esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### Characterization of the Surface Water System Network  
Based on Figure 6, the river network within the inner loop of the study area originates from the northeast, extends along the southwest, converges to the southwest outlet, and discharges downstream, creating a relatively closed hydrological system. The morphology of the river network takes on a tree branch-like appearance, exhibiting a lattice shape in the central part of the area. The northern part of the area has a denser river network, while sparser rivers characterize the southern part. Overall, the morphology of the river network in the study area is influenced by the topography and terrain features.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Title** & \begin{tabular}{c} **Area** \\ **(km2)** \\ \end{tabular} & **Perimeter (km)** & \
\begin{tabular}{c} **East-West** \\ **Width (km)** \\ \end{tabular} &
\begin{tabular}{c} **North-South** \\ **Length (km)** \\ \end{tabular} \\ \hline Inner Ring Line (R1) & 13.997 & 15.044 & 4.152 & 4.846 \\ Outer Ring Line (R2) & 34.750 & 27.919 & 7.459 & 7.863 \\ R1/R2 & 0.403 & 0.539 & 0.557 & 0.616 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical table of characteristic parameters of ridgelines and valley lines.  
Figure 5: Major valley lines and ridgelines in the study area.  
The network can be roughly divided into three parts: the first part is a \"bow\"-shaped fourth-order branch channel in the north, which has a dense network, with more and longer tributaries along the right side of the downstream direction; the second part is a \"Y\"-shaped fourth-order branch channel in the south-central part of the river; and the third part is a dendritic tertiary branch channel in the south. The tributary systems of the three components meet in the mainstem (Class V) and flow out of a single outlet in a southwesterly direction.  
According to the statistics in Table 4, the total length of the water system is 46.519 km, with 281 rivers in total. The water system is graded into five levels, which are positively proportional to the number of rivers. Among them, there are 144 first-class water systems with a total length of 21.400 km, which account for more than half of the river network, indicating that the first-class water systems play a dominant role in geography and geomorphology. There are 64 second-class water systems with a total length of 12.574 km, or 4/9 of the first-class water systems, which means that about every two first-class water systems converge into a second-class water system. There are 41 third-class water systems with a total length of 7.982 km, 23 water systems with a total length of 3.307 km, and 9 water systems with a total length of 1.257 km.
## 5 Applications and Challenges
### Applications
#### 5.1.2 Microscopic Imaging  
Microscopic imaging systems play an irreplaceable role in life sciences, materials research, medical diagnosis, and industrial inspection by their high-precision observation and analysis capabilities. By visualising the details of the microscopic world, microscopic imaging systems not only promote breakthroughs in basic research but also become a core support tool for technological progress and industrial upgrading. Traditional microscopic imaging systems rely on two-stage magnification of the objective lens and eyepiece and usually need to replace the objective lens with a different magnification to achieve zoom. The dielectric elastomer-driven tuneable lens can continuously and smoothly adjust the focal length of the lens by controlling the voltage to constantly zoom in on tiny samples. [PERSON] et al. [58] developed a droplet lens with a zoom ratio of 139.1% and good imaging of biological sample slides. [PERSON] et al. [83] developed a dielectric elastomer-driven Alvarez lens, as shown in Figure 13c, with a zoom magnification of up to 10x and a resolution of up to 1141 p/mm during the zoom process. They also conducted imaging tests on pineal and bio-intestinal tissue slices at different magnifications, achieving good imaging quality. In the future, the dielectric elastomer-driven microimaging system is expected to be applied in the fields of in vivo cell detection, material structure analysis, and non-destructive testing. To this end, DETL needs to improve its performance in terms of resolution enhancement, aberration correction, and precise control of focal length.
## 2 Characteristic Signal Selection and Wavelet Denoising Processing
### Wavelet Denoising  
Wavelet denoising is a method of signal analysis in the time domain and frequency domain. It has the features of multi-resolution, and it has the capacity to represent the local features of the signal in both the frequency domain and time domain. It has a higher frequency resolution and lower time resolution in the low-frequency part and lower frequency resolution and higher time resolution in the high-frequency part, which is suitable for analyzing and extracting the local features of the signal and non-stationary signals. In the monitoring of the tool grinding process, due to the complexity of the environment in the processing site and process parameters in the grinding process, the acoustic emission signal and the vibration signal are seriously interfered with by noise during the tool grinding process, and the collected state signal is usually a non-stationary signal with a certain noise. In view of this, the feature signal is processed using the wavelet denoising method. The process of wavelet denoising is to decompose the signal into each scale by using wavelet decomposition, remove the wavelet coefficient belonging to noise in each scale, retain and enhance the wavelet coefficient belonging to the signal, and reconstruct the processed wavelet coefficient by using inverse wavelet transformation to obtain the denoised signal [7; 8; 9].
# Binary and Multi-Class Malware Threads Classification
## 5 Results and Discussion
### Performance Evaluation Metric  
In order to evaluate the proposed method, Classification accuracy is the percentage of samples that are correctly classified to all categories. The accuracy rate is less effective when classes are not balanced. It does give crucial information when the classes are balanced. In order to calculate Classification Accuracy, the following equation has been used [37]:  
\[\text{Accuracy}=\frac{\left(\text{TP + TN }\right)}{\left(\text{TP+TN+FP+FN}\right)}\times 100\ \% \tag{3}\]  
The terms of TP, TN, FP and FN can be calculated by the following formulas [38]:  
1. The term \"TP\" (True Positive) refers to the variety of malware types that may be considered to be positive.
2. The term \"TN\" (True Negative) refers to the variety of malware types that may be considered to be negative.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Class ID**} & \multirow{2}{*}{**Family**} & \multicolumn{2}{c}{**Details**} \\ \cline{3-4}  & & **Malware Category** & **Sample No.** \\ \hline
#1 & Adposhel & Adware & 350 \\ \hline
#2 & Agent & Trojan & 350 \\ \hline
#3 & Allaple & Worm & 350 \\ \hline
#4 & Amonetize & Adware & 350 \\ \hline
#5 & Androm & Backdoor & 350 \\ \hline
#6 & Autorun & Worm & 350 \\ \hline
#7 & BrowseFox & Adware & 350 \\ \hline
#8 & Din wood & Trojan & 350 \\ \hline
#9 & Elex & Trojan & 350 \\ \hline
#10 & Expiro & Virus & 350 \\ \hline
#11 & Fasong & Trojan & 350 \\ \hline
#12 & HackKMS & Riskware & 350 \\ \hline
#13 & Hlux & Worm & 350 \\ \hline
#14 & Injector & Trojan & 350 \\ \hline
#15 & InstallCore & Adware & 350 \\ \hline
#16 & MultiPlug & Adware & 350 \\ \hline
#17 & Neoreklami & Adware & 350 \\ \hline
#18 & Neshta & Virus & 350 \\ \hline
#19 & Other & - & 350 \\ \hline
#20 & Regrun & Trojan & 350 \\ \hline
#21 & Sality & Virus & 350 \\ \hline
#22 & Sarasite & Trojan & 350 \\ \hline
#23 & Stantinko & Trojan & 350 \\ \hline
#24 & VBA & Macro Malwares & 350 \\ \hline
#25 & VBKrypt & Trojan & 350 \\ \hline
#26 & Vilsel & Trojan & 350 \\ \hline  & Total & - & 9100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Explanation of the MaleVis Dataset Categories.  
3. The term \"FP\" (False Positive) refers to the variety of malware types that may be considered to be negative and positive.
4. The term \"FN\" (False Negative) refers to the variety of malware types that may be considered to be negative and positive.
## 4 Findings and Accuracy
the kinetic energy formula, any error in the time domain will be magnified exponentially upon conversion, providing a less accurate representation of the code's ability to accurately make an estimation of the maximum proton energy. Additionally, slight deviations in the distance value may be magnified within the kinetic energy equation, thus decreasing the accurate representation of the code further. When run locally, this code is capable of processing the initial 15 datasets in under 0.5 s, a time which can be greatly improved if run on a more specialized device. Nevertheless, the sufficient processing time, as well as the consistently low deviation between the manually selected and calculated values of minimum time-of-flight puts forth the algorithm as a suitable substitute for manual identification and strongly affirms its reliability for the processing of large-scale dataset quantities.
# Spatiotemporal Variation of Land Surface Temperature Retrieved from FY-3D MERSI-II Data in Pakistan
## 1 Introduction
[19] considered the normalized difference vegetative index (NDVI) as a primary factor of LULC change to identify the role of urban land cover in the context of LST change for the cities of Lahore, Faisalabad, and Multan in Pakistan. From the above literature, it has been observed that most studies have been related to urbanization, and LULC and its contribution to the LST at city levels except for at the country level. As global LST has been rising, the LST can no longer be believed to be static. As a result, the country requires a focus of LST monitoring to support various aspects of climate change and environmental studies in the future. Hence, the objective of the study is (i) the MERSI-II data that are available, which are important for LST; these (ii) data are cross-validated with MODIS data and then (iii) applied to assess the spatiotemporal analysis of LST during the years from 2018 to 2021 for different regions of Pakistan.
# Evaluating Retrieval-Augmented Generation Models for Financial Report Question and Answering
### Thesis Statement and Research Questions  
Our study aims to explore the potential of RAG for improving the accuracy and reliability of LLMs for analyzing half-yearly or quarterly reports. Through a range of experiments featuring various RAG model configurations, such as different LLMs and retrieval methods, we aim to examine and gauge differences and improvements in context relevance, answer faithfulness, and answer relevance in financial report analysis. Ultimately, the goal is to empower users to make informed decisions by providing accurate answers. Toachieve this, a thesis statement and main research question were formulated, supplemented by additional sub-questions.  
Thesis Statement: The effectiveness of RAG in enhancing context relevance, answer faithfulness, and answer relevance for analyzing half-yearly or quarterly reports can be evaluated through empirical experiments with different model configurations.  
Main Research Question: How can RAG be adapted to increase the context relevance, answer faithfulness, and answer relevance of LLM conclusions about several types of financial reports?  
1. How can the retrieval component of RAG be set up to extract relevant information from half-yearly or quarterly reports?
2. What strategies can be employed to ensure that the generation component of RAG produces answers to the questions posed about half-yearly or quarterly reports?
3. How can the effectiveness of the RAG system for analyzing half-yearly or quarterly reports in the banking sector be reliably evaluated and validated?
4. How accurately do RAG-generated responses represent the information extracted from the half-yearly or quarterly reports of banking institutions?
# The Use of Earth Observation Data for Railway Infrastructure Monitoring--A Review
## 2 Railway Infrastructure Monitoring Use Cases and Satellite Data Potential
### Monitoring of Railway Bridges (B)  
The structural capacity of bridges built decades after the Second World War is considered deficient. They were designed for a smaller traffic volume and according to criteria that are less strict compared to today's criteria. They are also facing challenges, such as reduced maintenance and investment and changes in climate, reflected in increased temperatures, flooding, and strong winds [19]. Therefore, inadequate monitoring of the condition of these bridges can lead to damage and collapse. Bridges are, in general, inspected on a regular basis with a frequency that is often not sufficient to detect defects. On-site sensors can be used for monitoring bridges but not on a large scale, due to lack of funds, and installing sensors on some bridges, like long-span bridges, can be challenging [31]. Satellite SAR systems, providing real-time, absolute displacements of structures, can be used for early warning of potential problems. High-resolution optical data could provide detailed visual monitoring of surface conditions and bridge surroundings (e.g., [32]).
## 2 Materials and Methods
### Analysis of Total Hemispherical Reflectance (THR)  
THR was assessed by a SOC-410 directional hemispheric reflectometer (Surface Optics Corporation, San Diego, CA, USA). The device measures reflectance (total, diffuse, and specular) in seven discrete wavelength ranges during each analysis, along UV range (335-380 nm band), visible light (400-540 nm, 480-600 nm, and 590-720 nm bands), and infrared (700-1100 nm, 1000-1700 nm, and 1700-2500 nm bands) for a beam at an angle of 20\({}^{\circ}\). These discrete spectral ranges were established by the manufacturer. The THR result for each type of tablet analyzed was the average of all measurements obtained for 20 tablets (3 measurements per tablet). THR values were obtained using solar energy distributions for an air mass of 1.5, corresponding to a solar zenith angle of 48\({}^{\circ}\). According to the manufacturer's recommendations, before the measurement, the reflectometer was calibrated with the mirror and scattering coupons certified by the American National Institute of Standards and Technology. The 99.9% repeatability of the results was observed during the measurements.
## 5 Case Study: An Actual Deployment at the City Campus of the University of Auckland
### System Setup  
The deployment includes three sensor nodes, two relay nodes, and one central processing module arranged in a hierarchical topology. As shown in Figure 10, the central processing module, marked in red, consists of a bridge node and a Jetson Nano. The green icons represent the relay nodes responsible for forwarding data between each other to ensure that the sensor nodes can connect to the central processing module. The blue icons represent the sensor nodes used for data collection. The distance between the central processing module and Relay 1 is approximately 15 m, while the distance from Relay 1 to Relay 2 is around 60 m. The distance from Relay 1 to Sensor 1 is about 25 m, and from Relay 2 to Sensor 2 and Sensor 3, the distances are approximately 22 m and 20 m, respectively. Since the central processing module is deployed indoors, it is separated from Relay 1 by a transparent glass barrier, while there are no obstacles between other connections.  
Figure 10 also illustrates the communication methods, where red lines represent WiFi connections, while blue lines indicate BLE (Bluetooth Low Energy) links. This topology was explicitly designed to address the practical constraints of urban environments, where direct communication between all sensor nodes and the central processing module is often hindered by physical obstructions or distance. In this configuration, one relay node, located closer to the central processing module, was directly connected to one sensor node and could communicate with the central module without issue. The second relay node, positioned farther away and connected to two additional sensor nodes, could not communicate directly with the central processing module. Instead, it relied on the closer relay node to forward its data, showcasing the hierarchical communication structure employed in the network.  
In addition to sampling the acoustic signal, we gather other environmental parameters, such as air quality, brightness, temperature, humidity, and atmospheric pressure, to fully utilize the built-in sensors on the SLTB004A devices. Details regarding the sensors, in  
Figure 10: Node locations in the case study.  
cluding their types and operating conditions, are available in the SLTB004A datasheet [39]. These environmental parameters do not need high-frequency updates compared to acoustic information, so the workload can generally be ignored. In this case study, we sampled these parameters every 30 s, providing important contextual information for the acoustic data and allowing for a more comprehensive environmental monitoring system.
###### Abstract  
Multiple-hop tests are commonly used in both performance and rehabilitation settings to assess neuromuscular function. This study aimed to explore the relationship between hop performance and sprint ability. Specifically, it focused on three goals: (1) examining the connection between 3-Hop and 5-Hop distances and sprint performance and comparing the strength of relationship between hop kinetics and sprint times; (2) investigating two methods of calculating the 3-Hop and 5-Hop Reactive Strength Indexes (RSI\({}_{\text{hor}}\)s) and their relationship to sprinting; and (3) assessing whether hop ratios or kinetic variables could distinguish sprimiters of varying abilities. Forty-four male sportsmen participated, completing 3-Hop and 5-Hop tests and sprint times (5-45 m) over 54 inground force platforms. Ground reaction forces (GRFs) were collected during hop trials and horizontal and vertical hop propulsive and braking kinetics were determined. Results showed strong negative correlations between hop distances and sprint times (\(r=-0.700\) to \(-0.796\)), while kinetic variables showed weaker relationships with sprint performance (\(r=-0.554\) to 0.017). RSI\({}_{\text{hor}}\), derived from hop distance, correlated more strongly with sprint performance than RSI\({}_{\text{hor}}\) from flight time. Hop ratios (5-Hop/3-Hop) did not differentiate fast from slow sprimters, and maximal vertical force and horizontal propulsive impulse were the best predictors of 10 m and 40 m sprint times. These findings suggest that hop distance and RSI\({}_{\text{hor}}\) are valuable tools for assessing sprint performance and reactive strength.  
multiple-hop; sprint performance; reactive strength index; kinetics +
Footnote †: journal: applied sciences
## 3 Results
### Analysis of Extraction Results by D-InSAR
#### 3.1.2 Mining Subsidence Depth Extraction Results  
By superimposing the experimental data from phase 152 and isolating the range of collapse in the superimposed results, we can determine the total amount of surface collapse in the research area from 2017 to 2021, as depicted in Figure 4. The findings indicate that the highest degree of surface collapse observed in the study region was 417.77 cm between 2017 and 2021. The areas experiencing significant collapse within the Zhaogu Mining Area are concentrated in the southwestern and central sections of the mine, as well as the southern portion of the Zhaogu No.2 Mine. Additionally, there are clusters of collapse basins located in the central part of the Zhaogu No.1 Mine, as well as the southwestern and southern regions of the Zhaogu No.2 Mine. The subsided depression in the mine is situated above the air-mining zone, and its extent is greater than that of the air-mining region. In the Zhaogu Mining Area, the stratum is thicker than the coal. The mining method used is called multi-layer horizontal mining, where the coal seams are divided into layers based on their levels, and mining is performed layer by layer. This results in a deeper mining area underground. However, when the surface of the mining area collapses beyond a certain depth, the surrounding soil loses its support and collapses downward. As a result, the range of ground surface collapse is larger than the actual mining area.  
In the Zhaogu No.1 Mine, the southwestern part of Jitun Township has experienced a collapse with a surface depression of over 350 cm in its central area. The central area of Zhaogu Township forms the largest collapse basin in the mine, with an area of 637.36 \(\mathrm{h}\mathrm{m}^{2}\). The depth of this collapse area is deeper, ranging from \(-417.77\) cm to \(-200.00\) cm, with the dominant depth being in this range.  
The southern section of Beiyunmen Township, located within the Zhaogu No.2 Mine, is experiencing the most severe collapse condition. After applying D-InSAR processing, it was determined that there is no collapse volume within the affected area. This is due to the fact that this specific region is consistently unstable and waterlogged. As a result, it lacks consistency during D-InSAR processing, leading to a complete absence of meaningful results following the D-InSAR processing. Adjacent to the perennial waterlogged area is the periphery of the collapse basin, characterized by a non-waterlogged collapsed region.  
Because there is a permanent water body that experiences subsidence in Beiyunmen Town of Zhaogu No. 2 Mine, it is not possible to determine the exact amount of deformation. However, this location is categorized as a subsidence area, and its location is illustrated in Figure 5.
## 2 Materials and Methods
### Analytical Procedures  
The radiation and air temperature in the greenhouse were recorded by a recorder (TR-74 Ui; T&D Corporation, Matsumoto, Japan). Dissolved oxygen (DO) was measured using a multimeter (HQ30d) and a probe (LDO10101; Hach Co., Loveland, CO, USA). The absorbance spectrum was measured using a UV-visible light spectrophotometer (UV-2600; Shimadzu Corp., Kyoto, Japan). A calibration curve was obtained by measuring the absorbance of a RY86 solution with defined concentrations at 416 nm wavelength [15; 16; 17].  
Evaporation (%) in unplanted CWs and evapotranspiration (%) in the planted CWs for each run were determined by the following equation:  
\[\text{Evaporation/Evapotranspiration}\left(\%\right)=100\times(V_{\text{i}}-V_{\text{e}})/V_{ \text{i}}, \tag{1}\]  
The mass-based removal efficiency of RY86 in the CW was calculated using the following equation:  
\[\text{Removal (\%)}=100\times(C_{\text{i}}V_{\text{i}}-C_{\text{e}}V_{\text{e}})/C _{\text{i}}V_{\text{i}}, \tag{2}\]  
Therein, \(C\) represents the RY86 concentration (mg/L). \(V\) stands for the wastewater volume (L/batch). Subscripts i and e respectively denote influent and effluent.