esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Recent Advancements in Applications
### Forest Management  
Trees, as the fundamental building blocks of forests, are the primary focus of studies in MMS forest and green area applications. Recognizing individual trees from point clouds is an important task that requires special attention [114]. Counting the number of trees can help estimate forest biomass and is also beneficial for wood production industries [115]. Identifying tree species is equally crucial, as it provides insights into biodiversity and ecosystem health [116]. Investigating the spectral characteristics of trees can enhance our understanding of biophysical and biochemical properties of forests [117].  
Many different aspects related to individual trees can be measured remotely by MMSs. For instance, indicators such as irrigation status, dehydration levels, and leaf area index can be assessed by analyzing the spectral data of trees [118; 119]. Additionally, pest detection is possible using spectral analysis, which can help in early intervention and forest management [120]. These applications underscore the importance of integrating MMSs with spectrometry sensors, as they enable comprehensive and efficient monitoring of forest ecosystems, contributing to sustainable management and conservation efforts.
## 2 Methodology
### Node Adjustment  
To adjust the nodes, overlay the random nodes obtained in the previous step with the interface and boundary nodes, and discard the nodes within a half radius of any boundary node, as shown in Figure 3b. After the redundant nodes are discarded, the nodes at the internal interface and the boundary will be unevenly distributed. Therefore, we propose a node adjustment algorithm based on the centroid Voronoi structure. This algorithm divides Voronoi polygons into multiple polygonal regions formed by the boundary and interfaces, and uses the centroid point of the Voronoi polygon as the new node location. Since the distribution of the nodes before adjustment is relatively uniform, only 10-15 iterations are needed to evenly distribute the nodes near the interface. This method has good stability and can obtain a more uniform node distribution, however, since the conventional Voronoi polygon division is borderless, the Voronoi polygon division requires computing the intersection of the Voronoi diagram and the irregular boundary, which leads to a high computation cost. To solve this problem, GPU was used to accelerate the Voronoi polygon division and the adjustment of the centroid points in the irregular boundary area.  
The algorithm takes the internal node and boundary node coordinates as its inputs, and its main steps are as follows:  
1. Calculate the coordinates of the \(k\) points closest to each node, and connect the node and the \(k\) points into a line segment Voronoi unit (see Figure 3);
2. Calculate the intersection between each Voronoi element and the boundary polygon, and trim the Voronoi diagram (see Figure 3);
3. The centroid of the Voronoi element is calculated and used as the new node position;
4. Repeat the above three steps 10-15 times to get the final node distribution.  
Figure 4 shows the node generation steps for the two-layer simple model. It can be seen from the figure that the algorithm first generates variable density nodes through Poisson disks in the boundary region (Figure 4a). Then, the nodes that are too close to the interface nodes are deleted, and nodes with an uneven distribution at the boundary are obtained (Figure 4c). Finally, variable density internal nodes, and a uniform distribution of boundary nodes, are obtained through the centroid Voronoi adjustment algorithm (Figure 4d). The figure shows not only that the final node distribution is accurately arranged at the interface location, but also that the overall nodes layout is evenly distributed across the different regions.  
Figure 3: Generation steps for the Voronoi element under the restriction of an irregular boundary (step (a) and step (b)) [30]. \(X_{i}\) is the point to be adjusted, and \(X_{1}\)-\(X_{5}\) are the five points surrounding \(X_{i}\).
# Bi-Directional Point Flow Estimation with Multi-Scale Attention for Deformable Lung CT Registration
## 3 The Proposed Approach
### The Training Objective
#### 3.5.3 The Final Training Objective  
Our complete multi-scale training objective integrates these two losses across all resolution levels:  
\[\mathcal{L}=\sum_{l=1}^{L}\Big{(}\mathcal{L}_{\text{flow}}^{(l)}+\lambda\cdot \mathcal{L}_{\text{cycle}}^{(l)}\Big{)}, \tag{18}\]  
where the hyperparameter \(\lambda\) controls the influence of the cycle consistency regularization and is empirically set to 0.1.
# Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT
## 3 System Models and Problem Formulation
### Communication Latency Considering Mobility  
The communication latency for each client to upload its local model update to the BS depends on its communication conditions and mobility. The achievable data rate for client \(u\), denoted as \(r_{u}(t+k)\), is given by the following:  
\[r_{u}(t+k)=B\log_{2}\left(1+\frac{p_{u}h_{u}^{2}}{N_{0}}\right), \tag{3}\]  
where \(B\) is the total available bandwidth allocated to all participating clients, \(p_{u}\) is the transmission power of client \(u\), \(h_{u}\) is the channel gain, which varies based on the location and mobility of client \(u\), and \(N_{0}\) is the background noise power.  
The uploading latency, \(t_{u}^{\text{upload}}\), for client \(u\) to transmit its local update to the BS is as follows:  
\[t_{u}^{\text{upload}}=\frac{s}{r_{u}(t+k)}, \tag{4}\]  
where \(s\) is the size of the model update. Clients with higher data rates experience lower transmission delays, while those with weaker connections require more time to complete the upload or may fail. MACS selects clients with stable connections to ensure reliable participation and reduce delays in global model aggregation.
# Multi-Objective Optimization of Production Objectives Based on Surrogate Model
## 4 Results  
### Derivation of Surrogate Models of Production Objectives  
To fit the response surface, we performed DoE with two types of response surface designs, Face-Centered Design (FCD) and Box-Behnken Design (BBD) for four factors in Design-Expert(r) software by Stat-Ease (Minneapolis, MN, USA). It provided the polynomial regression models for four selected production objectives as models of the input-output behavior of the underlying simulation model. Based on the ANOVA analysis by F-test of the overall statistical significance of the model and t-tests of the statistical significance of individual regression coefficients, Equations (11)-(18) in terms of actual factors represent obtained response surface models for both types of design. The Lack of Fit testcould not be performed due to no variance in the central point, because the applied simulation model has been deterministic.  
As for BBD, the fitting model was linear for the average flow time and machine utilization responses according to Equations (11) and (13); others were reduced quadratic models for costs per part unit and number of products regarding to Equations (12) and (14).  
\[\begin{split}\textbf{Flow time}=435.07580+125.17042\ ^{*}\text{ LotSizeP1}+120.09642\ ^{*}\text{ LotSizeP2}-138.29592\\ \
# Challenges and Implications of Virtual Reality in History Education: A Systematic Review
## 1 Introduction
### Research Questions and Purpose  
This review aims to identify the most common challenges associated with the application of VR in history learning environments. Through a comprehensive analysis of the recent literature, this study seeks to address the following research questions:  
* What are the most prevalent technical, usability, and economic challenges faced by educators when integrating VR into history instruction?
* What psychological, social, and ethical issues emerge from the use of these technologies in history education?
* Do history educators require specialized training to effectively use VR technologies?  
The purpose of this review is to contribute to the field of VR by addressing gaps identified in prior studies and reviews. Specifically, the main objective is to highlight the challenges these technologies present in history and heritage education, offering a more comprehensive perspective on their implementation.  
This work is arranged as follows: in Section 2, we describe the methodology used for conducting the systematic review, including the criteria for article selection, search strategy, and analysis process. Section 3 provides a detailed synthesis and discussion of the findings, categorizing the studies based on key themes and identifying the challenges and limitations of using VR in history education. Finally, Section 4 concludes the paper by summarizing the key insights and offering recommendations for further investigation in this area.
## 3 Results
### Key Findings
#### 3.3.2 Insertional Achilles Tendinopathy
While 64% of patients with IAT who executed ESWT treatment were satisfied, the percentage of satisfied patients who executed HECT increased by an additional 18% [14]. Based on the claim from [PERSON] et al. [68], the most efficient monotherapy protocol for treating IAT was by using radial ESWT, which delivered 2000 impulses at 2.5 bars (with an energy flux density of 0.12 mJ/mm\({}^{2}\)) at 8 Hz for 3 sessions per week. The effects of ESWT treatment were found to be less effective when patients with IAT used a local anaesthetic before the treatment [52]. Even greater effects were observed after combining HECT and ESWT treatment [15; 39]. The combination of ESWT and ECC exercise showed a greater decrease in pain and improvement in function [15; 39] compared to ECC exercise alone [39]. The likelihood of a successful outcome after executing the combined protocol was up to 6.5-times greater than in the case of ECC exercise as a monotherapy [15]. As high as 74.3% of patients with IAT who executed ECC exercise with the ESWT protocol reported good-to-excellent patient satisfaction, while just 45.6% of patients were similarly satisfied after finishing just the ECC exercise protocol [39]. Similarly, as an implementation of ESWT to ECC exercise, the implementation of cold air and high-energy laser therapy to ECC exercise caused faster and greater pain decrease and greater patient satisfaction compared to each treatment as a mono intervention [39]. On the other hand, [PERSON] et al. [40] and [PERSON] et al. [39] reported that the implementation of ASTYM therapy to either HECT or modified HECT (with a lowered number of repetitions, series, or sessions) also caused an additional decrease in pain and improvement in function compared to exercise treatment alone. It was found that all participants who executed the combined ECC exercise and ASTYM therapy protocol had decreased pain and improved function, whereas just 50% of patients who executed the ECC exercise-only protocol were found to be similarly successful at a 12-week follow-up [40]. Statistically significant differences between both groups were observed in the short term and as long as 26 to 52 weeks after treatment ended [15; 39; 40]. Nevertheless, each of the treatments, cryoultrasound therapy, carbon dioxide laser, or TECAR therapy, decreased the pain of all patients with IAT who were treated that way, which was seen at an 8-month follow-up (55, 61). The cryoultrasound therapy was found to be significantly better than carbon dioxide laser or TECAR therapy, whereas there was no significant difference between the latter two (55, 61). Table S4 contains an overview of the included reviews that researched the effects of conservative nonpharmacological interventions for treating IAT.
## 2 Materials and Methods  
### Overview  
In general, images without reference objects may contain different objects or scenes, making it difficult to match feature points. To find an appropriate match between different objects, a more sophisticated feature point extraction and matching algorithm is needed. Non-reference images can have various transformations and distortions, which can make accurate position estimation challenging when stitching images or creating 3D point clouds. Therefore, in this experiment, we attempted to address these limitations by applying Laser Points to the images. Laser Points were used to correct images with various deformations and distortions and served as corresponding points for image correction using a homography matrix.  
Through this, the accuracy of image matching and location estimation can be improved, images are corrected using distortion correction and matching algorithms, and the location of cracks is estimated. Finally, we intend to conduct a position estimation experiment via the spatial information constructed using the aerial image obtained by operating the unmanned aerial vehicle equipped with the laser pointer model and the image processing technique. Figure 1 depicts the visualized flow chart for estimating the location of cracks in the exterior walls of buildings using unmanned aerial vehicles without the reference objects in the image. The proposed method comprises the following steps: (1) Data Acquisition: UAV-based aerial photography. (2) Construction of analysis data using image processing technique: (a) Distorted image correction using homography matrix and (b) Feature point extraction using SIFT algorithm. (3) Crack localization: (a) Construction of spatial information using image processing techniques, (b) Merging of spatial information layers based on point cloud technique and image stitching technique, and (c) Crack localization and Data validation.
# Cooperative Multi-Objective Optimization of DC Multi-Microgrid Systems in Distribution Networks
## 2 System Structure
### Bidirectional DC/AC Converter  
The bidirectional DC/AC converters are used to connect the MMGS and the distribution network, which can output active and reactive power with the distribution network. DC/AC converters use power factor correction (PFC) to obtain the unity power factor [21]. Therefore, the DC/AC converters are set to the unity power factor in this paper when the reactive power is not optimized [22]. However, by using the appropriate pulse-width modulation (PWM) switching technique, the power factor is adjusted to control the reactive power output of the DC/AC converters to the distribution network [22]. This is the basis for reactive power optimization.
# Process Optimization and Wear Performance of Plasma-Cladding Fe5 Coatings on Rotary Tillage Blades
## 3 Results and Discussion
### Structure and Properties of Fe5 Coatings Under Optimized Conditions
#### 3.2.1 Microstructure of Fe5 Coatings
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Location**} & \multicolumn{5}{c}{**Atomic Percent (\(\%\))**} \\  & **Fe** & **Cr** & **C** & **Si** & **Ni** \\ \hline a & 36.4 & 52.1 & 11.5 & - & - \\ b & 36.5 & 52.8 & 10.3 & - & - \\ c & 35.7 & 51.7 & 12.4 & - & - \\ d & 75.1 & 20.4 & - & 2.9 & 1.6 \\ e & 74.3 & 12.1 & 7.0 & 4.6 & 2.1 \\ \hline \end{tabular}
\end{table} 
Table 6: Energy spectral analysis of the plasma-cladding Fe5 coatings. Figure 4: XRD diffraction pattern of the plasma-cladding Fe5 coatings. Figure 3: Cross-sectional morphology of the plasma-cladding Fe5 coatings observed by SEM: (**a**) whole coatings; (**b**) upper part of the cladding layer; (**c**) middle part of the cladding layer; (**d**) lower part of the cladding layer. due to their low concentration, they are not prominently visible in the X-ray diffraction pattern and, thus, have not been labeled. Figure 6 presents the elemental distribution map of the cladding layer. Analysis of the figure reveals that Cr is predominantly concentrated in the hard phases, and it exhibits needle-like, hexagonal, and triangular cellular aggregation zones, existing primarily in the forms of Cr\({}_{7}\)C\({}_{3}\) and Cr\({}_{2}\)Fe\({}_{14}\)C. These compounds provide excellent wear resistance to the cladding layer. C, on the other hand, is dispersed throughout both the hard phases and the matrix phase. The distribution areas of Si and Ni are largely similar to those of Fe, as they are present in the matrix phase as compounds and solid solutions. The microstructures formed by these elements contribute to maintaining a certain level of toughness in the cladding layer, thereby enhancing its overall mechanical properties.
## Materials and Methods  
### Samples, Standards and Solvents  
Fresh mature orange (_Citrus sinensis_) cv. Changyecheng (Long-leaf Orange) fruits were purchased from a local orchard. The long-leaf orange, a recently-developed cultivar that is being increasingly planted in China, had been selected as a mutation from a common orange seedling in Chongqing,China. This cultivar was approved by the Crop Variety Approval Committee of Chongqing in 2014 as having excellent potential for both fresh eating and juice processing.  
The orange peel oil was obtained by using a cold-pressing method mechanically similar to that used in industry. The fruit flavedo layer was manually peeled and extracted using a household squeezer. The aqueous peel oil emulsion was collected and quickly centrifuged at 12,000 rpm at 4 \({}^{\circ}\)C for 10 min. The upper-most oil layer was then collected into large 50-mL tapered centrifuge tubes and frozen at \(-80\)\({}^{\circ}\)C for two weeks to remove residual water. Anhydrorus orange peel oil (OPO) was stored at 4 \({}^{\circ}\)C until subsequent sample preparation, chromatographic analysis and animal experiments.  
HPLC-grade standards of nine polymethoxyflavones (PMFs) were used to identify the constituents. Isosinensetin, tetramethyl-O-isoscutellarein, tetramethyl-O-scutellarein, heptamethoxyflavone and 5-demethylnobiletin were purchased from Yuanye Biotech (Shanghai, China); tangeretin was purchased from ChromaDex (Irvine, CA, USA); sinenstein and nobiletin were purchased from Toronto Research Chemicals (Toronto, ON, Canada); and 3,5,6,7,3\({}^{\prime}\)A\({}^{\prime}\)-hexamethoxyflavone was purchased from Purify Tech Co. Ltd. (Chengdu, China). Stock solutions were prepared of the individual standard at concentrations of approximately 1 mg MI\({}^{-1}\) in methanol and stored at \(-20\)\({}^{\circ}\)C. All reference standards were analyzed by employing a Thermo Ultimate 3000 HPLC with a Thermo Quantiva triple quadrupole electrospray ionization tandem mass spectrometer; the results showed that the purities of the stock solutions were all \(\geq\)98%. The stock solution of every standard compound was diluted in series so the calibration equation could be determined.  
A GC-grade \(D\)-limonene reference standard was purchased from Toronto Research Chemicals (Toronto, ON, Canada), and its purity was found to be \(\geq\)95% using an Agilent 7890B GC equipped with an MS detector.  
HPLC-grade acetonitrile, methanol, hexane and tetrahydrofuran (THF) were purchased from Honeywell (Morris Plains, NJ, USA). Analytical-grade phosphoric acid was purchased from Kelong Co. (Chengdu, Sichuan, China). Water was freshly purified using a Milli-Q plus Advantage A10 system (Molsheim, France).
## 7 Conclusions  
The propagation properties of waves in saturated soil are the theoretical basis for analyzing the dynamic problems of saturated soils. The waves and their propagation properties are also different in saturated soils with different permeabilities. In this paper, the following research works are carried out for saturated soils with different permeabilities, and the corresponding conclusions are obtained:  
1. Based on the _u-w-p_ formulation of saturated porous medium, the wave equations and the corresponding velocities of P1, P2, and S waves are acquired under zero, finite, and infinite permeabilities, respectively.
2. The differences in the permeability and loading frequency of saturated soils have different influences on propagation properties, such as the velocities, dispersion and attenuation characteristics.  
Figure 4: Velocities obtained from _u-p_ and _u-w-p_ formulations and relative errors of velocities in saturated gravel soil.  
3. For zero and infinite permeabilities, P1, P2, and S waves are not dispersed and attenuated in the propagation process. Wave velocities keep constants.
4. For the finite permeability, the solutions of wave equations have real and imaginary parts, which represent the wave shape and attenuation during the propagation process, respectively. The actual velocities are the real parts of the velocities.
5. In different saturated soils, the variation tendency of wave velocities and attenuation coefficients are similar, but the amplitudes of velocities and attenuation coefficients have obvious discrepancies. The harder the soil, the faster the wave velocity. The softer the soil, the faster the attenuation wave is.
6. As a simplified formulation, the application scope of the \(\mathbf{u}\)-\(p\) formulation is further discussed. The \(\mathbf{u}\)-\(p\) formulation can be safely used as permeability \(k\) is less than \(10^{-2}\) m/s and frequency \(\omega\) is less than 1 Hz.  
More simplified models, such as artificial boundaries, contact models, and algorithms, can be obtained based on the u-p formulation by selecting a suitable application scope in the future.  
Conceptualization, J.S.; data curation, J.S. and L.L.; funding acquisition, J.S. and C.X.; methodology, J.S. and L.L.; project administration, C.X.; software, J.S.; supervision, C.X.; validation, J.S. and L.L.; writing-original draft, J.S.; writing-review and editing, L.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China, grant numbers 51808006 and 51722801, and the Natural Science Foundation of Beijing, China, grant number 8192012.  
The authors would like to thank the National Natural Science Foundation of China (Grant No. 51808006 and No. 51722801) and the Natural Science Foundation of Beijing, China (Grant No. 8192012) for funding the work presented in this paper.  
The authors declare no conflict of interest.
## 2 Clinical Background
### Measuring EAT and PAT  
The most common way to quantify EAT is to measure EAT thickness from echocardiography. EAT thickness is usually measured as the thickness of EAT perpendicular to the free wall of the right ventricle, usually end-systole (at the end of a heartbeat). EAT thickness is used as a middle-ground that provides better accuracy than surrogate markers like waist circumference but is more practical than CT or MRI in a clinical setting [22]. Still, estimating EAT volume from thickness at a single point can produce inaccurate results.  
CT and MRI have greater sensitivity and measure 3D volumes, meaning they can more accurately measure EAT directly [22]. However, manual accurate quantification of EAT and PAT from CT or MRI is time-consuming and prone to inter-observer variability because of how distributed these tissues are around the heart. In literature, inter-observer variability for EAT volume measurement ranged in percentage from 7% to 14% [6,23,24] and in Dice scores from 0.89 to 0.91 [7,25,26]. Manually segmenting EAT from CT scans can last from a few minutes to an hour, depending on the number of slices in a scan and used software [9]. As another surrogate marker, [PERSON] et al. [27] shows that it is possible to use machine learning to predict the volume of paracardial adipose tissue volume from EAT  
Figure 3: The anatomy of the pericardium, EAT and paracardial adipose tissue on a CT scan [20].  
volume and vice-versa. Their method achieves good correlation (\(r=0.9683\) for predicting EAT) but still requires measuring one of the tissues directly. However, this might reduce the need to measure both tissues, thus saving either time to manually segment the tissues or computing power in the case of automatic methods.