esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methodology
#### 2.4.5 Labelling and Classification  
Rice field and non-rice field areas were differentiated visually based on the representative profiles of VH and NDVI time-series data. Figure 3 shows an example of the temporal evolution of the VH polarization and NDVI cluster profile for four land-cover classes: built-up, tree, water body, and rice field. Rice fields have a unique time-series cluster profile in which the VH polarization and NDVI values fluctuate seasonally. In contrast, signals on other land covers were relatively constant, as shown in Figure 3. The median VH polarization and NDVI values for water bodies were \(-\)28.714 dB and 0.040, trees about \(-\)15.049 dB and 0.678, and built-up areas about \(-\)15.899 dB and 0.185. These values are in agreement with past studies [11; 13; 14; 15; 22; 26; 61]. The time that was required for interpretive cluster labelling took longer--about half an hour for each ROI--and depended on the number of rice field clusters.  
Figure 3: Temporal evolution of the (**a**) VH and (**b**) NDVI polarization cluster profile of four land-cover classes: built-up, tree, water body and rice field; T = tillage and planting, V = vegetative, R = reproductive, M = maturity, MS = main season, OS = off season.  
2.4.6. Identifying Rice Cropping Calendar  
We determined the rice cropping calendar based on representative profiles of the VH and NDVI time-series data. The rice cropping calendar can be derived from rice cropping intensity and growth stages, including soil tillage and planting, as well as vegetative, reproductive, and maturity stages. For growth stages, first, the soil tillage and planting stage should be identified via VH backscatter and then followed by the next growth stages.  
2.4.7. Accuracy Assessment  
The accuracy of the produced maps in this study was evaluated using three types of assessment: (1) validation using virtual observations based on Google Earth VHR (very high resolution) and street view images, (2) comparison with existing rice map by NESEA-Rice10 [40], and (3) comparison with known planting and harvesting schedule.  
We generated 760 random points across all ROIs across Peninsular Malaysia for the first assessment. After this, we manually evaluated each location based on VHR images from the year 2020 and Google Earth street view images, where 348 points were identified as rice fields and 392 points as non-rice. The accuracy of the rice maps was then evaluated in these virtual field data using the confusion matrix and Kappa value. Figure 4 shows an example of validating the rice area by using the Google Street View image obtained in Sungai Besar, Selangor (3 deg42'43.92''N, 101 deg03'10.93''E) in July 2019, where rice was harvested in July 2019, corresponding to Figure 3.  
In the second assessment, this study's overall accuracy and kappa coefficient were compared with those from the NESEA Rice 10 map. Finally, the third assessment compared the cropping calendar generated from this study with reference data from the granary area in Selangor state (IADA BLS) and Kedah state (MADA).
### Structure  
The subsequent sections of this paper are structured as follows. Section 2 describes the system model. The investigation of the secrecy outage performance for single-antenna BS is presented in Section 3. Sections 4 and 5 detail the TAS schemes and provide the exact and asymptotic closed-form expressions for the SOP of secondary networks. Section 6 proposes the SA algorithm. In Section 7, we showcase representative numerical results based on the analytical findings and SA optimization models. Finally, Section 8 concludes this article.
## 2 Internet of Vehicles (IoV)
### Layered Architectures and Standards  
Several IoV architectures have been devised by researchers [1; 36; 37; 38; 39]. [PERSON] [40] proposed a four-layer architecture that includes embedded devices and sensors, multi-service edge, core, data center and cloud. [PERSON] et al. [1] proposed a five-layer architecture (perception layer, coordination, artificial intelligence, application, business). A 13-layer scheme with three-dimensional system architecture (dimension of IoV, intelligent computing, and real-time big data analytics) has even been proposed [39]. [PERSON] et al. [41] presented an SDN/NFV-enabled network architecture for IoV. [PERSON] et al. [42] proposed a Cognitive Internet of Vehicles architecture.  
Various researchers and industries are pursuing the development of novel network architectures and solutions that could facilitate IoV implementation [43]. The European Telecommunications Standards Institute (ETSI) in Europe and the Federal Communication Commission (FCC) in the US have proposed architectural standards for IoV. Approaches including cooperative awareness messages (CAMs) for transmitting geographically aware information with relevant data for other vehicles, decentralized environmental notification messages (DENMs), local dynamic maps (LDMs), a key technology for integrating static, temporary, and dynamic information in a geographical context, and geonetworking addressing for geolocation-based routing, have been added to the traditional TCP/IP-based model.  
Despite ongoing efforts, there is no universal and comprehensive architecture for IoV. To fully implement IoV, different sectors (researchers, automotive manufacturers, standardization organizations, city planners, etc.) must collaborate.
## 3 Proposed Method
### Multiclass Classification for PQDs  
After feature selection, a support vector machine (SVM) is utilized as the classifier to identify the pattern of PQDs. It is essential to adjust the strategies that are used for multiclass classification since the original SVM was designed for binary classification. When compared with binary classification problems, multi classification is more complex and it contains large datasets [31]. In this paper, the strategy \"one-versus-rest\" (OVR) is adopted to expand the SVM for multiclass classification. For an \(N\)-class problem, \(N\) hyperplanes are generated for \(N\) classes. When generating the \(i\)th hyperplane of the \(i\)th class, SVM treats the \(i\)th class as the positive case. Meanwhile, the rest are treated as a negative class. During the classifying process, all of the samples are required in each quadratic programming problem. The new sample is assigned to the corresponding class by calculating the closest distant to the hyperplane.  
Figure 3 gives an example of the OVR strategy. When considering a data set with four classes marked C1, C2, C3, and C4, the OVR strategy treats each class as the positive example and the rest as negative examples. Therefore, four classifiers will be trained to give the predicted result. If there is only one classifier like \(f_{3}\) predicts the sample as positive, the final result is determined. Otherwise, it is usually referred the forecast confidence given by each classifier. Subsequently, choose the maximum value as the final result.  
In this paper, the radial basis function (RBF) is chosen as the kernel function of the SVM, because the selected features are nonlinear and the quadratic programming problem is not linearly separable. By choosing a nonlinear kernel, the SVM model will build a nonlinear classification boundary. Furthermore, the penalty parameter \(C\) and the adjustable parameter \(\gamma\) in the SVM with an RBF kernel are optimized by conducting the grid search algorithm. In particular, \(C\) is the penalty coefficient, which is representative of the tolerance for error. The higher \(C\) is, the less error is tolerated, which makes the classifier model over-fit. The smaller \(C\) is, the easier it is to under-fit. If \(C\) is too large or too small, the generalization ability of the classifier would be poor. Parameter \(\gamma\) comes with the RBF function when it is selected as kernel. It implicitly determines the distribution of data after mapping to the new eigenspace. The larger the gamma is, the less the support vector is, the smaller the gamma value is, and the more the support vector is.
## 4 Results
### Erjiayan Village (Site 2)  
#### 4.2.1 Site Description and Fault Geomorphology  
Site 2 is located on the western segment of the JRNF. A distinct NEE-trending linear scarp is formed where the fault cuts through a Quaternary first-order alluvial fan (Fan 1) on the eastern side of a piedmont river channel. The scarp crosses the river channel. However, vertical displacements caused by the fault on both terraces were not well preserved due to water conservancy projects along the channel. The western segment has been anthropogenically altered, obscuring surface traces, but the eastern segment retains a clear linear feature extending approximately 500 m north of Erjiayan (Figure 7A).  
Along the eastern segment of the scarp, three RTK GPS survey lines (EJY-E, EJY, and EJY-W) were conducted perpendicular to the linear fault trend to quantify the geomorphic offset caused by fault activity at Site 2. The topographic profiles show that the fault scarp on the alluvial fan surface has a height of 5.2-7.7 m (Figure 7B).  
Additionally, high-precision UAV photogrammetry was conducted centered on the location where the scarp crosses the river channel. The orthomosaic image and DEM images clearly show surface traces of the scarp extending about 200 m on the eastern riverbank. On the western bank, where the scarp cuts the volcanic platform, the scarp is less distinct in the orthomosaic image but is visible in the DEM. The overall trend of the scarp aligns with the fault's orientation, and its displacement of the alluvial fan indicates Quaternary fault activity (Figure 8).  
Figure 7: High-resolution satellite image of the trench in Erjiayan Village (image data from Google Earth (accessed on 2 August 2023)) and its geomorphological units (**A**). Topography profiles (EJY-E, EJY, and EJY-W) of fault scraps are set along the EJY trench (**B**).
# Development of a Simplified Human Body Model for Movement Simulations
## 1 Introduction
It is a complex musculoskeletal model that takes into account the muscles, flexibility, and deformation of the body (the segments are not rigid), thus reflecting the actual shapes and densities of human body parts. It can be individually adapted for every person but requires more computational power and, most importantly, a lot of specialist knowledge to configure and simulate the model [19]. Many human body models have been considered over the years, but obtaining a particular design that is suitable for various applications, including rehabilitation, biomechanics, robotics, and virtual reality, is still a subject of research [21]. Complex models, like Hatze [17] or Anybody [19], while providing high fidelity, often demand significant computational resources, hindering their use in real-life cases. Moreover, despite the existence of relatively simple models like Hanavan and Yeadon, which yet require a considerable number of measurements, a further simplified model is still necessary for time-demanding applications and cases where ease of use is crucial. This study aims to address this need by developing a simplified human body model suitable for performing kinematic and dynamic analysis of movement activities that maintains the necessary basic level of anatomical detail while significantly reducing the model's complexity. Therefore, the main contribution of this paper is the development of a simplified human body model with a set of parametric equations that requires only two body measurements (height and mass). The following part of the paper presents in Material and Methods, Section 2.1, the developed model consisting of simplified geometric solids that are connected mainly with spherical joints. The paper then shows in Section 2.2, an exemplary experimental 3D dynamic numerical model (built-in Adams 2020 software) that would allow visualization of movement. A set of inertial sensors is described in Section 2.3 as being used for an experiment where a volunteer performs a particular human activity--lifting an object with a bent spine from the ground with both hands. In Section 2.4, the IMU gathered data are applied, and displacements of individual body parts are implemented for each joint of the model to perform a motion simulation. In the Section 3, an analysis of kinematic and dynamic parameters, with the outcomes presented as plots and numerical results, is performed. The paper is completed with a Section 4 presenting a summary and comparison with existing models and future works. In the end, the Conclusions highlight the main paper's contributions, outlining the developed model's features, advantages, and limitations, as well as possible applications.
# A Spatio-Temporal Model for Forest Fire Detection Using HJ-IRS Satellite Data
## 3 Methodology  
### Contextual Algorithm  
Before presenting our proposed spatio-temporal model, we will begin with an introduction to the contextual method. The spatial contextual method combines a series of absolute threshold tests to identify cloud, water and potential fire pixels. It then uses contextual tests to identify fires among the potential fire pixels. Finally, it uses additional algorithms to filter out false positives. Two contextual tests (Equations (1) and (2)) are among the key parts of the method. They attempt to predict the background BT of a potential fire pixel by calculating the average intensity of the valid neighboring pixels in a window. The valid neighboring pixels are defined as those pixels that: (i) contain usable observations; (ii) are located on land; (iii) are not cloud-contaminated; and (iv) are not background fire pixels [8]. The mean absolute deviation is also used to detect fire in the thresholds.  
\[T_{7}>\mu_{7}+\lambda_{1}\delta_{7} \tag{1}\]  
\[\Delta T>\Delta\mu+\lambda_{2}\Delta\delta \tag{2}\]  
where \(\Delta T=T_{7}-T_{8}\); \(T_{7}\) and \(T_{8}\) denote BTs from band 7 and band 8 for the inspected pixel, respectively; \(\mu_{7}\) and \(\Delta\mu\) denote the mean values of \(T_{7}\) and \(\Delta T\) of valid neighboring pixels, respectively; \(\delta_{7}\) and \(\Delta\delta\) denote their mean absolute deviations; and \(\lambda_{1}\) and \(\lambda_{2}\) are learning parameters that determine the thresholds.  
Additional details on the implementation of the contextual algorithm for HJ-IRS data are given in [24].
## 2 Materials and Methods
### FDTD Method  
Domain constitutive equations can represent a linear fluid without any losses, and may be described in terms of the Euler's continuity equation and the momentum equation [29], respectively, as  
\[\rho_{0}\frac{\partial\mathbf{u}}{\partial t}=-\
abla p \tag{20}\]  
Figure 1: Computed space exploration for 1D simulation with a melamine foam porous absorber of 60 mm thickness. Figure shows the quadratic error, in percentage, with respect to input data measured in an impedance tube with ISO 10534-2 [28].  
\[\frac{1}{\rho_{0}c_{0}^{2}}\frac{\partial p}{\partial t}=-\
abla\mathbf{u} \tag{21}\]  
To solve Equations (18) and (21), a classic FDTD method can be used, approximating the partial derivatives on constitutive equations using central finite differences schemes with staggered grids for the pressure and the particle velocity vector, both in space and time [30; 31; 32]. For example, in a 1D space along the \(X\)-axis, NEAM and domain momentum in Equations (18) and (20), respectively, with a first-order error, yield the next FDTD update equations  
\[\left(1+\frac{\Delta t\Omega_{B}}{2\Omega_{A}}\right)u_{x}|_{i+1/2}^{n+1/2}= \left(1-\frac{\Delta t\Omega_{B}}{2\Omega_{A}}\right)u_{x}|_{i+1/2}^{n-1/2}- \frac{\Delta t}{\rho_{0}\Delta x\Omega_{A}}\big{(}p_{x}|_{i+1}^{n}-p_{x}|_{i}^ {n}\big{)} \tag{22}\]  
\[u_{x}|_{i+1/2}^{n+1/2}=u_{x}|_{i+1/2}^{n-1/2}-\frac{\Delta t}{\rho_{0}\Delta x }\big{(}p_{x}|_{i+1}^{n}-p_{x}|_{i}^{n}\big{)} \tag{23}\]  
where subscript \"n\" denotes time steps an \"i\" space steps. The same goes for the other update equations.  
Since the simulation domain has finite dimensions, a useful working space has to be limited to ensure that reflections do not disturb the interest region. To avoid such spurious reflections, a perfectly match layer (PML) is used according to [31]. Algorithm stability is given by the Courant-Friedrich-Levy (CFL) condition, from which is defined the critical time step that ensures information propagation between cells. In a 2D Cartesian coordinates system, CFL condition is established as in [32]. Since an equally-spaced square grid is used, spatial increments are the same in both directions, and so, CFL condition is simplified. To setup a simulation, the minimum points per wave length (PPWL) must be defined to provide an adequate signal and domain resolution. Then, given a maximum CFL number and minimum PPWL, it is possible to calculate spatial and temporal increments to ensure stability.
## 3 Efficient LFSR Implementation
### Computation of Seeds  
Let us consider that the shift matrices \(A^{(j)}=A^{\theta_{j}}\) have already been precomputed. For any given seed \((d_{0},d_{1},\ldots,d_{L-1})\), the seeds of the binary LFSRs can be represented as:  
\[(d_{0,j},d_{1,j},\ldots,d_{n\cdot L-1,j})=(d_{0,0},d_{1,0},\ldots,d_{n\cdot L -1,0})A^{(j)}, \tag{13}\]  
where \(d_{L,j},\ldots,d_{n\cdot L-1,j}\) are unknowns for every \(0\leq j\leq n-1\). The values \(d_{L,0},\ldots,d_{n\cdot L-1,0}\) can be obtained solving the linear system composed with the \((n\cdot L-
# A Review of the Water Desalination Technologies
## 2 The State of the Art
### Ion-Exchange Resin (IXR)  
The term \"ion-exchange resin\" (IXR) indicates a variety of organic compounds, which have been chemically treated to react with the ions of a solution, capturing ions from the solution and releasing other ions from the resin into the solution. In the past, zeolites were used, i.e., minerals having this characteristic.  
Ion-exchange resins are used in industrial and domestic applications like soft water production, sugar purification, and the extraction of precious elements, such as gold, silver, and uranium from mineral ores.  
IXR can be classified according to functional group [77]:  
* Strongly acid, realized with sulfonic acid groups
* Strongly basic, based on quaternary amino groups
* Weakly acidic, realized with carboxylic acid groups
* Weakly basic, based on primary, secondary, or tertiary amino groups.  
The acid resins (also called cation resins) are designed to capture positive ions (Ca\({}^{2+}\), Na\({}^{+}\), Mg\({}^{2+}\), K\({}^{+}\), Mn\({}^{2+}\), Fe\({}^{3+}\)... ) and release H\({}^{+}\) ions. Consequently, the hardness of the water is reduced, and the acidity is increased, since the pH is increased by the greater concentration of H\({}^{+}\) ions. The basic resins (also called anion resins) are utilized to capture negative ions such as Cl\({}^{-}\), NO\({}_{3}\)\({}^{2-}\), SO\({}_{4}\)\({}^{2-}\), SiO\({}_{2}\)\({}^{-}\), CO\({}_{3}\)\({}^{2-}\) and release OH\({}^{-}\) ions.  
This technology was developed at the end of the 1960s [78]. A classical arrangement of a desalination plant based on IXR is reported in Figure 29.  
The saline water flows firstly through the weakly acid resin and the strongly acid resin. After this step, the water's acidity is increased. A degasser is required, as bicarbonates inside the water react with H\({}^{+}\) ions, producing carbon dioxide. After this step, water flows through the weakly basic resin and the strongly basic resin, reducing the water's acidity.  
Figure 29: A schema of an Ion-Exchange Resin desalination plant.  
An amphoteric resin (a mix of acid and basic) is normally added to complete the removal of ions.  
During the normal process, resins are progressively saturated by the ion exchange. Thence, a regeneration is periodically required to restore the resins. The regeneration uses acid solutions (H\({}_{2}\)SO\({}_{4}\) and HCI) for the acid resins and basic solutions (NaOH and NH\({}_{4}\)OH) for the basic resins.
# An Intelligent Approach to Allocating Resources within an Agent-Based Cloud Computing Platform
## 2 Related Technologies and State-of-the-Art Approaches  
Usually, in a CC environment, the term hardware infrastructure refers to the virtualized infrastructure [24; 25], which means that there is a layer of abstraction between the actual hardware infrastructure and the computation nodes. In this way, each of the offered services is deployed in virtual nodes belonging to that layer of abstraction, and these are called virtual machines (VMs). Therefore, services are usually distributed and this distribution of services in the different nodes requires a system that provides the necessary load balance to distribute the requests among the different computer nodes that attend them.  
Each VM has a set of dedicated hardware resources and is completely independent, making it possible to run different operating systems, where the software is completely independent. With this hardware in any given physical equipment, the virtualization process allows one to share and encapsulate the physical resources among a set of VMs by following a pattern similar to the master-slave model [26], in which the (real) server hosts different VMs. This capability, made possible by the virtualization technology, allows the computational resources in the virtualization layer to be considered unlimited resources, while the physical resources or hardware infrastructure are limited to the real infrastructure. The only disadvantage of this technology is that the management of the virtual environment requires a highly-specialized software called hypervisor [27]. This component is responsible for managing the hardware abstraction in each physical node, and this also consumes resources [28]. Nevertheless, this computational consumption depends on the selected virtualization model. Currently, the additional cost does not pose any significant problem as it does not exceed 2% of the computational power of the physical environment [24; 29]. However, it is necessary to have dedicated hardware, which is also widely extended as a result of technologies such as INTEL-VT or AMD-V [25].  
Virtualization techniques greatly simplify both the management and control of IT resources at the infrastructure level, supporting the dynamic creation or removal of VMs on demand or even enabling the migration of a VM from one physical machine to another at runtime, without stopping or pausing the machine or service. Therefore, thanks to virtualization technology, this complex problem is actually simple to solve, since it is only based on the efficient redistribution of physical (real) resources among the different computational (virtual) nodes. Due to its complexity and its contemporaneity, this problem has attracted the attention of the scientific community, which has led to the proposal of many different solutions.