esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Project overview and In-Situ Monitoring
### Monitoring Arrangement  
Rigidly connected with the primary support, pre-support structure forms a reinforcement circle in the ground. An overall bearing structure of \"surrounding rock-pre-support structure-primary support\" is composed. The effect of pre-support on loess tunnel can be determined by comparing the primary support displacement and the ground settlement. A total of 12 monitoring sections (from DK70+829 to DK70+887) with the monitoring projects of arch settlement, horizontal convergence, and GSS were arranged as shown in Table 3 and Figure 8. As a kind of widely used surveying instrument in tunnel construction, total station has the functions of angle measurement, distance measurement and height difference measurement. Lycra TCRA1102 Total Station with a measuring accuracy of 1 mm was selected as the monitoring instrument. The frequency of data acquisition was 1/24 h, with an acquisition time of 8 to 9 a.m. When the tunnel face reached the monitoring section, GSS and tunnel displacement recording were started. After the tunnel face passed, the monitoring of GSS continued for 10-20 days to ensure the gradual stabilization of GSS. Due to the damage to the monitoring points during the installation of geotextile and waterproof board, the tunnel displacement monitoring work could only be continued until the installation of vault secondary support.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Monitoring** & **Pre-Support** & **Buried Depth** & **Arch** & **Horizontal** & **Ground** \\
**Section** & **Type** & **(m)** & **Settlement** & **Convergence** & **Surface** \\ \hline DK70+829 & & 16 & \(\bigstar\) & \(\bigstar\) & \(\bigstar\) \\ DK70+834 & & 16 & \(\bigstar\) & \(\bigstar\) & \\ DK70+839 & leading & 16 & \(\bigstar\) & \(\bigstar\) & \(\bigstar\) \\ DK70+844 & ductile & 16 & \(\bigstar\) & \(\bigstar\) & \\ DK70+849 & & 16 & \(\bigstar\) & \(\bigstar\) & \(\bigstar\) \\ DK70+854 & & 16 & \(\bigstar\) & \(\bigstar\) & \\ \hline DK70+864 & & 12.4 & \(\bigstar\) & \(\bigstar\) & \\ DK70+869 & & 12.4 & \(\bigstar\) & \(\bigstar\) & \(\bigstar\) \\ DK70+872 & pipe roof & 11.3 & \(\bigstar\) & \(\bigstar\) & \\ DK70+877 & & 11.1 & \(\bigstar\) & \(\bigstar\) & \(\bigstar\) \\ DK70+882 & & 6.2 & \(\bigstar\) & \(\bigstar\) & \(\bigstar\) \\ DK70+887 & & 2.1 & \(\bigstar\) & \(\bigstar\) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Monitoring procedures of each section.  
Figure 7: Schematic image of the leading ductile (unit: mm): (**a**) Cross section; (**b**) Longitudinal section.
# Laboratory Testing and Analysis of Clay Soil Stabilization Using Waste Marble Powder
## 3 Results and Discussion
### Compacted Behavior
#### 3.2.1 Mdp  
The use of WMP to stabilize clay resulted in an increase in MDD values from 1.65 to 2.31 Mg/m\({}^{3}\) at 60% marble powder and no change at 75% marble powder. As such, this suggests that clay strengthening and compaction can be achieved using WMP. While some researchers, such as [PERSON] et al., have found that MDD values increase with marble dust content, others, such as [PERSON] and [PERSON], have found that they reduce [41]. Some researchers have suggested that differences in the physical and chemical properties of the WMP and clay used in the various tests may be responsible for the conflicting results.  
The increasing trend in MDD values observed when clay is stabilized with WMP in this study can be attributed to several factors: First, marble powder (WMP) is known to have a higher specific gravity than clay. This suggests that the addition of WMP to clay increases the overall density of the mix, resulting in higher MDD values [48]. Second, WMP particles' angularity is greater than clay particles. This angularity improves particle interlocking, resulting in increased compaction and higher MDD values [49]. In addition, WMP contains fine particles that can fill the spaces between clay particles, resulting in a denser and more compact mix [43; 50].  
It is crucial to note that the mineralogy, particle size distribution, and chemical composition of the WMP and clay used in the study can also influence the observed trend. These properties can vary between different sources of WMP and clay, resulting in different results between studies.
## 4 Results and Discussion
### Simulations of Dam Outflow
After considering hydraulic structures, the contribution of dam outflows from the Northern part of the Imjin river basin is expected to dominate the downstream discharge at Gunnam station. For example, during the flood event in 2010, in the release period, the dam outflow from Hwanggang dam contributed 98.8% to the inflow in Gunnam station, whereas in the storing periods, the percentage of contribution was slightly decreased but still very high (98.3%). The contribution is similar for the other high flow events.
# Assessment of Grassland Degradation on the Tibetan Plateau Based on Multi-Source Data
## 1 Introduction
For example, significant differences in global vegetation trends were found by comparing the NDVI from Terra (2001-2015) and EVI from Aqua Satellites (2003-2015) [26]. The analysis of NDVI with three different sensor systems showed that although the temporal consistency of all examined datasets increased after 2000 by using advanced platforms and sensors, the NDVI values remained different trends at both global and regional scales [27]. Therefore, it is of great significance to use multi-source remote sensing information combined with measured data to obtain more accurate grassland degradation information. This aim of this article was to analyze long-time series of multi-source remote sensing products by spatial superposition and raster trending to assess the spatial-temporal distribution of grassland degradation across the TP and to test the accuracy of remote sensing indexes (Figure 1). The objectives of this study were to (1) assess spatial-temporal variations in grassland area based on multi-source land cover products at the pixel level across the TP; (2) evaluate spatial-temporal variations in grassland quality based on six vegetation indexes at the pixel level across the TP; (3) analysis the consistency of six vegetation indexes based on the observation points of grassland degradation on the TP.
## 6 Conclusions  
In this work, an observer-based robust fault-tolerant predictive control (ORFTPC) approach is proposed for quarter-car active suspension systems with input constraints subject to sensor faults and disturbances. The proposed approach uses two observers:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Suspension Types**} & \multicolumn{2}{c}{**Body Acceleration**} & \multicolumn{2}{c}{**Suspension**} & \multirow{2}{*}{**Tire Deflection (m)**} \\  & & **(m/s)** & & & **Deflection (m)** \\ \hline \multirow{3}{*}{Passive} & Max & RMS & Max & RMS & Max & RMS \\  & 8.695 & 2.432 & 0.0450 & 0.0102 & 0.058 & \(3.452\times 10^{-3}\) \\ Active FTPC & 6.3232 & 1.317 & 0.0200 & 0.0066 & 0.0025 & \(4.7913\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: RMS comparison of the vehicle’s dynamic responses with sensor faults.  
Figure 7: The disturbance and its estimation.  
a virtual observer is used to reduce the impact of disturbances and sensor failures, and a real observer is employed to obtain the various states and faults.  
In the proposed approach, a Linear Parameter-Varying (LPV) model is used to describe the nonlinear quarter-car active suspension system and to develop the proposed controller. Based on the LPV model, an augmented virtual observer was first proposed to improve the estimation of the system's states, while a real observer was designed based on the parameters of the virtual observer to provide a better estimation of both the system's states and sensor faults without the use of a fault isolation module. Furthermore, the stability conditions were derived to ensure the robust stability of the overall closed-loop system. In addition, both the observer gain and the fault-tolerant controller gain were obtained by solving a set of linear matrix inequality constraints. The proposed observer-based controller design was tested on a nonlinear quarter-car active suspension system, and the results demonstrate its ability to improve the vehicle's ride comfort in terms of vibration elimination and fault tolerance. Furthermore, the proposed approach showed its ability to handle faults and external disturbances while satisfying the applied hard constraints, such as a suspension deflection limitation and actuator saturation. In conclusion, the simulation results under external disturbances and sensor faults show that the proposed control method provides improved robustness and performance.  
Conceptualization, A.A., S.B. and I.B.; methodology, A.A., S.B. and M.C.; validation, A.A., S.B., I.B. and M.C; formal analysis, A.A., S.B., I.B. and M.C.; investigation, A.A., S.B., I.B. and M.C.; writing--original draft preparation, S.B., I.B., B.N. and M.C.; writing--review and editing S.B., I.B., B.N. and M.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Implementation
### Synchronization Mechanism  
To ensure the SNN receives the latest events and the command to the servo is generated based on the latest output spikes, a synchronisation mechanism is proposed, as shown in Figure 5. This mechanism sets a limit for the maximum transmission speed of injecting spikes. For example, this limit is set to 2000 packets per second. As shown in Figure 5, the microcontroller keeps receiving events but only stores one event into the buffer every 500 us. For injecting spikes, the period is also 500 us and the microcontroller will reset the buffer if the time difference between current stored event and sent spike is greater than 1000 us. For servo command, it will be overwritten and not be executed until the time difference from last executed command is equal to or greater than 150 ms. Through this mechanism, events, spikes and commands are synchronised to reduce response latency.  
The buffering process for events enables the system to adapt to changing the speed of receiving events but also brings some limitations. Compared to injecting events directly, the microcontroller spends computation power on writing, reading and resetting the buffer. Thus, the maximum transmission speed is further limited. Furthermore, information will be partially dropped out if the  
Figure 4: The algorithm/pseudo-code above is used to generate and execute servo commands.  
speed of receiving events is higher than the pre-defined transmission speed. This will reduce the accuracy of the system.  
The setup of the neuromorphic goalkeeper is shown in Figure 5(a). The width of the baffle is approximately 4 cm and the movable range of the robot arm is 30 cm. Figure 5(b) shows a photo of the connections between the different modules.
# Recent Web Platforms for Multi-Omics Integration Unlocking Biological Complexity
## 2 Methods
### Platforms for Disease-Specific Research and Drug Discovery
#### 2.1.4 iODA: A Cancer-Focused Platform for Multi-Omics Pathway Analysis  
For researchers looking into cancer, a more specialized platform like iODA provides essential tools for multi-omics cancer pathway analysis. iODA (integrative Omics Data Analysis) is a platform designed to facilitate the analysis of multi-omics data in cancer research. The tool integrates genomics, transcriptomics (mRNA and miRNA), and protein-DNA interaction data from ChIP-Seq experiments to help researchers explore the molecular mechanisms driving cancer progression. iODA excels in cross-level omics consistency analysis, combining ChIP-Seq and mRNA data to uncover transcription factor regulatory mechanisms in cancer. For example, its applications in breast cancer research have identified transcriptional regulators of therapy resistance. Despite these capabilities, iODA does not currently support metabolomics or single-cell data integration, which may limit its use in highly heterogeneous cancers [17].  
iODA is particularly valuable in cancer studies due to its ability to handle heterogeneous multi-omics data, providing statistical tools for differential analysis and pathway enrichment. The platform supports the discovery of dysfunctional genes, miRNAs, and DNA-binding proteins across different data levels, making it an essential tool for exploring cancer heterogeneity and developing personalized medicine strategies. Although iODA is effective for transcriptomics and ChIP-Seq data, it does not currently integrate other types of omics data like metabolomics or epigenomics.
## 2 Materials and Methods
### Plastic Hinge Model  
The distributed plastic hinge model was chosen as the nonlinear plastic hinge model. This section focuses on the selection and explanation of the distributed plastic hinge model as the nonlinear plastic hinge model.  
The distributed plastic hinge model considers plasticity distributed across all cross-sections and the entire length of the structural element, as illustrated in Figure 10. The model encompasses three distinct types of structural behavior: modeling of longitudinal steel reinforcing bars, modeling of confined concrete's nonlinear behavior (core concrete), and modeling of unconfined concrete (cover concrete) [63]. In structural element design, numerical models are employed to analyze situations where stress-strain curves extend beyond the elastic region after deformation. These models can be broadly classified into distributed and lumped plasticity. The 'Evaluation and Design Based on Deformation' framework for existing or newly constructed structures involves creating internal force-strain relationships using nonlinear modeling techniques.  
The strength and deformation capacities of the structural system during earthquake movements are evaluated using both static and dynamic methods. These capacities, aligned with performance targets, are then compared with internal force and strain demands [65]. In summary, the adoption of the distributed plastic hinge model is based on its ability to account for distributed plasticity across the element's sections and length, providing a more accurate representation of the structural behavior. This allows for a comprehensive understanding of the complex response of structural elements beyond the elastic region, ensuring the design meets performance targets and safety requirements. Utilizing the plastic hinge model for analysis offers the advantage of enhanced accuracy, although it comes with the drawback of increased analysis duration due to its intricate focus on structural components. Despite this potential drawback, the model was chosen to achieve more precise analysis outcomes.  
The bond between rebar and concrete is crucial for the strength and stability of reinforced concrete (RC) structures. It allows for load-bearing and coordinated deformation. To analyze the mechanical performance of important components in the plastic stage of RC structures, such as beam-column joints, column-foundation connections, and beam-shear wall connections, a clear understanding of the bond-stress-slip relationship is necessary [66; 67; 68]. In order to consider the bond-slip relationship of rebars, the nonlinear link definition was defined alongside the plastic hinge definition. The multilinear curve, which is the polygonal hysteresis loop explained in the study of [PERSON] and [PERSON] [69], was chosen as the response curve for this definition in SeismoStruct software [44].  
Figure 10: Fiber modeling and cross-section details [63; 64].
# Pentane Depletion by a Surface DBD and Catalysis Processing
## 3 Experimental Results
### Gas Chromatograms  
Several chromatogram measurements were repeated at different treatment times, plasma conditions, and pentane concentrations to improve the statistics of the results. The initial pentane concentrations chosen were \(c=300\), \(600\), \(1200\) ppm. The experiments were performed at two plasma power levels, i.e., \(16.9\) W and \(44.2\) W. We assigned \(c\) to be the nominal concentration that we add to the chamber, while \(\rho_{5}\) represents the concentrations estimated by the GC sampling.  
In Figure 4, we show a few examples of chromatograms. The initial pentane concentration was \(1200\) ppm, and the plasma was lit up at \(44.2\) W. We considered the two experimental conditions: depletion without a catalyst (Figure 4a) and depletion with a catalyst (Figure 4b).  
Figure 4: Gas chromatograms vs. retention time [s]. The shown traces correspond to pentane and intermediate products, for treatment times \(t=0\), \(1\), \(5\) min; the plasma lit up at \(44.2\) W, and initial pentane concentration was at \(1200\) ppm. (**a**) Without catalyst. (**b**) With catalyst. The duration of a gas chromatogram is \(300\) s. The insets show the traces for the intermediate reaction products (CO\({}_{2}\), acetylene, propane, water, and other unidentified species) and pentane.  
The chromatograms shown in Figure 4a suggest that before the plasma treatment, except for the water peak, only the pentane peak was essentially present. After the plasma was switched on, the pentane concentration decreased, while intermediate reaction products such as carbon dioxide (CO\({}_{2}\)), acetylene (C\({}_{2}\)H\({}_{2}\)), propane (C\({}_{3}\)H\({}_{8}\)), and water (H\({}_{2}\)O) increased. The vertical positions of the lines are relative ones as they depend on the GC measurement. Only the peaks emerging from the red, blue, and black lines are relevant. It was not possible to identify other species, and their traces are reported in the upper right boxes of Figure 4.  
In Figure 4b, we report the results in the presence of the catalyst. In both experimental conditions, pentane was completely abated after less than 5 min. It is interesting to note that the concentrations of some residual intermediates increased after 1 min of plasma treatment, while after 5 min, they were converted into CO\({}_{2}\) and water. We will discuss these features in Section 3.3.
## 3 Denoising Diffusion Probabilistic Model (DDPM)
### Mathematical Foundations of DDPM
#### 3.2.6 Connection to Markov Chain Monte Carlo  
The denoising diffusion process in DDPM can be connected to the framework of Markov Chain Monte Carlo (MCMC). Specifically, the diffusion process can be viewed as a discretization of Langevin dynamics, a continuous-time stochastic process used in MCMC. This connection provides insights into the relationship between DDPM and traditional MCMC methods [51].
## 3 Model Concepts and Dynamic Routing Setup
### Computed Score of a Waypoint  
In addition to the base score of each waypoint, each waypoint also has a computed score. The computed score is between 0.0 and 1.0 and is only computed and applied for unvisited waypoints. The computed score of a waypoint is used in the dynamic routing of the UAV. Simply, the computed score of a waypoint is the weighted average of all the waypoints weighted by the influence matrix score. Mathematically, let the computed score of waypoint \(i\) be \(s_{i}\), the base score of waypoint \(j\) be \(B_{j}\), and the influence of waypoint \(j\) on waypoint \(i\) be \(\mathbb{I}_{ij}\). The computed score of waypoint \(s_{i}\) is \(s_{i}=\frac{\sum_{i}|I_{ij}B_{j}}{\mathbb{I}_{ij}\cdot\mathbb{I}_{ij}}\).
## 2 LiCSAR System Architecture
### LiCSAR FrameBatch Processing
#### 2.3.1 Data Preparation  
At the initial stage, base frame data (pre-processed primary epoch SLC, MLI, DEM-derived frame products etc.) are copied from LiCSAR internal storage to a temporary BatchCache folder. Any products stored in LiCSAR internal storage covering the frame during the requested time period are either linked (interferometric products) or decompressed (coregistration LUTs or RSLCs, compressed using 7 zip). We then use a data-filling script to search for the existence of relevant Sentinel-1 SLC data by querying Copernicus SciHub ([[https://scihub.copernicus.eu](https://scihub.copernicus.eu)]([https://scihub.copernicus.eu](https://scihub.copernicus.eu))), comparing the SciHub list to information on the data already ingested to the LiCSInfo metadata database, checking and optionally auto-downloading the relevant SLC data, and then refilling the LiCSInfo database. Optionally, the routine can be set to request the data available at CEDA storage from their Near Line Archive system (NLA, see Section 2.5) automatically, though the user is expected to perform such a request prior to the FrameBatch processing (non-autonomous NLA requests are preferred as the complex mechanism of the NLA may induce significant delays in the whole frame processing).  
Figure 5: LiCSAR FrameBatch processing chain flowchart—automatic generation of interferometric products for given frame and date limits.
### Objective  
WSN-IIoT has the distinct benefit of enabling the continuous surveillance and oversight of industrial operations, which boosts productivity, safety, and effectiveness [15]. With the use of WSNs, preventative upkeep may be carried out on manufacturing equipment and processes, resulting in less downtime. WSNs can also aid in waste minimization and energy optimization, which can save money and assist the environment. WSN-IIoT may, in general, automate and revolutionize manufacturing operations by facilitating decision-making based on data. In order to balance the power usage and avoid unauthorized access, data manipulation, and exposure by malicious nodes on the network, an energy-efficient routing (EER) protocol solution should be created for IoT-based WSNs [16; 17]. The purpose of this study is to propose a WSN routing technique that is secure and energy-efficient. The primary goals of the proposed MHSEER protocol are energy efficiency, stability, and reliability, in data transfer despite limited resources. To obtain the best possible data routing, some heuristics are utilized. To learn the routing decision, the MHSEER protocol uses integrated minimal resources, hop count, and connection integrity measures [18]. The suggested IIoT protocol has advantages such as improved security, decreased power consumption, and increased reliability. A secure routing protocol can shield critical data from cyberattacks and stop unauthorized users from connecting to the network. It helps IIoT devices use less energy, which extends their lifespans and minimizes their operating expenses, and by lowering the likelihood of network congestion and packet loss, the protocol can increase the reliability of IIoT systems. The MHSEER protocol is reliable thanks to encoding and the detection and segregation of hostile nodes; furthermore, it offers a method for dynamic routing [13]. The network monitoring between the BS and its surroundings and the energy-efficiency signals together provide effective load balancing and lessen the issue of network segregation. We will show that, compared to existing state-of-the-art solutions, the MHSEER protocol improves the performance of low-power SNs in terms of different performance metrics [19]. The suggested Protocol in IIoT also has several drawbacks, such as complexity and compatibility. A safe routing protocol's implementation might be difficult and expensive because it calls for specialized knowledge and skills. Certain protocols might not work with some IIoT devices, which could restrict their operation or necessitate extra hardware modifications.