The measurements were done in the United Kingdom, Germany, The Netherlands and Spain.
# Automatic Design of Collective Behaviors for Robots that Can Display and Perceive Colors
## 4 Experimental Set-Up
### Missions
#### 4.1.3 foraging  
The robots must select and forage from the most profitable of two sources of items. The swarm operates in a squared arena of \(2.25\,\mathrm{m}^{2}\) and gray floor. A rectangular white region of about \(0.23\,\mathrm{m}^{2}\) is located at the bottom of the arena and represents the nest of the swarm. A rectangular black region of \(0.23\,\mathrm{m}^{2}\) is located at the top of the arena and represents the two sources of items--the sources are separated by a short wall segment that does not display any color. This wall segment divides the black region in half. We account that an item is transported and successfully delivered when a robot travelsfrom any of the sources to the nest. The walls lining the nest are red, the walls lining the left source are blue, and the walls lining the right source are green--the colors do not change during the mission. We consider then two types of sources of items: the _blue source_--the black region lined by blue walls; and the _green source_--the black region lined by green walls. At the beginning of each run, the robots are randomly positioned in the center of the arena--between the white and black areas. Figure 2 (right) shows the arena for foraging.  
The performance of the swarm \((C_{f})\) is measured by the objective function described by Equation (3); the higher the better.  
\[C_{f}=(\kappa)I_{b}+(-\kappa)I_{g}; \tag{3}\]  
\[\kappa=1.\]  
\(C_{f}\) indicates the aggregate profit of the total of items collected from the two sources. \(I_{b}\) corresponds to the number of items collected from the blue source, and \(I_{g}\) corresponds number of items collected from the green one. We added the factor \(\kappa\) to balance the profit of the items available in each source. In our study \(\kappa=1\). Items from the blue source account for a profit of \(+1\) and items from the green source account for a penalization of \(-1\). We expect that TuttiFruti produces collective behaviors in which swarms use the blue walls as a reference to navigate towards the blue source, the green walls for avoiding the green source, and the red walls to navigate towards the nest.
# Model-Based Assessment of Energy Efficiency in Industrial Pump Systems: A Case Study Approach
## 1 Introduction
### Optimising Electric Drive Systems for Higher Efficiency  
Electric drive systems (EDSs) are key to improving energy efficiency, as they connect the power supply with mechanical and hydraulic processes. In industry, EDSs are responsible for up to two thirds of electricity consumption [6], making their efficient operation essential. EDSs contribute between 43% and 46% of global electricity consumption, resulting in approximately 6040 million tonnes of CO\({}_{2}\) emissions. Without robust energy efficiency measures, these emissions could increase by 40% by 2030 [7]. Improving the efficiency of industrial processes involves several steps, such as using energy-efficient motors, installing frequency converters, optimising system components such as piping and conductors, and using energy-efficient mechanical equipment such as pumps and compressors. It has been reported that the use of the high-efficiency motors could reduce overall electricity consumption by 4% to 5%, with further savings of 15% to 25% possible through additional electromechanical solutions [7]. Achieving these savings requires a thorough assessment of the unique requirements of each system.  
Pumps that convert mechanical energy into kinetic and static pressure energy of fluids are among the most-used fluid machinery [8]. Centrifugal pumps, which account for more than 75% of all pumps, are widely used in various industries, including the petroleum, chemical, and pharmaceutical industries, and therefore offer significant energy-saving potential [9]. Pump systems are reported to consume more than 22% of the electricity generated [10], but their efficiency is often below the levels achievable with the state of the art. Therefore, improving the efficiency of pumping systems is becoming increasingly important due to both energy-saving strategies and end-user requirements [11; 12; 13]. One approach to improving pump efficiency is to redesign the impeller blades. For example, a method to improve the efficiency of centrifugal pumps has been proposed based on the suppression of secondary flow, showing a strong correlation between the internal flow structures and energy efficiency [14]. Similarly, it has been shown that optimising the shape of the inlet pipe can significantly reduce energy losses [15].  
In addition to optimising impeller and piping designs, the use of motors with higher efficiency classes can further improve system efficiency. Studies on fixed-speed pump applications have developed methods for evaluating energy savings and environmental impact that highlight the importance of considering actual operating conditions rather than relying solely on standard efficiencies [16; 17]. While optimising EDSs is important, further efficiency improvements can be achieved through the adoption of variable-speed drives, as discussed in the next section.