esa-sceva/satcom-chunk-collection huggingface dataset:
# Incorporating Drone and AI to Empower Smart Journalism via Optimizing a Propagation Model
## 3 Proposed Drone-Enabled AI Framework
### The Proposed Conceptual Architecture  
Propagation models predict signal attenuation as a measure of the power density of an electromagnetic wave as it propagates through space from a transmitter, which represents a drone in our scenario. Calculating PL is useful for monitoring system performance, network planning, and coverage to achieve perfect reception for users such as journalists. Figure 2 shows the proposed conceptual architecture that comprises three main parts. Part one is the aerial part which consists of a drone along with its payloads (e.g., communication transceiver such as MIMO antenna, GPS sensors, and camera). Part two consists of the ground control unit that was controlled by an admin to support some of the drone's operations functions, aerial imaging, and flight control via a user interface. Part three consists of an AI framework that helps in making smart decisions, which can save time and efforts when using such a technology, as will be discussed in the upcoming section.  
The five selected propagation models are representatives of their types and exhibit better performance across different terrains in relation to other models. These models are programmed in smart decisions, unite to achieve a wider coverage footprint, efficient RSS, higher throughput, and better LoS and NLoS, which collectively help in aerial journalism. These propagation models are: ITU-R P.529-3, Hata-Davidson, Okumura, ATG, and Two-Ray models, which are denoted as M1, M2, M3, M4, M5, respectively. The first three models are examples of empirical models, while the other two are examples of free-space-based models. Further, these selected propagation models that we have considered are representative of their respective types along with their main parameters of upper limit transmission coverage distance, transmitter and receiver antennae heights, and high scale of frequency band. Mathematical representations of these models are discussed in Equations (1)-(25) [29].
## 2 Materials and Methods
### Sample Preparation  
The soil sample underwent drying at a temperature of 105 \({}^{\circ}\)C using an oven. Postdrying, it was subjected to fine grinding and passed through sieve No. 4, ensuring uniformity in particle distribution. A methodical process was utilized to prepare the samples, aiming to examine the properties of the soil after mixing it with a magnesium chloride (MgCl\({}_{2}\)) solution. Firstly, the MgCl\({}_{2}\) was dissolved in distilled water to achieve 4%, 9% and 14% solutions. Subsequently, the dry soil was mixed with the MgCl\({}_{2}\) solutions to create the desired soil samples.  
Manual mixing was carefully executed to ensure uniformity at every stage. To enhance the absorption of the solution and to achieve a homogeneous mixture, the prepared mixtures were sealed in plastic bags and allowed to rest for about 24 h before compaction. This resting period allowed for the thorough absorption of the solution by the soil particles.  
After compacting the treated and untreated samples to at least 98.0% of the maximum dry density (MDD) and the optimum water content (OWC) as obtained by the standard Proctor test (ASTM D-698) [52], each sample was wrapped in plastic film to avoid moisture loss. The samples were then divided into three groups with two designated for curing (one set for 7 days and another for 28 days) to study the effect of curing time on soil properties in a controlled environment. The third group was used for immediate testing to compare the soil characteristics directly with and without the curing process. Following preparation, all samples, both cured and uncured, were subjected to unconfined compression testing to evaluate the effect of curing on the samples' strength. Additionally, only the samples cured for 28 days were tested for freeze-thaw cycles to assess their mechanical properties and durability.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Physical Properties** & **Values** \\ \hline Silt (\%) & 61 \\ Clay (\%) & 39 \\ Specific gravity & 2.72 \\ Maximum dry density (kN/m\({}^{3}\)) & 17.4 \\ Optimum water content (\%) & 19 \\ Liquid limit (\%) & 37.2 \\ Plastic limit (\%) & 19.7 \\ Plasticity index (\%) & 17.5 \\ pH & 8.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical properties of used soil.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Constituents** & **Specification** \\ \hline Magnesium Chloride (MgCl\({}_{2}\)) & Min 46.5\% \\ Calcium Chloride (CaCl\({}_{2}\)) & Max 2.2\% \\ Sodium Chloride (NaCl) & Max 0.9\% \\ Potassium Chloride (KCI) & Max 0.6\% \\ Water of Crystallization & Balance to 100\% \\ Sulfates (SO\({}_{4}\)) & Max 0.025\% \\ Iron (Fe) & Max 5 ppm \\ Mercury (Hg) & Max 0.2 ppm \\ Lead (Pb) & Max 0.1 ppm \\ Arsenic (As) & Max 0.1 ppm \\ Cadmium (Cd) & Max 0.1 ppm \\ pH of aqueous Solution & 8.0–9.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: General properties of used MgCl\({}_{2}\).
## 4 Materials and Methods
### Experimental Setup and Root Inoculation  
After significant root biomass development, plants were arranged into 6 groups with 5 replicates, potted individually. The first group of plants composed of a set of three groups of five plants each were inoculated with the previously prepared rhizobacterial consortium solution (Table 1), by soaking the roots, previously washed with deionized water, in the bacterial suspension for 6 h at room temperature (25 \(\pm\) 1 \({}^{\circ}\)C). The bacterial suspension was diluted with 0.9% (\(w/v\)) saline solution to achieve a concentration of 10\({}^{7}\) CFU mL\({}^{-1}\), prepared as described previously [76]. The second group of plants composed of a set of three groups of five plants each were soaked similarly in 0.9% (\(w/v\)) saline solution without bacterial inoculation. This process occurred for 10 days. After this period, _H. portulacoides_ individuals from both inoculated and non-inoculated groups were separated into three sets with five replicate individuals and were kept under the same described conditions. Thenutrient solution was replaced and supplemented with NaCl to attain the desired target salinities (0, 400 and 600 mM NaCl):  
0 mM NaCl non-Inoculated (0 - I)  
0 mM NaCl Inoculated (0 + I)  
400 mM NaCl non-Inoculated (400 - I)  
400 mM NaCl Inoculated (400 + I)  
600 mM NaCl non-Inoculated (600 - I)  
600 mM NaCl Inoculated (600 + I)  
Salinity exposure trials lasted for 7 days after which chlorophyll fluorescence measurements were made and consecutively and plants were harvested. Leaf samples for biochemical measurements were immediately flash-frozen in liquid N\({}_{2}\) and stored at \(-\)80 \({}^{\circ}\)C until analysis.
# Solar Energy in Urban Planning: Lesson Learned and Recommendations from Six Italian Case Studies
## 4 Results and Discussion
### Photovoltaic Village in Alessandria
#### 4.1.7 Approaches, Methods, and Tools  
A monitoring campaign on the PV systems was carried out by researchers of Politecnico di Torino for a period of 12 months (September 2004-August 2005) to measure the electricity generated during the buildings' operation phase and correlate it to the actual energy consumption. Data were collected every 15 min and function curves for daily, weekly, monthly, and annual periods were generated. The analysis shows that PV systems can fulfill the electricity demand for seven months, but an electricity supply through the grid is still needed. In addition, in the absence of regional, national, or community incentives, the investment can be paid off in a long-term period (more than 30 years).
# The Effects of _Anthyllis vulneraria_ Hydroalcoholic Leaf Extract as an Adjuvant in Wound Healing
## 3 Results
### Experimental Animal Study
#### 3.2.3 Histopathological Analysis  
Histopathological analysis was conducted on skin biopsies collected from the wound center on days 7 and 14. The parameters assessed included reepithelialization, suppuration, epithelial acanthosis, fibrin crust presence, and calcification (recorded as absent [0] or present [1]), as well as granulation tissue, fibrosis, congestion, chronic inflammation, and the presence of skin adnexa (scored on a 0-3 scale: 0 = absent, 1 = mild, 2 = moderate, 3 = severe).  
Statistical analysis revealed no significant differences between the four groups for any histopathological parameter. The \(p\)-values for each parameter are presented in Table 4, with all \(p\)-values > 0.05, indicating no statistically significant differences.  
Repithelialization was observed in 25 out of 32 samples across all groups, with no significant variation (\(p\) = 0.917), indicating that epithelial regeneration occurred similarly, regardless of treatment. Similarly, granulation tissue scores ranged from 0 to 3 across samples; however, the distribution showed no significant difference between groups (\(p\) = 0.796). Chronic inflammation, scored as mild (1) in most samples, also showed no significant variation (\(p\) = 0.292), suggesting that the inflammatory response was not markedly altered by the extract. The presence of skin adnexa, which varied widely (scores of 0 to 3), also did not differ significantly between groups (\(p\) = 0.486), indicating that the regeneration of skin appendages was not influenced by the treatment.  
In Figure 7, the microscopic aspect of the wounds can be seen. We chose one representative photo from each group on day 7, because on day 7 there were significant macroscopic differences between the groups.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Evaluated Parameter** & \(p\)**Value** \\ \hline Reepithelialization & 0.917 * \\ \hline Supuration & 0.801 * \\ \hline Epithelial acanthosis & 0.943 * \\ \hline Fibrin crust presence & 0.710 * \\ \hline Calcification & 0.456 * \\ \hline Granulation tissue & 0.796 ** \\ \hline Fibrosis & 0.537 ** \\ \hline Congestion & 0.542 ** \\ \hline Chronic inflammation & 0.292 ** \\ \hline Presence of skin adnexa & 0.486 ** \\ \hline \hline \end{tabular}  
* Pearson chi-squared test; ** independent samples Kruskal-Wallis (k samples).  
\end{table}
Table 4: Histological evaluated parameters and the respective \(p\) values between the groups.
# Advanced Security Framework for 6G Networks: Integrating Deep Learning and Physical Layer Security
## 2 System Model
### Codebook Detection Using Deep Learning  
A key aim of this scheme is to minimise the manual tasks involved in detecting and estimating decoded private information from noisy received signals. This is achieved by automating the process using AI/ML algorithms. Specifically, we utilise LSTM networks and a hybrid model combining LSTM with RF algorithms for the detection and recovery of secret information. The LSTM model is designed to handle the temporal dependencies of the received signals, as shown in Algorithm 1.  
Moreover, the hybrid LSTM and RF Model combines the feature extraction capabilities of RF with the temporal dependency management of LSTM. By using RF, features are extracted from the received signals before they are fed into the LSTM. The LSTM model is trained with the extracted features from RF to improve detection accuracy.  
The selection of LSTM and RF models for our 6G security framework is based on the specific requirements of 6G communication systems, which demand both high accuracy in signal classification and the ability to handle complex temporal dependencies in dynamic network environments. LSTM networks, a variant of RNNs, are specifically designed to capture long-range dependencies in time-series data, making them particularly well-suited for analysing sequential data, such as the rapidly changing patterns of communication signals in a 6G environment. This capability allows LSTM to efficiently model the temporal aspects of the physical layer, where patterns of interference or attack may emerge gradually over time. On the other hand, RF provides a robust mechanism for feature extraction due to its ensemble learning nature, which aggregates decision trees to improve classification performance. By leveraging multiple decision paths, RF reduces the risk of overfitting and ensures more reliable classification even when the data are noisy or incomplete--conditions that are common in wireless communication. Finally, RF is computationally efficient, which is crucial for real-time applications in 6G networks where latency and speed are paramount.
## 2 Smart Homes
### Home Application Areas
#### 2.2.1 Energy Management
[PERSON] and [PERSON] present a hardware design SHEMS with the applications of communication, sensing technology, and machine learning algorithm so consumers can easily achieve a real-time, price-responsive control strategy for residential home loads such as electrical water heater (EWH), heating, ventilation, and air conditioning (HVAC), electrical vehicle (EV), dishwasher, washing machine, and dryer [16]. [PERSON] _et al_. proposed a framework through M-Bus communication system that aims to manage the energy consumption of controllable appliances in groups of Smart Homes belonging to the same neighbourhood [15]. They also propose a lightweight algorithm with the purpose to share the available alternative power between the houses.
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 2 Related Work  
Handling class imbalance in Intrusion Detection Systems (IDS) has been a persistent challenge in both traditional and deep learning-based approaches. Early studiesprimarily focused on data-level solutions, such as oversampling and two-stage classification pipelines. For instance, [PERSON] et al. [2] proposed a two-stage classifier that combines SMOTE with ensemble learning, improving the detection of rare attacks through stage-wise specialization. These traditional methods, however, often suffer from overfitting or poor feature generalization.  
More recently, Generative Adversarial Networks (GANs) have emerged as a promising approach for Intrusion Detection Systems (IDS), particularly in addressing data imbalance and generating high-quality synthetic attack samples. Existing research on GAN-based IDS can be categorized into two main directions: (1) applying GANs for minority-class data augmentation without significant modifications to their loss functions, and (2) modifying the GAN framework, including loss functions and architectural adjustments, to enhance training stability, sample quality, and minority-class detection. In this section, we review relevant studies in both categories, focusing on their contributions and limitations in handling class imbalance.
## 8 Multi-Objective Optimization Integrated with Neural Networks ANN
### Optimizing Nutritional Profiles with MOO-ANN  
Food products are increasingly required to meet diverse nutritional requirements, accommodate dietary restrictions, and satisfy consumer preferences. MOO integrated with ANNs allows for the precise formulation of food products by analyzing vast datasets of ingredients, nutritional properties, and sensory attributes. For instance, MOO-ANN can optimize recipes to enhance protein content while reducing sugar and fat, ensuring products align with health-conscious trends without sacrificing taste [77].  
Quantitatively, studies using MOO-ANN to optimize functional foods have reported up to a 30% improvement in nutrient density while maintaining consumer acceptability ratings above 85%. For example, an application in optimizing baby food formulations resulted in a 20% reduction in added sugar and a 15% increase in essential micronutrients, directly contributing to Sustainable Development Goal (SDG) 2: Zero Hunger by addressing malnutrition [78].
## 2 Theoretical Background
### Analysis of a Circular-Rectangular Waveguide
The cut-off wavenumbers \(k_{c}\) thus obtained are reference values for comparison when the infinitesimally thin strip is replaced by an \"elliptic strip\" with ratio \(d/w=a^{\prime}/b^{\prime}\ll 1\) or by a strip of small but finite thickness with ratio \(d/w\ll 1\). The cut-off wavenumbers for problems (b) and (c) with strips of non-zero thickness \(d\) were obtained using the general solution (36)-(38), where we set \(M=1\) and use a smooth parameterization of the contours \(L_{0}\) and \(L_{1}\) of the outer and inner conductors. The infinitesimally thin strip results are discussed in the next section and those for the strips of non-zero thickness are in the following. The ratio \(b/a\) of the sides of the outer conductor is fixed at the value \(2\).
# Analysis of the Water Quality Improvement in Urban Stream Using MIKE 21 FM
###### Abstract  
Domestic urban streams face insufficient base flow and consequently become dry streams in drought season, and vulnerable to water quality deterioration and ecological impairment, due to contaminants introduced from the urban pollutants. Many efforts are being made to improve the natural flow by actively enforcing restoration projects of urban streams. Gulpocheon is a national stream flowing through Incheon-si and Gimpo-si. As of March 2019, the reclaimed wastewater or the ozone-processed Gulpoc treated sewage has formed the upper part of Gulpocheon. This study aimed to analyze the improvement in water quality of Gulpocheon before and after supplying the reclaimed water by collecting the water quality data of the target area. Before and after providing the base flow, the water quality was analyzed using the two-dimensional numerical analysis model, i.e., MIKE 21 FM. The water quality one year before and after supplying the reclaimed water was compared, with a focus on DO, BOD, TN, and TP; they are used as water quality standards for stream water. The concentration of DO at all spots of Gulpocheon increased on average. The concentration of BOD, TN, and TP water quality parameters decreased, indicating water quality improvement. In addition, accurate water quality assessment is possible using MIKE 21 FM model simulation for urban stream analysis.  
ozone treatment; reclaimed water; water quality analysis; numerical analysis Article Analysis of the Water Quality Improvement in Urban Using MIKE 21 FM  
[PERSON]  
Department of Civil & Environmental Engineering, Incheon National University, Incheon 22012, Korea; [EMAIL_ADDRESS]; Tel.: +82-32-835-8085
# Improved Electrochemical-Mechanical Parameter Estimation Technique for Lithium-Ion Battery Models
## 2 Materials and Methods
### Mechanical Deformation
In reality, during battery operation, the lithiated electrode has more lithium content near the separator, while the delithiated electrode has a higher lithium concentration near the current collector. In fact, the diffusion of lithium ions is less constrained in the separator area (Figure 2). Figure 2: Electrochemical–mechanical model framework: from the lithium concentration obtained from P2D model to particle deformation at different positions along the direction of electrode’s width (z). The superscript \(c\) and \(a\) indicate cathode and anode, while \(l\) and \(p\) indicate lattice and particle, respectively. \(n\) and \(m\) denote the number of discretization points. Equation (6) remains valid if \(e_{v}^{p}(t)\) represents the average deformation of the particles across the electrode thickness. To achieve this, the P2D model is used to extract the lithium concentration \(c_{s}(z,r,t)\) at different positions in this direction. The concentration is then averaged over the particle radius (\(\epsilon_{s}^{avg}(z,t)\)) to determine the crystal lattice strain of the material, and consequently the particle strain \(e_{v}^{p}(c_{s}^{avg}(z,t))\), as shown in Figure 2. Finally, the particle strain is averaged across the electrode thickness to obtain the overall mean particle deformation \(e_{v}^{p}(t)\). The volumetric strain propagates across the battery scales, resulting in a macroscopic thickness change that is evaluated by accounting for the deformation of both the electrodes and the presence of \(N\) layers [29; 30]. Due to the thinness of the electrode with respect to in-plane geometry, the volumetric strain of the electrode is assumed to be responsible for just the out-of-plane strain (\(\epsilon_{z}^{e}=\epsilon_{v}^{e}\)) [16], resulting in the overall battery thickness change modeled as presented in Equation (7). \[\Delta t_{bat}(t)=N(t_{p}\epsilon_{z,p}^{e}(t)+t_{n}\epsilon_{z,n}^{e}(t)) \tag{7}\]
###### Abstract  
In this paper, we present a novel compression method based on partial differential equations complemented by block sorting and symbol prediction. Block sorting is performed using the Burrows-Wheeler transform, while symbol prediction is performed using the context mixing method. With these transformations, the range coder is used as a lossless compression method. The objective and subjective quality evaluation of the reconstructed image illustrates the efficiency of this new compression method and is compared with the current standards, JPEG and JPEG 2000.  
block sorting; compression; objective and subjective quality assessment; partial differential equations; symbol prediction
## 4 Results and Discussion
### Accuracy Assessment
We also quantitatively evaluated the classification accuracy for these 283 glacial lakes in the area covered by the GF-2 PMS images, and Table 2 summarizes the statistical accuracy results. Generally, the higher omission errors relative to the commission errors demonstrated that the missing glacial lake pixels were the main source of classification errors for all three methods. The highest accuracy, with a kappa coefficient of 0.95 and overall accuracy of 96.54%, was achieved for the classification results obtained using the proposed method. The commission and omission errors for the water and land classes were also very low at no more than 4%. The lowest kappa coefficient and overall accuracy were for the map produced using the ISODATA method (kappa = 0.79, overall accuracy = 88.87%). Better results were achieved for the SVM classification method, which needed a lot of user expertise and proper training samples, and had a kappa coefficient and overall accuracy of 0.85 and 92.13%, respectively. These quantitative evaluation results show that the proposed median-based composite method was effective and accurate enough to extract glacial lakes from the SAR intensity data. Figure 5: Comparison of the glacial lake extent extracted from Sentinel-1A/1B data in 2018 using SVM, ISODATA, and the proposed method for three sampled glacial lakes at: (**a**) 94.25\({}^{\circ}\)E, 30.05\({}^{\circ}\)N; (**b**) 93.64\({}^{\circ}\)E, 30.24\({}^{\circ}\)N; and (**c**) 94.28\({}^{\circ}\)E, 29.91\({}^{\circ}\)N.