esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Category-Specific Method  
To tackle the computational intensity inherent of the patient-specific method, we propose a novel category-specific method based on the mean correlation between inputs (CeProp/CeRemi) and the output (MAP). The reasoning for developing a category-specific method is based on the significant impact of correlation rates between inputs and outputs on estimation accuracy. This leads to an increased complexity of neural network architectures [28; 29]. The mean correlation is computed for pairs (CeProp/MAP and CeRemi/MAP), and the resulting values are categorized into five distinct ranges. Each category represents a specific range, providing insights into the relationship between inputs and output.  
The correlations between CeProp, CeRemi, and MAP are calculated as follows:  
\[\text{Corr}(CeProp,MAP)=\frac{\sum_{i=1}^{n}(CeProp_{i}-\overline{CeProp})(MAP_{ i}-\overline{MAP})}{\sqrt{\sum_{i=1}^{n}(CeProp_{i}-\overline{CeProp})^{2}\sum_{i=1}^{n}(MAP_{ i}-\overline{MAP})^{2}}}, \tag{11}\]  
\[\text{Corr}(CeRemi,MAP)=\frac{\sum_{i=1}^{n}(CeRemi_{i}-\overline{CeRemi})( MAP_{i}-\overline{MAP})}{\sqrt{\sum_{i=1}^{n}(CeRemi_{i}-\overline{CeRemi})^{2} \sum_{i=1}^{n}(MAP_{i}-\overline{MAP})^{2}}} \tag{12}\]  
where \(n\) is the number of samples, and \(CeProp_{i}\), \(CeRemi_{i}\), and \(MAP_{i}\) represent the data points for CeProp, CeRemi, and MAP at the \(i\)-th time point, respectively. \(\overline{CeProp}\), \(\overline{CeRemi}\), and \(\overline{MAP}\) represent the means of the corresponding sets. The obtained mean correlation value is classified into the appropriate range as given in Table 2.  
In contrast with the patient-specific method, within the category-specific method, Algorithm 1 is executed 5 times only instead of 70. Consequently, at the conclusion of this process, we obtain one distinct architecture for every correlation category. This method significantly reduces the total number of iterations (1440) compared to the examination of all 70 patients.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Categories** & **Correlation Range** & **Patients** \\ \hline
1 Weak or no correlation & 0 to 0.10 & Patient 15 \\ \hline
2 Weak correlation & 0.10 to 0.20 & Patient 59 \\ \hline
3 Mild correlation & 0.20 to 0.40 & Patients 10, 28, 30, 52, 60, 62, 65, 68 \\ \hline
4 Moderate correlation & 0.40 to 0.70 & Patients 2, 4, 6, 12, 13, 19, 20, 21, 23, 26, 27, 29, 31, 32, 33, 34, 37, 40, 41, 42, 44, 45, 46, 47, 49, 50, 51, 53, 55, 57, 58, 61, 67, 69 \\ \hline
5 Strong correlation & 0.70 to 1.00 & Patient 1, 3, 5, 7, 8, 9, 11, 14, 16, 17, 18, 22, 24, 25, 35, 36, 38, 39, 43, 48, 54, 56, 63, 64, 66, 70 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Correlation groups and patients.
# Synthesis, Structure, and In Vitro Biological Evaluation of Semi-Synthetic Derivatives of Betulin
## 3 Results and Discussion
### Computational Studies
#### 3.3.4 Mechanistic Studies  
Programmed cell death (apoptosis) is a process in which all unnecessary cells in the body are eliminated. The apoptosis pathway can be activated by intracellular signals (mitochondrial pathway) and extracellular signals (death receptor pathways). Apoptosis occurs with the participation of caspases, which have the ability to cleave various proteins [46]. Currently, many drugs are being designed to trigger apoptosis in cancer cells that normally do not undergo it. Among the molecules of this nature are betulin derivatives active in various types of cancer [48; 49; 50; 51].  
To evaluate the mechanism of antiproliferative action, compound **9** was selected, showing the highest activity against MV-4-11 cells. An apoptosis induction test was performed using the Annexin V (AnV)/Propidium iodide (PI) staining method after 24 and 72 h of incubation with derivative **9** at the following concentrations: 55 \(\upmu\)M and 75 \(\upmu\)M (about 1.5 \(\times\) IC\({}_{50}\) and 2 \(\times\) IC\({}_{50}\) value). The obtained results are presented in Figure 2.  
The incubation of MV-4-11 cells with derivative **9** for 24 h did not induct cell apoptosis. An incubation time extended to 72 h slightly increased the level of apoptotic cells using compound **9** at a concentration of 75 \(\upmu\)M. Betulin had much higher pro-apoptotic properties than compound **9**. After 48 h at a concentration of 40 mM, betulin induced the death of 39.1% leukemia U937 cells [52]. On other types of cancers cells (e.g., melanoma, breast, lung, gastric, bladder, colon, prostate), betulin also induced apoptosis [53].  
In order to better understand the effect of compound **9**, an analysis of its effect on the cell cycle of MV4-11 cells was performed. The cell exposure time was 24 and 72 h (Figure 3).  
In the case of MV-4-11 cells, after 24 h of incubation, compound **9** decreased the number of cells in the S and G2/M phases in comparison to control cells and increased the number of dead cells observed. The extension of the incubation time to 72 h with compound **9** resulted in a strong increase in the percentage of dead cells with a statistically significant decrease in the cell number in the G2/M, S and G0/G1 phases. [PERSON] et al., in studies on U937 leukemia, showed that betulin caused cell cycle arrest in the G2/M phase and strongly increased the percentage of cells in the subG1 phase after 48 h at concentrations of 20-40 mM [52]. On ovarian ONACAR-3 cancer cells, betulin, after 24 h at concentrations of  
Figure 2: Induction of apoptosis with derivative 9 after 24 and 72 h. * statistically significant versus control cells, \(p<0.05\).  
15, 60, and 120 mM, also arrested cells in the G2/M phase [54]. Compound **9** appears to have a different effect on the cell cycle than betulin.
# Design of Infinite Impulse Response Filters Based on Multi-Objective Particle Swarm Optimization
## 2 Methodologies and System Description
### Multi-Objective Particle Swarm Optimization (MOPSO) Algorithm  
MOPSO is widely used in engineering, economics, science, and other fields. The difference between MOPSO and PSO is that PSO is used to find a single best solution with a single objective function, while MOPSO uses multiple objective functions to find a set of solutions. In this case, the objectives are usually conflicting with each other; so, it is necessary to seek a set of equilibrium solutions to handle multi-objective optimization problems by introducing external files and domination sorting. In order to effectively handle multi-objective optimization problems, some specific strategies need to be introduced to ensure that well-diversified and evenly distributed Pareto fronts are found. These strategies include the following:  
1. External Archive  
The external archive is used to store the non-dominated solutions found so far. The external archive usually has a fixed size and needs to be updated regularly to maintain its diversity and representativeness. When the size of the external archive exceeds the predetermined capacity, strategies such as fitness will be used. Ranking or diffusion distance are used to select which solutions to retain, ensuring the solution set's diversity.
2. Selection of Guide Particles  
When updating the particle speed, a non-dominated solution is selected from the external file as the particle's reference (guide) particle. It can be selected randomly or based on specific criteria (such as fit, distance, etc.). The purpose is to promote search diversity and distribution uniformity.
3. Fitness Assessment and Dominance Judgment  
Each particle calculates its fitness based on the values of all objective functions to determine whether each particle is dominated by other particles (that is, it is not inferior to another particle on all objectives and is better than another particle on at least one objective).
4. Archive Update Strategy  
After each generation iteration, the new non-dominated solutions are merged with the external archive, and then the optimal solution is retained according to the reduction strategy. Strategies such as fitness ranking, or diffusion distance ensure that the solutions in the archive are evenly distributed in the target space.
5. Distance Measure and Goodness of Fit Ranking  
Crowding distance measures the crowding degree of solutions in the target space and prioritizes retaining solutions with lower crowding degrees to ensure the uniformity of distribution of the Pareto front. Fitness assignment assigns a fitness value to each particle based on the particle's dominance relationship and distance measurement for selection and update.
## 3 The Proposed Method and Simulation Verification
### The Proposed Method
To prove the advantages of the hybrid neural network, when different network structures train the same dataset, the advantage of the proposed method is self-evident, as in Figure 4. More specifically, when the proposed method reaches the fifth iteration, the value of the loss function is close to the optimal, and the curve is smoother than other network structures, indicating that this structure has the best training effect and the fastest convergence speed. As expected, the training times of CNN and LSTM are the longest and shortest, respectively. The training time of the proposed method and GRU is similar to that of LSTM. Interestingly, the training time of the proposed method is far less than the sum of the training times of CNN and GRU, indicating that the proposed method perfectly inherits the advantages of CNN and GRU. In addition, according to Equations (47)-(49), the test MRE and MARE of the proposed method is the smallest, indicating that the performance of this structure is the strongest. The comprehensive evaluation shows that the proposed method is the best. \[\text{RE}=y_{i}-\hat{y}_{i} \tag{47}\]  
\[\text{MRE}=\text{max}(\text{RE})=\max_{i\in(1,n)}\left(y_{i}-\hat{y}_{i}\right) \tag{48}\]  
\[\text{MARE}=\frac{1}{n}\sum_{i=1}^{n}\left(\text{RE}\right)=\frac{1}{n}\sum_{ i=1}^{n}\left(y_{i}-\hat{y}_{i}\right) \tag{49}\]
## 4 Results and Discussion
### First Application: Tilt and Sidelodes Control  
#### 4.2.1 Operation Conditions  
In the first application, the objectives are to position the main lobe of radiation in the desired direction of the elevation plane or E-plane (to determine the tilt angle, \(\theta_{0}\)) and reduce the level of sidelobes (increase RSLL) as the objective function given in Equation (5). Then, GA-MMC determines the optimum value of the amplitudes (\(A_{n}\) between 0 and 7, with increment equal to 1) and progressive phase (\(\beta\) between \(-\pi\) and \(+\pi\), with increment 0.01 rad) for a linear PA excitation. This linear PA possess 40 isotropic elements (\(N=2M=40\)), symmetrically distributed, and an uniform spacing of a quarter wavelength (\(d=\lambda/4\)). To illustrate, Figure 12 contains an operating condition in which a PAR tracks a target in \(\theta_{0}=45\,^{\circ}\)and details the associated linear PA used in the first GA-MMC application tests [13].  
To analyze the GA-MMC effectiveness in the E-plane, tests with five directions of \(\theta_{0}\) (\(90\,^{\circ}\), \(60\,^{\circ}\), \(45\,^{\circ}\), \(30\,^{\circ}\)and \(10\,^{\circ}\)) were conducted, which correspond to the main operation angles. The criterion adopted for considering the simulation results approved were: (i) maximum error of \(\pm 0.5\,^{\circ}\)for the \(\theta_{0}\), due to the resolution available (\(\Delta\theta=0.573\,^{\circ}\)or 0.01 rad), (ii) RSLL \(\geq 30\) dB, for \(\theta_{0}=90\,^{\circ}\)(broadside), (iii) RSLL \(\geq 27\) dB (3 dB less than broadside) to \(\theta_{0}<90\,^{\circ}\). As can be seen in (iii), major changes of the radiation pattern are required. These RSLL values are used only to test GA-MMC, because they are very high if compared to common values of parabolic reflector radars (PRRs) in rocket tracking systems (RTS), which have typical RSLL of 15-20 dB.
# Nonsmooth Optimization-Based Hyperparameter-Free Neural Networks for Large-Scale Regression
## 3 Theoretical Background and Notations
### Nonsmooth Optimization  
Nonsmooth optimization refers to the problem of minimizing (or maximizing) functions that are not continuously differentiable [39]. We denote the \(d\)-dimensional Euclidean space by \(\mathbb{R}^{d}\) and the inner product by \(x^{\top}y=\sum_{i=1}^{d}x_{i}y_{i}\), where \(x,y\in\mathbb{R}^{d}\). The associated \(L_{2}\) and \(L_{1}\)-norms are \(\|x\|_{2}=(x^{\top}x)^{1/2}\) and \(\|x\|_{1}=\sum_{i=1}^{d}|x_{i}|\), respectively.  
A function \(f:\mathbb{R}^{d}\to\mathbb{R}\) is called _locally Lipschitz continuous_ on \(\mathbb{R}^{d}\) if for any bounded subset \(X\subset\mathbb{R}^{d}\) there exists \(L>0\) such that  
\[|f(x)-f(y)|\leq L\|x-y\|_{2}\quad\text{for all }x,y\in X.\]  
The _Clarke subdifferential_\(\partial f(x)\) of a locally Lipschitz continuous function \(f:\mathbb{R}^{d}\to\mathbb{R}\) at a point \(x\in\mathbb{R}^{d}\) is given by [39; 40]  
\[\partial f(x)=\text{conv}\,\Big{\{}\lim_{i\to\infty}\
abla f(x^{i})\mid x^{i} \to x\text{ and }\
abla f(x^{i})\text{ exists }\Big{\}},\]where \"conv\" denotes the convex hull of a set. A vector \(\xi\in\partial f(x)\) is called a _subgradient_. The point \(x^{*}\in\mathbb{R}^{d}\) is _stationary_, if \(0\in\partial f(x^{*})\). Note that stationarity is a necessary condition for local optimality [39].
# DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic
## 3 Methodology
### People Detection
#### 3.1.2 Backbone Architecture  
As illustrated in Figure 3, YOLOv4 offers the best trade-off for the speed and the accuracy for a multi-class object detection purpose; however, since YOLOv4 is an aggregation of various techniques, we undertook an in-depth study of each sub-techniques to achieve the best results for our single-class people detection model, and to outperform the state-of-the-art.  
A basic way to improve the accuracy of CNN-based detectors is to expand the receptive field and enhance the complexity of the model using more layers; however, using this technique makes it harder to train the model. We suggest using a skip-connections technique for the ease of training, instead.  
Various models use a similar policy to make connections between layers, such as Cross-Stage-Partial (CSP) connections [73] or Dense Blocks (consisting of Batch Normalisation, ReLU, Convolution, etc.) in DenseNet [96]. Such models have also been used in the design of some recent backbone architectures, such as CSPResNeXt50, CSPDarknet53 [73] and EfficientNet-B3, which are our supported architectures options for YOLOv4.  
Table 2 summarises the brief report of our investigations for the above-mentioned backbone architectures in terms of the number of parameters and the processing speed (in _fps_) for the same input size of \(512\times 512\).  
Based on theoretical justifications in [53] and several experiments by us, we concluded that CSPDarknet53 is the most optimal backbone model for our application, in spite of higher complexity (due to more number of parameters). Here, the higher number of parameters leads to the increased capability of the model in detecting multiple objects while at the same time we can maintain real-time performance.
# Design of Wideband Decoupling Antenna Array for 5G Smartphones at N77/N78/N79/WLAN 5 GHz Bands
## 3 Performance of the Eight-Element Antenna System
### Eight-Element MIMO Antenna System Prototype
#### 3.1.5 Radiation Pattern  
Due to the mirror symmetry of the arrangement of the eight-element antenna array, we measured the 2D directional maps of Ant 1 and Ant 2 at the three resonance points of 3.5, 4.5, and 5.6 GHz, respectively. Figure 17a shows the simulated and measured xoy-plane radiation patterns, and Figure 17b shows the simulated and measured xoz-plane radiation patterns. From Figure 17, we can see that there is only a slight difference between the simulated and actual measured antenna radiation direction diagrams, which is caused by manufacturing and testing errors. From Figure 17a,b, we can clearly observe that, thanks to the symmetric structure of the antenna pairs, the maximum radiation directions of Ant 1 and Ant 2 are well complementary thanks to the symmetric structure of the antenna pairs. That ensures that the proposed eight-element MIMO antenna system has good omnidirectionality and spatial independence, and the overall performance of MIMO antenna arrays is good and suitable for the application of 5G mobile terminals.
# Emergence of Nano-Dentistry as a Reality of Contemporary Dentistry
## 4 Discussion
### Nanotechnology in the Surgical Field
#### 4.2.2 Nanotechnology in Dental Implants  
Structural and functional fusion of the surface of the dental implant with the surrounding bone (osteointegration) is crucial for short-term and long-term results. Titanium dental implants have been used successfully for the past 30 years, but they still have disadvantages due to their full osseointegration and the fact that their mechanical properties do not match the properties of the bones [80].  
Advances in the production of nanoparticles for implant surface coating and nano formatting of dental implants lead to better osseointegration and improved physiological functions of implants [81]. The application of nanotechnology on the surfaces of dental implants has many different mechanisms. In particular, the surfaces can potentially take an organized (isotropic) or unorganized (anisotropic) pattern. Due to the difficulty of applying standardized sequences to complex structures, the template for dental implants is usually anisotropic. Titanium dioxide (TiO\({}_{2}\)) nanoparticles have been explored in recent years as antimicrobial agents [82].  
A wide variety of methods are used to create nano-features on the surface of dental implants. They can be divided into chemical (anodic oxidation, combinations of acids (bases) and oxidants) and physical (plasma spray, blasting) [83, 84].  
Antimicrobial peptides (AMPs), such as LL37 peptides, may be immobilized on the surface of medical devices, as dental implants, to render them with antimicrobial and angiogenic properties.  
Both soluble and immobilized LL37 peptides have potent antimicrobial activity against Gram-positive and Gram-negative bacteria in the presence of 10% human serum (HS). However, the immobilized LL37 peptides showed less cytotoxicity to endothelial cells (ECs) at a concentration that was able to kill bacteria [85].
# Rainfall Induced Landslide Studies in Indian Himalayan Region: A Critical Review
## 1 Introduction  
Landslides are the most frequent naturally occurring hazards that affect people and their livelihood worldwide. The frequency of occurrence in the Himalayan context is very large when compared with global events [1]. This review paper is an attempt to understand the research being undertaken tounderstand, assess, and mitigate landslide scenarios in the Indian Himalayan region. The need for such a review was raised from the compilation of the global landslide disaster database by [PERSON] and [PERSON], [1]. In their database, a total of 5318 non-seismic landslides occurred from 2004 to 2017, of which 3285 landslides were triggered by rainfall. In the context of the Indian Himalayas, during the same period, 580 landslides occurred with 477 triggered by rainfall, thereby contributing 14.52% of the global landslides. These number could be even higher; for instance, based on NASA GLC data, the number of landslides in the Indian Himalaya during 2007-2015 is 691 with 6306 casualties ([[https://data.nasa.gov/Earth-Science/Global-Landslide-Catalog/h9d8-neg4](https://data.nasa.gov/Earth-Science/Global-Landslide-Catalog/h9d8-neg4)]([https://data.nasa.gov/Earth-Science/Global-Landslide-Catalog/h9d8-neg4](https://data.nasa.gov/Earth-Science/Global-Landslide-Catalog/h9d8-neg4))).  
This region covers more than 12% of India's landmass and is very prone to landslides due to the fragile lithology, the complex geological setting, the high energy of the relief with steep slopes, and the high topographic roughness. Moreover, most of the area is seismically active and subject to extreme precipitations, and the situation has been further worsened with the increase in anthropogenic activities and the advent of climate change. Since it is a well-established fact that most of the landslides in this area have been primarily triggered by rainfall [2; 3; 4], the focus of the present review is only on the studies considering rainfall triggered landslides. Moreover, one of the main practical purposes of this review is to serve as a starting point for future projects which consider implementing territorial landslide early warning systems, and rainfall triggering landslides are the only ones that at present can be forecasted with a certain confidence. Studies on earthquake-induced landslides, snow avalanches, and Glacier Lake Outburst Flood (GLOF) were not considered in this review. This work also covers the various methodologies being adopted for landslide monitoring and analysis, as well as reports on various mitigation measures being undertaken along with the role of Geographic Information Systems (GIS), remote sensing, and the recent use of computational techniques.
# Characteristics of Removal of Lead, Cadmium and Chromium from Soil Using Biosorbent and Biochar
## 3 Result and Discussion
### Characterizations of Materials
Based on the FTIR spectral analysis, the surface functional groups of the sorbent materials were characterized before and after the removal of heavy metals. Peaks with absorption maxima around 3333 cm\({}^{-1}\) indicate the presence of stretching vibrations, likely associated with hydroxyl groups (O-H) from alcohols, phenols, amines, or acids [33]. Bands around 2916 cm\({}^{-1}\) and 2849 cm\({}^{-1}\) suggest the presence of asymmetric and symmetric stretching vibrations of the methylene group (CH\({}_{2}\)) [34; 35]. Peaks in the range of 1740 cm\({}^{-1}\) to 1590 cm\({}^{-1}\) indicate the presence of asymmetric stretching vibrations of the C=O group, which may be attributed to acids, aldehydes, ketones, or esters [36; 37]. Peaks in the range of 1500 cm\({}^{-1}\) to 1450 cm\({}^{-1}\) may suggest the presence of O-H groups (related to phenols) and deformation vibrations or C-O groups and their stretching vibrations. Peaks around 1029 cm\({}^{-1}\) may correspond to hydroxyl groups (O-H) in sugars, C-O-C stretching vibrations in lignin or hemicellulose, C-H bending vibrations in aromatic rings, and S=O stretching vibrations [38; 39; 40]. Figure 3: Results of FTIR analysis of sorbents before and after the sorption process. The FTIR spectra also revealed noticeable differences that reflect changes in the surface structure of the materials after the removal of metal ions. These differences are attributed to the adsorption of metal ions on the surfaces of the studied materials. For example, shifts in peak positions and changes in signal intensity may indicate interactions between the functional groups on the adsorbents' surfaces and metal ions. After the adsorption process, the intensity of existing peaks changes. A change in the stretching frequency around 3333 cm\({}^{-1}\) indicates the coordination of -OH with metal ions [41]. Shifts in peaks around 2916 cm\({}^{-1}\) and 2849 cm\({}^{-1}\), corresponding to asymmetric and symmetric stretching vibrations of the methylene group (CH\({}_{2}\)), may suggest chemical interactions between the functional groups of the adsorbents and the metal ions. Additionally, changes in the range of 1590 cm\({}^{-1}\) to 1735 cm\({}^{-1}\), where asymmetric stretching vibrations of the C=O group occur, may suggest the formation of metal-carbonyl complexes [42]. Peaks corresponding to hydroxyl groups (O-H) and C-O groups also show shifts and changes in intensity, which could be related to the binding of heavy metal ions by these functional groups [41]. These observations indicate that the process of heavy metal ion adsorption on the surfaces of the studied materials leads to distinct changes in FTIR spectra, confirming the effectiveness of these materials in removing heavy metals from the soil.