esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing Voltage Stability in Distribution Networks:
## 2 Modeling of Stochastic Variables Considering Correlation
### Risk Preference Assessment Indicator  
The risk index can quantitatively reflect the system's security state by synthesizing the probability of incident occurrence and the severity of its aftermath. This paper adopts a risk preference utility function based on utility theory, and constructs a severity function model in conjunction with the exceedance of the LMI. This allows the quantitative calculation of the risk of static voltage instability, thereby enabling the accurate assessment of voltage stability in the distribution network with distributed generators [39].  
The severity function \(S(I_{\text{LM}})\) of the load margin index \(I_{\text{LM}}\) is defined as [39]:  
\[S(I_{\text{LM}})=\frac{\text{e}^{\mu(I_{\text{LM}})}-1}{\text{e}-1} \tag{28}\]Here, \(I_{\text{LM}}\) is the LMI of the distribution network. The calculation formula of the index lower limit \(h(I_{\text{LM}})\) is as follows:  
\[h(I_{\text{LM}})=\left\{\begin{array}{l}(I_{\text{c}}-I_{\text{LM}})/(1-I_{ \text{c}}),I_{\text{LM}}<I_{\text{c}}\\ 0,I_{\text{LM}}\geq I_{\text{c}}\end{array}\right. \tag{29}\]  
where \(I_{\text{c}}\) is the alarm limit of \(I_{\text{LM}}\) index. The severity function curve under this definition is shown in Figure 2.  
Combined with the probability density function \(f(I_{\text{LM}})\) of \(I_{\text{LM}}\) index, the system static voltage instability risk can be defined as \(R\), according to the \(I_{\text{LM}}\) index-exceedance risk [40]:  
\[R=\int f(I_{\text{LM}})S(I_{\text{LM}})\text{d}I_{\text{LM}} \tag{30}\]  
Using \(R\) as an indicator, it is possible to effectively analyze the impact of the stochasticity of PV output and load power on the load margin, as well as the system-operational risks of the distribution network under different PV connection methods and capacities.
## 2 Materials and Methods
### Landslide Conditioning Factors
#### 2.3.1 Seismic and Geological Factors  
Peak ground acceleration (PGA) and the distribution of local faults were considered as critical factors influencing co-seismic landslides, which are typically triggered by intense ground shaking. PGA serves as a quantitative indicator of seismic hazard and shaking intensity. For this study, PGA data associated with the 2015 Gorkha Earthquake were obtained from the USGS ShakeMap database ([[https://www.usgs.gov/](https://www.usgs.gov/)]([https://www.usgs.gov/](https://www.usgs.gov/)) (accessed on 24 January 2025)). The original PGA values (in units of gravitational acceleration, g) were rasterized to a 30 m spatial resolution to align with the other thematic layers used in the analysis. As illustrated in Figure 4b, the PGA values within the study area ranged from approximately 0.3 g to 0.74 g, with the highest intensities concentrated near the epicenter. These elevated shaking levels likely contributed to the widespread initiation of co-seismic landslides in the region.  
In addition, slope stability tends to deteriorate near active faults due to structural weakening, leading to increased landslide susceptibility [27]. To capture this, a geological map from the Department of Mines and Geology (DMG) of Nepal was used to digitize the thrust and fault lines. A Euclidean distance map was generated from these faults (Figure 4a) to represent proximity to geological structures.  
Furthermore, geology is an indispensable function in determining the spatial distribution of landslides [28]. This is because different lithological units have different physical properties, and they result in dissimilar permeability and strength values of rocks and soils. The influence of geological parameters on landslides has been well documented in numerous studies. For instance, in the context of Nepal, the lithological layers were identified and analyzed through the modification of existing geological maps [20] and the geomorphological data from the geological map produced by the Department of Mines and Geology (DMG), Nepal. The dataset generated from these maps was further classified into 14 lithological units, as depicted in Figure 5, along with the details of each unit and its corresponding formations. Detailed geological descriptions of each geocode used in the analysis are provided in Supplementary Table S1.  
Figure 4: Seismic causative determinants: (**a**) distance to fault lines and (**b**) peak ground acceleration (PGA) distribution map.
# Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison
## 3 Overview of Available Datasets  
The development and deployment of autonomous vehicles heavily rely on vast and comprehensive datasets to train, validate, and test their perception, decision-making, and control systems. These datasets encompass various forms of sensor data, including images, LiDAR point clouds, radar echoes, and GPS coordinates, capturing diverse driving environments, conditions, and scenarios, enabling the development of robust AI models for safe and effective autonomous driving. Image datasets are essential for tasks such as object detection, classification, semantic segmentation, and lane detection. The most used datasets include Cityscapes for understanding urban scenes, nuScenes for providing data for spatial object detection, and CARLA for testing models in realistic simulations. Radar datasets, though less common, provide valuable information on object speed and distance, exemplified by nuScenes, which includes radar data for comprehensive perception tasks. Multisensor datasets like Argoverse [64] offer synchronized data from cameras, LiDAR, radar, and GPS, enhancing sensor fusion techniques. Simulated datasets from platforms like CARLA and TORCS offer cost-effective and controlled data collection in diverse virtual environments, essential for training, planning, and control tasks. These datasets are crucial for training AI models by providing diverse examples, enabling rigorous validation and testing, offering standardized benchmarks, handling edge cases, and advancing research through rapid iteration. The proliferation of such datasets has accelerated research, demonstrated by thousands of papers (Tables 1-3) on applications like object detection, semantic segmentation, sensor fusion, and adversarial robustness, making datasets the cornerstone of autonomous vehicle development and innovation. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Description**} & \multirow{2}{*}{**Sensors Used**} & **Samples Number** & **Classes Number** & \multirow{2}{*}{**2D/3D**} & \multirow{2}{*}{**Cited**} & \multirow{2}{*}{**Ref.**} \\ \cline{3-3} \cline{5-8}  & & & & & & & \\ \hline \multirow{3}{*}{Cityscapes} & Provides semantic, instance-wise, and & & & & & & \\  & dense pixel annotations for 30 classes & Camera & 25,000 & 30 & 2 Dk3D & 3411 & [65] \\ \cline{1-1}  & grouped into 8 categories. & & & & & & \\ \cline{1-1}  & High-resolution sensor data collected & Camera, & & & & & \\ \cline{1-1}  & by Waymo Driver in various & & & & & & \\ \cline{1-1}  & conditions. & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Available image datasets that can be used to train deep neural models for implementation in autonomous vehicles.
## 2 Modeling
### Dynamics of Satellite Mounted with Reaction Wheel and DRC Antenna  
In this study, we assume that Reaction wheel (RW) are installed in each three axis of satellite. The angular momentum of RW are shown as follow:  
\[\mathbf{h}_{\text{RW}}=\text{diag}(I_{\text{RW}},I_{\text{RW}},I_{\text{RW}})\mathbf{ \Omega}=\mathbf{I}_{\text{RW}}\mathbf{\Omega} \tag{9}\]  
\(I_{\text{RW}}\) represents moment of inertia of one RW. \(\mathbf{\Omega}=\left[\Omega_{1}~{}~{}\Omega_{2}~{}~{}\Omega_{3}\right]^{T}\) represents wheel speed of RW in each axis. The angular momentum of whole satellite mounted with RW and DRC antenna can be rewritten by following equation:  
\[\mathbf{H}_{c}=\mathbf{I}_{c}\mathbf{\omega}_{s}+\mathbf{I}_{ac}\mathbf{e}_{\phi 1}\dot{\mathbf{\phi}}_{1}+\mathbf{I}_{ac}\mathbf{e}_{\phi 2 }\dot{\mathbf{\phi}}_{2}+\mathbf{h}_{\text{RW}} \tag{10}\]  
Using Equations (1) and (10), the dynamics of the satellite mounted with RW and DRC antenna can be expressed by following equation:  
\[\begin{split}&\mathbf{l}_{c}\mathbf{\omega}_{s}+\mathbf{I}_{c}\dot{\mathbf{ \omega}}_{s}+\mathbf{I}_{ac}\mathbf{e}_{\phi_{1}}\dot{\mathbf{\phi}}_{1}+\mathbf{I}_{ac}\mathbf{e}_ {\phi_{1}}\ddot{\mathbf{\phi}}_{1}+\mathbf{I}_{ac}\mathbf{e}_{\phi_{2}}\dot{\mathbf{\phi}}_{2}+ \mathbf{I}_{ac}\mathbf{e}_{\phi_{2}}\ddot{\mathbf{\phi}}_{2}+\mathbf{I}_{RW}\mathbf{\Omega}\\ &+\mathbf{\omega}_{s}\times\left(\mathbf{I}_{c}\mathbf{\omega}_{s}+\mathbf{I}_{ac }\mathbf{e}_{\phi_{1}}\dot{\mathbf{\phi}}_{1}+\mathbf{I}_{ac}\mathbf{e}_{\phi_{2}}\dot{\mathbf{\phi} }_{2}+\mathbf{I}_{RW}\mathbf{\Omega}\right)=\mathbf{0}\end{split} \tag{11}\]
## 5 Experiments
### Experimental Setup  
The vision/inertial module used in these experiments is the INDEMIND M1, developed by Beijing Indemind Technology Co., Ltd. based in Beijing, China. It offers advantages such as low cost and high data update rates, consisting of a tightly coupled binocular camera and IMU with microsecond-level synchronization precision. The binocular camera utilizes the OV9281 sensor from OmniVision Technologies, Inc. (located in Santa Clara, CA, USA), featuring a sampling frequency of 50 FPS and a resolution of 640 \(\times\) 400. The IMU is ICM20602, model from TDK-InvenSense (formerly InvenSense Inc., based in San Jose, CA, USA), operating at a sampling frequency of 1000 Hz. Magnetic field data is collected using Xsens sensors from Xsens Technologies B.V. (situated in Enschede, The Netherlands) and transmitted wirelessly to the computing module.  
The computing module is an NVIDIA Jetson TX2 NX development board, produced by NVIDIA Corporation (headquartered in Santa Clara, CA, USA), which is installed with NVIDIA Jetpack 5.1 (also from NVIDIA Corporation), Ubuntu 20.04, and the ROS operating system. The INDEMIND M1 vision-inertial module is connected to the development board via a USB 3.0 interface, and the development board is powered by a 9 V power supply. The connections between modules are illustrated in Figure 14.  
The device modules are securely attached via a custom wearable test to reduce movement-induced device shake. The test personnel wear the integrated equipment, secure it on their chest, connect the power supply module, and turn on the power. After the computing module is powered on, the system automatically boots, starting the vision/inertial modules and the localization algorithm. The localization results are sent to the host computer via the wireless transmission module, and once the data is received, the test personnel begin their movement. The experimental scenario, in which personnel wore test vests to conduct trials, is shown in Figure 15a.  
After completing the experiment, in order to facilitate comparison and error analysis, we calculate the average error, minimum error, maximum error, standard error, and endpoint error for each experiment. The verification tests were conducted in multiple scenarios, including indoor corridors, underground parking lots, and tunnels, as shown in Figure 15a,c.  
Figure 14: Device and module connections.
## 4 HIPERLN/2 with Open Loop Transmit Diversity Technique  
HIPERLN/2 system has been modified to include the Alamouti technique as shown in Figure 3 for both two transmit antennas and one receiver antenna for both users. Besides, the method also applied when each user has two transmit antenna and two receive antenna. The transmit and receive antennas are assumed to be closely located. Each user has transmitters that transmit the symbols to the other user. The number of independent channels between the two users depends on the number of transmitting and receiving antennas. It is equal to the multiplication of the number of the antennas at the transmitter side by the number of the receive antennas. There are two groups of the channels in the modelling that are used; the first group is between the transmitter and the receiver within the same user, whereas the second group is between the transmitter of a user and the receiver of the other user.  
More details of the channel box in Figure 3 is shown in Figures 4 and 5 when the number of transmitting and receive antennas is varying. Figure 4 show the channels when the users have two transmit antennas and one receiver antenna, whereas Figure 5 shown the channels when each user has two transmit antennas and two receiver antennas. The channel modelling depends on the gain and attenuation block diagram, where the gain block will be used to model the channels between the transmit and the receive antennas within the same user, whereas the attenuator will be used to model the channels between the transmit and the receive antennas between the users. As a result, the receiver antenna of the first user received two signals; the signal transmitted by the same user and the signal transmitted by the other user in addition to the noise. It can be noticed from Figures 4 and 5 that the transmitted symbols for the first user will be amplified by using a gain block. After that, the amplified symbols will be transmitted through the Rayleigh channel [18; 19]. Meanwhile, the transmitted symbols by the second user will be attenuated by the same gain block. However, the amount of the gain will be less than one; in this case, the gain will be attenuation. The next stage is to send the attenuated symbols through other independent channels.  
After that, the output of the channel will be added together, and this will represent the input to the received antenna. It can be seen that at the user 1, the magnitude of the transmitted symbols by the same user will be higher than the magnitude of the transmitted signal by the other user, due to the attenuation and the gain. It should be noticed that the feedback knows the transmitted symbols and the channel state information (CSI) [18; 19], because of the position of the transmitter and the receiver within the user.  
Figure 3: Modified HIPERLAN system.  
Figure 4: The channels implementation for both users when each one has two transmit antennas and one receive antenna.  
Figure 5: The channels for both users when each one has two transmit antennas and two receive antennas.
## 3 Results and Discussion
### Evaluation of the Changes in the Microstructure  
Micrographs shown in Figures 12 and 13 were obtained by SEM (in secondary electron mode) for the surface and fracture (perpendicular to the surface) of carbonated samples at 1 h and 9 h carbonation curing and also for reference samples. The samples were prepared at the age of 28 days. The analysis of these micrographs allows the observation of the cement phase development and the impact of carbonation on the interface between the fibers and cement matrix. For the surface of the non-carbonated samples, Figure 12a shows needle shaped ettringite (Arrow 1) and small sized CaCO\({}_{3}\) around the matrix pores (Arrow 2). Figure 12b shows large sized CaCO\({}_{3}\) (Arrow 3), which decreased the AVV of the sample. The ettringite was not observed either in 1 h or 9 h carbonation samples (Figure 12b,c), which suggests that the ettringite was entirely carbonated under accelerated conditions to form CaCO\({}_{3}\) phase. Furthermore, we observed that the structure of 9 h carbonated composites was much denser compared to 1 h carbonated samples.  
Figure 12: Scanning electron micrographs on the surface of fiber-cement composites: (**a**) non-carbonated; (**b**) 1 h carbonated sample; (**c**) 9 h carbonated sample.  
SEM analysis of fractured samples allows observations of the changes of fiber-matrix bond (i.e., ITZ) and how deep the samples were carbonated. The fractures were prepared at the age of 28 days within 1 mm distance from the surface. Figure 13a shows the micrograph of a fractured non-carbonated sample, which exhibited poor bonding between the fiber and matrix. As a result, the fiber was pulled out during fracture preparation, which created holes (dashed ellipse) on the fracture as shown by Arrow 5 in Figure 13a. After 1 h carbonation curing, calcite precipitated around the interfacial transition zone between the fiber and matrix and other pores. Therefore, there appeared some calcites precipitated around the holes (dashed ellipse) created due to the pulling out of fibers, as indicated by Arrow 6 in Figure 13b. Figure 13c shows a better fiber-matrix bonding, which prevented the fibers pulling out from the matrix. Arrow 7 was a filament of cellulose fiber, which was kept in the matrix during SEM fractured sample preparation. This indicates that carbonation helped create a better adhesion of fiber and cement matrix because of the refinement of composite microstructure. Note that the fibers used in this study were refined ones, which contain a large number of filaments as shown in Figure 13d. These filaments had sizes (diameter) ranging from 1 micron to 30 microns, which are shown in Figure 13c.
# Six Sigma-Based Frequency Response Analysis for Power Transformer Winding Deformation
## 3 Results--Verify Phase
### Case 2
#### 3.2.3 Case 2: Analysis of 10,000-100,000 Frequency Range (Hz)  
This band is crucial for diagnosing axial and radial winding deformation, disc space variation, and insulation compression failure, as per Table 7. Any irregularities in this range could indicate a loss of insulation integrity, affecting transformer longevity.  
In Figure 13a, the FRA measurement deviates significantly from the baseline in specific segments, particularly between 20 kHz and 70 kHz. The observed dip suggests a possible alteration in winding geometry or an emerging insulation issue.  
Further validation from Figure 13b confirms that \(C_{p}\) and \(C_{pk}\) values drop below 1.0 in certain regions, categorizing the process as being on the threshold of instability, according to Table 4. The upper R-limit is exceeded, signaling that mechanical deformation is progressively worsening. These findings necessitate a targeted investigation of insulation integrity and potential remedial action.  
Remarks: It should be noted that the transformer is undergoing structural deformation in the windings, which could impact dielectric performance. Preventive maintenance is required to mitigate further deterioration.
# Constructed Technosols: A Strategy toward a Circular Economy
## 4 Waste Materials in Technosols Design
### Issues Related to the Use of Constructed Soils  
#### 4.3.1 Contamination  
The use of recycled materials is subordinated to the verification of their non-toxicity or non-contamination. Materials such as compost, digestate, sewage sludge, and sediments can be contaminated by inorganic (Cd, Cr, Hg, Pb, Cu, Zn, As) or organic pollutants such as biphenyl, pesticides, and PAHs [110; 111]. Contaminants legal threshold values depend on national legislations; however, it is important to maintain a low content of pollutants or, at the very least, to ensure their low bioavailability in constructed soils and their components. In fact, these substances can accumulate in the food chain and result in hazards to human health or interfere with soil functions and services.  
[PERSON] et al. showed that the use of sewage sludge as amendments could cause metals contamination suggesting thus the co-planting of different species of trees for a phytoremediation of the soil [110]. [PERSON] et al. found chromium contamination during pot experimentson some constructed technosols obtained with furfural residue and fly ash. In particular, the fly ash was found to have Cr concentrations near the critical limit for harmful substances, however the content of this metal decreased significantly after 6 months [92].  
A study by [PERSON] et al. [112] investigated biotic, abiotic, and functional parameters of six derelict soils, comparing native soils from industrial French plant sites and artificial soils. One of the examined artificial soils was constructed with a bottom layer of paper mill waste, a layer of remediated former PAH-contaminated soil, and a topsoil layer of compost. The soil was placed on the site of a former coking plant and planted with grass. Among all the six investigated soils, this mixture displayed the highest quality in terms of fertility and functional parameters, with suitable values of organic matter, water holding capacity, CEC and phosphorous content. The pollutants concentrations in this soil were similar to others from industrial sites, with a content of Cd, Pb, Zn above geochemical backgrounds, but lower than the metal-polluted soils. However, unexpected high PAHs concentration were detected (170 mg kg\({}^{-1}\)), probably due to the aerial deposition or an accumulation from the remediated soil to the topsoil. These results encourage the use of constructed soils but remark the need to monitor them after their application.  
The addition in constructed technosols of components such marble waste and biochar allows using structural material arising from contaminated soil or containing naturally high concentrations of metals. In the study of [PERSON] et al. [69] the mobility of metal(loids) in matrices made from mine tailings was strongly reduced (75-99%) with the addiction of marble waste, due to the pH increase and to biochar adsorption.
## 3 Results and Discussion
### Principal Component Analysis (PCA)  
A Principal Component Analysis (PCA) was performed on the variables included in Tables 1 and 3-5 to explore the relationships among them and the general effect of the organic practices, harvesting method and harvesting time.  
Results show a different distribution of the samples according to the organic practices and harvesting method. The Ground oils were plotted in the positive PC1 semiaxis (Figure 2a), where the quality parameters and most of the fermentation and oxidation compounds had large loadings (Figure 2b). Conversely, the oils from fruits collected from the trees (both the Conventional and Organic oils) appeared in the negative PC1 semiaxis, where the total phenols, oil stability and most LOX compounds reached large absolute loadings (Figure 2b). Figure 2b shows that most LOX compounds were positively related to the total phenol content and oil stability (most of them had negative loadings, generally under \(-0.5\)) and negatively related to the quality parameters and most of the fermentation and oxidation compounds (most of them had positive loadings, generally above \(0.5\)). Therefore, factors which favour fermentation or oxidation (for example, collecting the fruits from the ground) seem to hinder the LOX-pathway compounds.  
In addition, the Organic oils tended to reach negative loadings in the PC2, whereas the Conventional ones tended to be displayed in the positive PC2 semiaxis. The variables with the largest absolute loadings in the PC2 were mostly LOX compounds, two oxidation compounds ((E)-hepten-2-al and hexa-2,4-dienal) and two fermentation compounds (2-methylprop-2-enal and 3-methylbutanal), all of them with positive values (Figure 2b). Therefore, these compounds seem to be more related to the Conventional than in the Organic oil according to the variability explained by the PC model.  
Regarding the harvesting time, samples from the first weeks generally reached lower scores in the PC1 axis and higher in the PC2 axis, whereas the ones from the last weeks tended to follow the opposite trend. Therefore, at the beginning of the harvesting period, the oils tended to have higher scores in the variables positively related to oil quality, such as stability and phenol content, whereas at the end there was a slightly stronger relation to variables considered detrimental to oil quality (quality parameters, and fermentation and oxidation compounds).