esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this study, the monitoring results of the Hongya Village landslide along the LOS direction were acquired according to the SBAS-InSAR technique, using data from both the ascending and descending orbits of the Sentinel-1 satellite. Moreover, the spatial deformation characteristics of the landslide were analyzed with the aid of displacement derived from SBAS-InSAR and optical imagery. The time-series analysis of the deformation revealed a positive correlation between the rate of landslide deformation and rainfall, with correlation values of 0.64 and 0.72 for two representative monitoring points. In addition, the stratigraphic lithology with loess and mudstone is extremely sensitive to changes in water content, particularly considering water from precipitation, thus forming a weak sliding surface and a slippery layer. According to the combined effects of natural factors and the interpretation of the InSAR monitoring results, the Hongya Village landslide remains unstable to some extent. Therefore, it is crucial to continue conducting high-frequency monitoring of the Hongya Village landslide and to focus on the prevention and early identification of geological hazards.  
Figure 8: (**a**) Stratigraphic lithology and site investigation map; (**b**) A field photo of loess soft and hard interbedded; (**c**) A field photo of pebble matrix; (**d**) A field photo of elastic rock formations.  
**Author Contributions:** Conceptualization and design, writing of paper outline, and coordination of teamwork, [PERSON]; collection of data, data analysis, and analysis of results, [PERSON]; data processing, analysis, and initial draft of paper, [PERSON]; technical guidance and paper revision., [PERSON]; participation in data analysis and chart drawing., [PERSON]; provision of basic data, and modification and verification of information., [PERSON]; provision of theoretical support and case studies, and participation in discussions and revisions, [PERSON]; optimization of images and translation of content, [PERSON]; partial writing and submission of the paper, [PERSON]; revising the manuscript, [PERSON]; revising the manuscript, H.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was supported in part by the National Natural Science Foundation of China (NSFC) (Code: 4187/7273).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available because the project is not yet finished.  
**Acknowledgments:** We are very grateful to the European Space Agency (ESA) for providing the Sentinel-1 satellite data free of charge, to the Japan Aerospace Exploration Agency (JAXA) for providing the \"ALOS World 3D--30m\" DEM at 30 m resolution free of charge, and to the 2023 Qinghai Province \"Kunlun Talents\" Leading Talent Program Support.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Error Sources of Interferometric Synthetic Aperture Radar Satellites
## 2 Intrinsic Height Errors and Location-Induced Errors
### Location-Induced Errors
#### 2.2.1 Foreshortening  
Foreshortening is a distortion inherent in radar imaging [51]. Foreshortening can occur even on flat terrain. It can be observed that if the local terrain slope \(\alpha\) increases, terrain \(A\) in Figure 2 lying within two concentric curves will be lengthened accordingly. The signals within the segment of terrain (\(A\)) are projected to radar image plane (\(A^{\prime}\)). Since the range difference within \(A\) is lengthened by the terrain slope, the signal will be compressed in plane \(A^{\prime}\) and thus foreshortened. If \(\alpha\) rises and approaches incidence angle \(\theta\), more terrain will be recorded between two radar echoes, resulting in more compression in the range direction and thereby symmetric fore-slope patterns which lean toward the sensor in SAR images.  
Foreshortening occurs when \(\alpha\) is smaller than \(\theta\). Given the same area of interest, a larger incidence angle implies a greater likelihood that the slope angle will be smaller than the incidence angle, resulting in a greater likelihood of foreshortening effects. Foreshortening is the most common geometric distortion type because most of the imaged topography falls within the geometric condition of foreshortening, i.e., \(\alpha<\theta\), except for very steep terrain and areas in backslope.
# Co-Efficient Vector Based Differential Distributed Quasi-Orthogonal Space Time Frequency Coding
## 2 Distributed Space-Time-Frequency Coding
### Multipath Channel Model  
The channel at the \(t\)th OFDM time slot between the source node and the \(p\)th relay node is described by the impulse response vector \(\mathbf{f}_{p,t}=\left[f_{p,t}(0),\ldots,f_{p,t}(L_{SR}-1)\right]^{T}\). Similarly, the channel at the \(t\)th OFDM time slot between the \(p\)th relay node and the destination is described by the impulse response vector \(\mathbf{g}_{p,t}=\left[g_{p,t}(0),\ldots,g_{p,t}(L_{RD}-1)\right]^{T}\), where \(L_{SR}\) and \(L_{RD}\) denote the number of independent channel tops on the source-relay link and relay-destination link, respectively. The multipath fading channel between the source node and the \(p_{th}\) relay node is modelled as \(f_{p}(t)=\sum_{l=0}^{L_{SR}-1}f_{p,t}(l)\delta(t-\alpha_{l})\). Similarly, the multipath fading channel between the \(p_{th}\) relay node and the destination is modelled as \(g_{p}(t)=\sum_{l=0}^{L_{RD}-1}g_{p,t}(l)\delta(t-\beta_{l})\), where the complex amplitudes \(f_{p,t}(l)\) and \(g_{p,t}(l)\) are assumed to be independent zero-mean complex Gaussian random variables with variances \(E\left(\left|f_{p,t}(l)\right|^{2}\right)=\sigma_{SR}^{2}(l)\) and \(E\left(\left|g_{p,t}(l)\right|^{2}\right)=\sigma_{RD}^{2}(l)\), respectively. The delay of the \(l_{th}\) path is denoted by \(\alpha_{l}\) and \(\beta_{l}\) while \(\delta(\cdot)\) is the Dirac delta function.  
Figure 1: \(P\)-relay Cooperative Network.
## 3 Materials and Methods
### Quality Control Data (QCD)  
Analytical precision and accuracy were monitored by including procedural blanks, replicate samples, and certified reference materials (CRMs) such as GBW07309 (stream sediment). The recovery rates for all elements ranged between 90% and 110%, and the relative standard deviations (RSDs) were generally below 5%, indicating satisfactory data quality for environmental assessment.
## Biomedical Applications
### Tissue Engineering  
Research into the engineering of marine ulvan or marine polysaccharides or materials sourced from marine plants has been developed due to the nature of marine polysaccharides, namely biocompatibility, biodegradability, non-toxicity, low cost, and abundance. Most marine polysaccharides are derived from natural sources, such as fucoidan, alginate, carrageenanan, agarose, porphyry, ulvan, ulcers, chitin, chitosan, and chitoligosaccharides [125; 126; 127].  
_U. lactuca_ represents a source of three-dimensional porous structure based on ulvan, which might be applied as a medical device, especially for tissue engineering applications [105]. Ulvan-based hydrogels can be used in cartilage tissue engineering [128]. Ulvan also contains polysaccharides that are similar to alignates, so it can be used as a hydrogel polymer [73]. Ulvan extracted from _U. lactuca_ and chitosan extracted from _Loligo forbesii_ squid-pen produced 98% carboxymethyl ulvan (CMU) and 87% carboxymethyl chitosan. These compounds are not cytotoxic and might represent suitable materials for in vitro and in vivo evaluation as tissue candidate [104]. Ulvan, combined with poly(D-lactic acid) (PDLLA), may be used to produce new scaffolding for bone tissue engineering applications [74].
## 2 Literature Review  
The BAP has been studied for over 60 years and there are extensive publications in the literature. In this brief review, it is only possible to present some directly relevant works. A comprehensive review of the extant works on this topic can be found in the following publications: [PERSON] and [PERSON] [4], [PERSON] et al. [5], and [PERSON] et al. [13]. Various types of BAPs exist, depending on the optimization goal. [PERSON] and [PERSON] [4] proposed two variants of BAP: a primal problem, i.e., minimizing total buffer capacity subject to a throughput constraint, and a dual problem, i.e., maximizing throughput subject to a total buffer capacity constraint. Other variants include maximizing profit, minimizing buffer cost, and minimizing WIP, as shown in Table 1. In this study, we study the dual problem, which is widely used in practice and is of most interest in the literature. A typical solution scheme for the BAP is to integrate a generative method with an evaluative method [5; 16]. The generative method searches for candidate solutions, while the evaluative method evaluates the system performance corresponding to the candidate solutions. The two methods are deployed in an iterative way: the generative method uses the evaluation result to generate a new candidate solution, which is further evaluated. This process is repeated iteratively until specific stopping conditions are met. The most commonly used evaluative methods include exact methods [1; 34], decomposition methods [1; 35; 36; 37; 38; 39], aggregation methods [40; 41], generalized expansion methods [42; 43], and simulation methods [9; 28; 44; 45; 46; 47]. The application of exact methods is only feasible for small systems due to the high computational complexity involved. The decomposition and aggregation methods are very fast, but they are approximate performance evaluation methods and may lead to errors in some situations. Moreover, these approximate methods have some application limitations. For example, they cannot be applied to production lines with non-exponential failure and repair models. The employment of simulation methods facilitates the evaluation of system performance with a high degree of accuracy. Moreover, these methods can be applied to complex, realistic manufacturing systems. We therefore use simulation as a performance evaluation tool. The generative methods can be categorized into four distinct classifications: enumeration methods, dynamic programming, metaheuristics, and search algorithms [5; 13]. Table 1 presents an overview of some typical generative methods. Enumeration methods are suitable only for production lines that have a very limited number of machines. The utilization of dynamic programming has been demonstrated to be an effective solution for
## 2 Materials and Methods
### Proposed System Model  
In this subsection, the joint receiver design for JCAS system is investigated, where the communication signal and target echo signal are simultaneously received and processed to achieve balanced performance between both functionalities. However, interference may occur in spatial simultaneous functioning due to concurrent operation. The JCAS scenario is described in Figure 2.  
In Figure 2, the joint design system processes the downlink signal from the base station to the communication user and the echo signals reflected from the target simultaneously.  
In general, the receiver of the joint design system should be able to transmit the communication signal to the user equipment (UE) and estimate the states of the targets at the same time. These two tasks are likely to occur concurrently, which can result in mutual interference. Therefore, it is necessary to incorporate interference cancellation methods when considering the joint design system for communication and sensing. The downlink joint communication and sensing (D-JCAS) system comprises two stages. In the initial stage, the BS transmits a combined signal containing both the communication and radar waveforms to the UE and radar targets. Subsequently, in the second stage, the BS extracts environmental information from the reflected radar echoes.  
The downlink communication signal transmitted by the BS and received at the UE is denoted as \(s(t)\). Let us define \(s(n)\in\mathbb{C}\) as the \(n-\text{th}\) discrete-time index, \(n=1,2,...,N\). Let \(x(t)\) be the radar signal at the BS and \(x(n)\in\mathbb{C}\) be the \(n-\text{th}\) discrete-time index, \(n=1,2,...,N\).To illustrate the mutual interference between these functionalities, the received signal at the UE is expressed as  
\[\mathbf{y}(n)=\sqrt{\beta}\mathbf{h}_{c}(s(n)+x(n))+\sqrt{\gamma}\alpha(n) \mathbf{a}_{R}(\theta)\mathbf{a}_{T}^{H}(\theta)\mathbf{f}x(n)+\mathbf{w}(n), \tag{1}\]  
where parameters \(\beta\) and \(\gamma\) represent the power of communication and sensing signals, respectively; \(w(n)\) is the AWGN. The communication signal \(\mathbf{h}_{c}s(n)\) and the sensing signal \(\alpha(n)\mathbf{a}_{R}(\theta)\mathbf{a}_{T}^{H}(\theta)\mathbf{f}x(n)\) are both accordingly normalized and are expressed more clearly in the following section.
## 4 Results and Discussion
### Comparison of the Energy Consumption  
We also compared the various frameworks using energy consumption metrics, as shown in Figure 4. The results indicate that the energy consumption of the ACRLPC framework is less than that with other constraints in energy, like 120 Joules. The highest energy of 160 Joules was consumed by the UW-ALOHA-QM framework. The high diversity in rapid growth demonstrates the efficiency of the ACRLPC framework, which may prove advantageous in contexts where conserving energy is the leading concern. Intermediate energy consumption results for the FDU-MAC and TCH-MAC frameworks are 135 Joules and 120 Joules measured in this case, respectively. These results illustrate that both of the frameworks outperform UW-ALOHA-QM but still yield lower energy efficiency in comparison to the ACRLPC.
## 4 Particle Filter Model for Experiments
### Weight Calculation and Update  
At each measurement step, the distance and angle measurements from the LiDAR were used to calculate errors and update particle weights using a normal distribution probability density function [30]. The noise standard deviation for distance measurements was set to 0.1, and for angle measurements it was set to \(\pi\)/36.  
\[\omega_{t}^{(i)}=\frac{1}{\sqrt{2\pi\sigma^{2}}}e^{-\frac{(\pi_{t}-h(x_{t}^{(i )}))^{2}}{2\sigma^{2}}} \tag{2}\]  
Equation (2) depicts the particle's weight using the probability density function of the normal distribution. Here, \(z_{t}\) is the actual measurement value from LiDAR, \(h\left(x_{t}^{(i)}\right)\) is the expected measurement value based on the particle state, and \(\sigma\) is the standard deviation of measurement noise. This means that the difference between actual and expected measurements is normalized by the standard deviation of noise, determining the weight based on how closely the particle matches the measurement data.
# Learning Weighted Forest and Similar Structure for Image Super Resolution
## 3 The Image Super Resolution Based on Random Forest  
[PERSON] et al. [21] proposed to study mapping relationship from LR to HR patches based on random forest. The training dataset was partitioned into \(N\) groups of the training sample. Let \(X=\left\{{{x_{l}}^{i}}|i=1,2,\ldots,N\right\}\), \(Y=\left\{{{y_{h}}^{i}}|i=1,2,\ldots,N\right\}\), which denotes LR patches and corresponding HR patches training datasets, respectively. The estimation of HR patches can be considered the local linear regression problem for LR patches as below:  
\[y_{h}=W(x_{l})\cdot x_{l} \tag{3}\]  
where \(x_{l}\) denotes the LR patch, \(y_{h}\) denotes the corresponding HR patch, and \(W(\cdot)\) is the local linear regression function.  
The least square model can be adopted to study the local linear regression function as below:  
\[\underset{W(x_{l})}{\operatorname{argmin}}\sum\limits_{i=1}^{N}{{{\left\|{y_{ h}}^{i}-W({x_{l}}^{i})\cdot{x_{l}}^{i}\right\|}_{2}^{2}}} \tag{4}\]  
This optimization is implemented using the SR forest algorithm, which utilizes a splitting function to recursively split the input data into disjoint subspaces to obtain a tree structure, and establishes the linear regression function to express data dependence between LR and HR patches.  
When random forest is trained, the target HR image patch \(y_{h}\) is estimated as below:  
\[y_{h}=\frac{1}{T}{\sum\limits_{t=1}^{T}{{m_{l(t)}}(x_{l})}} \tag{5}\]  
where \(T\) denotes the number of decision tree, and \({m_{l(t)}}\) denotes the local linear regression function for the sampled LR image patches \(x_{l}\) in the \(t\)-th tree.
# Self-Configuring IoT Service QoS Guarantee Using QBAIoT
## 3 QoS Guarantee within Internet of Things Environment
### IoT Service Level Agreement
As shown in the MSC (see Figure 6), the IoT-C initiates the iSLA establishment by sending a request concerning the desired IoT service with its requirements. Then, the IoT-SP will classify the requirements into three types; cloud requirements, network requirements and gateways/sensing requirements. After this classification step, the IoT-SP chooses among the best offers available from  
Figure 6: IoT components interaction for e-health application iSLA establishment. the different CSPs and NSPs in order to serve the IoT-C. The IoT-SP initiates a cSLA request to the corresponding CSP and waits for his proposal. If the proposed cSLA meets the specified requirements, a cSLA Acceptance will be sent by the IoT-SP. Similarly, an nSLA request is initiated by the IoT-SP. The corresponding NSP should send its proposal and the IoT-SP accepts it if the requirements are satisfied. Afterwards, the IoT-SP evaluates the available resources on the corresponding HL-Gw to be used by the IoT-C requested service. A report about gateway resources' availability is collected by the IoT-SP. Based on this report, the IoT-SP concludes an internal SLA called gateway SLA (gSLA) with the existing HL-Gw or with a new implemented one. Thus, the IoT-SP has the capability to propose an iSLA to the IoT-C based on the cSLA, nSLA, and gSLA. If this offer is accepted by the IoT-C, the IoT-SP concludes the cSLA and nSLA with the CSP and NSP, configures the HL-Gw, LL-Gw, nodes and the application instance. Finally, the e-health IoT service is available for usage by the IoT-C users.
## 3 Materials and Methods  
The majority of the models applied are mechanistic models (process-based), which satisfactorily describe dynamic processes and exploit accurate data used for model initialization. Empirical models rely on data correlations for the final outcome, while deterministic models make use of defined and well-established scientific laws in soil water and chemical agents' dynamics to obtain the predictions. Stochastic models take into account variability and randomness concepts (input variables), providing a broader range of potential outcomes. Each type of model has its strengths/drawbacks and ideal use cases, depending on the specific research, the implementation scale area, or management accuracy and goals.  
Mechanistic models are categorized as progressive conversion models where particle size remains unchanged during the leaching process without any penetration resistance to fluids/agents, a shrinking core model with constant particle size, a shrinking core model with shrinking particle size, etc. [17].  
The way of approaching each case selected is to determine conceptual, empirical, physically based, hybrid models, steady (snapshot-like pictures) or dynamic (ongoing) simulation models. Another significant categorization depends on the extent of area appliance, i.e., pasture, cultivated plot, river catchment/basin, estuarine, coastal waters, aquifers, regional, countrywide, etc., and dimensional fate 1-D, 2-D, spatial analysis employed, e.g., distribution of lumped and huge heaps of data collected and tested from databases or models with minimum data requirements [18; 19]. Groundwater modeling, when numerical models are employed, entails certain approaches, e.g., finite difference [20], finite volume [21], finite element, and element-free [22] methods.  
Chemical agents, e.g., herbicides, complex ions, organic pollutant concentration, saline and fate, runoff, drainage, infiltration, percolation, seepage phenomena, etc., determine water quality deterioration, monitoring network, for integrated strategy and remedial actions. In complex soil layers, anion retention capacity varies, and surely soil nitrate flux does not correspond directly to the water soil infiltration flux and it is rather a function of the organic matter content. However, major categories located and analyzed in published papers of modeling are given below.
# W-IFL: An Improved Maximum Power Point Control Model to Promote Renewable-Powered Vehicles
## 3 The W-IFL Methodology
### IFL Controller  
In this controller, as summarized in Equation (5), inputs need to be fuzzed to generate control variables, i.e., ke, kec, and kdu.  
\[\begin{array}{l}dP/dU=ke=\frac{P_{k}-P_{k-1}}{k-k_{k-1}}\\ \Delta dP/dU=kec=\hat{x}_{k}-U_{k}\\ \Delta U=kdu=e_{k}-e_{k-1}\end{array} \tag{5}\]  
Precisely, \(P_{k}\), \(I_{k}\), \(U_{k}\), and \(\hat{x}_{k}\), are the power, voltage, current, and predicted voltage of the PV cell at time \(k\), respectively. The fuzzy domain factor, kdu, can, accordingly, reflect the offset voltage; thus, it is clear that \(e_{k}=0\) means the PV cell works at MPP (time = \(k\)).  
The fuzzy logic, in general, specifies some rules that each variable should correspond to a fuzzy subset and a membership function to control the unknown output [43]. In addition, As considering the influence of environmental factors such as temperature and irradiance, the PV cell's output may change rapidly, and the controller should choose a triangular function to improve controlling resolution.  
Therefore, the definition of fuzzy rules and corresponding membership functions in the proposed controller is described in Table 2 and Figure 2.  
Notably, since there are 2 inputs to the fuzzy controller for calculation, and each input is divided into five fuzzy subsets, the fuzzy rules can, accordingly, be determined as 5 \(\times\) 5 = 25 in Table 2. What is more, based on the predicted voltage from the wavelet predictor, the role of the variables defined above is to quickly adjust the duty cycle, \(d\), of the filter, thereby controlling the system's work at the MPP.
## 4 Simulations and Results Analysis
### Methodology  
The validation is carried out by measuring the accuracy of SDSE security estimates regarding the actual security status of the simulated networks. More specifically, to measure how close an estimated value (\(V_{E}\)) is from an actual value, measured in a simulation, in this case, (\(V_{R}\)), we use the following equation:  
\[\text{Accuracy}=100\times\left(1-\frac{|V_{R}-V_{E}|}{V_{R}}\right) \tag{23}\]  
Since in each of its equations, SDSE uses several configuration parameters of the network security mechanisms, various scenarios have been simulated, so that we could validate SDSE estimates under different circumstances.  
Each scenario has been simulated 100 times, and the average result of the obtained accuracies has been considered for analysis. Since in each scenario the results obtained in the 100 simulation runs have presented approximately the same pattern, we conclude that 100 simulation runs can give us reliable results.
# Simulation Study of Dynamic Rotation and Deformation for Plasmonic Electric Field-Skyrmions
## 3 Results and Discussions
### Dynamic Rotation for Skyrmions
Even considering the strong dependence of rotation direction on orientation control, we can further synthesize rotation direction to generate more directions, which provides an interesting discovery for manipulating topological structures in the sub-wavelength scale.