esa-sceva/satcom-chunk-collection huggingface dataset:
# Studying the Impact of a UDP DoS Attack on the Parameters of VoIP Voice and Video Streams
## 3 Research Methodology and Used Tools  
### Research Methodology  
For the purpose of this work, the GNS3 IP network modeling platform [19; 20; 21] is used. The use of platforms for modeling different systems and the processes in them makes research related to different areas of science easy to conduct and increasingly applied in the process of studying different problems [22; 23; 24; 25; 26; 27; 28]. Using GNS3 will solve several key problems such as the following:  
* The attack will be isolated to the \"territory\" of the modeled network. The modeled network will not be connected to the Internet or other IP networks. Thus, the attack action will be isolated, controlled and co-focused only on the territory of the modeled network;
* Real network device models will be used, which are very expensive, and not everyone can afford them. The main advantage of GNS3 is working with disk images of real operating systems of different network devices. Real/physical network devices are very expensive, and not every researcher can afford to buy them. This limiting factor is eliminated by the use of the GNS3, through which these network devices can be used by researchers, in the form of models. In this way, the modeled networks with GNS3 will be closer to the real networks.  
The present study is divided into two parts. In the first part, only voice traffic will be exchanged in the modeled network, and in the second part, only video traffic will be exchanged. As mentioned in the beginning, Asterisk FreePBX will be used as the VoIP server. During both studies, Asterisk will be attacked/flooded with UDP packets. The packet size is 950 bytes, which is the maximum packet size that can be proceeded with by the emulated disk images of the modeled network devices. Beyond this size, packets begin to be dropped by the network devices. These packets are sent every 10 \(\upmu\)s.  
Compared to the previous study, more results from the Asterisk FreePBX monitoring menu that were missing in the previous study were added--Live network usage; Asterisk's CPU load and memory load. A network analyzer will be used to monitor the load on the Asterisk FreePBX network card.  
In addition, packets exchanged between the Asterisk FreePBX and the subscribers during the video and audio calls will be captured using Wireshark. After the end of the study, Wireshark's RTP stream analysis feature will be used to analyze/show how the UDP DoS attack affects the parameters of the individual streams. The parameters to be monitored are maximum and mean jitter and packet loss. These are the main parameters that are monitored when monitoring VoIP traffic. The values of these parameters determine the quality of service--the mean jitter should not exceed 30 ms, and the packet loss should be around 1% [29; 30].
# Implementation of Climate Change Effects on Slope Stability Analysis
## 3 Example of Landslide  
The case under consideration is that of a slope where a landslide was triggered below the local road in the fall of 2021 due to persistent heavy rainfall (Figure 2). Since the landslide had already occurred in Slake (Slovenia) and the causes of the landslide were being researched, the following was chosen for the investigation. Geological and geotechnical investigations were conducted on the slope. It should be noted that geotechnical investigations were also conducted near the site where the landslide occurred and where the soil properties were not affected by the landslide. This included field prospecting, a geodetic survey, soil soundings and sampling, ground water measurement, field testing (SPT), and laboratory testing (classification of soil, determination of unit weight, direct  
Figure 1: Components of the water balance on the slope.  
shear test, permeability test, oedometer test). It was determined that the soil cover of the slope consists of layers of sandy clay in a slightly to moderately kneaded state. The base of the marl is at a depth of approximately 6 m. The height of the water table depends on the season and the amount of precipitation.  
The geomechanical properties of the soil layers in the slope were investigated. The geomechanical properties (Table 2) are given based on field and laboratory tests and feedback stability analysis on the slope for the soil cover and bedrock. Based on the values from SPT and literature, the properties of marl were estimated, while the properties of sandy clay were estimated based on laboratory tests, SPT and literature. The soil model considers the peak friction angle and the zero angle of dilatation.  
According to the Van-Genuchten and Nielsen method [28], the soil-water characteristic curve of the slope was determined, and then the permeability function curve was obtained. As shown in Figure 3, the hydraulic conductivity decreases with the increase of the matrix suction, while the matric suction decreases with the increase of the volumetric water content (VWC). Therefore, the VWC is an important factor affecting the matric suction (Figure 3) and the hydraulic conductivity (Figure 4).  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Symbol (Unit)** & **Sandy Clay** & **Marl** \\ \hline
**Unit weight** & \(\gamma\) (kN/m\({}^{3}\)) & 18.5 & 24 \\ \hline
**Cohesion** & c (kPa) & 2 & 200 \\ \hline
**Friction angle** & \(\varphi\) (\({}^{\circ}\)) & 20 & 45 \\ \hline
**Volumetric water content** & VWC = V\({}_{\mathrm{w}}\)/V\({}_{\mathrm{s}}\) (\(-\)) & 0.2 & \\ \hline
**Permeability** & k\({}_{\mathrm{y}}\) = k\({}_{\mathrm{x}}\) (m/s) & 5\(\cdot\)10\({}^{-7}\) & 5\(\cdot\)10\({}^{-11}\) \\ \hline
**Compressibility** & m\({}_{\mathrm{v}}\) (1/kPa) & 5\(\cdot\)10\({}^{-4}\) & 1\(\cdot\)10\({}^{-8}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Input data for slope soil cover and bedrock assigned to the FEM model.  
Figure 2: Studied slope with activated landslide.
# Wi-Fi Fingerprint-Based Indoor Localization Method via Standard Particle Swarm Optimization
## 3 System Overview
### Two-Panel Fingerprint-Homogeneity Model  
In Wi-Fi fingerprint-based indoor localization system, similarity characterization is essential for the test fingerprint \(Fin_{testi}\) to match the \(K\) most similar training fingerprints \(Fin_{traini}^{simk},(k=1,2,\ldots,K)\) in the offline database \(\mathbf{Fin}_{train}\). Generally, it is expressed in terms of Euclidean distance; the closer the distance, the more similar it is to the fingerprints. Then, the location estimation can be calculated using Equation (9).  
\[(x,y)=(\frac{\sum_{k=1}^{K}x_{traink}}{K},\frac{\sum_{k=1}^{K}y_{traink}}{K}) \tag{9}\]  
To further constrain the bias in fingerprint similarity characterization, a two-panel fingerprint-homogeneity model [19] was adopted to gauge the similarity of different fingerprints. In contrast to [19], for the first panel, Euclidean distance was used to gauge the homogeneity of different data. Further, the cosine distance was used to reflect the divergence of different vectors from a directional aspect in another panel. For vectors with the same dimension \(n\), the two distances are denoted by Equations (10) and (11), and the similarity can be expressed as Equations (12) and (13).  
\[dis_{\text{Euc}}(\mathbf{v_{1}},\mathbf{v_{2}})=\left\|\mathbf{v_{1}}-\mathbf{v_{ 2}}\right\|_{2}=\sqrt{\sum_{i=1}^{n}\left(v_{1i}-v_{2i}\right)^{2}} \tag{10}\]  
\[dis_{\text{cos}}(\mathbf{v_{1}},\mathbf{v_{2}})=1-\frac{\mathbf{v_{1}}\cdot \mathbf{v_{2}}}{\left\|\ \mathbf{v_{1}}\ \|\cdot\|\ \mathbf{v_{2}}\ \right\|} \tag{11}\]  
\[sim_{\text{Euc}}(\mathbf{v_{1}},\mathbf{v_{2}})=10^{-dis_{\text{Euc}}(\mathbf{ v_{1}},\mathbf{v_{2}})} \tag{12}\]  
\[sim_{\text{cos}}(\mathbf{v_{1}},\mathbf{v_{2}})=10^{-dis_{\text{cos}}(\mathbf{ v_{1}},\mathbf{v_{2}})} \tag{13}\]  
For a specific test fingerprint \(Fint_{test}\) and a specific training fingerprint \(Fint_{train}\), their Euclidean and cosine distances are denoted by Equations (14) and (15). Next, the corresponding location coefficients (as weights) were obtained based on the two distances, as Equations (16) and (17).  
\[dis_{\text{Euc}}(Fint_{test},Fint_{train})=\sqrt{\sum_{j=1}^{M}\left(RSS_{test}^ {APj}-RSS_{train}^{APj}\right)^{2}} \tag{14}\]  
\[dis_{\text{cos}}(Fint_{test},Fint_{train})=1-\frac{\sum_{j=1}^{M}RSS_{test}^{APj }\cdot RSS_{train}^{APj}}{\sqrt{\sum_{j=1}^{M}\left(RSS_{test}^{APj}\right)^{2} }\sqrt{\sum_{j=1}^{M}\left(RSS_{train}^{APj}\right)^{2}}} \tag{15}\]  
\[\omega_{\text{Euc}\ k}=10^{\left[-dis_{\text{Euc}}(Fint_{test},Fint_{train}^{ sim_{\text{Euc}}k})\right]} \tag{16}\]  
\[\omega_{\text{cos}\ k}=10^{\left[-dis_{\text{cos}}(Fint_{test},Fint_{train}^{ sim_{\text{cos}}k})\right]} \tag{17}\]  
Actually, if only one panel of the two-panel fingerprint-homogeneity model or other distance metrics were used, the localization results would be affected. Different combinations will result in different performances. The details are discussed in Section 4.
## 1 Introduction  
Many reports on the development of wide band gap materials and the design of novel edge-termination structures for a high breakdown voltage have been published. Several structures have been investigated to spread the electric field at the edge of the corner [1]. One example is a conventional floating field ring (FFR) structure with a periodically heavily doped region, which can be made in the same processing step as the active region during device fabrication. This structure is used to improve the breakdown voltage characteristics by shifting the maximum electric field from the surface to the substrate and reducing the peak value [1; 2; 3]. On the other hand, when a high voltage structure is designed, this edge structure is needed for the many rings due to spreading of the electric field.  
This study examined the design of novel edge-termination structures, including the trenched ring structure filled with polysilicon or oxide. The poly-trenched structure was formed using the same process as that for the active region. The newly proposed structures contained heavy doping at the bottom of the trench to reduce the crowding electric field [3; 4; 5; 6; 7; 8; 9; 10] and to add a quasi-equipotential region. By varying the trench width and ring space, these structures enable deeper and wider field-spreading profiles at the edge region and improve the breakdown voltage compared to the FFR structure as the same cross-section.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CR-WSN & Cognitive Radio Wireless Sensor Network \\ SFDR & Spurious-Free Dynamic Range \\ RF & Radio Frequency \\ WSN & Wireless Sensor Network \\ MEMS & Micro-Electro-Mechanical Systems \\ ISM & Industrial Scientific Medical \\ CR & Cognitive Radio \\ HCRSN & Heterogeneous Cognitive Radio Sensor Network \\ SU & Secondary User \\ PU & Primary User \\ I/Q & In-Phase/Quadrature \\ DCR & Direct-Conversion Receiver \\ ADC & Analog-to-Digital Converter \\ DDC & Digital Down-Conversion \\ DSP & Digital Signal Processing \\ FIR & Impulse Response Filter \\ CDF & Cumulative Distribution Function \\ LNA & Low Noise Amplifier \\ SNR & Signal Noise Ratio \\ IFFT & Inverse Fast Fourier Transform \\ WII & Weighed Iterative Improvement method \\ IF & Intermediate Frequency \\ BPF & Band-Pass Filter \\ DFT & Discrete Fourier Transform \\ IDFT & Inverse Discrete Fourier Transform \\ FFT & Fast Fourier Transform \\ RLS & Recursive Least Squares method \\ SDR & Software Defined Radio \\ ED & Energy Detection \\ SINR & Signal to Interference plus Noise Ratio \\ EVM & Error Vector Magnitude \\ BER & Bit Error Rate \\ ROC & Receiver Operating Characteristics \\ \end{tabular}
## 3 Results and Discussion
### Cr(VI) Adsorption Experiments
#### 3.1.2 pH of the Solution  
As in the case of the stirring speed, the pH of the solution could play a significant role in the adsorption process of heavy metals [33] because this variable influences the surface charge of the adsorbent, the degree of ionization, and the species that the adsorbate forms in the corresponding solution [34]. In the case of Cr(VI), as shown in Figure 2, the pH value influenced the element speciation. In this figure, it can be observed that, at the concentration of 0.01 g/L Cr(VI), the same concentration  
Figure 1: Influence of the stirring speed on the Cr(VI) adsorption.  
used in the present investigation, the metals are present as HCrO4\({}^{-}\) and CrO4\({}^{2-}\) species, at pH values of 0-6 and 8-14, respectively.  
Thus, metal uptake onto the carbon was investigated as a function of this variable, and the results of these series of tests are represented in Figure 3. These results indicate that the adsorption percentage dramatically increased from pH 0 to 3, with the maximum Cr(VI) adsorption percentage (90%) being at pH 3. Thereafter, metal uptake decreased until a constant percentage of 38% resulted at pH values higher than 8. These results are due to the carbon surface becoming positively charged at acid pH values [36; 37]; thus, when the solution pH increases, the surface losses its positive charge. These results are in good agreement with those previously reported [36], where the obtained value of the isoelectric point for the investigated activated carbon was 1.42.  
Accordingly, at pH \(\geq\) 8, Cr(VI) adsorption became constant, the reason probably being due to one (or a combination) of three effects: (i) decrease of the positive charge of the AC; (ii) less adsorption  
Figure 3: Cr(VI) adsorption percentage as a function of the solution pH. Aqueous phase: 0.01 g/L Cr(VI) at different pH values. Carbon dosage: 0.5 g/L. Temperature: 20 \({}^{\circ}\)C. Time: 5 h. Stirring speed: 1000 min\({}^{-1}\).  
Figure 2: Cr(VI) speciation as pH function [35].  
capacity of the carbon to adsorb CrO\({}_{4}\)\({}^{2-}\) species; or (iii) a competition of CrO\({}_{4}\)\({}^{2-}\) and OH\({}^{-}\) species to be adsorbed onto the carbon. Based on the above, a pH of 3 was used in the subsequent experiments.
## 3 Case Study Application
### Data and Characteristics Used within the Vulnerability Conceptual Framework
#### 3.2.4 Crisis Management Vulnerability  
To ensure emergency vehicle access, a 3.5 m buffer was created around the roads within the case study area, adhering to the normative guidelines of Decreto-Lei n\({}^{\circ}\) 409/98 de 23 December 1998 (Law n\({}^{\circ}\) 409/98 of 23 December 1998) [32]. This process led to the development of the \"Accessible roads\" layer, which was utilized in network analysis.  
Using the shortest route tool in QGIS and incorporating the \"Accessible roads\" layer along with the previously mentioned infrastructure and building layers, a network analysis was performed. This analysis aimed to identify the shortest routes from the infrastructure points to each building in the study area. The result was a new layer containing distance measurements from each of the five infrastructures to all the buildings.  
To visually represent the accessibility routes for pertinent authorities, the result layer was categorized and color-coded accordingly. Furthermore, to enhance the visualization of the assessment results, the fire inception index of each building was integrated into the analysis. The fire inception index was categorized into low, moderate, and high-risk levels.  
This approach yielded four informative maps, showcasing the shortest routes, in meters, from each piece of infrastructure to every building in the case study area. The maps also displayed the Fire Inception Index categorization for each building, providing a comprehensive and visually intuitive overview of the fire risk vulnerability assessment. These maps will prove instrumental in supporting decision-making and emergency planning efforts to enhance the resilience and safety of the historic city center of Guimaraes.
## 3 Results and Discussion
### Dewatering Mechanism and Model  
The increase in the dewatering ratio and reduction in sludge volume resulting from thermal solidification treatment is supposed to improve the sludge dewatering by changing the inner interactions and microstructure of flocs. Based on the properties and microstructure of flocs, the sludge dewaterability and the floc's inner interaction are schematically described in Figure 7. During thermal solidification treatment, sludge dewatering processes can be divided into two steps according to the temperature:  
1. When the temperature increased to 45 \({}^{\circ}\)C, the particles aggregated and formed large flocs, resulting in better dewaterability. This is mainly because the hydrogen bonding between hydrophilic groups in the sludge (such as carboxyl and hydroxyl) and water molecules was destroyed due to temperature increases, which reduced the water-absorbing capacity of the sludge flocs. As the temperature continued increasing (below 70 \({}^{\circ}\)C), the thermal motion of water molecules increased, and part of the bound water within the sludge was transformed into free water and could be easily removed. Accompanied by the reunion of particles, the volume of sludge decreased, and a large, loosely clumped sludge was obtained.
2. As the temperature was elevated above the softening point of acrylic resins (70 \({}^{\circ}\)C), the polymer chains were curled up and folded rather than being stretched out. The softening and shrinking of the polymer chains can connect or wrap the suspended solids in the sludge by means of van der Waals forces, hydrogen bonding, or intermolecular cohesion, leaving more chances for the floccules to contact and aggregate. During this process, a large number of hydrophilic carboxyl groups of polymer chains might be enclosed within the sludge or form internal hydrogen bonding, which reduces the interaction with water molecules and increases the hydrophobicity of the sludge particles. Therefore, the bound water content of sludge with carboxyl groups was released. At the same time, the sludge particles were packed closely together, thus reducing the water-holding capacity and volume of sludge; a large amount of surface water was also released. Less water was absorbed on the sludge surfaces and held by the sludge particles, which released more water molecules. The released free water can diffuse from the inner portions of the flocs to the solution through the internal pore within the sludge due to the shrinkage of acrylic chains and the extrusion of sludge particles. Finally, the particles adhered to each other to form a clot of compacted cake with a large number of pores within the interior and high hardness, thereby improving the dewaterability of sludge.  
Figure 7: Schematic diagram of the enhanced sludge dewatering by thermal solidification treatment.
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 6 Scheme Analysis
### Performance Analysis  
This section evaluates the proposed scheme in terms of feature comparison, time overhead, storage overhead, and throughput. The experiments were conducted based on the Hyperledger Fabric v2.4 framework. The experimental platform hardware consisted of an Intel(r) Core(tm) i5-10400 CPU @ 2.9 GHz and 16 GB of RAM, running on a 64-bit operating system. The Hyperledger Fabric network was deployed using Docker on a virtual machine running Ubuntu 20.04.
###### Abstract  
The time series of Synthetic Aperture Radar data acquired by four satellite missions (including ERS, Envisat, TerraSAR-X and Sentinel 1) were processed using Persistent Scatterer interferometric synthetic aperture radar (InSAR) techniques. The processed datasets provide a nearly continuous coverage from 1992 to 2017 over the Brussels Region (Belgium) and give evidence of ongoing, slow ground deformations. The results highlight an area of uplift located in the heart of the city, with a cumulative ground displacement of \(\pm\)4 cm over a 25-year period. The rates of uplift appear to have decreased from 2 to 4 mm/year during the ERS acquisition period (1992-2006) down to 0.5-1 mm/year for the Sentinel 1 data (2014-2017). Uplift of the city centre is attributed to a reduction of groundwater extraction from the deeper (Cenozoic-Paleozoic) aquifers, related to the deindustrialization of the city centre since the 1970s. The groundwater levels attested by piezometers in these aquifers show a clear recharge trend which induced the uplift. Some areas of subsidence in the river valleys such as the Maelbeek can be related to the natural settlement of soft, young alluvial deposits, possibly increased by the load of buildings.  
Persistent Scatterer Interferometry; Radar Interferometry; InSAR; uplift; subsidence; groundwater recharge; Brussels +
Footnote †: journal: _Aticle_  
Article  
A Study of Ground Movements in Brussels (Belgium) Monitored by Persistent Scatterer Interferometry over a 25-Year Period
## 3 Results
### Detection of Oxidation Biomarkers in Body Fluids Using SPRi  
All oxidation biomarkers were successfully detected in fresh and ageing semen and saliva (Figure 3). However, for urine, the lipid oxidation markers could not be detected. In semen, the LP marker HNE (1) could be measured for at least seven out of ten samples for all time points (Figure 3A) and HNE (2) could be measured in eight out of ten semen samples. The other LP marker, MDA, could be measured in at least six out of ten semen samples for all time points. The detection of LP markers in semen was expected to be successful as semen contains a large amount of polyunsaturated fatty acids (PUFAs), which are highly susceptible to oxidation [26]. The presence of the protein oxidation markers AGE and dityrosine appears to be low in semen samples (Figure 3A). The number of semen samples in which AGEs could be detected increased to five after four days and remained constant up to 21 days. The presence of dityrosine appears to be lower in semen samples, with the minimal number of samples (two) positive on days two and four and the maximum number of samples (five) at day 14. No clear pattern for the detection of dityrosine could be observed over time. With regard to urine, dityrosine could be detected in two urine samples (out of six) and over time increased to six samples (out of six) (Figure 2B). There can be multiple factors that can contribute to formation of dityrosine in urine including  
Figure 2: Overview of the number of samples in which body fluid-specific markers could be detected from semen, urine, and saliva samples. (**A**) Semen-specific markers were PSA and semenogelin-1. (**B**) Urine-specific markers were Osteopontin and THP. (**C**) Saliva-specific marker was AMY-1.  
oxidative stress, non-enzymatic mechanisms under certain conditions, such as exposure to UV light or metal-catalyzed oxidation, or the enzymatic action of peroxididases such as myeloperoxidase which are present in certain cells and can catalyze the cross linking of tyrosine residues [27; 28; 29]. AGEs could not be detected in any sample at each time point, while the LP markers HNE and MDA were measured in only one to two urine samples over time. With regard to saliva, all oxidation markers could be detected and the detection of the oxidation markers increased over time (Figure 3C). HNE showed the highest number of positive samples and could be measured in at least four out of nine samples. Over time, the number of samples increased in which HNE could be detected. Divrosine could be measured in at least three out of nine fresh saliva samples and increased up to eight out of nine samples after 14 days. AGEs were detected in two out of nine fresh saliva samples but after 14 days increased to seven out of nine samples. MDA had the lowest number of positive samples in which it could be detected, varying from two fresh samples to four samples after 14 days and then reducing to three samples after 21 days.