esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Circuit Modeling of the BPF and the WG ZOR, and Its Geometry
### Required Specifications of the BPF for Satellite Wireless Communication
\begin{bmatrix}ABCD_{\text{Res.1}}^{\text{Circ.}}\end{bmatrix}  = \begin{bmatrix}ABCD_{\text{L1}}^{\text{Circ.}}\end{bmatrix} \begin{bmatrix}ABCD_{\text{C1}}^{\text{Circ.}}\end{bmatrix} = \begin{bmatrix} 1&0\\ 1/jwL1&1\end{bmatrix}\begin{bmatrix}1&0\\ jwC1&1\end{bmatrix} \] \[ \begin{bmatrix}ABCD_{\text{Im}12}^{\text{Circ.}}\end{bmatrix}  = \begin{bmatrix}\begin{matrix}cos\beta&jZ_{0}sin\beta l\\ jY_{0}sin\beta l&cos\beta l\end{matrix}\end{bmatrix} ,\,\text{where}\,\,l= length2\] \[ \begin{bmatrix}ABCD_{\text{Tot.}}^{\text{Circ.}}\end{bmatrix}  = \begin{cases}N-1\\ P=1\end{bmatrix}\begin{bmatrix}ABCD_{\text{Res.p}}^{\text{Circ.}}\end{bmatrix} \begin{bmatrix}ABCD_{\text{Im}_{\text{Im}_{\text{Im}_{\text{Im}_{\text{Im}_{ \text{Im}_{\text{Im}_{\text{Im}_{\text{Im}_{\text{Im}_{\text{Im}_{\text{Im}_{ \text{Im}_{\text{Im}_{\text{Im}_{\text{Im}_{\text{Im}_{\text{Im}}}}}}}}}}}}}}}}} ^{\text{Circ.}}\end{cases} \end{cases} \tag{2}\] \[= \begin{bmatrix}A_{\text{Tot.}}&B_{\text{Tot.}}\end{bmatrix} \] \[ \begin{bmatrix}C_{\text{Tot.}}&D_{\text{Tot.}}\end{bmatrix} \] \[ \begin{bmatrix}S_{\text{11}}^{\text{Circ.}}&=\frac{A_{\text{Tot.}} +B_{\text{Tot.}}/Z_{0}-C_{\text{Tot.}}Z_{0}-D_{\text{Tot.}}}{A_{\text{Tot.}} +B_{\text{Tot.}}/Z_{0}+C_{\text{Tot.}}}\\ S_{\text{21}}^{\text{Circ.}}&=\frac{A_{\text{Tot.}}+B_{\text{Tot.}}/Z_{0}+C_{ \text{Tot.}}Z_{0}+D_{\text{Tot.}}}{A_{\text{Tot.}}+B_{\text{Tot.}}/Z_{0}+C_{ \text{Tot.}}Z_{0}+D_{\text{Tot.}}}\end{bmatrix} \]  
The ABCD-parameter matrix of each block of the circuit is multiplied sequentially from \(P_{in}\) to \(P_{out}\). S\({}_{11}\) and S\({}_{21}\) are functions of the elements of the finalized ABCD matrix. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Value** & **Variable** & **Value** \\ \hline \(L_{\text{P1}}\) & 0.998 nH & \(L_{\text{P2}}\) & 0.516 nH \\ \(L_{\text{P3}}\) & 0.417 nH & \(L_{\text{P4}}\) & 0.408 nH \\ \(C_{\text{P1}}\) & 0.442 pF & \(C_{\text{P2}}\) & 0.859 pF \\ \(C_{\text{P3}}\) & 1.064 pF & \(C_{\text{P4}}\) & 1.088 pF \\ _length1_ & 20 mm & _length2_ & 15.6 mm \\ _length3_ & 16.6 mm & _length4_ & 16.5 mm \\ _length5_ & 16.6 mm & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Calculated unknowns of Figures 1 and 2 for the required BPF. Figure 2: Modified equivalent circuit and its frequency response. (**a**) Circuit. (**b**) S\({}_{11}\) and S\({}_{21}\).
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 4 Fair Editions of Logistic Regression
### Constraint Logistic Regression  
The authors of [19] extended the fairness-enhanced Logistic Regression model proposed in [18]. They introduced a fairness constraint based on Disparate Impact (also discussed in [18]) as well as a constraint to enforce the equalized odds fairness criterion. Experiments conducted on real-world datasets, such as Adult and COMPAS, demonstrated significant improvements in fairness, with up to a 58% reduction in bias, while incurring only a modest decrease in accuracy (approximately 5-7%). In comparison to the model in [18], which relies solely on the Disparate Impact constraint, their approach achieves better equalized odds when applied to datasets that are inherently unfair, and performs comparably on datasets that are fair or slightly unfair.
# A Critical Review of the Status of Current Greenhouse Technology in China and Development Prospects
## 6 Analysis of Key Regions of China's Greenhouse Market  
### Eastern China  
China has a vast territory, with a total land area of about 9.6 million square kilometers; its southernmost point at the Nansha Islands in the South China Sea has a latitude of about 4 degrees. The country's latitude has a range of 50 degrees and crosses five climatic zones, including tropical, subtropical, warm temperate, temperate, and cold temperate zones. The climatic conditions of each specific location require different greenhouse structures.  
Shandong Province is in east China, where the average winter temperature range is \(-4\) to \(-2\)\({}^{\circ}\)C, with strong winds and heavy snow, poor sunlight, and cloudy winters. Shandong is influenced by the ocean, and the winter is mostly cloudy; the soil layer is deep, the groundwater level is deep, and a shallow groundwater level can only be found near the Yellow River. These are the basic reasons why east China implemented small-span arched greenhouses: Shandong-type sheds [61]. In Shandong Province, the types of greenhouses used are simple bamboo and wood-structure greenhouses (discussed in Section 3.2), which are mainly distributed around small towns and rural areas and used for spring, autumn, and winter long-season cultivation, mainly growing vegetables, such as tomatoes and cauliflower. Steel sheds (discussed in Section 3.3) are also popular in Shandong Province as they are suitable for all kinds of crops (including edible mushrooms), flower production, dwarf fruit trees, such as peaches, apples, and pears, and forestry seedlings. Glass greenhouses (discussed in Section 3.4) are also used for the cultivation of vegetables, fruits, and flowers.  
In northern China and China as a whole, vegetable cultivation is most prominent in Shouguang City, Shandong Province. Shouguang City, Shandong Province, is a famous vegetable production base in China and is home to the largest multi-span intelligent greenhouse (8 ha) in China, which represents high-level modern agriculture in Shandong Province and the whole country. Shouguang City not only has a large area of greenhouse cultivation, with a vegetable growing area of 40,000 ha and an annual output of 4.5 million tonnes, but also has a high technological level, with a focus on energy saving, emission reduction, and environmental protection through the adoption of advanced greenhouse climate control technology, reduced use of chemical fertilizers and pesticides, improved vegetable quality and safety, and the application of full traceability of all aspects of the processes, from seedling breeding to production, harvesting, packaging, transportation, and sales. Furthermore, several growers have introduced vegetable branding, comprehensively enhancing the competitiveness of their products.
## 2 Site Selection and Water Specifications  
The Jordan Valley is an import region for agriculture in Jordan and possesses substantial resources of underground brackish water with a salinity of 2000 to 4000 ppm [13]. Additionally, many locations are not connected to the electricity grid, or the electrical grid is not stable, resulting in prolonged power outages. To desalinate water for irrigation purposes, farmers in these regions depend on an alternative energy source for their desalination systems. The proposed site of the project is in South Shouna in the Jordan Valley north of the Dead Sea (see Figure 3).  
Figure 3: The specified location [14].  
The area possesses desirable climatic conditions for growing different types of crops, but suffers from the availability of suitable irrigation water, and even the available resources are not managed in an optimized way to save water and energy. An overall water desalination system with a brine processing mechanism is highly needed at the distribution level.  
The water source at the project site is underground brackish water, and it is neither useful for human purposes nor for irrigation of some vegetables and fruit plants. Several samples of this water were collected and analyzed as shown in Table 1.
# AI Chatots for Mental Health: A Scoping Review of Effectiveness, Feasibility, and Applications
## 3 Results
### Panic Disorder Management  
[PERSON] et al. [70] assessed a mobile app-based interactive CBT chatbot for panic disorder. The study was a randomized controlled trial comparing a newly developed mobile app chatbot for cognitive-behavioral therapy (CBT) to a paperback book with information about panic disorder. The chatbot was offered as a standalone intervention for the patients, while the control group received a paperback book with comprehensive information about panic disorder, including sections on symptoms, different types of treatments, and coping skills for emergencies. In total, 45 patients with panic disorder were randomized to either the chatbot group (\(n=21\)) or book group (\(n=20\)). After 4 weeks, the chatbot group showed significantly greater reductions in panic disorder severity measured by the Panic Disorder Severity Scale compared to the book group. The chatbot group also had improvements in social phobia symptoms and perceived control over feelings of helplessness. The chatbot received lower usability ratings and faced technical challenges; the chatbot group reported a lower mean System Usability Scale score (64.5) compared to the book group (69.5). However, qualitative feedback highlighted several advantages of the chatbot, including the availability of coping tools, interactive learning, and self-management features. The mobile CBT chatbot shows promise as an accessible intervention to help manage panic symptoms.
###### Abstract  
The importance of Land Cover (LC) classification is recognized by an increasing number of scholars who employ LC information in various applications (i.e., address global climate change and achieve sustainable development). However, studying the roles of balancing data, image integration, and performance of different machine learning algorithms in various landscapes has not received as much attention from scientists. Therefore, the present study investigates the performance of three frequently used Machine Learning (ML) algorithms, including Extreme Learning Machines (ELM), Support Vector Machines (SVM), and Random Forest (RF) in LC mapping at six different landscapes. Moreover, the Geometric Synthetic Minority Over-sampling Technique (G-SMOTE) was adopted to deal with the class imbalance problem. In this work, the time-series of Sentinel-1 and Sentinel-2 data were integrated to improve LC mapping accuracy, taking advantage of both data. Moreover, Support Vector Machine-Recursive Feature Elimination (SVM-RFE) was implemented to distinguish the most informative features. Based on the results, the RF integrated with G-SMOTE showed the best result for four landscapes (coastal, cropland, desert, and semi-arid). SVM integrated with G-SMOTE had the highest accuracy in the remaining two landscapes (plain and mountain). Applied ML algorithms showed good performances in various landscapes, ranging Overall Accuracy (OA) from 85% to 93% for RF, 83% to 94% for SVM, and 84% to 92% for ELM. The outcomes exhibit that although applying G-SMOTE may slightly decrease OA values, it generally boosts the results of LC classification accuracies in various landscapes, particularly for minority classes.  
Machine Learning (ML); Geometric Synthetic Minority Over-Sampling Technique (G-SMOTE); land cover mapping; European Space Agency (ESA); class imbalance problem +
Footnote †: journal: Applied Sci.  
0021
## 1 Introduction
The random digit string task highlights one of the most serious causes of feature uncertainty, which is the short duration of the given speech samples [15]. The conventional i-vector is known to suffer from severe performance degradation when short duration speech is applied to the verification process [16, 17, 18]. It has been reported that the i-vectors extracted from short-duration speech samples are relatively unstable (as the duration of the speech is reduced, the vector length of the i-vector decreases and its variance increases; this may cause low inter-speaker variation and high intra-speaker variation of the i-vectors) [16, 17, 18]. The short duration problem can be critical when it comes to real-life applications, since in most practical systems, the speech recording for enrollment and trial is required to be short [16]. In this paper, we propose a novel approach to speech embedding for speaker recognition. The proposed method employs a variational inference model inspired by the variational autoencoder (VAE) [19, 20] to non-linearly capture the total variability of the speech. The VAE has an autoencoder-like architecture which assumes that the data are generated through a neural network driven by a random latent variable (more information on the VAE architecture is covered in Section 3). Analogous to the conventional i-vector adaptation scheme, the proposed model is trained according to the maximum likelihood criterion given the input speech. By using the mean and variance of the latent variable as the utterance-level features, the proposed system is expected to take the uncertainty caused by short-duration utterances into account. In contrast to the conventional deep learning-based feature extraction techniques, which take the acoustic features as input, the proposed approach exploits the resources used in the conventional i-vector scheme (e.g., universal background model and Baum-Welch statistics) and remaps the relationship between the total factor and the total variability subspace through a non-linear process. Furthermore, since the proposed feature extractor is trained in an unsupervised fashion, no phonetic or speaker label is required for training. Detailed descriptions of the proposed algorithm are given in Section 4. In order to evaluate the performance of the proposed system in the random digits task, we conducted a set of experiments using the TIDIGITS dataset (see Section 5.1 for details on the TIDIGITS dataset). Moreover, we compared the performance of our system with the conventional i-vector framework, which is the state-of-the-art unsupervised embedding technique [21]. Experimental results showed that the proposed method outperformed the standard i-vector framework in terms of equal error rate (EER), classification error, and detection cost function (DCF) measurements. It is also interesting that a dramatic performance improvement was observed when the features extracted from the proposed method and the conventional i-vector were augmented together. This indicates that the newly proposed feature and the conventional i-vector are complementary to each other.
# An Empirical Evaluation of Ensemble Models for Python Code Smell Detection
## 1 Introduction
Our proposed ensemble framework aims to address these challenges, improving robustness and generalizability. Despite the success of ML models in detecting Java code smells, many still struggle with complex smells requiring deeper semantic understanding (e.g., Feature Envy, Data Class). Moreover, adapting such techniques to dynamically typed languages like Python has not been systematically studied. This research presents a novel contribution by rigorously evaluating the transferability and adaptation of ensemble learning, Chi-square FST, and SMOTE in the Python context. Given the fundamental differences between Java and Python in terms of syntax, type system, and code structure, this research provides new empirical insights into the behavior and generalizability of ML-based code smell detectors across programming languages. To the best of our knowledge, this is among the first comprehensive efforts to benchmark these techniques on Python datasets, contributing valuable insights for cross-language ML model transferability. The primary goal of the proposed research is to identify code smells from the Python code smell dataset using ensemble learning approaches with an applied Chi-square FST and SMOTE methods. This research is significant as it aims to enhance the understand ing of code smells in Python, a crucial step toward improving software quality and system efficiency.
# Investigation of the iCC Framework Performance for Solving Constrained LSGO Problems +
## 2 Related Work
### \(\varepsilon\)DE Constrained Handlings  
Many constrained-handlings techniques have been proposed [15]. In this study, we used the \(\varepsilon\)DE approach [16] which transforms the selection operator in DE using Formula (5).  
\[(f(X_{1}),v(X_{1}))<_{\varepsilon}(f(X_{2}),v(X_{2}))\leftrightarrow\left\{ \begin{array}{l}f(X_{1})<f(X_{2})\text{ {if }}v(X_{1}),v(X_{2})\leq\varepsilon\\ f(X_{1})<f(X_{2})\text{ {if }}v(X_{1})=v(X_{2})\\ v(X_{1})<v(X_{2}),\text{ {otherwise}}\end{array}\right. \tag{5}\]  
where \(f(X)\) is a fitness value of X solution and \(v(X)\) is a value of constraints violation Formula (6):  
\[v(X)=\frac{\sum_{i=1}^{p}G_{i}(X)+\sum_{j=1}^{k}H_{j}(X)}{p+k} \tag{6}\]  
\[G_{i}(X)=\left\{\begin{array}{l}g_{i}(X),\text{ {if }}g_{i}(X)>0\\ 0,\text{ {otherwise}}\end{array}\right. \tag{7}\]  
\[H_{i}(X)=\left\{\begin{array}{l}\left|h_{j}(X)\right|,\text{ {if }}\left|h_{j}(X)\right|-\varepsilon>0\\ 0,\text{ {otherwise}}\end{array}\right. \tag{8}\]  
In Formula (8), \(\varepsilon\) is the tolerance threshold, and it is equal to 0.0001 for all equality constraints. Analyzing Formula (5), we can conclude that the violation values have higher priority than the fitness function values as comparing with the two-candidate solutions. To control the \(\varepsilon\) parameter in Formula (5), we use the following modified Formula (9), inspired by [24].  
\[\begin{split}\varepsilon&=\left\{\begin{array}{ll}E, \ if\ FEV\leq 0.8\text{-}MaxFEV\\ 0,\ otherwise\end{array}\right.\\ E&=\left(\left(1-\frac{FEV}{MaxFEV}\right)^{cp}\cdot v\left(X_{[\theta \text{-}pop\_size]}\right)\right)\end{split} \tag{9}\]  
where \(FEV\) and \(MaxFEV\) are the current number of fitness evaluations and a maximum budget of fitness evaluations for the current run, respectively; \(v\left(X_{[\theta\text{-}pop\_size]}\right)\) is a violation value for solution \(X\) with index [\(\theta\text{-}pop\_size]\) after sorting population (from best to worse); \(c_{p}\) is a parameter for controlling the speed of constraints; \(c_{p}\) is equal to 3. _Pop_size_ is the population size; and \(\theta\) is equal to 0.8.  
Different values for \(c_{p}\) and \(\theta\) parameters have been tested. The following set of parameters has shown better performance: in Formula (9), \(E\) is equal to 0 after evaluation of 80% of the total budget of FEV. After 80% of fitness evaluation in the last generations, EA should concentrate search in a local area.
## 5 Results and Discussion
### Influence of Temperature Gradient  
According to the Equations (15) and (16), the contact pressure of each contact surface of multi-layer interference cylinder is influenced by temperature of inner and outer surfaces. In Figure 6, the influence of temperature gradient on the contact pressure of multi-layer cylinder is analyzed by applying the temperature on inner and outer surfaces, then the matrix model is verified by comparing the numerical solution with the analytical solution.  
According to Table 3, the temperature gradient is applied between the inner and outer surfaces respectively, and the temperature gradient is gradually increased. Ultimately, the influence of the same temperature gradient on the contact pressure of multi-layer interference cylinder is simulated and analyzed.  
Figure 10 is the contact pressure diagram of model \(M_{1}\), \(M_{2}\), \(M_{3}\), and \(M_{4}\) under different temperature gradients. It indicates that the contact pressure of multi-layer interference cylinder decreases with increase of temperature gradient, which is consistent with the centrifugal force.  
As shown in Figure 11, the contact pressures \(p_{1}\), \(p_{2}\), \(p_{3}\), and \(p_{4}\) of model \(M_{4}\) decreases linearly with the increase of temperature gradient, and the rate of change decreases from \(p_{1}\) to \(p_{4}\) in turn, which indicates that temperature gradient will reduce interference magnitude of multi-layer interference cylinder, thereby affecting the performance of torque transmission of multi-layer interference components.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Inner Surface of \(C_{i}\)/Outer Surface of \(C_{i}\)**} & \multicolumn{6}{c}{**Temperature of Inner and Outer Surfaces**} \\  & & & \multicolumn{6}{c}{\(\mathbf{t/t+\Delta t}\)} \\ \hline \(\Delta T(^{\circ}\)C) & 0 & 10 & 20 & 30 & 40 & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Temperature gradient parameters of model.  
Figure 10: Contact pressure of each model under the temperature gradient.  
In order to validate the correctness of the relationship between contact pressure and interference magnitude of any multi-layer interference cylinder under temperature gradient, the relative error between the numerical solution and the analytical solution under the temperature gradient \(\Delta t=10\)\({}^{\circ}\)C is compared and analyzed. The result is shown in Table 4.  
Table 4 shows the analytical solution is basically consistent with the numerical solution. The maximum relative error is only \(-7.69\%\), which is within the allowable range of design. So, the matrix model of multi-layer interference cylinder under temperature gradient is of high accuracy.
## 2 Zone-Based Registration Scheme
### Type II: Clustered Shape
#### 2.2.2 Case 2: Processing Implicit Registration inside Clustered-Shaped RA  
If an implicit registration occurs in Type II, it is sufficient to define only the transition from \(I_{21}^{II}\) to other states, as shown in Figure 9. After the UE moves from 2\(\rightarrow\)1 and processes the implicit registration, the Network_DB becomes \(\framebox{1|2|3}\) at which point the transition process from state \(I_{21}^{II}\) includes the following states:  
Figure 8: State transition probability for Case 1 of Type II.  
(1) \(I_{21}^{II}\): When a call occurs in zone 1, the UE's state becomes \(I_{21}^{II}\) again (Network_DB = \(\overline{1|2|3}\) ). If an incoming call occurs while staying in zone 1 before moving to another zone with a probability of \(1-m^{\prime}\), one paging procedure is required.  
(2) \(E_{12}^{II}\): UE can transit to state \(E_{12}^{II}\) with a probability of \(\theta m^{\prime}\) if it returns from zone 1 to zone 2. Since Network_DB is \(\overline{1|2|3}\) ), two paging processes (1\(\rightarrow\)2) are required to set the call in zone 2.  
(3) \(E_{21}^{I}\): The UE can move to a new zone and form a serial-shaped RA of Type I. A new registered zone is stored in the Network_DB (\(\overline{\text{new}|1|2}\) ) with a probability of \(3(1-\theta)m^{\prime}\overline{5}\). With the arrival of an incoming call, one paging procedure is performed in the new zone.  
(4) \(E_{13}^{II}\): The UE's state changes to \(E_{13}^{II}\) with a probability of \((1-\theta)m^{\prime}\overline{5}\) if it moves from zone 1 to zone 3. Three paging procedures (1\(\rightarrow\)2\(\rightarrow\)3) are requested using Network_DB = \(\overline{1|2|3}\) in zone 3.  
(5) \(E_{21}^{II}\): If the UE constructs a new cluster-shaped RA, its state changes to \(E_{21}^{II}\) with a probability of \((1-\theta)m^{\prime}\overline{5}\). One paging procedure is processed for an incoming call occurring in the new zone that has been moved.  
In the following section, we will describe the method of computing a signaling cost for mobility management (registration and paging) on a radio frequency based on the transition probability between each state, as explained in Figures 5-9.