esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Literature Analysis Results
#### 4.1.2 Status by Institution  
Further analysis of GAI research related to security was conducted by examining the contributions of academic and research institutions worldwide. Figure 6 presents the numbers of documents published by leading institutions, offering insight into the primary contributors in the field.  
The Chinese Academy of Sciences emerges as the most prolific institution, with over 20 publications, reflecting China's strong emphasis on advancing GAI research, particularly in the context of security. It is closely followed by the Ministry of Education of the People's Republic of China, underscoring the impact of national education policies and initiatives on driving research output. This demonstrates a coordinated national effort to prioritize GAI research.  
The National University of Singapore is also a major contributor, reflecting Singapore's strategic investment in technology and innovation. Tsinghua University and the University of the Chinese Academy of Sciences are other prominent Chinese institutions contributing significantly to the field, further reinforcing China's leadership in GAI research.  
Figure 5: Distribution of research publications by country/region.  
Internationally, institutions such as the University of Illinois Urbana-Champaign, University College Dublin, and UNSW Sydney are key players, each contributing between 10 and 15 publications. These institutions reflect the global nature of GAI research, with active participation from leading universities in the United States, Europe, and Australia. Other institutions, including Nanjing University and Universiti Sains Malaysia, also make noteworthy contributions, demonstrating the diversity of actors involved in advancing GAI and security research.  
This analysis highlights the dominance of Chinese institutions in GAI security research, while also recognizing the significant contributions of leading institutions from other regions. This global participation is vital for the continued development and application of GAI technologies to address security challenges.
# Predicting Heating Load in Energy-Efficient Buildings Through Machine Learning Techniques
## 3 Model Development
### Model Assessment Approaches  
To evaluate the reliability of early estimated heating load in energy-efficient residential buildings, five well-known (as used mostly in academic studies) statistical indices including root mean square error (RMSE, relative absolute error (RAE in %, mean absolute error (MAE), root relative squared error (RRSE in %) and coefficient of determination (R\({}^{2}\)) are used to help to rank the network performances. The outputs of these statistical indexes are also used for color intensity ranking. Equations (7)-(11) designate the equations of R\({}^{2}\), MAE, RMSE, RAE, and RRSE, respectively.  
\[R^{2}=1-\frac{\sum_{i=1}^{s}{\big{(}Y_{i_{practical}}-Y_{i_{practical}}\big{)}^{ 2}}}{\sum_{i=1}^{s}{\big{(}Y_{i_{observed}}-\overline{Y}_{observed}\big{)}^{2}}}, \tag{7}\]  
\[MAE=\frac{1}{N}\sum\
olimits_{l=1}^{s}{\big{|}Y_{i_{observed}}-Y_{i_{practical }}\big{|}}, \tag{8}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum\
olimits_{i=1}^{s}{\big{|}\big{(}Y_{i_{observed}}- Y_{i_{practical}}\big{)}\big{|}^{2}}}, \tag{9}\]  
\[RAE=\frac{\sum_{i=1}^{s}{\big{|}Y_{i_{practical}}-Y_{i_{observed}}\big{|}}}{ \sum_{i=1}^{s}{\big{|}Y_{i_{observed}}-\overline{Y}_{observed}\big{|}}}, \tag{10}\]  
\[RRSE=\sqrt{\frac{\sum_{i=1}^{s}{\big{(}Y_{i_{practical}}-Y_{i_{observed}}\big{)} ^{2}}}{\sum_{i=1}^{s}{\big{(}Y_{i_{observed}}-\overline{Y}_{observed}\big{)}^{2 }}}}, \tag{11}\]  
where, \(Y_{i_{observed}}\) and \(Y_{i_{practical}}\), represented in Equations (6) to (10), are the actual and estimated values of heating load in energy-efficient buildings, respectively. The term S in the above equations stands for the number of instances and \(\overline{Y}_{observed}\) denotes the mean of the real amounts of the heating load. Weka software environment was employed to perform the machine learning models.
## 3 The End-to-End Performance Analysis of the System
### Average Bit Error Rate  
For the multi-hop parallel hybrid FSO/RF system, two binary modulation scheme is used for data transmission in any FSO or RF link. According to Refs. [20; 35], the mathematical expression of the average bit error rate (ABER) can be expressed as:  
\[P_{b}=\frac{q^{p}}{2\Gamma(p)}\int_{0}^{\infty}\exp(-q\gamma)(\gamma)^{p-1}F_{ \gamma}(\gamma)d\gamma \tag{16}\]  
where \(p\) and \(q\) are used to describe the ABER parameters of different binary modulation schemes, as shown in Table 1.  
Substituting Equation (15) into Equation (16), the end-to-end ABER of the multi-hop parallel hybrid FSO/RF system with structure \((N,M)\) is obtained where \(N\) is the total number of paths and \(M\) is the total number of hops of each path:  
\[\begin{split} P_{b}^{mml}=\frac{q^{p}B_{s,d}}{2\Gamma(p)\Gamma(m^{ RF})}\int_{0}^{\infty}\exp(-q\gamma)(\gamma)^{p-1}\zeta_{0,0:1:2:3:7}^{0:0:1:1:6 1}\Bigg{(  
By substituting Equation (15) into Equation (21), it can be obtained that the end-to-end outage probability of the multi-hop parallel hybrid FSO/RF system with structure \((N,M)\) is:  
\[\begin{split} P_{out}^{mml}&=\frac{B_{sd}}{\Gamma(m^{ RF})}C_{00:12:37}^{00:11:61}\Bigg{(}\begin{array}{c}1\\ m^{RF},0\end{array}\Bigg{|}\begin{array}{c}C_{sd}\\ D_{sd}\end{array}\Bigg{|}\frac{m^{RF}\gamma_{th}}{\overline{\gamma}_{sd}^{ RF}},\frac{A_{sd}\gamma_{th}}{\overline{\gamma}_{sd}^{FSO}}\Bigg{)}\\ &\times\Bigg{[}1-\Bigg{[}1-\frac{B_{ij}}{\Gamma(m^{RF})}C_{00:12:37}^{00:11:61 }\Bigg{(}\begin{array}{c}1\\ m^{RF},0\end{array}\Bigg{|}\begin{array}{c}C_{ij}\\ D_{ij}\end{array}\Bigg{|}\frac{m^{RF}\gamma_{th}}{\overline{\gamma}^{RF}},\frac{A _{ij}\gamma_{th}}{\overline{\gamma}^{FSO}}\Bigg{)}\Bigg{]}^{M}\Bigg{]}^{N} \end{split} \tag{22}\]  
By substituting Equation (10) into Equation (21), the outage probability of the hybrid FSO/RF direct link is  
\[P_{out}^{SD}\!=\!\frac{B_{sd}}{\Gamma(m^{RF})}C_{00:12:35,7}^{00:11:61}\!\! \left(\begin{array}{c}1\\ m^{RF},0\end{array}\Bigg{|}\begin{array}{c}C_{sd}\\ D_{sd}\end{array}\Bigg{|}\frac{m^{RF}\gamma_{th}}{\overline{\gamma}_{sd}^{RF}}, \frac{A_{sd}\gamma_{th}}{\overline{\gamma}_{sd}^{FSO}}\Bigg{)} \tag{23}\]  
By substituting Equation (4) into Equation (21), the outage probability of the FSO-only direct link is  
\[P_{out}^{FSO}=B_{s,d}C_{3,7}^{6,1}\Bigg{(}A_{s,d}\frac{\gamma_{th}}{\overline{ \gamma}_{s,d}^{FSO}}\Bigg{|}\begin{array}{c}C_{s,d}\\ D_{s,d}\end{array}\Bigg{)} \tag{24}\]
## 3 Results and Discussion
### Multivariate Statistical Analysis
[29] characterized and differentiated wines from Chardonnay, Sauvignon Blanc, Pinot Noir, Merlot, and Cabernet Sauvignon based on volatile aroma composition obtained by GC\(\times\)GC-TOF-MS analysis and extracted the following 12 volatile compounds as the most useful for their differentiation: diethyl succinate, 2,3-butanediol, nerol, 3-penten-2-one, diethyl malonate, \(\beta\)-santalol, ethyl 9-decenoate, alcohol-C\({}_{9}\), 4-carene, tetrahydro-2(2H)-pyranone, dihydro-2(3H)-thiophene, and 3-methyl-2(5H)-furanone. It is probable that the main reason for such discrepancy between this and the study from [PERSON] et al. [29] was the fact that the mentioned authors mutually compared wines from white and red varieties, which greatly differ with respect to the production technology, which, besides variety, certainly greatly contributed to the differences between wines. [PERSON] et al. [29] also obtained a SLDA model that differentiated wines according to variety with a 100% correct recognition ability, while some other authors who applied conventional GC-MS for the same purpose, such as [PERSON] et al. [61] and [PERSON], [PERSON] and [PERSON] [14], did not succeed completely. [PERSON], [PERSON], and [PERSON] [15] obtained a 100% correct discrimination among Syrah, Malbec, and Bonarda red wines by the application of SLDA on GC-MS data with ethyl hexanoate, ethyl octanoate, 1-hexanol, benzyl alcohol, and isoamyl acetate as the most useful differentiators. Terpenes were not analyzed. [PERSON], [PERSON], and [PERSON] [19] obtained a relatively good differentiation of red wines, with the ability of the LDA model to correctly classify and predict their varietal origin based on HS-SPME/GC-MS data of 95%, while the model built for white wines was not that successful. The compounds most useful for the differentiation of white wines (Chardonnay, Sauvignon Blanc, and Muscat) were isoamyl acetate, furfural, ethyl octanoate, ethyl decanoate, and ethyl dodecanoate, while red wines (Cabernet Sauvignon and Merlot) were differentiated mainly by 1-hexanol, ethyl decanoate, and 2-phenylethanol. It should be noted that the samples of the same variety were collected across several countries, which was certainly a factor that introduced large variability.
## 4 Experimental Results and Discussion
### Comparison between Results of Analytical and Numerical Analysis
ribbed reinforcement bars are better anchored in the concrete, which leads to a shorter length where relative deformation occurs, and thus to a smaller angle of rotation of the end cross-section. To compare the ratio of moment and cross-sectional rotation of smooth and ribbed reinforcement under cyclic loading, comparative M/\(\varphi\) diagrams of two specimens with smooth and one specimen with ribbed reinforcement are shown in Figure 14 (below). In addition, the locations where the yield strength of the reinforcement is reached are marked according to different analysis methods. For cyclically tested specimens, the difference between the angle of rotation when the yield point is reached, which was determined by experimental analysis, and those determined numerically and analytically for smooth and ribbed reinforcement is significantly smaller than for monotonously tested specimens. This leads to the conclusion that these two types of reinforcement around the plastic joint behave similarly with regard to bond strength under cyclic loading. Alternating load cycles with a compression-tension effect causes the concrete to be crushed near the reinforcement ribs, which causes the non-anchored part of the ribbed bar to increase in size and thus behave like smooth reinforcement, so that the results for both smooth and ribbed reinforcement are closer together. The results of measurements on cyclically tested specimens made with smooth reinforcement show similar behavior near the end cross-section as monotonically tested specimens. The rotations of the end section and the section where the first crack occurs are dominant. For cyclically tested specimens, the difference between the experimental results of the end section rotation when the yield strength is reached, and the analytical and numerical results is somewhat smaller compared to monotonically tested specimens. On average, the difference is four-and-a-half-times higher than the difference between the rotation of the experimental measurements compared to the analytical and numerical results. As with monotonically tested specimens, the reason for this discrepancy is the loss of bond strength between concrete and reinforcement in the anchorage area of the smooth reinforcement. Differences between the cyclically tested specimens and the monotonically tested specimens can be seen in the specimens made with ribbed reinforcement. A 50% larger rotation of the end cross-section of the cyclically tested specimens can be seen in comparison to the monotonically tested specimens made with ribbed reinforcement. Since the influence of a higher axial force reduces the rotatability of the cross-section, it can be assumed that the difference would be even greater since the cyclically tested specimen was loaded with a 50% higher force. This proves that ribbed reinforcement with high ductility properties (B500 B) behaves much better under cyclic loading in terms of element ductility than under monotonic loading.
# Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
## 3 Methods
### Denoising Diffusion Probabilistic Models  
The denoising diffusion probabilistic model (DDPM) is a latent variable model that uses a T-step Markov chain to transform data into noise and then back into a high-quality output [52]. In the forward process, Gaussian noise is progressively added to the data to degrade it over several steps. Starting with an initial data distribution \(x_{0}\sim q(x_{0})\), the forward process can be described by:  
\[q(x_{t}|x_{t-1}):=\mathcal{N}(x_{t};\sqrt{\alpha_{t}}x_{t-1},(1-\alpha_{t}) \mathbf{I}), \tag{2}\]  
where \(\alpha_{t}\in(0,1)\) represents the noise reduction factor at step \(t\), \(\alpha_{t}\geq\alpha_{t+1}\) ensures noise increases progressively, and \(\mathbf{I}\) is the identity matrix representing isotropic noise. To simplify, let \(\bar{\alpha}_{t}=\prod_{i=1}^{t}\alpha_{i}\), which denotes the cumulative noise reduction factor up to step \(t\). The marginal distribution \(q(x_{t}|x_{0})\) is then given by:  
\[q(x_{t}|x_{0}):=\mathcal{N}(x_{t};\sqrt{\bar{\alpha}_{t}}x_{0},(1-\bar{\alpha} _{t})\mathbf{I}). \tag{3}\]  
Here, \(\sqrt{\bar{\alpha}_{t}}\) scales the original data \(x_{0}\), while \(1-\bar{\alpha}_{t}\) represents the variance of the added noise. As \(\sqrt{\bar{\alpha}_{t}}\) approaches zero, the data distribution effectively converges to an isotropic Gaussian distribution.
## 3 Results
### Performance Evaluation
It likely benefits from enhanced data preprocessing and augmentation methods, which improve data quality and variety. The use of a more sophisticated architecture, such as deeper networks or those with residual connections, allows the model to capture complex patterns more effectively. Additionally, optimized training techniques, including better weight initialization and effective regularization methods like dropout and batch normalization, contribute to faster convergence and improved generalization. These combined advancements result in superior performance compared to earlier models.
## 3 Proposed Converter Topology
### Bi-Directional Converter Circuit
The bi-directional boosting DC-DC conversion is adjusted to keep the DC link voltage (\(\hat{V}_{DC}\)) at the required level. If there is surplus power in the DC link, the converter is controlled in charging mode, and if electricity from the grids is required, it operates in the buck method. Figure 2: The bi-directional DC–DC converter design.
# A Traceable High-Accuracy Velocity Measurement by Electro-Optic Dual-Comb Interferometry
## 1 Introduction
The Doppler frequency shift, which occurs in the repetition frequency of the backward reflected light, is detected after the laser is illuminated on the moving target. By utilizing the Doppler effect formula, the target's velocity can be calculated. Inspired by this idea, we deduce some formulas that prove the correctness of the measurement principle. Then, we propose a measurement method based on it. A verified experiment is set up, and the experimental results show that our idea is feasible and that the proposed measurement method makes full use of the high-accuracy characteristic of the optical comb. The remainder of this paper is organized as follows. In Section 2 we give original rational deduction towards the Doppler effect of optical comb repetition frequency. In Sections 3 and 4 we introduce the measurement method and our experimental system, followed by a detailed analysis of the experimental data. In Section 5 we conclude with a discussion and future outlook.
# Biomechanical Analysis of Two Types of Oseointegrated Transfemoral Prosthesis
In both cases, after the fixture implantation, the bone-implant interface is irregular and consists of regions with gaps between the bone and the stem and regions of direct contact between the bone and the implant [27]. The gaps between the bone and the fixture will be later filled by compact bone thanks to bone-remodelling processes [27]. The long term success of OI implants is highly related to the mechanical loading acting on the bone and on the prosthesis during patients' lifetime. Daily activities and unexpected critical events may produce, obviously, loadings of different kinds and magnitude that could cause failures. Two of the most common complications, in fact, are implant failures and peri-prosthetic bone fractures [4; 21]. For these reasons, it is very interesting to fully understand the stress and strain distribution on the bone, the prosthesis and at the bone/fixture interface. Due to the complexity of this kind of investigation, numerical simulations are often used to assess the risks that patients can face. For several years, the Finite Element Method (FEM) has been used largely to investigate medical problems, especially in the field of orthopaedic implants [28; 29; 30]. Nowadays, the level of reliability and accuracy of FEM analyses is very high and, in many cases, it is comparable with that of the experimental studies [31; 32]. Several studies used FEM to investigate the stress distribution on bone and implants during normal daily activities [4; 25; 27; 33; 34; 35; 36; 37; 38; 39]. Nevertheless, no relevant studies in the literature have focused on the effects of critical events, such as falls, on osseointegrated transfemoral implants. The objective of this study was twofold. Firstly, different types of falls were investigated to identify the most dangerous fall scenario, and secondly, how the stress distributions during a fall vary depending on the specific prosthetic design was investigated. Two different implants, OPRA and OPL, have been compared.
## 5 Conclusions  
In this study, we predicted the spatial distribution of STN content in the middle reaches of the HRB in northwestern China by comparing the BRT, RF, and SVM models. We were able to come to the following main conclusions from this study: (1) Compared to the SVM model, tree-based models (RF and BRT) performed better at predicting STN content; (2) the combination of the multi-temporalSentinel-1A and Landsat-8 OLI data using the BRT model compared to the application of optical data alone improved the RMSE and MAE by 17.2% and 17.4%, respectively. The inclusion of multi-temporal Sentinel-1A data in the RF and SVM models achieved similar improvements in the prediction of STN. The predictive power of multi-temporal SAR images was as strong as the optical remote sensing variables and was identified as one of the most important predictor variables for the best prediction of STN content in this study area; (3) the combination of all environmental variables achieved the highest prediction accuracy, with the BRT model having the highest R\({}^{2}\) (0.57) value and the lowest RMSE (0.24) and MAE (0.18) values; (4) the most important environmental variables affecting the spatial distribution of STN were the predictor variables extracted from remotely sensed images (including SAR and optical data). These predictor variables in the RF and BRT models explained 59% and 50% of the STN variation, respectively; and (5) the STN distribution maps obtained using the three machine learning techniques had similar distribution patterns, and the agro-ecosystem along the river had a higher STN content. Based on the results obtained in this study, we recommend using multi-temporal Sentinel-1 images to map STN content, especially in those areas that are susceptible to cloud. Although the use of variables derived from remote sensing improved the prediction performance, this study did not achieve a high prediction accuracy. Future research may explore the feasibility of incorporating new environmental variables to improve the prediction accuracy.  
Data curation, T.Z., Y.G., J.C. and C.S.; writing--original draft preparation, T.Z.; supervision, A.L. and D.H.  
This research was funded by China Scholarship Council.  
We are particularly grateful to CARD for their support of this research data.  
The authors declare no conflict of interest.
## 6 Simulation Results
### Communication Overhead
#### 6.2.2 Communication Overhead of Network
The total data transmission in the network is shown as a function of average number of hops between aggregators and the ratio of false data to legitimate data. Figure 8 shows that, as the ratio of false data to legitimate data increases, the total data transmission increases. Moreover, under the same conditions, compared to SDA-PH and DAA, FESA scheme has less total data transmission, which leads to the less energy consumption of sensor nodes. The reason is that each data packet of DAA is larger than FESA, thus more data are transmitted in the network. SDA-PH scheme detects false data only in the BS, therefore, data would not be detected until it transmitted into BS. In this condition, when more sensor nodes are compromised, FESA can quickly detect false data than SDA-PH, which results in the reduction of data transmission and energy consumption in the whole network.
# An Improved Routing Approach for Enhancing QoS Performance for D2D Communication in B5G Networks
###### Abstract  
Device-to-device (D2D) communication is one of the eminent promising technologies in Beyond Fifth Generation (B5G) wireless networks. It promises high data rates and ubiquitous coverage with low latency, energy, and spectral efficiency among peer-to-peer users. These advantages enable D2D communication to be fully realized in a multi-hop communication scenario. However, to ideally implement multi-hop D2D communication networks, the routing aspect should be thoroughly addressed since a multi-hop network can perform worse than a conventional mobile system if wrong routing decisions are made without proper mechanisms. Thus, routing in multi-hop networks needs to consider device mobility, battery, link quality, and fairness, which issues do not exist in orthodox cellular networking. Therefore, this paper proposed a mobility, battery, link quality, and contention window size-aware routing (MBLCR) approach to boost the overall network performance. In addition, a multicriteria decision-making (MCDM) method is applied to the relay devices for optimal path establishment, which provides weights according to the evaluated values of the devices. Extensive simulation results under various device speed scenarios show the advantages of the MBLCR compared to conventional algorithms in terms of throughput, packet delivery ratio, latency, and energy efficiency.  
D2D communication; IoT; 5G network; MCDM; routing; MBLCR Article  
An Improved Routing Approach for Enhancing QoS Performance for D2D Communication in B5G Networks.  
_Electronics_**2022**, _11_, 4118. [[https://doi.org/10.3390/Electronics11244118](https://doi.org/10.3390/Electronics11244118)]([https://doi.org/10.3390/Electronics11244118](https://doi.org/10.3390/Electronics11244118))