esa-sceva/satcom-chunk-collection huggingface dataset:
# Land Cover Mapping in Southwestern China Using the HC-MMK Approach
## 3 Methodology
### Remote Sensing Data Collection and Mapping Units

Table 1: Land cover classification system of the CLC-SW2010 product. scenes, neither Landsat image scenes nor HJ image scenes, but rather the intersection regions between HJ and Landsat image scenes were chosen to divide Southwestern China into 190 mapping units. All satellite images and ancillary data were clipped by these mapping units. Meanwhile, all processes including segmentation, automatic classification and quality control were conducted in these mapping units. Notably, a part of the overlapping regions needed to be retained to meet the requirements for mosaicking the final classification results for each neighboring mapping unit.
## 3 Archimedean Spiral Antenna  
Spiral antennas are generally classified as frequency-independent antennas [15]. This kind of antenna has the property of maintaining some of their radiation parameters constant, such as the impedance. Thus, they are extensively used in applications where a large bandwidth is required. Their constant impedance along the frequency facilitates the design of the multiplier circuit, since the circuit dependence on the antenna impedance is eliminated. The two spiral-shape antennas most typically used in practice are the Archimedean spiral [11; 16] and the equiangular spiral [17], both circularly polarized. The design of the harvesting element will be based on the Archimedean spiral antenna due to its simpler geometry.
## 2 Methods
### Data Generation Process
#### 2.1.2 CT Simulator  
A 2D equiangular fan-beam CT simulator was used to generate rational motion blur patterns in the motion-blurred CT images. It is a complex physical process, in which X-rays from the source pass through an object and are collected by the detector. In this study, we only focused on the motion blur caused by the motion of the aortic valves. Therefore, the complex physical process can be well-approximated by taking collections of line integrals and combining them in appropriate ways [31]. The geometry of the 2D equiangular fan-beam CT is shown in Figure 2e. The X-ray source was rotated from \(-90^{\circ}\) of the imaging plane. The parameters of the CT simulator are shown in Table 4.  
We used a ray-driven CT simulation method. The ray-driven method connects a line from the focal point through the image to the center of the detector cell of interest of the X-ray. CT values along the X-ray were calculated by interpolation of adjacent pixels. In the projection process of the CT simulator, the projection data is given by the following:  
\[p(r)=\int\mu(x,y)ds \tag{1}\]  
where \(\mu(x,y)\) is the attenuation coefficient at the point \((x,y),s\) is the ray from the source to the detector, and the projection data \(p(r)\) is the line integral along with the X-ray. In the reconstruction process, the filtered back projection (FBP) [32] and half scan reconstruction [33] algorithms were used to reconstruct motion-blurred CT images from projection data. The back-projection data is given by the following:  
\[b(x,y)=\int p(s,\theta)d\theta \tag{2}\]  
where \(b(x,y)\) is the back-projection data at the point \((x,y)\), \(\theta\) is the rotation angle of X-rays.  
A single motion-blurred CT image requires about 183 ms of video stream data (Figure 2g). Considering that the CT scan cannot start at the precise moment the aortic valve starts to open in the real world, we randomly selected the scan start moment to generate motion-blurred CT images. The time range for random scan start is during the closed phase of aortic valves, because motion images in the rapid opening phase are relatively important to understanding the dynamics of aortic valves. Images from the video stream data were set in the CT imaging plane in the simulator, and the center of images in the video stream data was kept the same as the center of the CT simulator (Figure 2f). After inputting the video stream data to the CT simulator, we can acquire motion-blurred CT images. Additionally, motion images were evenly taken from the video stream data used in CT imaging. Finally, we selected a 128 \(\times\) 128 region around the center of the images to make the dataset. In this way, a dataset (N = 8000) containing motion-blurred CT and their corresponding 60 motion images was generated. The dataset was further divided into a training dataset (N = 6400), validation dataset (N = 800), and test dataset (N = 800).
###### Abstract
Although these methods have shown promising results, their training datasets were not target-oriented; rather, they aimed to achieve a general solution by creating subsurface models with randomly selected box-shaped anomalies or multiple layers. However, because the inverse problem is ill-posed in nature, a deep-learning approach still requires constraints, such as regularization terms in traditional inversion, to ensure the stability and reliability of the solution. In the deep-learning approach, these constraints can be incorporated when generating training data by adding prior knowledge, such as information about background layers obtained from borehole data in the application area. Additionally, these methods do not incorporate borehole data, which can be utilized to improve the inversion results when available. Furthermore, although the deep-learning model is well-trained using a training dataset that closely resembles realistic settings, the trained model is not guaranteed to obtain the optimal solution because the distribution of the target field data may differ from that of the training data. To address the abovementioned limitations, we propose a new workflow that combines deep-learning technology with traditional deterministic inversion to enhance fault detection in electrical resistivity surveys. First, we create a target-oriented training dataset for fault detection using field borehole data. Then, we develop a deep-learning model based on the U-Net architecture using the training dataset for ERT inversion. In addition, we include an additional network called borehole mixer at the end of the U-Net, which incorporates borehole information to enhance the inversion results if borehole data are available. Finally, we use the prediction of the trained model as the initial subsurface model for deterministic inversion to predict the final subsurface model. Through numerical examples of synthetic and field data, we have obtained encouraging experimental results, which indicate that the proposed workflow has better performance than the traditional inversion in fault detection.
## 5 Evaluation
### Experimental Setup  
#### 5.1.1 Prototype  
In this experiment, we simulated the SEC environment using CloudSim [47], which is an open-source simulation platform for the cloud computing environment. We have made some modifications to CloudSim from the source code to make it suitable for the research environment of this article.  
We consider an SEC network with 60 LEO satellites, and it can provide 5 LEO satellites as workers for edge-distributed inference one time. Given the limited size, each LEO satellite is attached with lightweight accelerators, such as NVIDIA Jetson Orin NX [21; 53]. We obtained the computing power of an NVIDIA Jetson Orin NX using the method mentioned in [37]. Considering the inconsistent computing power of edge servers, we use one-fifth of the obtained value as the computing power of a CPU core in edge servers and control the CPU core number of each worker to an integer between 2 and 10 randomly. We control the out-bandwidth for each worker between 1000 and 2000 MB/S [54]. We selected five out-bandwidth values, 1000 MB/S, 1250 MB/S, 1500 MB/S, 1750 MB/S, and 2000 MB/S, and randomly assigned them to the five workers. Therefore, by fixing a worker as the requester, it can avoid randomness and make the environment configuration more realistic. Table 3 shows the parameters of our simulation environment.
# The Role of the Directivity of Various THz Detectors in Multiplexing Systems
## 1 Introduction  
In recent decades, terahertz (THz) radiation and its applications have attracted the interest of a constantly increasing number of scientists and entrepreneurs [1]. The ever-growing global demand for innovation has already found many applications for THz technologies in areas such as medicine [2, 3], non-destructive testing [4, 5], detection of dangerous materials and objects [6, 7], or telecommunication [8, 9, 10, 11]. Despite a consistent progress in the methods of generation, detection and manipulation of the THz waves, the need for the improvement of existing technologies and the development of new ones is as easy as ever. One of the interesting possibilities is the use of diffractive optical elements (DOEs) for the formation of almost arbitrarily chosen amplitude and phase patterns [12]. This is especially appealing in the lower-frequency part of the THz spectrum, because of the wavelength dependence of diffractive patterns. The size of the smallest details of DOEs depends on the design wavelength (DWL) and it is usually in the order of small fractions of it. An analogy to the visible spectrum may be useful, where optical elementswith a roughness not exceeding \(\lambda/4\) are considered sufficiently good, for example, in holography [13]. Therefore, with the DWL in the order of single millimeters or its fractions, DOEs can be fabricated with a relatively simple and very cost-efficient technique of additive manufacturing, whereas structures designed for visible or near-infrared light require much more advanced manufacturing methods, such as photolithography, laser ablation [14; 15] or electron-beam lithography [16].  
One of the unique solutions achievable with diffractive optics is multi-focal-point lenses with a large \(f\)-number value (the ratio of focal length to the diameter of the structure). Such elements allow radiation to be focused on several precisely defined spots in a single focal plane, which could be used in dedicated systems for multiplexing of the wireless THz data transmission channel, tomography or imaging. The concept of three-focal-spot diffractive THz lenses, as well as the iterative optimization algorithm, have already been described in our previous papers [17; 18]. These results have shown that a reliable characterization of such elements can be a challenge on its own. The precise knowledge of the effects introduced by the detector parameters (such as, for example, directivity) on the distribution of registered focal points is even more important from the perspective of the application of such structures to multiplexing systems. In this article, we focus on the issue of the applicability of various detectors to multiplexing setups based on multi-focal-point lenses. It has been realized not only by the characterization of the detectors but also by experimental verification of three-focal-point lenses with high f numbers, analyzing and comparing different types of THz receivers, focal point distributions and detector configurations.
# A Blockchain-Based Secure Inter-Hospital EMR Sharing System
## 3 The Proposed Scheme
### Inter-Hospital Authentication and Communication Phase
* After the diagnosis of the patient, hospital B stores the medical records of the patient in the server and generates the encrypted basic inspection report \[c_{HPB2}=E_{SEK_{BP}}(EMR,Cert_{HPB}),\] (70)  
\[Sig_{HPB}=S_{SK_{HPB}}(EMR,Cert_{HPB}), \tag{71}\]  
If the identity \(ID_{PAT}\) is valid, hospital B calls the smart contract mrins and p and sends \((ID_{HPB},c_{HPB2},Sig_{HPB})\) to the patient. Step 11: The patient decrypts the received message,  
\[(EMR,Cert_{HPB})=D_{SEK_{BP}}(c_{HPB2}), \tag{72}\]  
verifies the signature,  
\[(EMR,Cert_{HPB})\stackrel{{?}}{{=}}V_{PK_{HPB}}(Sig_{HPB}). \tag{73}\]  
and receives the encrypted basic inspection report from hospital B.
## 3 Smart Feasant Farming System and Data-Mining Process
### IoT-Based Hardware Platform  
The foundation of this layer is a general purpose microcontroller-based development board wired to the transceiver module of the WSN acting as the root node. In this regard, some general purpose electronic prototyping platforms such as Arduino and the family of Discovery Boards offered by STMicroelectronics have microcontroller-based development boards especially designed for the IoT, such as Arduino 33 IoT and B-L475E-IOT01A, respectively.  
These microcontroller boards usually facilitate integration with IoT platforms, cloud services or mobile and web development platforms such as Blynk, Amazon Web Services, or Google Firebase, respectively; nevertheless, they tend to be relatively more expensive than general purpose microcontroller boards, e.g., Arduino UNO.  
Furthermore, one of the major components of the IoT-based hardware platform layer is the wireless networking module which must enable access to the Internet to communicate with the cloud (represented by the cloud-computing platform layer).  
Due to the characteristics of the domain of application of our system, it will not normally be located near WiFi access points, so using Wireless Local Area Networks to connect it to the Internet would not be a viable option. Therefore, we have chosen to use cellular networks as an alternative networking technology in this regard.
# Preliminary Insights into 3D Cheiloscopy for Forensic Applications: A Pilot Study
## 1 Introduction
This pilot study aimed to explore the feasibility and advantages of applying 3D technology to cheiloscopy. Specifically, it evaluated the precision, repeatability, and potential for standardization of 3D cheiloscopy in comparison to traditional analog methods. By addressing these aspects, this research sought to establish a foundation for future studies and forensic applications of 3D cheiloscopy. The findings could contribute to the development of a standardized lip print database, ultimately improving the accuracy and reliability of forensic lip print analysis.
## 4 Discussion
### Comparison with Existing Studies and Implications for Future Research  
Several prior studies have examined the impact of IVR-based learning compared to traditional instructional methods in physics education [67; 68]. Research has shown that IVR environments significantly enhance student engagement, motivation, and conceptual understanding by allowing learners to directly interact with scientific phenomena in immersive settings [69, 70]. For instance, studies such as [8, 71] have demonstrated that students using IVR for physics learning often outperform those in conventional classroom settings, particularly in areas requiring spatial reasoning and conceptual visualization.  
However, while IVR-based learning provides an interactive and engaging alternative, it is not intended to replace traditional teaching methods but rather to complement them [60]. Physics education requires a structured learning approach, where lectures, problem-solving exercises, and theoretical discussions remain crucial for deepening conceptual understanding [72]. The integration of IVR-based serious games as a supplementary tool allows students to visualize and interact with abstract concepts, making the learning process more intuitive and engaging [73, 74].  
While our study demonstrated significant learning gains using \"Parabolic Basketball VR\", the lack of a control group prevents direct comparisons with traditional instructional methods. Future research should address this gap by conducting randomized controlled trials (RCTs) that compare IVR-based learning with traditional classroom instruction, measuring not only immediate conceptual understanding but also long-term knowledge retention and the transferability of learning outcomes [75]. Additionally, factors such as cognitive load, engagement levels, and user experience should be analyzed to optimize the effectiveness of IVR-based educational tools [76, 77].  
By expanding the scope of experimental designs and incorporating control groups, future research can provide stronger empirical evidence on the comparative advantages of IVR-based physics education, ultimately informing best practices for integrating immersive learning technologies into academic curricula. A balanced approach, where IVR complements traditional teaching methods, has the potential to create a more effective and engaging physics education framework.
## 3 Numerical Algorithms and Computational Methodologies
### Dimensionless Parameters  
As detailed in [38], it is desirable to report the results with respect to dimensionless parameters. This allows the results to be translated more generally to pipes/geometries of arbitrary size and also to general flow conditions. Results that are based on specific geometric dimensions and specific flow conditions would otherwise be case-specific and not easily generalisable. The relevant dimensionless parameters for the core region are, respectively, the Reynolds number (\(Re_{c}\)), the Prandtl number (\(Pr_{c}\)), the Deborah number (\(De\)), the ratio of polymer to total viscosity (\(\beta\)), and the Giesekus nonlinear parameter (\(\epsilon\)):  
\[Re_{c}=\frac{\rho_{c}^{*}U_{t_{0}}^{*}L^{*}}{\eta_{c}^{*}},\ Pr_{c}=\frac{C_{p_{c }}^{*}\eta_{c}^{*}}{K_{c}^{*}},\ De=\frac{\lambda^{*}U_{t_{0}}^{*}}{L^{*}},\ \beta=\frac{\eta_{p}^{*}}{\eta_{c}^{*}},\ \epsilon=\frac{L^{*}}{\eta_{c}^{*}\mu_{c}^{*}U_{c _{0}}^{*}}\alpha^{*},\]  
where \(U_{c_{0}}^{*}\) is a reference flow velocity in the core region, say, the constant core-fluid velocity at the inlet. The relevant dimensionless parameters for the shell region are, respectively, the Reynolds and Prandtl numbers, as well as the ratio of nanoparticle to base-fluid thermal conductivities:  
\[Re_{s}=\frac{\rho_{s}^{*}U_{s_{0}}^{*}L^{*}}{\eta_{s}^{*}},\ Pr_{s}=\frac{C_{p_{c }}^{*}\eta_{s}^{*}}{K_{f}^{*}},\ \kappa=\frac{K_{s}^{*}}{K_{f}^{*}},\]  
where \(U_{s_{0}}^{*}\) is a reference flow velocity in the shell region, say, the constant shell-fluid velocity at the inlet.  
Figure 3: Illustration of mesh independence.  
Unless otherwise indicated, the subsequent simulations are carried using the following parameter values:  
\[\begin{array}{ll}\text{core-fluid:}&Re_{c}=0.6,\ Pr_{c}=0.8,\ De=0.4,\ \varepsilon=0.2,\ \beta=0.5,\\ \text{shell-fluid:}&Re_{s}=90,\ Pr_{s}=3,\\ \text{thermal-conductivity:}&\kappa=654.16,\ \aleph=3,\ \alpha=0.2,\ A_{2}=0.5,\ \varphi=0.2. \end{array}\]  
The thermal conductivity ratio, \(\kappa\), is calculated from the values, \(K_{s}=401\ \text{Wm}^{-1}\text{K}^{-1}\) and \(K_{f}=0.613\ \text{W}/(\text{m}\cdot\text{K})\). In the subsequent graphical results, if any of the above parameters are varied, it is understood that the other parameters will be kept at the given values.
###### Abstract  
The aim of this study was to analyse different success models and split time on cut-off point values on physical demands to keep category in semi-professional football players. An ad hoc observational controlled study was carried out with a total of ten (840 match data) outfield main players (25.2 \(\pm\) 6.3 years, 1.79 \(\pm\) 0.75 m, 74.9 \(\pm\) 5.8 kg and 16.5 \(\pm\) 6 years of football experience) and monitored using 15 Hz GPS devices. During 14 official matches from the Spanish division B in the 2016/2017 season, match data were coded considering the situational variable (score) and classified by match results (winning, losing or drawing). The results show significant differences between high-intensity attributes criteria that considered split time in velocity zones of 0-15 min (\(p\) = 0.043, \(\eta\)p\({}^{2}\) = 0.065, medium), 30-45 min (\(p\) = 0.010, \(\eta\)p\({}^{2}\) = 0.094, medium) and 60-75 min (\(p\) = 0.015, \(\eta\)p\({}^{2}\) = 0.086, medium), as well as sprint 60-75 min (\(p\) = 0.042, \(\eta\)p\({}^{2}\) = 0.066, medium) and 75-90 min (\(p\) = 0.002, \(\eta\)p\({}^{2}\) = 0.129, medium). Decision tree induction was applied to reduce the disparity range of data according to six 15-min intervals and to determine the cut-off point values for every parameter combination. It was possible to establish multivariate models for the main high-intensity actions criteria, allowing the establishment of all rules with their attributes and enabling the detection and visualisation of relationships and the pattern sets of variables for determining success.  
competition analysis; GPS technology; decision tree; fuzzy set; football 2020; 10.3390/app10155299
# Scaling of FAPAR from the Field to the Satellite
## 3 Methodology
### Temporal Normalization
#### 3.2.2 Estimation of Skylight Proportion  
Based on the FAPAR-P model, the most relevant factors accounting for the diurnal variations of FAPAR are SZA and skylight proportions. The proportion of skylight is also highly variable with SZA, as well as aerosol optical depth (AOD), visibility (VIS) and cloud cover. As real-time measurement of skylight proportions are not existed, it is difficult to determine the value of skylight proportion. The major task becomes how to determine the proportion of skylight from relevant environmental factors.  
The 6S atmospheric radiative transfer model is employed to model the skylight proportion. Occasional cloud was not considered here, as it is random and hard to measure, which would further complicate the modeling. For a small study area stretching several kilometers, the AOD was regarded as constant spatially and temporally, while the VIS was assumed to be constant in space at the same day. Three VIS cases, including mildly hazy, moderately clear and clear air conditions, were considered, corresponding to VIS = 5 km, 15 km and 30 km in 6S. The atmospheric condition and aerosol model in 6S were set to standard midlatitude summer and continental type, respectively. By running 6S with three VIS cases and varying SZAs, the relationships between the skylight proportions and SZAs were derived and were further fitted using exponential equations (Figure 3), expressed as:  
\[\beta =0.473\times\exp\left(0.172\times\sin\left(\theta\right)\right)+2.17 5\times 10^{-4}\times\exp\left(7.62\times\sin\left(\theta\right)\right) \tag{2}\] \[\beta =0.254\times\exp\left(0.400\times\sin\left(\theta\right)\right)+1.0 80\times 10^{-7}\times\exp\left(15.43\times\sin\left(\theta\right)\right)\] (3) \[\beta =0.186\times\exp\left(0.249\times\sin\left(\theta\right)\right)+7. 322\times 10^{-9}\times\exp\left(18.08\times\sin\left(\theta\right)\right) \tag{4}\]  
which corresponds to VIS = 5 km, 15 km and 30 km, sequentially. The goodness of the fits were evaluated in root mean squared error (RMSE) and the correlation coefficient (\(R^{2}\)), where RMSE = 0.007, 0.014, and 0.013 and \(R^{2}\) = 0.999, 0.996, and 0.996 for Equations (2)-(4), respectively.