esa-sceva/satcom-chunk-collection huggingface dataset:
# A Dual-Beam Leaky-Wave Antenna Based on Squarely Modulated Reactance Surface
## 3 Dual-Beam Leaky-Wave Antenna Implementation
This means that this antenna can be considered as a single-beam antenna pointing at \(-34^{\circ}\), due to beam 2 actually being a side lobe (\(\theta_{2}=16^{\circ}\)). It can be concluded that the radiation power of each beam is related to the surface impedance coefficients. For the last case, the direction and gain of the radiation beams are obviously deviated from the theoretical values. These problems are not only caused by unequal surface impedance coefficients but also by the coupling between the two coexisted radiation patterns. In order to solve these problems, the surface impedance coefficients (\(a_{1}\) and \(a_{2}\)) and modulation periods (\(p_{1}\) and \(p_{2}\)) should be optimized from the initial values. The radiation efficiency from CST Microwave Studio simulation under these conditions is 84% (\(a_{1}=a_{2}=0.5\)), 71% (\(a_{1}=0.6\), \(a_{2}=0.4\)) and 48% (\(a_{1}=0.7\), \(a_{2}=0.3\)). This means that the radiation efficiency is related to the balance of radiation energy from each beam. The prototype of the dual-beam LWA based on squared modulation was fabricated, as shown in Figure 8a. The modulation pattern was designed with the parameters \(X_{s}=400\)\(\Omega\), \(M=0.4\), \(p_{1}=18\) mm (6 unit cells per period) and \(p_{2}=30\) mm (10 unit cells per period). Here \(a_{1}=a_{2}=0.5\) is applied to ensure the equal radiation energy of each beam. According to Equation (11), the modulation pattern  
Figure 7: The simulated radiation patterns with different modulation depth at 8.5 GHz. with varying \(b\) was designed to match the distribution of surface impedance, as shown in Figure 8b. The measurement of S-parameters was conducted by using an Agilent N5227A microwave vector network analyzer (VNA, Agilent Technologies Inc., Santa Clara, CA, USA), and the simulated and measured results are presented in Figure 8d. The reflection coefficient of the proposed antenna is almost below \(-10\) dB in the frequency range of \(8\) GHz to \(8.6\) GHz, which means a good impedance match is provided. Meanwhile, S\({}_{21}\) keeps below \(-10\) dB, indicating that little energy is transmitted to the output port. The far-field pattern was measured in the anechoic chamber with a linearly polarized horn antenna as the transmitting antenna, as shown in Figure 8c. The measured gain of the antenna at \(8.5\) GHz is shown in Figure 8e. The directions and beam widths of the measured main beams are in close agreement with the simulated patterns. The measured gains are \(10\) dBi and \(8.2\) dBi at \(\theta_{1}=-30^{\circ}\) and \(\theta_{2}=18^{\circ}\), respectively, and the radiation efficiency is about \(83\%\). This may be caused by the additional loss brought by manufacturing error.
# Recent Advances in Wearable Devices for Non-Invasive Sensing
## 2 Materials and Structural Design
### Structural Design
#### 2.2.5 Three-Dimensional (3D) Structure  
3D structure designs, such as 3D coiled structures and 3D sponge structures, have many advantages. In the case of a 3D coiled structure, deformation of the coil alleviates the maximum strain of the coil, enabling it to be more stretchable than the 2D serpentine structure [86]. [PERSON] et al. presented a yarn-derived, spring-like, long CNT rope that can produce tensile strains of 285% during external elongation by opening the loops, as shown in Figure 3j[80]. This coiled structure allows 20 times higher stretchability than the conventional pure CNT yarn structure, and it does so without strength and conductivity problems. In the case of the 3D sponge structure, the interconnected 3D porous scaffold structure maintains stretchability and conductivity through shape deformation. [PERSON] et al. introduced binary-network-structured polyurethane sponge-Ag nanowire-poly(dimethylsiloxane) (PUS-AgNW-PDMS) conductors, as shown in Figure 3k[81]. The key role of the PUS as a skeleton to support AgNW networks with a porous and junction-free structure. This structure accommodates the deformation without changes in conductivity under stretching conditions. The result shows high elongation (140%) and high electrical conductivity (19.2 S cm\({}^{-1}\)) under an external strain of 50%.
# Does Nanosilver Have a Pronounced Toxic Effect on Humans?
## 2 Results of Investigations Performed by Other Researchers
### General Toxicity of AgNPsPs for Cells, Tissues, and Organs
The bulk of studies demonstrated that AgNPs could cause defects in the neurological, cardiovascular, reproductive, and immune systems of the embryos and fetus; however, in some studies, no adverse effect on the development of AgNPs was found, even at high doses [27]. The development of oxidative stress, the occurrence of morphological defects, slowing heart rate, the appearance of pericardial edema, delayed hatching from eggs, and increased mortality were observed in studies on zebrafish embryos exposed to AgNPs [28; 29; 30]. Morphological changes in the ovary and testis were observed in rat offspring exposed to AgNPs [31]. Developmental exposure to AgNPs results in long-term gut dysbiosis, body fat increase, and neurobehavioral alterations in mouse offspring [32]. At the same time, it should be mentioned that silver ions (Ag\({}^{+}\)) provoke similar toxic effects at concentrations hundreds of times lower than those applied for AgNPs [29; 30]. It was suggested by some authors that the compounds used for NP coating significantly affected the level of AgNP toxicity. Thus, after contact with polyvinylpyrrolidone-coated AgNPs, zebrafish larva did not differ in morphology and behavior from control ones; however, when citrate was used as a coating agent, some disturbances were detected [33]. In a study by [PERSON] et al. [34], in which the level of AgNPs in solution was close to their environmental concentrations in water (0.03-3 ppm), no negative changes in the survival, hatching, or morphology of _Danio rerio_ were found. According to a number of studies, after a single oral administration, reproductive dysfunctions in males and females (inhibition of spermatogenesis, histopathological disorders in ovaries, etc.) were observed in rodents exposed to AgNPs. It is noteworthy that the smaller the dose and size of AgNPs were, the less pronounced were the effects [35]. Undoubtedly, the dose-response principle also works in nanotoxicology.
# Allocating Small Transporters to Large Jobs
## 2 Literature Review  
In this section, we outline how the transporter allocation problem studied in this paper ties in with the wider literature. As mentioned before, to the best of our knowledge, \((\mathrm{TAP})\) has not been studied in this form before. However, there are several optimization problems that bear a resemblance to \((\mathrm{TAP})\). Below, we focus on the two most closely related problems, namely divisible load scheduling, which can be seen as a version of \((\mathrm{TAP})\) where transporters may process the job in parallel rather than in sequence, and multiple knapsack problems, which relate to \((\mathrm{TAP})\) in the way that for each job, there is an upper limit of transporter sizes before adding more transporters no longer improves anything. We compare the problems to \((\mathrm{TAP})\) and explain which, if any, results can be transferred to our setting.  
We will not consider papers discussing capacitated field operations in general again, as in the beginning of Section 1, since other than providing the wider practical motivation, they have little relevance for \((\mathrm{TAP})\). Furthermore, note that even though both problems stem from logistic tasks, \((\mathrm{TAP})\) is not comparable to vehicle routing problems. In vehicle routing problems [14], each transporter is usually assigned several delivery jobs, while for \((\mathrm{TAP})\), each job is assigned several transporters. Due to this fundamental difference, even though the problems may look related in practice, they are not close from a mathematical perspective.
## 3 Results and Discussion
### In Silico Study
#### 3.3.3 Molecular Docking  
Molecular docking was performed to propose a probable mechanism of action of active compounds. The use of this method allows for the determination of possible interactions with selected cellular proteins without the need for expensive and time-consuming experimental methods. The obtained results may allow for further optimization of the structure of active compounds in order to improve biological activity [54].  
The time-of-addition experiment showed the activity of compound **3** in the third mode, where the final stages of assembly and maturation of new virions take place. One of the key replication enzymes of adenoviruses is adenine cysteine protease. It plays a critical role in the formation of important core and capsid proteins, which then form the mature, infectious virus particles [77].  
The target crystal structure of adenain was obtained from Protein Data Bank (PDB ID: 4 PIE) [50]. Betulin **1** and active compound **3** were ranked by their binding energy with protein (Table 10). The lower binding energy, represented as \(\Delta\)G, corresponds to a stronger binding affinity. Compound **3** exhibited a lower \(\Delta\)G value compared with reference betulin (-4.8 and -3.4 kcal/mol, respectively).  
Visual inspection of the docking modes reveals that botulin **1**, due its relative smaller hydrophilicity, binds deeper inside the protein pocket than compound **3** (Figure 7).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Compound** & \(\Delta\)**G [kcal/mol]** \\ \hline
**1** & \(-\)3.4 \\ \hline
**3** & \(-\)4.8 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Binding affinities of betulin **1** and derivative **3** in complexes with adenain.  
Figure 6: The MEP for compound **3**.  
To acquire a more in-depth insight into the binding model, an analysis of protein-ligands bonding has been performed (Figure 8). Compound **3** forms two hydrogen bonds between the oxygen atom at position C-28 of the betulin core and Gln82 and Val83 residues of protein. On the other hand, the betulin molecule lacks a hydrogen bond. Additionally, both ligands form numerous hydrophobic bonds with various residues. The introduction of a diethylphosphonate group to the betulin molecule changes the structure and influences the way compound **3** interacts with the receptor, increasing the stability of the formed complex.  
In general, in silico analysis confirms in vitro data. The most active compound **3** exhibited stronger binding affinity both in terms of \(\Delta\)G and hydrogen bonding. Further exploration of the molecular mechanism of action could lead to more potent anti-adenovirus compounds being obtained from phosphonate derivatives of betulin.
# Evaluation of Multi-Sensor Fusion Methods for Ultrasonic Indoor Positioning
## 3 Proposed Fusion Methods
### Loosely Coupled Approach
#### 3.1.1 Linear Kalman Filter Approach  
The linear Kalman filter (LKF) is used to merge the estimated positions obtained from the three ULPSs, after applying the GN algorithm. This filter is based on a loop of two steps: prediction and updating. The GN algorithm aims to converge into the correct estimated position after a number of iterations [31]. The state model of the filter is Equation (3):  
\[\mathbf{X}_{\text{k}}=\mathbf{A}\cdot\mathbf{X}_{\text{k}-1}+\mathbf{w}_{ \text{k}} \tag{3}\]  
\[\mathbf{Z}_{\text{k}}=\mathbf{H}\cdot\mathbf{X}_{\text{k}}+\mathbf{v}_{\text{ k}} \tag{4}\]  
\[\mathbf{P}_{\text{k}}^{-}=\mathbf{A}\cdot\mathbf{P}_{\text{k}-1}\cdot\mathbf{ A}^{\top}+\mathbf{Q} \tag{5}\]  
where \(\mathbf{K}_{\text{k}}\) is the Kalman filter gain; \(\mathbf{Q}\) and \(\mathbf{R}\) the process noise matrix and the measurement noise matrix, respectively; \(\mathbf{A}\) and \(\mathbf{H}\) are constant transition matrices; and \(\mathbf{P}\) is a dynamic matrix. To apply the LKF to fuse the independent estimated positions and obtain the final position, the process and measurement covariance matrices must be fixed experimentally.  
Figure 6: Block diagram of a loosely coupled fusion of the three positions estimated by the receiver \(R_{A}\).  
In our case, the measurement matrix is the covariance of the estimated position related to the specific ULPS. So, the measurement matrix is particular for each position. The process noise \(\mathbf{Q}\) is fixed experimentally as \(\frac{\mathbf{R}}{10}\) and computed according to Equation (7), so it is also particular for each position. For the hyperbolic case, it is assumed that the noise is Gaussian for distances and, applied to the distance differences, the noise is correlated, and \(\mathbf{R}\) and \(\mathbf{Q}\) are defined as Equations (6) and (7):  
\[\mathbf{R}=\left(\begin{array}{ccc}\sigma_{x,i}^{2}&0.5\;\sigma_{y,i}^{2}&0. 5\;\sigma_{x,i}^{2}\\ 0.5\;\sigma_{x,i}^{2}&\sigma_{y,i}^{2}&0.5\;\sigma_{x,i}^{2}\\ 0.5\;\sigma_{x,i}^{2}&0.5\;\sigma_{y,i}^{2}&\sigma_{z,i}^{2}\end{array}\right) \tag{6}\]  
\[\mathbf{Q}=\left(\begin{array}{ccc}\frac{\sigma_{x,i}^{2}}{10}&0.05\;\sigma_ {y,i}^{2}&0.05\;\sigma_{z,i}^{2}\\ 0.05\;\sigma_{x,i}^{2}&\frac{\sigma_{x,i}^{2}}{10}&0.05\;\sigma_{z,i}^{2}\\ 0.05\;\sigma_{x,i}^{2}&0.05\;\sigma_{y,i}^{2}&\frac{\sigma_{z,i}^{2}}{10} \end{array}\right) \tag{7}\]  
where \(i\) is the index of the corresponding ULPS, and \((\sigma_{x,i}^{2},\;\sigma_{y,i}^{2},\;\sigma_{z,i}^{2})\) are the variances of each position for the three axes \(x,y,\) and \(z\), respectively.
# Developments, Applications, and Innovations in Agricultural Sciences and Biotechnologies
## 4 Plastic Mulching and Fertilization for Dryland Crops
### Optimizing Resource Use in Semiarid Regions  
Plastic mulching combined with optimized fertilization offers a powerful tool for improving agricultural productivity in semiarid regions [15]. The study on tartary buckwheat cultivation demonstrates how these practices enhance soil water retention, extend the crop growth period, and boost yields. The study reveals that plastic mulching, combined with low fertilization rates, offers several advantages:  
Enhanced soil hydration: Plastic mulching reduced soil evaporation by 22% and increased soil water storage by 19%, allowing crops to thrive even in low-rainfall conditions. It improves water availability for crops by promoting soil water absorption from deep root zones.  
Improved nutrient use efficiency: The combination of mulching and fertilization optimized nutrient uptake, resulting in higher grain yields and better grain quality.  
Environmental sustainability: While plastic mulching offers agronomic benefits, the study emphasizes the importance of managing plastic residues to minimize environmental risks, emphasizing the need for biodegradable mulching materials.
# BrushGaussian: Brushstroke-Based Stylization for 3D Gaussian Splatting
###### Abstract  
We present a method for enhancing 3D Gaussian Splatting primitives with brushstroke-aware stylization. Previous approaches to 3D style transfer are typically limited to color or texture modifications, lacking an understanding of artistic shape deformation. In contrast, we focus on individual 3D Gaussian primitives, exploring their potential to enable style transfer that incorporates both color- and brushstroke-inspired local geometric stylization. Specifically, we introduce additional texture features for each Gaussian primitive and apply a texture mapping technique to achieve brushstroke-like geometric effects in a rendered scene. Furthermore, we propose an unsupervised clustering algorithm to efficiently prune redundant Gaussians, ensuring that our method seamlessly integrates with existing 3D Gaussian Splatting pipelines. Extensive evaluations demonstrate that our approach outperforms existing baselines by producing brushstroke-aware artistic renderings with richer geometric expressiveness and enhanced visual appeal.  
Keywords:three-dimensional Gaussian splatting; novel view synthesis; style transfer; stroke-based rendering; texture mapping +
Footnote †: journal: Applied sciences
# AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
## 5 Challenges and Future Directions
### Challenges
#### 5.1.6 Network Quality Susceptibility  
In real-time missions, UAVs depend heavily on data transmission. However, poor network conditions can disrupt stable system performance [221]. For example, in areas with severe signal blockage or interference, UAVs may experience communication disruption, resulting in critical data not being uploaded or received on time. Moreover, LLMs with networked search functions can falter when network delays or congestion block instant access to external data, slowing the decision-making process [222]. In addition, network instability is prone to data loss or misdelivery, making it difficult to maintain a consistent response to UAV group coordination and, thus, affecting the overall system performance.
# Machine Reading Comprehension Model Based on Fusion of Mixed Attention
## 6 In-Depth Discussion and Comparative Analysis of Experimental Results
### Model Adaptation and Challenges  
The machine reading comprehension model based on mixed attention fusion has shown significant performance improvements in understanding complex texts and capturing contextual associations, but its research and application still have multiple limitations. On the one hand, model training relies on a large amount of high-quality annotated data, and the acquisition and annotation of such data are often time-consuming and labor-intensive, and it is difficult to ensure the coverage of all domains and contexts, which limits the generalization ability of the model. Although the hybrid attention mechanism can enhance the model's capture of key information, it is challenging to achieve the efficiency and accuracy of attention allocation when dealing with long texts or cross document understanding, which may lead to information omission or misunderstanding. On the other hand, the current model has shortcomings in terms of interpretability, and its decision-making process is difficult to intuitively understand, which reduces the credibility and practicality of the model. The accuracy of reading comprehension in cross-linguistic and cross-cultural contexts still needs to be improved, and the performance of the model in processing non-native texts or culturally specific content is still unstable, limiting its application breadth in multilingual environments.
## 2 Materials and Equipment
### Experimental Equipment
#### 2.2.2 Noise Measuring Instrument--Online Digital Noise Meter  
The instrument used to measure the noise level in this experiment was an industrial-grade AR844 handheld online digital noise meter. This noise meter can be used for noise engineering, quality monitoring, health control and various environmental volume measurements. The products complied with: a. International Electrotechnical Commission standards: IEC PUB 651 TYPE2 b. American National Standards: ANSI S1.4 TYPE2. The technical parameters of the noise meter are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Particle Size** & **\textless{}0.075** & **0.075–0.15** & **0.15–0.3** & **0.3–0.6** & **0.6–1.18** & **1.18–2.36** \\ \hline Proportion & 23 & 21 & 24 & 21 & 7 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Blockage grading table.  
Figure 2: (**a**) The structure diagram of the dynamic water scour instrument. (**b**) The dynamic water scour instrument.  
The measuring speed of the noise meter has two gears for selection: fast and slow. The noise meter can lock the maximum value during the measurement process, and directly record 4700 noise data, and can also be connected to a computer to realize the functions of downloading data, collecting data in real time, and analyzing data.
## 4 Analysis and Interpretation of Results
### Dependence of the Occurrence of Negative Prices on RES Generation  
The analysis was based on the calculation of the proportion of hours, with negative electricity prices (%) depending on the sum of the output of wind and solar resources, which were divided into intervals of 100 MW. The hourly values were aggregated in some cases from 15 min data. The output is the relative proportions of negative prices in each power interval. The objective of this section was to analyse the relationship between hourly aggregated generation from renewable energy sources (RES)--specifically wind and solar--and the occurrence of negative electricity prices in the daily market. The generation was divided into power intervals and, for each interval, the proportion of hours in which a negative price occurred was calculated. The analysis was carried out for six selected European countries--Germany, France, Poland, Slovakia, the Czech Republic, and Estonia.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & & \multicolumn{4}{c}{**Average Volatility [EUR/MWh]**} \\ \hline
**Year** & **Germany** & **France** & **Poland** & **Slovakia** & **Czech Republic** & **Estonia** \\ \hline
2015 & 8.32 & 8.13 & 17.37 & 9.32 & 8.49 & 10.64 \\
2016 & 6.96 & 9.38 & 16.06 & 8.01 & 7.76 & 7.78 \\
2017 & 9.08 & 9.05 & 8.82
# Improving Streaming Video with Deep Learning-Based Network Throughput Prediction
## 4 Prediction of Network Throughput
### Long Short-Term Memory Networks
#### 4.2.1 Stacked Unidirectional LSTM  
An ANN can consist of multiple hidden layers, which include LSTM cells. In such a case, the output of one hidden layer is fed as the input into the subsequent hidden layer. Such deep ANN architecture allows progressively building up a higher level of representations of sequence data and mapping more complex, non-linear dependencies, which a single-layered, shallow network does not discover.  
A special case is stacked architecture, where a hidden layer is multiplied and stacked on top of each other, creating a grid of cells, as shown in Figure 4. Cells composing the grid share their connections' weights and biases. The outputs \(\{h_{t}\}\) are forwarded as inputs to the next immediate layer above. The top layer produces \(\hat{y_{t}}\), which corresponds to the expected unprocessed forecasts for the input time series. Each step of a training process calculates training error \(e_{t}=f(y_{t}-\hat{y_{t}})\) and accumulates it until the end of the time series \(E=\sum_{t=1}^{T}e_{t}\), where \(y_{t}\) is the actual value of the time series.