esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Principles of Operation
### Energy Flow in the System
#### 3.4.2 Energy Flow Chart  
An energy flow chart is shown in Figure 5. Wind energy is the only energy source for the whole system. Wind power is mainly used to supply electrical demand of the targeted household application. This portion of wind power denotes \(p_{dir}(k)\). The rest of wind power is stored in the form of hydrogen and oxygen via water electrolysis while the power consumed is \(p_{H_{2}O}(k)\). The extra energy generated by the wind turbine is fed to the smart grid. In case the demand exceeds the wind power, the fuel cell starts to generate electricity and act as the back-up power supply.  
Figure 5: Energy flow in the proposed system.
## 1 Introduction
The example provided in Section 3, from the WestConnex Motorway tunnels, demonstrates the first application of the V-GSI systems to tunnels. The purpose of this publication is to provide details on the method used to estimate the tunnel rock mass _VFC_ and the use of the V-GSI chart to determine the strength and deformability of the tunnel rock mass. The intent of this publication is not to provide details of the V-GSI ratings for the entire M8 Westconnex Motorway project. Rather, one hypothetical example is initially provided (Section 2) to demonstrate the method used to estimate _VFCs_ on this project in the hope that others can use the system in a tunnel environment. This is followed by providing tunnel mapping results from one actual location in the tunnel, along with the estimated _VFC_ (Section 3). Validation of the V-GSI system for use in tunnelling comes from undertaking 3-D modelling with RS3 by Roccience Version 4.024, (c 2022 Roccience Inc., Toronto, ON, Canada) in the same location. Input into the model includes the V-GSI rating in this location and the assessment of the intact rock strength--all from the tunnel mapping records. The tunnel deformation predicted at this location was estimated by the modelling and is compared to real time monitoring data in the same location. This shows that the V-GSI system can be used with some confidence in underground works as well.
## 2 The Design of the Element
### The Simulated Results  
The element is simulated under radiation boundary conditions, and the simulation results are shown in Figure 2. As shown in Figure 2a, the simulated S\({}_{11}\) of the element is less than \(-10\) dB from 107 to 117 GHz. As shown in Figure 2b, the peak gain is stable at 5.4-5.64 dBi within the bandwidth. As shown in Figure 2c, the E-plane half-power beam width within the bandwidth is between 85.4 and 98.3\({}^{\circ}\), and the H-plane half-power beam width is between 100 and 123\({}^{\circ}\), showing a wide-beam characteristic. Figure 2d presents the radiation pattern at 112 GHz, with a normal gain of 5.58 dBi, and the half-power beam widths in the E-plane and H-plane are 94.8\({}^{\circ}\) and 122.4\({}^{\circ}\), respectively. Additionally, the cross-polarization levels in both the E-plane and H-plane are below \(-40\) dB.  
The proposed element will eventually be used for PAA design, so the performance under periodic boundary conditions also needs to be considered [30; 31; 32]. The simulation results of the element under periodic boundary conditions are shown in Figure 3. As shown in Figure 3a, the S\({}_{11}\) bandwidth is 106-119 GHz, and the active VSWR is less than 2 within the range of 105.5-119 GHz. As shown in Figure 3b, the peak gain under periodic boundary conditions is stable at 4.1-5 dBi within the bandwidth.  
Figure 3: The periodic boundary simulation results of the element. (**a**) S-parameter and VSWR. (**b**) Peak gain.  
Figure 2: The radiation boundary simulation results of the element. (**a**) S-parameter. (**b**) Peak gain. (**c**) Beam width. (**d**) Radiation pattern at 112 GHz.  
As shown in the element simulation results, the element exhibits characteristics such as wideband, wide beam, stable gain, and low cross-polarization, meeting the basic requirements for the design of a wide-angle scanning PAA [33; 34; 35].
# What is 5G? Emerging 5G Mobile Services and Network Requirements
## 4 5G Mobile Services
### Autonomous 5G services
#### 4.4.2 Robots  
Robots are on a high-growth trajectory within both the industrial and the consumer sectors. 5G will provide networking functions that are necessary to allow various industries to leverage the next phase of robot evolution. Moreover, the information flow between assemblies and control systems with robots is another example of 5G services. 5G will pave the way for a new generation of robots. Some robots that can freely move are controlled via wireless rather than wired communication links and exploit the vast computing and data storage resources of the cloud. Armed with these capabilities, robots can be precisely controlled dynamically in near real time and be connected to people and machines locally and globally. The use cases of 5G in robots include teleoperation and smart industrialization services. Teleoperation refers to the concept in which a robot is controlled by a person or a control center remotely. Remote surgery in Figure 10 and remote sensing are applications of teleoperation [26]. To understand the synergy between 5G and robots, healthcare is one of the best examples where robotics has immense potential. Robots may not only perform mundane functions, such as transferring things, but also perform telesurgery aided by 5G communications and cloud networks. Remote surgery services also involve transmitting 3D video data of the surgical site and medical data related to the patient in the real time. It may also involve controlling the surgery robot based on received data and delivering control information to perform surgical actions. Robot services  
Figure 9: Smart transportation services.  
may also involve sending a robot in a radioactively-contaminated area and controlling it remotely based on the video and sensor data captured by the robot. Smart industrialization [27] refers to replacing human workforce with robots that can perform the same work repeatedly, such as assembly processes using robots. There are several limitations associated with the current technologies, such as latency, reliability and high throughput, for such applications of robots. Therefore, 5G for robot services provides solutions to these limitations.
## 1 Introduction
Hence, from the point of view of the highest Seebeck coefficient at room temperature and band structure, Bi-Sb thin films potentially can be used as a sensitive element for THz detection. The bismuth-antimony thermocouple has already been considered as a possible basis for far-infrared detection in 1982 and showed relatively low noise equivalent power. In [26], an antenna based on \(n\)-type \(Bi_{0.87}Sb_{0.13}\) and \(p\)-type Sb thin films on silicon substrate was proposed for 0.812-THz frequency detection. The simulations showed a possible temperature difference of up to 7.3 K in vacuum sealing under the input power of 7.5 \(\mu\)W. The responsivity was 30 \(V/W\) in the air and 119 \(V/W\) in vacuum for a pixel with 8 thermocouples. In [27], an antenna made of 8 \(Sb-Bi_{87}Sb_{13}\) thermocouples was fabricated with 30-\(\mu\)m length and 2-\(\mu\)m width. It showed an output voltage of 0.1-0.7 \(\mu\)V. The main advantage of this antenna was the possibility of room-temperature operation, low noise equivalent power (from 100 to 1000 pW\(\cdot\)Hz\({}^{-0.5}\)), and a fast response of 22 \(\mu\)s.  
Finite elements simulation method showed itself as a reasonable and effective tool for THz antenna modelling, making it possible to combine thermal, electrical, and optical phenomena in one multiphysical model. For example, in [28], THz-resonant antenna was simulated to investigate the photocurrent value as the function on the antenna length. The simulation results were in good agreement with the experimental measurements. In [26], an electromagnetic heating model was used in order to evaluate temperature increase due to THz radiation absorption. A line of works about FEM simulation of thermoelectric detectors of infrared radiation, which is close to THz range, has been presented in the literature [29]. The aim of this work is to simulate a THz detector based on Sb and \(Bi_{88}Sb_{12}\) TE thin films in order to evaluate possible heating of hot junction due to the electromagnetic heating at 0.3, 0.4, and 0.5 THz. Such a detector can be potentially used as a pixel of THz imaging system.
# Penetration Depth of Initiated and Non-Initiated Diode Lasers in Bovine Gingiva
## 3 Results  
The histological evaluation of the specimens showed differences in the penetration depth of the laser light between the continuous and pulsed emission modes at both laser wavelengths (Figure 1). The initiated tips created a more localized effect in the pocket epithelium. A histomorphometric comparison between the treatment groups demonstrating the amount of collagen fiber denaturation and the heat (energy) effects within the tissue is presented in Table 1. In the continuous mode, the initiated 445 nm laser presented a significantly different penetration depth compared to the 970 nm laser (\(p<0.0001\)). Similar effects were found for the non-initiated lasers (\(p<0.0001\)). In contrast, the pulsed mode lasers showed a statistically significant differences in penetration depth between initiated vs. non-initiated lasers for the 970 nm wavelength (\(p<0.001\)) but no statistical difference for the 445 nm laser (\(p=0.7939\)).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Experimental Group** & **N** & **Mean** & **Std. Dev.** & **Min.** & **Max.** & \(p\)**-Value** \\ \hline
970 nm C.W. Non-Initiated & 96 & 0.131 & 0.161 & 0 & 0.46 & \textless{}0.001 \\ \hline
445 nm C.W. Non-Initiated & 96 & 0.22 & 0.15 & 0 & 0.6 & \textless{}0.001 \\ \hline
970 nm C.W. Initiated & 96 & 0.20 & 0.14 & 0 & 0.8 & \textless{}0.001 \\ \hline
445 nm C.W. Initiated & 96 & 0.35 & 0.20 & 0.09 & 0.8 & \textless{}0.001 \\ \hline
445 nm Pulsed Initiated & 96 & 0.13 & 0.14 & 0 & 0.48 & \textless{}0.7939 \\ \hline
445 nm Pulsed Non-Initiated & 96 & 0.13 & 0.15 & 0 & 0.5 & \textless{}0.001 \\ \hline
970 nm Pulsed Initiated & 96 & 0.10 & 0.10 & 0 & 0.46 & \textless{}0.001 \\ \hline
970 nm Pulsed Non-Initiated & 96 & 0.01 & 0.05 & 0 & 0.25 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between treatment groups when measuring the amount of collagen denaturation and thermal effect within the tissue (photothermal effects), which could be observed and measured through EVG staining (all measurements in mm; C.W.: continuous-wave mode).  
Figure 1: Histological samples of all experimental groups in continuous and pulsed modes. Coagulation of underlying tissue is visible via dark purple staining through EVG methods (\(1360\times 1024\) pixels, \(27.105\) zoom, \(4.141\) mm wide for all histological images).
## 7 Results and Discussion
### Comparative Analysis of Client Selection Strategies
#### 7.3.4 Privacy Loss over 300 Rounds  
The privacy loss Figures 30-33 (CIFAR-10) show that all methods start from the same initial privacy level and exhibit a noticeable decline during the initial training rounds. This decline is attributed to the fact that, during FL, participating clients share updated model parameters (weights and biases) with the server. Although raw data is not transmitted, these shared parameters can still reveal information about the local data distribution, leading to potential privacy leakage. After a dataset-specific round, the privacy loss curves stabilize. This stabilization occurs because the server stops selecting certain clients for training beyond that point. As a result, those clients no longer share updates, and their privacy loss remains constant. The method labeled \"Privacy\" shows the lowest overall privacy degradation, followed by \"RBPS (Ours)\", indicating stronger privacy-preserving behavior. In contrast, methods such as \"Accuracy\" and \"Random\" lead to higher cumulative privacy loss across all datasets.  
Figure 31: Privacy loss over 300 rounds (EMNIST).  
Figure 30: Privacy loss over 300 rounds (MNIST).  
Figure 32: Privacy loss over 300 rounds (Fashion-MNIST).
## 5 Conclusions  
This paper proposes a method for building extraction with multi-features using convolutional neural networks and superpixels for PolSAR images. G\({}^{0}\) statistical texture parameters and PauliRGB in the proposed method are used as a new feature set as input to CNN for training. The combination of polarization features and statistical texture features can reduce the influence of other classes with the same spatial structure as the building on the building extraction results. The superpixel constraint applied to the preliminary results decreases the effect of noise, optimizes the boundary of building extraction results, reduces the appearance of false alarms, and improves the overall effect of building extraction.  
In this paper, we use the PolSAR data from ESAR, GF-3, and RADASAT-2 sensors for experimental verification. The F1 parameter of building extraction accuracy is improved by  
Figure 13: Building elements used for training account for the accuracy of building extraction results at different percentages of all building pixels.  
at least 6%. The false alarm rate is reduced by at least 10% when the building extraction accuracy is comparable, which proves that our method is more suitable for the extraction of PolSAR buildings. After using the G\({}^{0}\) texture parameter, F1 is increased by about 5%. Furthermore, after the superpixel constraint, the evaluation parameter F1 increases by 2% to 4%. The proposed method achieved better building extraction results using CNN and superpixel with G\({}^{0}\) statistical texture and polarization features from PolSAR images.  
For instance, there are still some misclassifications in some cases, such as the edge error phenomenon and small house erasure phenomenon caused by the introduction of CNN, and the incomplete phenomenon of the building caused by different scattering mechanisms in different parts of the building. At present, deep learning requires a large amount of data, but the amount of data used in PolSAR building extraction is limited. More data can be used for support in the future.  
[PERSON] provided the thought. [PERSON] and [PERSON] wrote the manuscript; [PERSON] supervised, edited, restructured; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] professionally optimized the manuscript; Q.C. acquired the funding. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC), grant number 41771467.  
Not applicable.  
The authors would like to thank the funding and support from the China University of Geosciences, which we gratefully appreciate.  
The authors declare no conflict of interest.
## 2 Results and Discussion
### Profiling of Volatile Compounds in Sweet Potato
#### 2.4.2 Aldehydes
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **Anna** & **Jieshu95-16** & **Ayamurasaki** & **Shuangzai** \\ \hline Aldehydes & 8.95 \(\pm\) 0.56 \({}^{\text{c}}\) & 13.27 \(\pm\) 0.93 \({}^{\text{a}}\) & 11.09 \(\pm\) 0.417 \({}^{\text{b}}\) & 12.23 \(\pm\) 0.97 \({}^{\text{a}b}\) \\ Alcohols & 4.31 \(\pm\) 0.23 \({}^{\text{a}}\) & 0.95 \(\pm\) 0.24\({}^{\text{c}}\) & 2.19 \(\pm\) 0.32 \({}^{\text{b}}\) & 1.32 \(\pm\) 0.07 \({}^{\text{c}}\) \\ Ketones & 0.62 \(\pm\) 0.19 \({}^{\text{b}}\) & 0.70 \(\pm\) 0.16 \({}^{\text{b}}\) & 0.9 \(\pm\) 0.021 \({}^{\text{b}}\) & 1.9 \(\pm\) 0.12 \({}^{\text{a}}\) \\ Terpenes & 5.23 \(\pm\) 0.16 \({}^{\text{a}}\) & 2.62 \(\pm\) 0.19 \({}^{\text{b}}\) & 3.24 \(\pm\) 0.08 \({}^{\text{b}}\) & 2.87 \(\pm\) 0.38 \({}^{\text{b}}\) \\ Acids & 4.02 \(\pm\) 1.23 \({}^{\text{a}}\) & 3.28 \(\pm\) 0.66 \({}^{\text{a}}\) & 1.75 \(\pm\) 0.36 \({}^{\text{b}}\) & 3.14 \(\pm\) 0.21 \({}^{\text{a}}\),\({}^{\text{b}}\) \\ Esters & 0.55 \(\pm\) 0.1 \({}^{\text{a}}\) & 0.23 \(\pm\) 0.05 \({}^{\text{b}}\) & 0.46 \(\pm\) 0.13 \({}^{\text{a}}\) & 0.21 \(\pm\) 0.03 \({}^{\text{b}}\) \\ Furan & 0.74 \(\pm\) 0.023 \({}^{\text{a}}\) & 0.64 \(\pm\) 0.21 \({}^{\text{a}}\) & 0.33 \(\pm\) 0.05 \({}^{\text{b}}\) & 0.88 \(\pm\) 0.13 \({}^{\text{a}}\) \\ Phenols & 0.47 \(\pm\) 0.08 \({}^{\text{b}}\) & 0.1 \(\pm\) 0.03 \({}^{\text{c}}\) & 0.97 \(\pm\) 0.14 \({}^{\text{a}}\) & 0.36 \(\pm\) 0.09 \({}^{\text{b}}\) \\ Alkanes & 0.06 \(\pm\) 0 & - & 0.21 \(\pm\) 0.05 & - \\ Ethers & - & - & 0.07 \(\pm\) 0 & - \\ Others & 0.08 \(\pm\) 0.01 \({}^{\text{c}}\) & 0.21 \(\pm\) 0.08 \({}^{\text{b}}\) & 0.11 \(\pm\) 0.01 \({}^{\text{c}}\) & 0.77 \(\pm\) 0.02 \({}^{\text{a}}\) \\ Total content & 24.98 \(\pm\) 0.21 \({}^{\text{a}}\) & 21.67 \(\pm\) 0.42 \({}^{\text{c}}\) & 21.33 \(\pm\) 0.55 \({}^{\text{c}}\) & 23.4 \(\pm\) 0.4 \({}^{\text{b}}\) \\ \hline \hline \end{tabular}  
* The value of each volatile class was represented by the mean of relative concentration (ug/g) \(\pm\) standard deviation, calculated by the peak area ratio between each compound and the internal standard, and the concentration of the internal standard. Different uppercase letters in the same line indicate significant differences among cultivars (\(p<\) 0.05).  
\end{table} 
Table 2: Classes of volatiles characterizing four different sweet potato cultivars. and Shuangzai. It was suspected that fatty acid oxidation products contributed a more unpleasant flavor for Jieshu95-16 and Shuangzai than for Ayamurasaki and Anna.
# Home-Based Lower Limb Exercises for Individuals with Diabetes: A Scoping Review
###### Abstract  
Background: Diabetic foot ulcers (DFUs) affect millions worldwide, significantly increasing the risk of amputation, mortality, and reduced quality of life. While conventional interventions such as specialized footwear and pediatric care can mitigate ulceration risks, they do not address the biomechanical factors contributing to ulcer recurrence. Emerging evidence suggests that lower limb exercises may play a role in secondary DFU prevention. This scoping review aims to synthesize available research on home-based lower limb exercise programs for individuals with diabetes mellitus, focusing on feasibility, adherence, and their impact on foot biomechanics and ulcer prevention. Methods: A search was conducted across six electronic databases (PubMed, Web of Science, Cochrane Library, EBSCO, Scopus, and ScienceDirect) for studies published between January 2014 and December 2024. Eligible studies included those assessing home-based lower limb exercises in diabetic individuals, with interventions lasting at least four weeks. Studies focusing on supervised exercises, pharmacological interventions, or non-diabetic populations were excluded. Results: Nine studies met the inclusion criteria, featuring a range of home-based exercise interventions, such as muscle strengthening, stretching routines, proprioceptive training, functional mobility exercises, and range-of-motion activities. These interventions demonstrated notable effectiveness, leading to improved foot biomechanics, more even plantar pressure distribution, enhanced balance, and reduced ulcer recurrence (in some cases). One study, for instance, reported a significant decrease in ulcer recurrence, with only 16% of participants in the intervention group experiencing relapse compared to 72% in the control group after 24 weeks. Adherence rates varied across studies but were generally higher when programs included structured guidance through educational booklets, mobile applications, or consistent phone follow-ups ranging from 41% to 92.5%. Nonetheless, the findings tend to be tempered by methodological differences between studies and a lack of robust long-term follow-up data. Conclusions: Home-based lower limb exercises show promise in improving foot function and preventing DFU recurrence. Further research is needed to standardize protocols, enhance adherence, and confirm long-term effectiveness.  
l owerml 2025, 15, 4552
# Observations on the Computation of Eigenvalue and Eigenvector Jacobians +
## 6 Simplified Cases
### Real Diagonal Parent Matrix
#### 6.2.1 Simplified Jacobians for a Diagonal Matrix  
To develop the simplified derivatives for a diagonal matrix, begin with the simplified derivatives for the symmetric case given in Equations (64) and (66). Now, recognizing that the pseudoinverse of a diagonal matrix is just the reciprocal of the non-zero diagonal elements, the eigenvector derivative simplifies to  
\[\frac{\partial\mathbf{v}_{i}}{\partial\mathbf{A_{vec}}} =-\mathbf{e}_{i}^{T}\otimes(\mathbf{A}-\lambda\mathbf{I})^{+}\] \[=-\left[\begin{array}{cc}\mathbf{0}_{n\times(i-1)n}&(\mathbf{A }-\lambda\mathbf{I})^{+}&\mathbf{0}_{n\times(n-i)n}\end{array}\right], \tag{67}\]  
where  
\[(\mathbf{A}-\lambda\mathbf{I})^{+} =\text{diag}\left[(\lambda_{1}-\lambda_{i})^{-1},\ldots,(\lambda_ {i-1}-\lambda_{i})^{-1},0,\right.\] \[\qquad\qquad\qquad\qquad\left.(\lambda_{i+1}-\lambda_{i})^{-1} \ldots,(\lambda_{n}-\lambda_{i})^{-1}\right] \tag{68}\]  
and where \(\mathbf{e}_{i}\) is the \(i^{th}\) standard basis vector and the derivatives presented are for the \(i^{th}\) eigenvalue and eigenvector (at this point, it becomes necessary to distinguish the eigenvalue and eigenvector being considered). Recall that the pseudoinverse of a non-zero scalar is the reciprocal, while the pseudoinverse of a zero scalar is 0.  
The eigenvalue derivative simplifies similarly:  
\[\frac{\partial\lambda_{i}}{\partial\mathbf{A_{vec}}} =\mathbf{e}_{i}^{T}\otimes\mathbf{e}_{i}\] \[=\left[\begin{array}{cc}\mathbf{0}_{1\times(i-1)n}&\mathbf{e}_ {i}^{T}&\mathbf{0}_{1\times(n-i)n}\end{array}\right]. \tag{69}\]
## 3 Compression Algorithm Design
### Performance Analysis of the Proposed Algorithm
#### 3.3.3 EkgMove ECG Signals  
EkgMove [24] is a psycho-physiological measurement sys-tem for research applications. From the various measurement capabilities of the sensor, the ECG signal of a single subject has been used.  
As a result, from the respective evaluation, Figure 2 presents the achieved compression rate of the three different compression algorithms over the dataset of eight different signals is presented. As depicted, the proposed RT-Huffman algorithm manages to offer the highest compression in all cases. Specifically, compared to LEC, RT-Huffman offers an increased compression rate varying from 1.5 up to 4.3% while the same variation with respect to ALEC reaches up to 3.5%.  
Table 1 presents the measurements concerning the processing delay of compression algorithms under evaluation. In this case, due to drastic processing delay differences of the different biosignals, in order to extract more objective conclusions Table 1 depicts the absolute delay for the algorithm offering the lowest measurement and the percentage deviation for the other two algorithms.  
As extracted from Table 1, LEC yields the lowest processing demands upon the processing unit. However, what is even more important is that in all cases the proposed RT-Huffman proves to be the second less resource demanding solution clearly outperforming ALEC and exhibiting a steady and relative small overhead compared to LEC thus able to meet time constrained performance demands.