esa-sceva/satcom-chunk-collection huggingface dataset:
# Tethered Balloon Cluster Deployments and Optimization for Emergency Communication Networks
## 3 System Model and Problem Formulation
### Geometrical Model
The 3D coordinates of the \(n\)th balloon base station and the \(m\)th user can be expressed, respectively, as \(\mathcal{P}_{n}=\left\{x_{n},y_{n},h_{n}\ |\ x_{min}\leq x_{n}\leq x_{max},y_{min}\leq y_{n} \leq y_{max},h_{min}\leq h_{n}\leq h_{max}\right\}\) and \(\mathcal{D}_{m}=\left\{x_{m},y_{m}\ |\ x_{min}\leq x_{m}\leq x_{max},y_{min}\leq y_{m} \leq y_{max}\right\}\). Therefore, the distance between balloon \(n\) and user \(m\) is \(d_{n,m}\) can be given by  
\[d_{n,m}=\sqrt{\left(x_{n}-x_{m}\right)^{2}+\left(y_{n}-y_{m}\right)^{2}+{h_{n} }^{2}}. \tag{1}\]
## 3 Results
### TBARS (Thiobarbituric Acid Reactive Substances)  
Unsaturated fatty acids are oxidized to form odor-free, tasteless hydroperoxides. Then, they are decomposed to flavorful secondary oxidation products, which are mainly aldehydes, such as hexanal, 4-hydroxynonenal (HNE), and malondialdehydedehyde (MDA) [35]. The most common method to determine MDA in foods is the spectrophotometric measurement of the colored adduct of MDA with 2-thiobarbituric acid (TBA). This methodology is not specific, but it has been reported to be a more accurate and sensitive parameter in assessing the oxidative deterioration than other methods [36].  
Figure 2 shows the TBARS results of hazelnuts and walnuts packaged for 21 and 42 days, respectively, in both types of laminates, control and active. It can be observed that the hazelnuts packaged in active bags (made with laminate B) released around 10% less MDA than the blanks (laminate A) after 21 days of being packaged, and up to 20% when they were measured after 42 days. In the case of the walnuts study, the bags made with laminate B (active) released 5% less MDA than the blanks (laminate A) after 21 days and around 25% less after 42 days. The values of MDA were higher in walnuts than in hazelnuts. This can be attributed to the fact that walnuts contain a higher amount of PUFA [25], which are more vulnerable to oxidation. Student T-test was performed, and the results were 0.005 for hazelnuts and 0.016 for walnuts. Thus, it could be confirmed that the results with laminate A and B had significant differences with a 95% confidence level.  
The results also demonstrated that the active packaging based on nanoSe prevents the oxidation of nuts and significantly extends the shelf life of the nuts.
# Engine Fault Detection by Sound Analysis and Machine Learning
## 3 Methodology
### Performance Evaluation  
The performance of the classification model is assessed using standard metrics, including accuracy, precision, recall, macro, and weighted averaged F1-scores. These metrics provide a comprehensive understanding of the model's effectiveness in correctly diagnosing vehicle faults. Accuracy measures the overall correctness of the model, precision indicates the proportion of true positive diagnoses out of all positive diagnoses, recall (or sensitivity) assesses the model's ability to identify true positives, and F1-score is the harmonic mean of precision and recall, offering a balanced evaluation metric [38]. Each metric is calculated as outlined in Equations (10)-(14). Five-fold cross-validation is employed in the performance analysis to provide a reliable estimate of the model's effectiveness across different subsets of the data. This method helps in ensuring that the evaluation is not biased by any particular partitioning of the data.  
\[Accuracy=\frac{Number\ of\ correct\ predictions}{Total\ number\ of\ predictions} \tag{10}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Values** \\ \hline Number of hidden neurons & 2, 3, 4, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, and 100 \\ Activation Function & sigmoid, sine, triangular basis, radial basis, and hard \\  & limit function \\ \hline \hline \end{tabular}
\end{table}
Table 4: Grid search parameters for optimizing ELM classifier.  
Figure 4: Structure of an ELM model.  
\[Precision\ (PR)=\frac{1}{N}{\sum_{i=1}^{N}\frac{TP_{i}}{TP_{i}+FP_{i}}} \tag{11}\]  
where \(N\) is the number of classes, \(TP_{i}\) is the number of true positives for class \(i\) and \(FP_{i}\) is the number of false positives for class \(i\).  
\[Recall\ (RE)=\frac{1}{N}{\sum_{i=1}^{N}\frac{TP_{i}}{TP_{i}+FN_{i}}} \tag{12}\]  
where \(FN_{i}\) is the number of false negatives for class \(i\).  
\[Macro\ F1\text{-}Score=\frac{1}{N}{\sum_{i=1}^{N}\frac{2\cdot PR_{i}\cdot RE_{i }}{PR_{i}+RE_{i}}} \tag{13}\]  
\[Weighted\ F1\text{-}Score=\sum_{i=1}^{N}w_{i}\frac{2\cdot PR_{i}\cdot RE_{i}}{ PR_{i}+RE_{i}} \tag{14}\]  
where \(PR_{i}\) and \(RE_{i}\) are the precision and recall for class \(i\), respectively. Additionally, confusion matrices, which display the model's classification results, were constructed to provide a detailed view of how well the classifier distinguishes between fault classes. This visual representation is crucial for understanding the classifier's strengths and areas needing improvement.
## 4 Target and Frequency Sweeping Jamming Classification Experiment  
In order to verify the performance of the presented approach for target and jamming signals classification, a support vector machine (SVM) classifier is used. SVM as a classifier has been previously demonstrated for radar target classification [31, 32]. The schematic of the proposed method is shown in Figure 7. The proposed method consists of several parts, i.e., signals data acquisition, signal pre-processing, wavelet packet transform, feature extraction, feature dimensionality reduction, classifier training, and classification recognition.  
Figure 6: Boxplot of the distribution of the values of each principal component. (**a**) Principal component 1. (**b**) Principal component 2. (**c**) Principal component 3. (**d**) Principal component 4. (**e**) Principal component 5. (**f**) Principal component 6. (**g**) Principal component 7. (**h**) Principal component 8.
## 5 Numerical Experimental Evaluation
### Mainshock-Integrated Fragility Function Model
To improve the accuracy of the numerical simulation results, more aftershocks are suggested to be used. In addition, more mainshock intensities are utilized in the nonlinear dynamic analysis. The fragility curves show that the probability of collapse of the bridge structures at different given values of the mainshock intensity, for example, the mainshock intensity in this work, is from 0.4 g to 1.6 g with incremental 0.2 g. The probability of failure at other mainshock intensities is unknown; for example, when mainshock intensity is 1.1 g and 1.5 g, this is because that in the nonlinear dynamic analysis, the probability of failure at this these intensities is not calculated. Therefore, it would reduce the calculation cost if the probability of failure at other mainshock intensities by using the results at targeted mainshock intensity. Hence, a fragility surface model is suggested to be created based on the known M-A intensities by fitting the values of \(\mu_{m}\), \(\sigma_{m}\) and \(\gamma_{m}\) at different mainshock intensity. In this work, we aim to propose a mainshock-integrated aftershock fragility function model for future research, and a fragility surface model may be generated based on this work. The accuracy of the developed mainshock-integrated aftershock fragility function model is analyzed by comparing the fragility curves with the numerical simulation data. The results, wherein in the mainshock intensities range from 0.6 g to 1.6 g, and the aftershocks are scaled to the corresponding mainshock intensity, show that the error is generally smaller than 5%. Only when the mainshock intensity is at 0.8 g, and the aftershock intensity is 0.5 g, the error is 5.33%. Therefore, the developed mainshock-dependent fragility function model is accurate to assess the vulnerability of a structure under M-A sequences. By using Equation (5), for each intensity of mainshock, three parameters (\(\mu_{m}\), \(\sigma_{m}\) and \(\gamma_{m}\)) are estimated through the maximum likelihood estimating method. Furthermore, the three parameters corresponding to each intensity of mainshock are presented in Figure 7. The quadratic polynomial equation (Equations (6)-(8)) is used to fit the three parameters, respectively. From Figure 7, it is concluded that the quadratic polynomial equation fits the numerical data well; for the three parameters, the R-square is 0.9256, 0.9314, and 0.9938, respectively. For the \(\gamma_{m}\), the quadratic polynomial equation almost exactly matches the numerical simulation data. To improve the accuracy of fitting numerical simulation data (\(\mu_{m}\) and \(\sigma_{m}\)), it is suggested that more mainshock and aftershock intensities are utilized in future work. By using Equations (5)-(8), the mainshock-aftershock fragility surface can be obtained numerically.
# Rotation Invariant Graph Neural Network for 3D Point Clouds
## 7 Discussion  
### Classification  
#### 7.1.1 Synthetic Dataset without Noise  
Figure 13a shows that the original RGCNN network had a much better accuracy than the rest when noise is small. This is because the large enough position noise causes massive changes in the distance graph and point coordinates. For the position noise tests, increasing the standard deviation of the noise lead to a sharp decrease of the performances for all networks. A visual representation of the distortions caused by position noise is shown in Figure 5.  
For the rotation noise datasets analyzed in Figure 13b, we see the effect of rotation-invariant networks. RGCNN had better accuracy for random rotations in the range \([-15,15]\)  
Figure 14: Results for classification networks trained with noise. (**a**) Position noise results for models trained with position noise. (**b**) Rotation noise results for models trained with position noise. (**c**) Rotation noise results for models trained with rotation noise. (**d**) Occlusion noise results for models trained with occlusion noise.  
degrees but its performance degraded fast with the increasing range of random rotations, whereas all the rest had stable performance regardless of the rotations applied on the point clouds. This happens because large enough rotations cause significant changes in the coordinates that are used as input for the first convolution. Rotation-invariant networks have similar input coordinates irrespective of the rotation.  
Finally, for occlusion noise, we see that the original RGCNN maintains high performance, and, apart from the network using the Gram matrix, all networks maintain constant accuracy despite the larger radius of the occlusion. The occlusion changes the distribution of the points in the point cloud, which leads to a significantly different Gram matrix.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 6 HARRF SPDT Design
### Scattering Parameters (S-Parameters)
#### 6.2.1 Network Power Relations  
For an \(N\)-port passive network, the scattered (reflected) signals must not exceed the incident signals in terms of total power. In the three-port case \((N=3)\), one of the passivity constraints can be expressed as  
\[\sum_{m=1}^{3}\left|S_{mm}\right|^{2}\ \leq\ 1,\quad\text{for each $n=1,2,3$.} \tag{42}\]  
when the network is _lossless_ and reciprocal, the S-parameter matrix is unitary and symmetric. Unitarity implies that  
\[\mathbf{S}^{\text{H}}\ \mathbf{S}\ =\ \mathbf{I}, \tag{43}\]  
where \(\mathbf{S}^{\text{H}}\) is the Hermitian (conjugate transpose) of \(\mathbf{S}\), and \(\mathbf{I}\) is the \(3\times 3\) identity matrix.  
Figure 17: DC characteristics of a FET switch around the 0 V region of operation.  
Because each S-parameter element \(S_{ij}\) is, in general, a complex-valued function of frequency \(\omega\) (or equivalently \(f\)), we have:  
\[S_{ij}\ =\ S_{ij}(\omega)\ =\ \left|S_{ij}(\omega)\right|e^{j\phi_{ij}(\omega)}, \tag{44}\]  
where \(\phi_{ij}(\omega)\) denotes the phase response. Examining how \(S_{ij}\) varies with \(\omega\) is crucial for designing wideband or multi-band circuits such as SPDT switches in reconfigurable front-ends.  
A useful measure is the derivative of the phase with respect to \(\omega\), often related to the group delay \(\tau_{g}\):  
\[\tau_{g}(\omega)\ =\ -\ \frac{d}{d\omega}\angle S_{ij}(\omega). \tag{45}\]  
Minimising large variations in group delay is beneficial for signal integrity.
## 1 Introduction  
The use of conventional laboratory assays--the Born aggregometry--for the assessment of the platelet function has the disadvantages of being time and resource consuming with poor quality assurance [1]. Whole blood multiple impedance aggregometry with the Multiplate(r) device (Roche, Mannheim, Germany) has shown a number of advantages in the evaluation of the platelet function. Use of whole blood samples avoids the need for the centrifugation step and the eventual activation of platelets during preparation. Moreover, time requirement for measurement is markedly reduced in comparison to Born aggregometry. In addition, the computerized design and the availability of standardized activators simplify the measurements and therefore allow bedside monitoring of the platelet function [2; 3].  
Impedance aggregometry has been demonstrated to be useful in several settings [4; 5; 6]. In cardiac surgery, an impedance aggregometry-based algorithm was demonstrated to reduce the requirements of blood, plasma and platelet transfusion [7; 8; 9]. In patients with coronary artery disease and percutaneous coronary intervention, impedance aggregometry was useful for the detection of aspirin responsiveness and resistance [10]. In 40 patients, who underwent transcatheter aortic valve implantation due to severe aortic stenosis, multiple aggregometry was useful to detect high reactivity despite the use of clopidogrel (in 42%) and aspirin (in 11%) and the risk of thromboembolism events [6; 11]. In patients with intracranial hemorrhage impedance aggregometry was used to evaluate the need for platelet concentrates [12].  
As whole blood samples subjected to impedance aggregometry are not corrected for platelet count, an effect of platelet count on the readings of the device can be assumed. However, it is the general opinion, that platelet count affects impedance aggregometry values only in thrombocytopenia with less than 100,000 mm\({}^{-3}\)[13; 14]. In contrast, a recent study demonstrates, that light transmission aggregometry, but not Multiplate(r) findings, are independent of platelet count in the physiological range [15].  
In our study we examined the effect of platelet count on impedance aggregometry findings. We hypothesized that impedance aggregometry findings are dependent on platelet count, and that the correction of impedance aggregometry findings for platelet count using the ratio of the variables results in a variable which is independent of platelet count.
## 3 Results and Analysis
### DPRS Load Distribution Analysis  
#### 3.2.1 Load Distribution in Ideal State  
To verify the correctness of the load distribution model presented in this paper, the results of the model calculations were compared with the results obtained using the direct stiffness method in [25]. Figure 13a,b show the load distribution of the DPRS under ideal conditions for an axial load of 30 kN and surface roughness of 0.8 for the two calculation methods. It is evident that the load distribution on the screw-roller side is essentially the same for both methods, while the load distribution on the roller-nut side is slightly different. Compared with the results obtained using the method described in the literature, the maximum load of this model is 1.15% greater and the minimum load is 1.81% lower, but the overall agreement remains basically the same. The same load distribution pattern is also found in [14], which verifies the reliability of the load distribution model presented in this paper.
## 4 Location Estimation-Based Congestion-Aware Routing Protocol (LECAR)--Detailed Proposal
### Defining Message Format and Interval
#### 4.2.2 Hello Message Interval  
We consider our previously proposed energy-efficient hello scheme with the instant hello message feedback mechanism for calculating the hello message interval [27]. Rather than sending hello messages after a fixed time interval, the hello interval is determined based on the speed of the UAV, airspace volume where UAVs are allowed to fly during the mission, and UAV transmission range. We encourage the interested readers to read details on the EE-hello scheme from [27].
## 5 Conclusions  
The development of video satellites has provided a new approach to transportation land planning and management. It provides a near-continuous and wide field of view for observing the traffic situation. Traditional moving object detection methods have to balance precision and false alarms. In this study, we proposed a new method, which uses the land cover information to guarantee high detection precision and low false alarms. To validate the effectiveness of the method, we conducted a series of experiments on the videos from the Jilin-1 and Skysat satellites. The F-measures achieved were 0.98 and 0.87 for Jilin-1 and Skysat, respectively, which are much better than those for GMM, IFD, and ViBe. The results highlight that the proposed method is practical and feasible for use in moving vehicle detection. This research can be used in the field of intelligent transportation, and can contribute to transportation land planning and management.  
Conceptualization, M.L. and Y.Y. ([PERSON]); methodology, J.T. and Y.Y.([PERSON]); software, Y.L.; validation, R.W.; formal analysis, H.W.; investigation, W.Z.; resources, M.L.; data W.Z.; writing--original draft preparation, M.L.; writing--review and editing, Y.Y.([PERSON]); visualization, M.L.; supervision, Y.Y.([PERSON]); project administration, M.L.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 42201442.  
Figure 12: The detected moving vehicles and their identification numbers.  
**Institutional Review Board Statement:** Not applicable for studies not involving humans or animals.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author due to confidentiality restrictions.  
**Acknowledgments:** We wish to thank the anonymous reviewers and editors for their detailed comments and suggestions.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# A Review on Anaerobic Digestion of Lignocellulosic Wastes: Pretreatments and Operational Conditions
## 2 Overview of AD Process
### Solid-State (ss) and Wet-State Anaerobic Digestion  
Wet AD and solid-state AD (ss-AD) are considered the two main types of anaerobic technologies that have been employed to digest solid organic matter for biogas production. The classification between the two technologies is based principally on the particular content of total solids (TS) concentration at below or higher than 15%, respectively [30; 31].
## 3 System Architecture
### CPM-OFDM Receiver  
With reference to Figure 2, the received signal \(r(t)\) passes through the A/D converter, the cyclic prefix remover, the S/P converter, and the FFT blocks. The output of the FFT block is the estimated complex numbers that can be represented as a vector \(C_{u}\), as shown below.  
\[\hat{C}_{u}=\left[\begin{array}{c}\hat{c}_{u,0}\\ \hat{c}_{u,1}\\ \vdots\\ \hat{c}_{u,N-1}\end{array}\right] \tag{17}\]  
Each of these estimated complex numbers are input into a Viterbi decoder which is a maximum likelihood decoder [56; 57; 58]. For all practical applications, the Viterbi decoder is used to estimate the symbols that have been transmitted using CPM [59]. The output of the Viterbi decoder passes through the P/S converter before being input into the BCH decoder. The \((n,m)\) BCH decoder takes \(n\) encoded bits as its input and outputs \(m\) decoded bits, which is the estimated binary transmitted data represented as \(\hat{B}_{i}\). When simulating this  
Figure 1: The proposed coded CPM-mapper-based OFDM transmitter.  
Figure 2: The proposed coded CPM-OFDM receiver.  
system, the difference between \(B_{i}\) and \(\hat{B}_{i}\) is used to compute the bit error rate (BER). It is noted that the proposed system does not employ any channel estimation and equalization techniques. This is to demonstrate the effectiveness of using a CPM mapper with OFDM. As will be shown in the section on simulation results, the performance of the proposed system is drastically better than the traditional systems even without incorporating channel estimation and equalization.
###### Abstract  
The progress of technology has played a crucial role in enhancing various fields such as education. Universities in Saudi Arabia offer free education to students and follow specific admission policies. These policies usually focus on features and scores such as the high school grade point average, general aptitude test, and achievement test. The main issue with current admission policies is that they do not fit with all majors, which results in high rates of failure, dropouts, and transfer. Another issue is that all mentioned features and scores are cumulatively calculated, which obscures some details. Therefore, this study aims to explore admission criteria used in Saudi Arabian universities and the factors that influence students' choice of major. First, using data mining techniques, the research analyzes the relationships and similarities between the university's grade point average and the other student admission features. The study proposes a new Jaccard model that includes modified Jaccard and approximated modified Jaccard techniques to match the specifications of students' data records. It also uses data distribution analysis and correlation coefficient analysis to understand the relationships between admission features and student performance. The investigation shows that relationships vary from one major to another. Such variations emphasize the weakness of the generalization of the current procedures since they are not applicable to all majors. Additionally, the analysis highlights the importance of hidden details such as high school course grades. Second, this study employs machine learning models to incorporate additional features, such as high school course grades, to find suitable majors for students. The K-nearest neighbor, decision tree, and support vector machine algorithms were used to classify students into appropriate majors. This process significantly improves the enrolment of students in majors that align with their skills and interests. The results of the experimental simulation indicate that the K-nearest neighbor algorithm achieves the highest accuracy rate of 100%, while the decision tree algorithm's accuracy rate is 81% and the support vector machine algorithm's accuracy rate is 75%. This encourages the idea of using machine learning models to find a suitable major for applicants.  
Keywords:students; university admission; major selection; data mining analysis; machine learning models +
Footnote †: journal: Applied Sciences