esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Tree-Based Entropy Structured Self-Organizing Maps--Tessom
As far as pyramid arrangement is concerned, an example is **TSSOM** (tree-structured self-organizing maps) [51; 52], where a multidimensional SOM has its dimensions organized in a pyramid shape using the **TSVQ** algorithm (_tree-structured vector quantization_). The topologies of some tree SOMs are exemplified in Figure 1, where (a) TSSOM, (b) SOTA, and (c) TTOSOM. In this work, we propose _tree-based entropy structured self-organizing maps_--TESSOM--an algorithm based on a hierarchical structure based on some of the studies carried out with SOM by [1] to identify relevant variables for the correct diagnosis of patients with COVID-19 and visualizations of the SOM model [53; 54], and studies with decision trees and the **k-NN** algorithm (k-nearest neighbors, classifier based on distance [22; 39], where the data are grouped by proximity in relation to one among \(k\) objects identified by the algorithm as centroids of the groups [55]). The proposed algorithm, as will be detailed below, is similar to the proposals _BTASOM_ and **SOTA** explained above in the sense of obtaining a tree whose segmentation of the structure is explainable and obtaining a complete model that can be analyzed at each node. However, this proposal also brings as a contribution the control of hierarchical level growth based on entropy. Thus, neurons that have entropy indicators below a threshold to be parameterized are subdivided into a new level so that objects with overlapping classes are separated. The proposal still resembles decision tree algorithms and their variations, such as random forest. The main difference is that is it viable to identify overlapping objects from a vector perspective, whereas in the decision tree each variable is analyzed individually to better understand the effect of variables in situations that cause overlap. It is worth noting that the nature of similarity analysis in relation to the information gain calculated in the decision tree is influenced in situations of class imbalance, as is the case for the problem addressed in this work, in which the number of positive cases is greater than negative cases.
## 2 Nonlinear Dynamic Modeling of Spur Gear Systems
### Time-Varying Meshing Stiffness  
In order to realize the continuous transmission of power and motion, the contact ratio of a spur gear pair is between 1 and 2. Therefore, when the number of meshing teeth pairs alternates during the meshing process, the meshing stiffness will periodically change.  
The parabolic method is used to solve the time-varying meshing stiffness of the gear pair. When a single pair of gear teeth are engaged in the meshing process, the meshing stiffness of the pitch point can be expressed as  
\[K_{c}=0.8\times 10^{3}b/q \tag{17}\]  
\[q=0.04732+0.15551/Z_{\text{v1}}+0.25791/Z_{\text{v2}} \tag{18}\]  
where \(b\) is the face width of the gear, \(Z_{\text{v1}}\) and \(Z_{\text{v2}}\) are the equivalent tooth numbers of the driving and driven gears.  
The meshing stiffness can be expressed as a parabolic function with respect to \(X\). When \(X\in[\varepsilon-1,1]\) (\(\varepsilon\) is the contact ratio of the gear pair), a single pair of teeth are engaged. The gear meshing stiffness can be described as  
\[K_{1}(X)=AX^{2}+BX+C \tag{19}\]  
When \(X\in[0,\varepsilon-1]\), a double pair of teeth are engaged. The meshing stiffness is  
\[K_{2}(X)=2 AX^{2}+2(A+B)X+(A+B+2C) \tag{20}\]  
Figure 7: Model of dynamic meshing force.  
where \(A=-2K_{0}/\epsilon^{2}\), \(B=2K_{0}/\epsilon\), \(C=K_{0}\); \(K_{0}\) represents the stiffness of the meshing-in point and the meshing-out point, and generally \(K_{C}=1.5K_{0}\).  
Within a range of engagement, let the average meshing stiffness be \(\overline{K}\), i.e.,  
\[\overline{K}=A\epsilon^{3}/3+B\epsilon^{2}/2+C\epsilon \tag{21}\]  
From Equations (17)-(21), one obtains  
\[\overline{K}=8\epsilon K_{c}/9 \tag{22}\]  
The gear parameters used in this study are listed in Table 1. The time-varying meshing stiffness of the gear system can be calculated according to Equations (19) and (20), as shown in Figure 8.  
In this study, the time-varying meshing stiffness is expanded into the Fourier series in order to facilitate further analysis and calculation. The formula of Fourier transformation is  
\[k(t)=k_{0}+\sum_{i=1}^{i}k_{i}\sin(a\omega t+\varphi_{i}) \tag{23}\]  
where \(k_{0}\) is the average mesh stiffness, \(k_{i}\) is the amplitude of the \(i\)-th harmonic term, and \(\varphi_{i}\) is the initial phase angle of the \(i\)-th harmonic component. \(i\) is the order of the harmonic term expanded by the Fourier series, and \(i=8\) in this study.
## 2 Study Area and Data  
### Study Area  
The study area comprises the Xuanhan section of the Zhou River, located in the southwestern part of Xuanhan County, Sichuan Province, China. The Zhou River is a first-class tributary of the Qu River, with the upper source divided into three branches: the front, middle and back rivers. The front river is the mainstream, with all originating from the southern foot of the Daba Mountains and flowing in a sinuous manner to the southwest (Figure 1). The Qu River is an important tributary of the Jialing River, with sufficient water supply, large flood and dry variability, and a relatively obvious runoff pattern. Its runoff series is suitable for use as the time series data for this study.  
The basin area of the study area is 3574 km\({}^{2}\), the mainstream length is about 108 km, the river bed width is about 60 m, the natural drop is 16.6-327 m, the average annual flow is 160 m\({}^{3}\)/s, the annual average temperature is 16.7 \({}^{\circ}\)C, and the annual average rainfall is 1230 mm, with the majority of it falling between May and October, accounting for roughly 70% of the total. There are 6 soil types, and 106 variants in the study basin, and the main soil types are purple soil, rice soil and loam. The study basin is influenced by the southwest monsoon, with abundant precipitation and frequent floods, including the historical floods in 1902, 1982 and 2004. Floods in this area are closely related to climatic precipitation, so it is important to simulate rainfall-runoff in the study area.
## 2 Background  
### Technical Fundamentals of Web Analytics  
Google introduced Google Tag Manager (GTM) to simplify the implementation of website tracking tags [18]. GTM allows website owners to embed a single JavaScript code (gtm.js), create multiple tags, and trigger and manage them through a user-friendly interface, thereby reducing the need for developer involvement. GTM also introduced autoevent tracking, such as link clicks, to further simplify user interaction tracking. Although GTM reduces technical barriers, setting up custom events and managing tag triggers still require technical knowledge and effort.  
Using GTM to implement Google Analytics tags offers an efficient solution for capturing essential website data, without the need for manual coding. The Pageview Tracking tag, a key feature of Google Analytics, facilitates basic reporting of user interactions. GTM allows developers to deploy and configure the Google Analytics tag via a user interface, reducing dependency on technical teams and accelerating the tracking setup process [19; 20; 21]. However, while GTM simplifies many aspects of tag management, it still requires users to define functional requirements (e.g., Pageview Tracking, Event Tracking, E-commerce Tracking), setup appropriate triggers, and manage additional data collection needs. Establishing custom tags and triggers for various user interactions requires careful planning, effort, and time to ensure an accurate digital data collection. Thus, although GTM reduces reliance on technical teams, its initial setup and ongoing management requires careful execution to meet specific web analytics requirements.  
Figure 1 illustrates the components and flow of data in a web analytics implementation using Google Tag Manager (GTM).  
* Client Side (Website): User interactions (e.g., page views, clicks, and other actions) on the website generate data. These data are passed to the dataLayer, which is an array that organizes and manages data before sending them to other systems.
* Google Tag Manager (GTM): This acts as an intermediate system that collects data from the website through the dataLayer and sends the data to analytics systems such as Google Analytics and other third-party analytics systems.
* Tags, Triggers, and Variables: Tags are snippets of the JavaScript code that GTM fires based on certain events or conditions to send data to analytics platforms, known as triggers. Triggers define when and under what conditions tags should fire (e.g., a page view or specific user action such as a click). The variables store dynamic data that collects custom information (e.g., user click details) and can be retrieved by tags and triggers.
# Characterization of Tropical Cyclone Intensity Using the HY-2B Scatterometer Wind Data
## 2 Data  
The HSCATs level 2B (L2B) data from the National Satellite Ocean Application Service (NSOAS) are used in this study. These data are produced by NSOAS with the adapted pencil-beam scatterometer wind processor (PenWP) developed by the Royal NetherlandsMeteorological Institute (KNMI) scatterometer team for the EUMETSAT Numerical Weather Prediction Satellite Application Facility (NWP-SAF), and disseminated through the NSOAS website [[https://osdds.nsoas.org.cn](https://osdds.nsoas.org.cn)]([https://osdds.nsoas.org.cn](https://osdds.nsoas.org.cn)) (last accessed on 30 January 2022). The wind quality of the different HSCATs has been assessed by [PERSON] et al. [22; 25], and found to be within the mission requirements. Moreover, the wind retrievals are found to be consistent among the different HSCATs. This paper mainly uses the HSCAT (HY-2B) observations of western Pacific TCs in 2019. Since the main objective of this study is to explore the feasibility of wind radii in estimating TC intensity, rather than to develop an automated TC identification algorithm, we only use the HSCAT observations that capture the entire TC structure. As such, nine TCs with 32 HSCAT acquisitions are collected, as shown in Figure 1.  
Both the L2B wind products of HSCAT and ASCAT have already included the European Center for Medium Range Weather Forecasts (ECMWF) background winds, which are acquired by interpolating three ECMWF 3 hourly forecast winds, both spatially and temporally, to the scatterometer data acquisition. In order to verify the general applicability of the developed methods, the collocated European Center for Medium Range Weather Forecasts (ECMWF) winds, as well as the ASCAT and the HSCAT-C data, are used to assess the validity of TC center identification and wind radii estimation algorithms. The above HY-2 data are in 25-km grid resolution and the ASCAT data are in 12.5-km grid resolution. The collocated ECMWF winds are already provided in the scatterometer L2B data. With the ASCAT swath being 40% smaller than that of HSCAT, only three TCs (Lingling, Faxai, Bualoi) with 9 ASCAT entire acquisitions were collected in 2019, as shown in Figure 1 too.  
The 6-hourly best-track data, including TC center position, MSW and minimum sea level pressure (MSLP) of TCs with at least tropical storm intensity (MSW \(>\) 25 m/s), are extracted and evaluated from the China Meteorological Administration (CMA) TC database [26; 27]. Both MSW and MSLP are common indicators of the TC intensity, e.g., TC intensification refers to an increase in MSW or a decrease in MSLP [28]. Note that the used MSW represents the maximum two-minute sustained wind speed at 10-m sea surface  
Figure 1: The observation data used in this study. The legend indicates the observing sensors, the TC names, and the TC durations in year 2019. The markers indicate the TC center location identified with the method in Section 3.  
height. Moreover, both MSW and MSLP are interpolated spatially and temporally to the scatterometer acquisitions for comparison purposes.
# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
## 3 Types of Nanomaterial-Based Sensors in Breath Analysis
### Selective and Cross-Reactive Applications  
Selective applications of colorimetric sensors have scarcely been studied. Lead acetate (PbAc\({}_{2}\)) NPs anchored to polyacrylonitrile nanofibers (NFs) comprise an example of a selective sensing element that was investigated for H\({}_{2}\)S colorimetric detection (a halitosis biomarker); a LOD of 400 ppb which is far lower than the 5 ppm of PbAc\({}_{2}\) paper tests,was achieved [192]. Concerning cross-reactive sensing systems, there are five possible categories to which an indicator may belong to: pH responsive, (Bronsted acidity/basicity); metal-ion-containing dyes (Lewis basicity--electron pair donation); redox-responsive metal salts; nucleophilic indicators (responsive to electrophilic analytes) and dyes with large permanent dipoles (e.g., solvatochromic dyes) [6]. Such optical sensors have been extensively investigated in the field of breath analysis, mainly for LC diagnosis, using both synthetic [140; 193] and real-world breath samples [127; 194]. [PERSON] et al. have achieved to discriminate at first 4 (Table 2) [140] and then 20 [193] LC-related VOCs in two separate studies, with accuracies of 100% and 90%, respectively. [PERSON] et al. achieved to differentiate LC patients and HC, with moderate accuracies [127; 194], as well as LC patients of different histologies with a higher accuracy percentage (Table 2) [127]. The main challenges for such sensors include low sensitivity, high LOD and low response times. Furthermore, their irreversible operation renders them disposable (single-use tests), which is something that should also be considered [7; 167]. Nanomaterials may be also used for enhanced sensing properties. For the detection of VOCs that have been identified as LC biomarkers the use of AuNRs-modified metalloporphyrins has proven to protect the device from photo-degradation, to provide good repeatability, increased long-term sensor stability, and increased shelf-life (Figure 13) [141].
# Quantum Neural Networks Approach for Water Discharge Forecast
## 3 Results and Discussion
### Results Comparisons
The values of the goodness of fit indicators presented above indicate a lower performance of the classical NN algorithms compared to the QNN, at least in terms of MSE and MAE. Similar comparisons can be made for the series So, So1, and So2, leading to a similar conclusion. Therefore, the proposed approach shows its potential in modeling the river discharge, even in the case of inhomogeneous data series.
# New Orthogonal Transforms for Signal and Image Processing
## 3 Exponential Form of the Transform Matrix  
If we select the powers of the real number \(a\) as elements of matrix \(A(n)\) defined by Equation (4), the basis sequence for this case is:  
\[a^{1},a^{2},a^{3},a^{4},\ldots,a^{N}\,\ N=2^{n}\,\ n=2,\ 3,\ 4,\ 5,\ \ldots \tag{12}\]  
The above sequence meets the condition expressed by Equation (5). This condition is also fulfilled for the more general case when the basis sequence is in the form:  
\[a^{k},a^{k+1},a^{k+2},a^{k+3},\ldots,a^{k+(N-1)} \tag{13}\]  
where  
\(a\)--any real number, \(a\
eq 0\).  
\(k\)--integer, \(k\in(-\infty,+\infty)\).  
For this case, Equation (5) is:  
\[a^{i}\cdot a^{i+3}=a^{i+1}\cdot a^{i+2}\qquad\text{for $i=k,\,k+1,\,k+2$, \,\,\,\ldots$} \tag{14}\]  
We now define the general form of the matrix \(A(n)\) having an exponential form of order \(N\), \(N=2^{n}\), for which the following recursive relationship holds:  
\[A(n)=\begin{bmatrix}A(n-1)&A(n-1)\cdot a^{2^{(n-1)}}\\ A(n-1)\cdot a^{2^{(n-1)}}&-A(n-1)\end{bmatrix} \tag{15}\]  
where \(n=1,\,2,\,3,\,\,\ldots\) and \(A(0)=a^{k},k\)--integer.  
The matrix \(A(n)\) defined by Equation (15) is symmetric and orthogonal.  
The coefficient \(C\) is equal to the energy of the row of matrix \(A(n)\) and is defined as follows:  
\[C=\sum\limits_{i=1}^{N}a^{2i}=const \tag{16}\]  
Using Equation (15) for \(n=1\) and assuming \(k=1\), the matrix \(A(1)\) is:  
\[A(1)=\begin{bmatrix}a&a^{2}\\ a^{2}&-a\end{bmatrix} \tag{17a}\]  
For \(n=2\) we have:  
\[A(2)=\begin{bmatrix}A(1)&A(1)\cdot a^{2}\\ A(1)\cdot a^{2}&-A(1)\end{bmatrix}=\begin{bmatrix}a&a^{2}&a^{3}&a^{4}\\ \cdot a^{2}&-a&1&a^{4}&-a^{3}\\ \cdot a^{3}&\cdot a^{3}&-a&-a^{2}\\ a^{4}&-a^{3}&\cdot-a^{2}&a\end{bmatrix} \tag{17b}\]  
For \(n=3\) we have:  
\[A(3)=\begin{bmatrix}A(2)&A(2)\cdot a^{4}\\ A(2)\cdot a^{4}&-A(2)\end{bmatrix} \tag{17c}\]  
The choice of element \(a\) depends on the specific case of the analysis. It should be noted that for the particular case when \(a=1\) matrix \(A(n)\) becomes the Hadamard matrix. For the basis sequence represented by Equations (12) and (13) we have the following relationship:  
\[a\overset{k+(\frac{N}{2}-1)}{\underset{\overset{\Sigma}{\rightarrow}}{\overset {\Sigma}{\rightarrow}}i}\cdot a^{k+(N-1)}\overset{k+(N-1)}{\underset{\overset{ \Sigma}{\rightarrow}}{\overset{\Sigma}{\rightarrow}}i}=a^{k+(\frac{N}{2}-1) } \tag{18}\]  
If two matrices \(A_{1}(n)\big{|}_{k}\) and \(A_{2}(n)\big{|}_{l}\) based on Equation (15) have the basis sequences \(a^{k},a^{k+1},a^{k+2},\ldots,a^{k+(N-1)}\) and \(a^{i},a^{l+1},a^{l+2},\ldots,a^{l+(N-1)}\), respectively, for \(k\
eq l\) then:  
\[A_{1}(n)\big{|}_{k}\cdot A_{2}(n)\big{|}_{l}=C_{0}\cdot I \tag{19}\]  
where  
\(C_{0}=\sum\limits_{i=1}^{N}a_{i_{1}}\cdot a_{i_{2}}\).  
\(a_{i_{1}}\), \(a_{i_{2}}\)--the elements of basis sequences for matrices \(A_{1}(n)\) and \(A_{2}(n)\), respectively.
# Machine-Learning-Enabled Intrusion Detection System for Cellular Connected UAV Networks
## 8 Conclusions  
We designed a model for a 5G software-defined security system to show the benefits of machine learning in a satellite and UAV network for threat detection. We used various types of ML algorithms in networked based intrusion detection to detect new types of intrusion in the UAV networks. The efficiency and performance of various ML algorithms has been verified based on different parameters. The results demonstrate that there is no unique machine-learning algorithm that succeeds in preventing all types of attacks. However, the decision tree obtained the minimum value of false negative rate of 0% with a maximum accuracy of 99.99% for all types of tested attacks, except infiltration, which had 86.57% accuracy. Among all ML classifiers, Gaussian Naive Bayes reached the lowest accuracy rate and the maximum false negative rate. Furthermore, to detect the intrusion in the network, the FN rate is very significant to provide availability and the confidentiality in addition to precision, recall and the accuracy rate parameters. These results show a  
Figure 19: The ROC curves of ML models for DoS.  
promising capability for the application of ML in network threat detection for cellular-based UAVs and satellite networks.  
Conceptualization, R.S., A.O., S.A.R. and R.A.; writing--original draft preparation, R.S., A.O. and S.A.R.; investigation, R.S., R.A. and S.K.; supervision, R.A. and S.K.; writing--review and editing, R.S., R.A. and S.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Korean government (MSIT) (No. 2021-0-01352) through Institute for Information & Communications Technology Planning & Evaluation (IITP) for development of technology to validate autonomous driving service and the APC was funded by the same funding source.  
This work was supported by Institute for Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korean government (MSIT) (No. 2021-0-01352, Development of technology for validating the autonomous driving services in perspective of laws and regulations).  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Outcome Variables  
Each subject was assessed for four primary outcomes: (1) peak aerobic capacity, (2) maximum heart rate, (3) oxidative stress status, and (4) erythrocyte antioxidant enzymes analysis. Peak aerobic capacity and maximum heart rate was assessed at pre-exercise and post-exercise with a graded exercise cycling test. A peripheral blood sample was obtained from the forearm vein of each subject into a BD Vacutainer(r) (BD Life Sciences, New Jersey, USA) tube coated with K2 EDTA before and immediately after each exercise bout. Blood samples were centrifuged at 3000 rpm for 10 min at 4 \({}^{\circ}\)C to separate plasma from red blood cell pellets. The resulting plasma and red blood cell samples were frozen and stored at \(-\)80 \({}^{\circ}\)C.  
Plasma malondialdehydedehyde (MDA) was used as an oxidative stress status marker, and the levels were determined according to [PERSON] et al. [34]. The intracellular antioxidant markers were measured using erythrocyte antioxidant enzymes activities, including superoxide dismutase (SOD), glutathione peroxidase (GPx), and catalase (CAT). Activity for one unit of SOD was designated as the amount of haemoglobin that inhibits the rate of nitro blue tetrazolium reduction by 50% [35]. GPx activity was determined spectrophotometrically by the method of [PERSON] [36]. H2O2 was added to a medium containing phosphate buffer, EDTA, NaN3, NADPH, GSH and erythrocyte hemolysis, followed by measuring the change in absorbance of the system at 340 nm. These antioxidant  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristics** & **Mean \(\pm\) SE** \\ \hline Age & 20.8 \(\pm\) 0.45 years \\ Bodyweight & 58.8 \(\pm\) 2.39 kg \\ Height & 167.5 \(\pm\) 1.12 cm \\ BMI & 20.9 \(\pm\) 0.84 (kg/m\({}^{-2}\)) \\ Fat Mass & 10.8 \(\pm\) 1.20 kg \\ Body Fat & 17.4 \(\pm\) 1.25\% \\ Measured VO2 pk & 36.6 \(\pm\) 1.17 (mL/min/kg) \\ Predicted VO2 pk & 48.1 \(\pm\) 1.10 \({}^{\circ}\) (mL/min/kg) \\ \hline
**Nutrient level** & **Pre-training** & **Post-training** \\ \hline Calorie & 1853 \(\pm\) 70 kcal & 1771 \(\pm\) 85 kcal \\ Protein & 112 \(\pm\) 3 g & 118 \(\pm\) 2 g \\ Carbohydrate & 281 \(\pm\) 18 & 272 \(\pm\) 16 \\ Fat & 38 \(\pm\) 3 & 39 \(\pm\) 2 \\ Retinol & 197 \(\pm\) 30 ug & 193 \(\pm\) 32 ug \\ Carotene & 1511 \(\pm\) 423 ug & 1482 \(\pm\) 512 ug \\ Ascorbic Acid & 83 \(\pm\) 12 mg & 88 \(\pm\) 13 mg \\ \hline \hline \end{tabular} N = 25; BMI—Body Mass Index; VO2 pk —Peak Oxygen Consumption; \({}^{\star}\) measured VO2 pk significantly different from predicted VO2 pk at \(p\) < 0.05.  
\end{table}
Table 1: Characteristics of all subjects and their nutritional intake pre- and post-training.  
enzymatic activities were expressed relative to the haemoglobin concentration. CAT activity was quantified using a spectrophotometer based on the method previously described by [PERSON] [37]. The decomposition of H\({}_{2}\)O\({}_{2}\) by the CAT enzyme was followed directly by the decrease in absorbance at 240 nm. The difference in absorbance per unit time was measured as CAT activity. All samples were performed in duplicate.