esa-sceva/satcom-chunk-collection huggingface dataset:
# ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru
## 3 Results
### Flooding
#### 3.1.3 Riverbank Collapses and Damage  
Cross-event SAR pairs also allowed us to capture seven radar backscatter changes (i.e., decrease) due to collapses of some sections of the riverbank that, in some cases, have also caused damage to critical infrastructure (e.g., bridges). They are located on alluvial deposits, near urban areas and with an A-class slope (\(\beta\leq 5^{\circ}\)). Figure 10 shows the example of an area, in the district of Lurigancho-Chosica at the intersection of Malecon Checa Eguiguren and Gran Chimu avenues, that has lost material due to the increase in the Rimac River water flow and the collapse of part of the riverbank. The post-event Google Earth image shows the detachment niche of the riverbank collapse and the new scarp cutting one of the buildings (Figure 10c-e) and making the stability of the neighboring houses quite precarious.  
Another example was found in the Talavera Bridge, in San Juan de Lurigancho. As also documented in a video published online [88], the part of the bridge collapsed down into the river water during the flood event occurred on 16/03/2017 (Figures 11 and 12). This bridge had been built only six and a half years before.  
Figure 9: Example of F2 “Riverbed flooded by increased water flow” changes occurred in relation to the 16/03/2017 flood event: (**a**) Sentinel-1 RGB composite and (**b**) ratio map highlight a clear change pattern of radar backscatter decrease (light green polygon) caused by the increase in the flow of the Rimac River. Google Earth images (**c**) 2020 Maxar Technologies) show the condition of the riverbed in (**c**) the dry (16/12/2016) and (**d**) rainy (17/03/2017) season. Contains modified Copernicus Sentinel-1 data.  
Figure 11: Google Earth imagery (c) 2020 Maxar Technologies) acquired on (**a**) 28/01/2017 and (**b**) 21/03/2017 showing the Talavera Bridge prior and after the collapse due to the 16/03/2017 flood event, respectively. In (**b**) the increase of the water flow of the Rimac River is apparent.  
Figure 10: Example of F3 “Riverbank collapses and damages” changes in relation to the 31/01/2017 flood event: (**a**) Sentinel-1 RGB composite and (**b**) ratio map highlight a pattern of decreased radar backscatter due to the increase in the water flow and level of the Rimac River (blue polygon) and local collapse of the riverbank (pink polygon). Google Earth imagery (c) 2020 Maxar Technologies) acquired (**c**) before (28/01/2017) and (**d**) after (18/03/2017) the event, with (**e**) matching profile views evidencing the detachment niche due to the riverbank collapse. Contains modified Copernicus Sentinel-1 data.
# Recent Progress of Metal-Air Batteries--A Mini Review
## 4 Electrolytes
### Non-Aqueous Electrolyte  
**Solid-state electrolyte**: Solid-state electrolytes are different from aqueous electrolytes in dual characteristics of wettability and ion conduction. For MABs, aqueous electrolytes with an excellent wetting property at three-phase boundaries could be in full contact with the cathode. For a solid-based electrolyte, the three-phase interface reaction can be restricted by the poor wetting property of the \"immobilized\" electrolyte, thereby, interfacial transporting resistance of OH\({}^{-}\) may be remarkably higher than that of an aqueous system [65]. Alkaline gel electrolytes (AGEs), consisting of low molecular weight polymer and alkaline solutions have been developed to mitigate these issues for primary lithium-air batteries [66].  
**Ionic liquid electrolyte**: Ionic liquids are non-aqueous liquid electrolytes, including two types of cations: large organic cations with organic/inorganic anions and alkali metal ions in an organic solvent, such as organic carbonates, ethers, and esters [67; 68]. Lithium salt, such as LiPF6, LiAsF6, Li(SO\({}_{2}\)CF3)2, and LiSO\({}_{3}\)CF3 are commonly used in LABs [69]. PYR14 TFSI-TEGDME-LiCF3 SO3 are also employed in LABs [70], consisting of LiCF3 SO3 in tetraethylene glycol dimethyl ether (TEGDME) and pure PYR14 TFSI. Ionic liquid electrolytes also face challenges because of the formation of carbonates, which consume the electrolyte and block the electrode pores. What is more, the understanding of the oxygen reaction in an ionic liquid is very limited. These hinder the practical application of ionic liquid electrolytes.  
In summary, neutral salt solution, acidic solutions, and hybrid electrolyte are rarely used in industrial application. Alkaline solutions electrolytes and ionic liquid electrolyte are usually employed. Aqueous electrolytes do not match LABs. Furthermore, solid-state electrolytes can work in all MABs.
## 4 Beyond the SIR Model
### Impact of Antiviral Drug Treatment on the Prevalence of COVID-19  
Another modification was made to the original SIR model, mentioned earlier in Equation (2), in order to explore how treatment would impact the spread of COVID-19 [31; 32], as well as to estimate the final size of the pandemic after the onset of antiviral drug treatment. The SIR model was adjusted by dividing the infected individuals into treated (\(I_{tr}\)) and untreated (\(I_{u}\)) cases, as per Equation (13). \(f_{0}\) represents the fraction of infected individuals to receive the antiviral drug treatment [31], with the assumption that the level of infection can be reduced by treatment factor \(\sigma\). Subsequently, this would lead to an increase in the number of recovered individuals after the onset of the treatment and, as a consequence, a decrease in the number of cases of infection.  
\[\begin{array}{l}\frac{dS}{dt}=-\frac{\delta S}{N}\left(I_{u}+\sigma I_{tr} \right),\\ \frac{dI_{u}}{dt}=(1-f_{0})\int_{N}^{\delta S}\left(I_{u}+\sigma I_{tr}\right) -\gamma_{u}I_{u},\\ \frac{dI_{tr}}{dt}=f_{0}\frac{\delta S}{N}\left(I_{u}+\sigma I_{tr}\right)- \gamma_{tr}I_{tr},\\ \frac{dI_{tr}}{dt}=\gamma_{u}I_{u}+\gamma_{tr}I_{tr}\end{array} \tag{13}\]  
The parameters \(\gamma_{u}\) and \(\gamma_{tr}\) are the average recovery rates for the untreated and treated individuals, respectively. The start date of the antiviral drug treatment was set to 8 July 2020 in order to examine the impact of treatment on the current number of cases of infection. Multiple treatment impacts are illustrated in Figure 7, showing different fractions of infected individuals assumed to receive the treatment. As there is still no antiviral drug that has been proven to be fully effective against COVID-19, the impact of treatment was investigated by assuming that such a drug would reduce the level of infection by 50% (\(\sigma=0.5\)) [31]. Meanwhile, \(f_{0}\) was varied between 0 and 1 with a step size of 0.1 in order to simulate different projections of the virus after the onset of antiviral drug treatment. The simulations showed that there were various significant reductions in the number of new daily cases of infection due to the treatment in July 2020, as shown in the 11 curves of different fractions of treatment in Figure 7.  
Figure 6: The impact of social distancing on the prevalence of COVID-19 in Saudi Arabia, as predicted by the modified version of the SIR model. (**A**) The small effect of the short-term model and (**B**) the higher impact of the long-term model on the number of cumulative cases, especially when using the nonlinear response term (\(\xi>1\)).
# An Enhanced Time Series Analysis to Improve the Performance of 5G Communication Systems +
## 3 Results and Discussion
### Throughput Management  
Through time series analysis, it is possible to identify the throughput of a network at any period and consequently make informed decisions on how to improve the capacity and performance of a system.  
Figure 5 exposes the comparison of throughput administration. In a computation cycle, the proposed TSA reached 99.27% throughput management. The existing FCS achieved 70.33%, MDM reached 49.20%, and NMAA reached 86.79%, and SBRA obtained 66.65% throughput management. Sudden traffic spikes or network anomalies can cause the accuracy of predictions derived from time series analysis for 5G system performance to suffer. This can happen when traffic data points are misinterpreted or when anomalous  
Figure 4: Interference administration.  
Figure 3: Bandwidth administration.  
data points skew the pattern recognition algorithms that rely on identifying trends in the data over time. This could produce inaccurate predictions due to the interruption of the predicted trend.  
Inaccurate predictions could mean the system is not optimizing system performance, causing system latency and leading to lower customer satisfaction. Some of the limitations are shown in the following:  
* Limited data availability: Time series analysis requires data points from multiple sources for a comprehensive view. In complex 5G networks, it can be difficult to collect and store the required data points due to the increased number of endpoints;
* Data quality: Data points collected from 5G networks can be noisy and unreliable, making it difficult to apply time series analysis to generate meaningful insights;
* Scalability: Applying time series analysis on large datasets is a computationally expensive operation. Computing resources for 5G networks are limited, making it difficult to scale and deploy such operations;
* Interpretability: Interpreting the results of time series analysis can be tricky, as it requires domain knowledge and expertise to understand the underlying patterns;
* Security: Time series analysis requires access to vast amounts of data, making it vulnerable to cyber attacks and data theft.
## 2 Materials and Methods
### Environmental Acoustic Patterns
Behavior Category 1 groups the locations related to main routes of road traffic, in particular, major thoroughfares and intersections with very high road traffic intensity. Noise levels are relatively consistent during the day, with peak noise levels occurring during rush hour when traffic is heaviest. At night, noise levels decrease somewhat due to reduced traffic volume, though they are still significantly higher than in quiet residential areas. The sound pressure level is high and fluctuation is low, as shown in Table 1.  
Behavior Category 2 groups those locations related to the regular areas in a city, which typically include medium-density residential, commercial, and office building areas along with public spaces such as parks, squares, and sidewalks. The noise pollution is moderate to high, with a wide range of noise sources. During the day the noise level is relatively consistent, with peak noise levels occurring during peak hours of activity such as rush hour and lunchtime. At night, noise levels decrease somewhat due to reduced activity, though they remain higher than in quiet residential areas. In this category, the sound pressure level is moderate to high and the fluctuation is moderate, as shown in Table 1.  
Behavior Category 3 groups the locations related to shopping, entertainment, and nightlife activity. The noise pollution is high throughout the day, evening, and night due to the high level of human activity and mix of commercial and entertainment venues. During the day, noise levels are high and relatively consistent, with peak noise levels occurring during peak hours of shopping and entertainment activity. In the evening, noise levels continue to be high, and are more fluctuating, with an increase in human activity as people go out for entertainment and nightlife. At night, noise levels are high and fluctuating, with an increase in human activity in nightlife venues such as bars, clubs, and restaurants. In this category, both the sound pressure level and fluctuation are high, as is shown in Table 1.  
Finally, Behavior category 4 is related to quiet residential areas in a city, where the noise pollution is low during the day, evening, and night periods. These areas are characterized by lower levels of human activity and fewer noise sources, providing a relatively peaceful and quiet environment for residents. Noise levels are low during the day, with occasional spikes from passing vehicles and aircraft or distant construction and maintenance work. In the evening noise levels decrease even further due to lower traffic and other human activities. Noise levels at night decrease significantly, as expected. However, occasional high noise level events, e.g., from passing vehicles or aircraft, can explain the elevated fluctuation of the sound pressure level shown in Table 1.  
In the current research, short-period measurement data are used to estimate the corresponding behavior recognized using long-term data, i.e., the environmental acoustic pattern category is the output variable of the proposed supervised learning algorithm.
## 1 Introduction
In this paper, the effects of horizontal transverse vibrations on the thermal behavior and the ampacity of ten different rectangular bus bars installed horizontally are modeled and analyzed analytically under the same steady-state service (operating and environmental) conditions, and the simulated results are compared with each other. The four bus bars of smaller cross-sections are assumed to be made of aluminum alloy 5052-O and have the same cross-sections as those of [21], so that the measurement results and empirical correlations from [21] can be used as an experimental background. The remaining six bus bars are assumed to be made of aluminum alloy 6101-T61 and identical to those of [22], so that the relevant data from [22] can be used for comparisons. The cases where the bus bars are installed horizontally in an indoor environment with a vertical and horizontal major axis are considered. The proposed analytical thermal model is implemented in the BUSBAR.m program, whose first version can only solve problems with pure free or forced convection without the effects of vibrations [22]. Whilst all FEM-based verifications are performed in COMSOL 4.3 [23]. Compared to the analytical model from [22], the most significant contributions made in the presented approach to the thermal modeling of rectangular bus bars are as follows. First, the effects of horizontal transverse vibrations on free convection from the top and bottom surfaces of current-carrying rectangular bus bars are modeled with existing empirical correlations for forced convection. This thermal modeling is carried out assuming that the top and bottom surfaces of the bus bars, regardless of their characteristic lengths, are non-adiabatic, which is contrary to an adiabatic assumption used in [21]. Second, the effects of different vibration amplitudes and different vibration frequencies on the bus bar ampacity are represented and quantified by means of the proposed analytical thermal model. The vibration amplitudes and vibration frequencies are chosen to correspond to the standard vibration classes A, B, and C, as well as frequencies that can cause the resonance phenomenon. Third, the standard vibration classes are introduced for the first time into the analytical and FEM-based thermal modeling of the rectangular bus bars at normal service conditions. Specifically, the effects of the vibration amplitudes and vibration frequencies corresponding to the standard vibration classes on the bus bar ampacity are modeled and quantified. Additional contributions of this paper are the results obtained for some vibration amplitudes that differ from the standard ones, as well as for vibration frequencies equal to some critical values and twice the frequencies of the second and third-order harmonic currents.
# Overlap Avoidance of Mobility Models for Multi-UAVs Reconnaissance
## 1 Introduction  
Exponential growth of recent technologies of computing, communications, and sensors has made it possible to perform various applications using multiple unmanned aerial vehicles (UAVs). Since a UAV is handled without human beings, it can perform various applications especially in inaccessible or perilous environments. Recently, multiple UAVs have been studied in civilian applications such as road patrol [1], load transportation [2], monitoring forest fire [3], etc. The UAV has been also used for military purposes such as patrol, surveillance, etc. [4; 5; 6; 7; 8; 9; 10; 11].  
One of the basic applications for many multi-UAVs applications is a reconnaissance mission. The reconnaissance scans a specific area through a camera mounted on a UAV. As multiple UAVs are available, it has been possible to reconnoiter the scanning area efficiently [12; 13; 14; 15]. Thus, many recent studies have been conducted on efficient multi-UAVs reconnaissance.  
The reconnaissance is classified in two ways: Path planning method and random mobility method. In the path planning method, UAVs are designed with pre-defined paths to be scanned by a mission planner before performing a mission. If the mission planner operates multiple UAVs simultaneously, areas should be allocated to each UAV. On the other hand, the random mobility method reconnoiters the area autonomously without any prior knowledge. It is not necessary to allocate an area to each UAV. In addition, it is also possible to perform a continuous mission even if some UAVs cannot be operated.  
Much recent work [12; 14; 15; 16] has provided random mobility models for multi-UAV reconnaissance as the random mobility model provides randomness and robustness. Since the random mobility model makes it difficult to predict each UAV's path, it is more useful for reconnaissance compared to thepath-planning method. In addition, it is also tolerable for the malfunctioning of UAVs. However, one weakness comes from a lower coverage rate since several UAVs occasionally reconnoiter a similar area at the same time. Thus, in this paper, we first define the overlapped reconnaissance problem in a multi-UAV reconnaissance application. Then, we provide appropriate overlap-avoidance schemes for well-known random mobility models for multi-UAVs reconnaissance.  
The remainder of the paper is organized as follows. Section 2 deals with work related to mobility models and research motivation. In Section 3, we explain four existing random mobility models and describe how to extend each model for overlap avoidance. Section 4 outlines the simulation environment and analyzes the simulation results. Finally, Section 5 provides the conclusions and future work.
## 3 FE Analysis
### FE Model with Solid Elements  
The pultruded GFRP deck tubes and adhesive joints between them are modeled using Element C3D8R (linear bricks of eight nodes with reduced integration) in ABAQUS [18]. As shown in Figure 5a,b, the FE models have four parts: (1) a deck tube discretized with high-density (HD) solid elements (red), (2) a deck tube discretized with low-density (LD) solid elements (blue), (3) an adhesive joint between the deck tubes (black lines between deck tubes), and (4) loading pads for the wheel loads (gray). High-density solid elements are used at the adhesive joints and loading pads to simulate local behaviors. Figure 6a shows the top (blue), bottom (red), and web plates (gray), the adhesive joints (blue line), and the loading pad (gray at top) in section.  
Figure 4: Dimensions and boundary conditions of the deck.  
In our previous weak-axis bending tests [11], failure was observed in the adhesive material but none at the bonding surface between the deck tubes and adhesive. Thus, the bonding between the deck tubes and adhesive is simulated by tie elements (Figure 6b) in ABAQUS, assuming complete bonding. However, to consider failures at the bonding surface between deck tubes and adhesive, nonlinear tie elements or contact elements should be used based on the corresponding test results.  
Figure 7 shows a partial section view of the discretized FE model. The HD part (right-hand side) is discretized with a larger number of elements than the LD part (left-hand side) to simulate local behavior near the loading area. Moreover, the bottom and top plates at the joints are discretized with a large number of elements to simulate local behavior, and the adhesive at the joint is discretized using four element layers throughout the thickness to properly simulate its local behavior. The entire FE model is discretized into 924,802 solid elements.  
Figure 5: Parts of FE model with different solid-element densities. (**a**) Load configuration \(1\) (LC1); (**b**) load configuration \(2\) (LC2).  
Figure 6: Detailed section views of the FE model. (**a**) Top (blue), bottom (red), and web plates (gray) and joint adhesive (blue lines) of the deck tube; (**b**) tie elements (red) between deck tubes and adhesives.
## 3 Methodology
### Dimensionality Reduction Algorithm  
1. PCA Principal Component Analysis (PCA) is an unsupervised multivariate statistical technique that reduces data dimensionality while preserving the main variation patterns. Ittransforms the original high-dimensional data into a new coordinate system composed of orthogonal principal components, which are ranked according to the amount of variance they capture [49]. This process simplifies the dataset, reduces redundancy, and helps extract essential features for tasks such as lithology recognition.  
In lithology analysis, PCA can integrate multiple conventional logging parameters into a few principal components that retain most of the geological information. For example, the first two components can often preserve more than 90% of the original variance, providing an efficient and interpretable representation of formation characteristics (Figure 3).  
2. t-SNE  
t-Distributed Stochastic Neighbor Embedding (t-SNE) is a nonlinear dimensionality reduction technique commonly used for visualizing high-dimensional data in two or three dimensions [50]. Unlike PCA, which preserves global variance, t-SNE focuses on maintaining the local neighborhood structure of data points. It is particularly effective at revealing clusters, hidden patterns, or manifold structures that are difficult to detect in high-dimensional space.  
The core idea is to convert the similarities between data points into probability distributions in both high- and low-dimensional spaces and then minimize the difference between these distributions using Kullback-Leibler (KL) divergence. This process results in a low-dimensional embedding where similar points remain close together (Figure 4).  
The optimization objective of t-SNE is defined by minimizing the KL divergence between the high-dimensional and low-dimensional probability distributions, as follows:  
\[C=KL(P||Q)=\sum_{i}\sum_{j}p_{ij}log\frac{p_{ij}}{q_{ij}} \tag{3}\]  
where \(p_{ij}\) and \(q_{ij}\) represent the similarity of point pairs in the high- and low-dimensional spaces, respectively. In lithology recognition, t-SNE serves as a powerful visualization tool to assess data separability among lithofacies, evaluate clustering effectiveness, and guide the design of classification models.