esa-sceva/satcom-chunk-collection huggingface dataset:
# Valorization Potential of Oilseed Cakes by Subcritical Water Extraction
## 3 Results
### Amino acid Composition Analysis  
The detailed amino acid profiles of pumpkin, flax and hemp seed extracts obtained under different extraction conditions were determined by chromatographic analysis. The content of total amino acids, essential and flavour amino acids were compared both regarding the raw material and the extraction approach (Table 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **K (mg/100 g)** & **Na (mg/100 g)** & **Ca (mg/100 g)** & **P (mg/100 g)** \\ \hline Pumpkin 1 1 & 24.89 \(\pm\) 0.13 & 0.46 \(\pm\) 0.03 & 3.72 \(\pm\) 0.06 & 2.29 \(\pm\) 0.03 \\ Pumpkin 2 & 24.59 \(\pm\) 0.13 & 0.65 \(\pm\) 0.03 & 5.26 \(\pm\) 0.03 & 2.45 \(\pm\) 0.02 \\ Pumpkin 3 & 27.90 \(\pm\) 0.11 & 1.53 \(\pm\) 0.06 & 6.45 \(\pm\) 0.05 & 2.36 \(\pm\) 0.02 \\ \hline Average \(\pm\) 2 SD & 25.79 \(\pm\) 0.12 & 0.88 \(\pm\) 0.04 & 5.14 \(\pm\) 0.05 & 2.37 \(\pm\) 0.02 \\ \hline Hemp 1 & 49.01 \(\pm\) 0.11 & 2.14 \(\pm\) 0.09 & 6.03 \(\pm\) 0.04 & 4.05 \(\pm\) 0.02 \\ Hemp 2 & 52.50 \(\pm\) 0.11 & 2.87 \(\pm\) 0.08 & 7.86 \(\pm\) 0.08 & 4.21 \(\pm\) 0.02 \\ Hemp 3 & 48.73 \(\pm\) 0.12 & 3.35 \(\pm\) 0.04 & 7.97 \(\pm\) 0.09 & 4.53 \(\pm\) 0.03 \\ \hline Average \(\pm\) 2 SD & 50.08 \(\pm\) 0.11 & 2.78 \(\pm\) 0.07 & 7.29 \(\pm\) 0.07 & 4.27 \(\pm\) 0.02 \\ \hline Flax 1 & 49.01 \(\pm\) 0.10 & 6.16 \(\pm\) 0.05 & 5.05 \(\pm\) 0.07 & 1.81 \(\pm\) 0.01 \\ Flax 2 & 57.09 \(\pm\) 0.10 & 3.72 \(\pm\) 0.08 & 10.19 \(\pm\) 0.11 & 1.99 \(\pm\) 0.01 \\ Flax 3 & 38.05 \(\pm\) 0.12 & 4.71 \(\pm\) 0.07 & 8.81 \(\pm\) 0.09 & 2.05 \(\pm\) 0.02 \\ \hline Average \(\pm\) 2 SD & 48.05 \(\pm\) 0.11 & 4.86 \(\pm\) 0.07 & 8.02 \(\pm\) 0.09 & 1.95 \(\pm\) 0.01 \\ \hline \hline \end{tabular}  
* 20 bar, 1 h.  
\end{table}
Table 2: Mineral content (mg/100 g dry weight) in oilseed cake extracts obtained by subcritical water.
# Combating Web Tracking: Analyzing Web Tracking Technologies for User Privacy
## 4 Future Research Directions
### Holistic Tracker Detection Across Browser, Network, and Device Levels  
Web tracking often functions across multiple layers, exploiting browser-level vulnerabilities, network-based identifiers (e.g., IP addresses), and device-specific characteristics like screen resolution or installed fonts. To ensure comprehensive protection against tracking, it is essential to adopt a holistic approach that safeguards users at all of these levels. At the browser level, advanced techniques such as fingerprint obfuscation and cookie isolation can prevent trackers from collecting identifying information. At the network level, encrypted DNS and VPNs can anonymize the user's location and network activity, further concealing their identity. Additionally, at the device level, periodically randomizing or obfuscating parameters like screen resolution or hardware information can disrupt device fingerprinting. By integrating protection across all these levels, this approach creates synergy between them, making it significantly more difficult for trackers to gather meaningful data from users.
# A Privacy-Preserving Record Linkage Method Based on Secret Sharing and Blockchain
## 4 Methods
### Output and Audit Module
#### 4.4.2 Audit and Verification  
To ensure the integrity and accuracy of computing nodes' results, the audit verification contract coordinates validation nodes to perform the audit process.  
1. Trigger and Sampling: Upon activation, validation nodes use a Verifiable Random Function to randomly select a portion of the result shares \(\left[D_{i,j}\right]_{k}^{A}\) for verification.
2. Data Retrieval: For sampled shares \(\left[D_{i,j}\right]_{k}^{A}\), validation nodes query the blockchain's index table to obtain CIDs and download from IPFS: the sampled share \(\left[D_{i,j}\right]_{k}^{A}\), corresponding Bloom filter boolean shares \(\left[a_{i,j,l}\right]_{\pi_{\text{gid}}^{B}(k)}^{B}\) and \(\left[b_{i,j}\right]_{\pi_{\text{gid}}^{B}(k)}^{B}\), mask boolean shares \(\left[r_{\text{gid},l}\right]_{\pi_{\text{gid}}^{B}(k)}^{B}\), mask arithmetic shares \(\left[r_{\text{gid},l}\right]_{\pi_{\text{gid}}^{A}(k)}^{A}\), constant \"1\" arithmetic shares \(\left[1\right]_{\pi_{\text{gid}}^{A}(k)}^{A}\), and permutation data \(\pi_{\text{gid}}\).
3. Recomputation: Using the retrieved input and mask shares, validation nodes independently recompute the core steps of the approximate matching module to generate a reference value, denoted \(\left[D_{i,j}\right]_{k,\text{ref}}^{A}\).
4. Verification: Validation nodes compare the uploaded share \(\left[D_{i,j}\right]_{k}^{A}\) with the recomputed reference \(\left[D_{i,j}\right]_{k,\text{ref}}^{A}\). If they satisfy \(\left[D_{i,j}\right]_{k}^{A}\equiv\left[D_{i,j}\right]_{k,\text{ref}}^{A}( \text{mod}\ p)\), the share passes verification; otherwise, it is marked as incorrect. The trustworthiness of a computing node's task execution is determined based on the verification results of all sampled shares [31].
## 2 Theoretical Background
### Curve Fitting Error Measures  
The first curve fitting error measure is the linear correlation coefficient. Let \(f_{\rm o}(b_{i})\) and \(f_{\rm a}(b_{i})\) denote the obtained discrete data and the values of the considered approximations, respectively, for the given \(b_{i}\) (\(i=1,\ldots,N\)). Then, the following formula applies:  
\[\rho_{f_{\rm o},f_{\rm a}}=\frac{\sum\limits_{i=1}^{N}\Big{(}f_{\rm o}(b_{i})- \overline{f_{\rm o}}\Big{)}\Big{(}f_{\rm a}(b_{i})-\overline{f_{\rm a}}\Big{)} }{\sqrt{\sum\limits_{i=1}^{N}\Big{(}f_{\rm o}(b_{i})-\overline{f_{\rm o}} \Big{)}^{2}}\cdot\sqrt{\sum\limits_{i=1}^{N}\Big{(}f_{\rm a}(b_{i})-\overline{ f_{\rm a}}\Big{)}^{2}}} \tag{5}\]  
where the general formula for the sample mean has the following form:  
\[\overline{f_{\rm o}}=\frac{1}{N}\sum\limits_{i=1}^{N}f_{\rm o}(b_{i}) \tag{6}\]  
The closer the value of the coefficient \(\rho\) is to \(1\), the more the considered data series are correlated and the approximation courses are closer to the trial points. Due to the above and the narrow range of values (\(-1\leq\rho\leq 1\)), greater visual differences are obtained when considering the value \(-\log_{10}\) (\(1-\mid\rho\mid\)).  
Another measure of approximation fitting is the root-mean-square error:  
\[RMSE=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}(r_{i})^{2}}=\sqrt{\frac{1}{N}\sum \limits_{i=1}^{N}(f_{\rm o}(b_{i})-f_{\rm a}(b_{i}))^{2}} \tag{7}\]  
where the magnitude \(r_{i}\) is called the residuum, error or remainder. The next measure, fitting variance, is also based on this value:  
\[Var(r_{i})=\frac{1}{N-1}\sum\limits_{i=1}^{N}(r_{i}-\overline{r})^{2} \tag{8}\]  
The coefficient of determination was also considered with the given formula:  
\[R^{2}=\frac{\sum\limits_{i=1}^{N}\Big{(}f_{\rm a}(b_{i})-\overline{f_{\rm o }}\Big{)}^{2}}{\sum\limits_{i=1}^{N}\Big{(}f_{\rm o}(b_{i})-\overline{f_{\rm o }}\Big{)}^{2}} \tag{9}\]while due to its range of values (\(0\leq R^{2}\leq 1\)), greater visual differences were obtained considering the quantity \(-\text{log}_{10}\mid R^{2}-1\!\mid\).
## 3 Circuit Design
### DC Offset Cancellation Loop
The input-referred noise current of the optical receiver contributed by the error amplifier \(i_{in,in_error}\) is derived as  
\[i_{in,in_{error}} =v_{n,error}\frac{H_{error}(s)\frac{g_{m5}}{1+g_{m5}R_{ES}}}{1+H_{ AMP}(s)H_{error}(s)\frac{g_{m5}}{1+g_{m5}R_{ES}}}\,, \tag{7}\] \[\approx v_{n,error}H_{error}(s)\frac{g_{m5}}{1+g_{m5}R_{ES}}\,\]  
where \(v_{n,error}\) denotes the input-referred noise voltage of the error amplifier. Since \(H_{AMP}(s)H_{error}(s)\) is far less than 1 at high frequencies due to the limited bandwidth of the DCOC loop, \(i_{in,in_{error}}\) can be ignored in Equation (7). The sink current source \(I_{sink}\) with emitter degeneration term \(g_{m}R_{E}\) helps to further reduce \(i_{in,in_{error}}\). Meanwhile, the input-referred noise current of the optical receiver introduced by the sink current source \(i_{in,in_{sink_{cur}}}\) is given by  
\[i_{n,in_{sink_{cur}}} =v_{n,sink_{cur}}\frac{\frac{g_{m5}}{1+g_{m5}R_{E}}}{1+H_{AMP}(s)H_ {error}(s)\frac{g_{m5}}{1+g_{m5}R_{ES}}}\,, \tag{8}\] \[\approx v_{n,sink_{cur}}\frac{g_{m5}}{1+g_{m5}R_{ES}}\]  
where \(v_{n,sink_{cur}}\) denotes the input-referred noise voltage of the sink current source. Compared with the sink current of the CE stage, the emitter degeneration term \(g_{m5}R_{ES}\) equips the proposed sink current source (\(I_{sink}\)) with noise suppression in Equation (8). The simulated input-referred noise current introduced by the 100 uA sink current source of a CE stage or a CE stage with emitter degeneration is given in Figure 12:0 \(i_{in,in_{sink_{cur}}}\) decreases by 0.82 pA/\(\surd{Hz}\) due to emitter degeneration.
# DGA Domain Name Classification Method Based on Long Short-Term Memory with Attention Mechanism
## 5 Experimental Evaluation
### Work Comparison and Discussion
\begin{table}
\begin{tabular}{l|c c|c c|c c|c c c|c} \hline \hline  & \multicolumn{3}{c|}{**[PERSON] et al. [8]**} & \multicolumn{3}{c|}{**[PERSON] et al. [16]**} & \multicolumn{3}{c|}{**Our Method**} & \multicolumn{1}{c}{**Support**} \\ \cline{2-13}
**Domain Type** & **Precision** & **Recall** & \(\mathbf{F_{1}}\)**Score** & **Precision** & **Recall** & \(\mathbf{F_{1}}\)**Score** & **Precision** & **Recall** & \(\mathbf{F_{1}}\)**Score** & **Support** \\ \hline Alex & 0.9612 & 0.9863 & 0.9736 & 0.9865 & 0.9970 & 0.9917 & 0.9956 & 0.9956 & 0.9927 & 91,119 \\ banjori & 0.9741 & 0.9889 & 0.9814 & 0.9998 & 1.0000 & 0.9999 & 1.0000 & 1.0000 & 0.9999 & 43,808 \\ Cryptolocker & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0018 & 0.0018 & 0.0035 & 562 \\ dyre & 0.9820 & 1.0000 & 0.9990 & 0.9975 & 1.0000 & 0.9987 & 1.0000 & 1.0000 & 1.0000 & 797 \\ locky & 0.0294 & 0.0038 & 0.0067 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 574 \\ murofet & 0.5139 & 0.4861 & 0.4996 & 0.7441 & 0.7477 & 0.7459 & 0.7207 & 0.7207 & 0.7418 & 1443 \\ neucurs & 0.2699 & 0.0253 & 0.0461 & 0.6225 & 0.1725 & 0.2701 & 0.1722 & 0.1722 & 0.2735 & 3241 \\ mymain & 0.1080 & 0.0409 & 0.0593 & 0.3621 & 0.1048 & 0.1626 & 0.1115 & 0.1115 & 0.1743 & 601 \\ Post & 0.9897 & 0.9913 & 0.9905 & 0.9995 & 1.0000 & 0.9998 & 0.9998 & 0.9998 & 0.9996 & 6644 \\ pykspa & 0.7253 & 0.5690 & 0.6377 & 0.9407 & 0.6827 & 0.7912 & 0.7207 & 0.7207 & 0.7994 & 1393 \\ qakhot & 0.7160 & 0.3483 & 0.4686 & 0.7970 & 0.4774 & 0.5971 & 0.5013 & 0.5013 & 0.6122 & 1973 \\ ramnit & 0.3541 & 0.5580 & 0.4333 & 0.4711 & 0.7360 & 0.5745 & 0.7525 & 0.7525 & 0.5777 & 4856 \\ ranbyus & 0.0000 & 0.0000 & 0.0000 & 0.4599 & 0.8522 & 0.5974 & 0.8455 & 0.8455 & 0.6018 & 1346 \\ shiotob & 0.9349 & 0.6584 & 0.7727 & 0.9873 & 0.9211 & 0.9531 & 0.9251 & 0.9251 & 0.9494 & 1268 \\ simda & 0.8203 & 0.6839 & 0.7459 & 0.9688 & 0.8533 & 0.9074 & 0.9669 & 0.9669 & 0.9462 & 1418 \\ tinba & 0.6709 & 0.8588 & 0.7533 & 0.9264 & 0.9912 & 0.9577 & 0.9920 & 0.9920 & 0.9578 & 6498 \\ \hline avg/total & 0.8960 & 0.9127 & 0.9001 & 0.9482 & 0.9504 & 0.9445 & 0.9505 & 0.9514 & 0.9458 & 167,541 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results of [PERSON] et al. [16]. Figure 5: Comparison of the precision of our method and [PERSON]’s method.
## 3 Adaptive Sliding Mode Control (ASMC) Design
### Proof of Stability  
Based on the model (4), the time derivative of \(\sigma\) in Equation (6) is derived as follows  
\[\begin{array}{ll}\dot{\sigma}&=\ddot{e}+\lambda_{1}\dot{e}+\lambda_{2}e\\ &=(-a_{1}x_{1}-a_{2}x_{2}+bu+f-\ddot{y}_{d})+\lambda_{1}(x_{2}-\dot{y}_{d})+ \lambda_{2}(x_{1}-y_{d})\end{array} \tag{14}\]  
In order to verify the stability of the above control scheme, consider the Lyapunov candidate function as follows  
\[V=\frac{1}{2}\sigma^{2}+\frac{1}{2\varphi}\widetilde{\eta}^{2} \tag{15}\]  
where \(\widetilde{\eta}=\hat{\eta}-\eta\). Then the time derivative of \(V\) is  
\[\begin{array}{ll}\dot{V}&=\sigma\dot{\sigma}+\frac{1}{\varphi}\widetilde{ \eta}\dot{\widetilde{\eta}}\\ &=s(-\eta sat(\sigma/\Delta)-k_{n}\sigma+f)+\frac{1}{\varphi}\tilde{\eta} \dot{\widetilde{\eta}}\end{array} \tag{16}\]  
Among them, when \(\dot{\widetilde{\eta}}=\hat{\eta}\), \(\dot{V}\) is expressed as  
\[\begin{array}{ll}\dot{V}&=-\hat{\eta}|s|-k_{n}\sigma^{2}+\frac{1}{\varphi} \widetilde{\eta}\dot{\hat{\eta}}+f|\sigma|\\ &=-\hat{\eta}|\sigma|-k_{n}\sigma^{2}+\widetilde{\eta}|\sigma|+f|\sigma| \end{array}. \tag{17}\]  
With the assumption of the external disturbance, \(|f|\leq f_{0}\), \(\dot{V}\) is then expressed as  
\[\begin{array}{ll}\dot{V}&\leq-k_{n}\sigma^{2}-\eta|\sigma|+|f||\sigma|\\ &\leq-k_{n}\sigma^{2}-\eta|\sigma|+f_{0}|\sigma|\end{array} \tag{18}\]  
Therefore  
\[\dot{V}\leq-k_{n}\sigma^{2}+(-\eta+f_{0})|\sigma|\leq 0 \tag{19}\]  
It is proved that the time derivative of the Lyapunov candidate function used is negative definite, which ensures the asymptotic stability of the control system and the existence of the sliding mode.
# Vertical Handover Algorithm for WBANs in Ubiquitous Healthcare with Quality of Service Guarantees
## 5 Evaluation and Discussion of the Results  
Having described the model architecture and the proposed approach, to illustrate its effectiveness, we first describe the WBAN scenario as well as the simulation setup; we then analyze and evaluate the results that are achieved.
## 4 Experiment and Results  
This section presents the experimental methods and results. It consists of two subsections, namely, MOO evaluation, which is presented in Section 4.1, and networking measures, which are presented in Section 4.2. The generation of the experimental results was performed using MATLAB 2020b using a computer i7 core 2.30 GHZ 16 GB RAM. The parameters that were used for conducting the experimental evaluation are presented in Table 7. As shown in the table, we used 100 iterations for all algorithms. Furthermore, the boundary dimension of all algorithms ranged between [1,2,2] and [3,4,6], which indicates the range of [_number of mobileSink_, _number of RV_, _number of hops_].
## 5 Conclusions  
In this paper, the MD-VRCRTM model is innovatively proposed and a new theoretical framework is provided. Through systematic search and analysis, the application effects of virtual reality technology in patients with stroke, Parkinson's disease, brain trauma, and Alzheimer's disease were demonstrated, emphasizing the selection of appropriate VR rehabilitation technology according to different patient needs to improve rehabilitation effects.  
Virtual reality technology has demonstrated broad application prospects in the field of cognitive rehabilitation and has proven to have significant clinical benefits. However, our research has certain limitations, such as reliance on specific databases and search terms, which may lead to the omission of relevant studies, and our focus on randomized controlled trials and case reports may have overlooked other research formats. Additionally, sample heterogeneity and the use of different VR technology devices may affect the comparability and consistency of the results. Nevertheless, a substantial body of research still indicates that VR technology has an motivational effect in cognitive rehabilitation, but clinical trial data are limited, and sample sizes are insufficient, necessitating more high-quality empirical research to comprehensively evaluate its effectiveness. At the same time, the increased cost associated with the enhancement of VR device clarity and immersion does not necessarily mean that fully immersive devices are always superior to other types. Therefore, when selecting cognitive rehabilitation systems or devices, a comprehensive consideration of patient needs and cost-effectiveness ratios should be taken into account.  
Future research should be dedicated to optimizing the application of VR technology to enhance its effectiveness and prevalence in the field of cognitive rehabilitation, and to overcoming the limitations of existing studies.  
Conceptualization, W.Q.; Formal analysis, S.L.; Funding acquisition, M.C.; Methodology, S.L. and W.Q.; Software, J.Z.; Supervision, W.Q.; Writing--original draft, S.L. and W.Q.; Writing--review editing, W.Q. and M.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China [62006121]; the National Social Science Fund of China [23 BFX038]; the Humanities and Social Science Fund of the Ministry of Education of China [23 YA870009]; the Significant Project of Jiangsu College Philosophy and Social Sciences Research [2021S]ZDA153]; and the National College Students Innovation and Entrepreneurship Training Program [202311287050Z].  
The data underlying this article will be shared on reasonable request to the corresponding author.  
The authors declare no conflict of interest.
## 3 Results and Discussions
### Damage Characterization Using the CWT of AE Signals  
During the tensile test, the AE signals of four groups of specimens were extracted. Figure 11 shows the representative waveforms of the AE signals received from the specimens constructed in four different orientations. The waveforms of the AE signals characteristic of the elastic and plastic stages are presented in Figure 12.  
Figure 10: (A) Elastic stage, (B) plastic stage considered for the AE analysis.  
Figure 9: Load-displacement curves of different groups of AlSi10 Mg specimens (Tx, Ty, Tz, and T45).  
Figure 13 shows the spectrograms of the AE signals for four specimen orientations obtained with the CWT. The observation of the AE signals and the spectrogram revealed that Tx and Ty can be considered equivalent. By examining the spectral content within a given class, an order of magnitude of 0.005-0.3 is observed for Tx in the frequency range of 200-300 kHz (see Figure 13a). For Ty, in the same frequency range and time interval as Tx, the magnitude varies by 0.005-0.05 (see Figure 13b). For Tz, from 250 to 300 kHz, the magnitude varies from 0.05 to 0.03 (see Figure 13c). Finally, for T45, in the frequency range of 50-300 kHz, the magnitude varies from 0.01 to 0.1 (see Figure 13d). A similarity is observed between the AE signals Tx and Ty, in contrast to the AE signals of Tz and T45. The same observation was made for the mechanical results of the tensile test (see Table 3). Figure 14 shows the scalogram of the different stages of elastic and plastic damage. During the elastic stage, a spectral energy is observed in the frequency range of 200 kHz to 300 kHz, varying in the order of 0.2-1.5 in terms of magnitude. A similar observation was made with the spectrogram of the plastic phase, where the frequency content is also found in the range of 200 kHz to 300 kHz, but a difference in magnitude of 0.005-0.04 is observed, which is considered small compared to the elastic stage (see Figure 14a,b).  
Figure 11: Representative waveforms of the received AE signals for the specimens built in four different orientations: (**a**) Tx, (**b**) Ty, (**c**) Tz, (**d**) T45.  
Figure 12: (**a**) Representative waveforms of the AE signal from the Elastic Stage, (**b**) Representative waveforms of the AE signal from the Plastic Stage.
## 2 Mathematical Formulation for Multiyear Network Expansion Planning
### Problem Constraints  
The nodal power balance for all buses should be satisfied by the following equations:  
\[\begin{split}& PU_{i,\textit{ls}}=\sum\limits_{j=1}^{N_{\textit{ l}}}\big{|}V_{1,\textit{ls}}\big{|}V_{j,\textit{ls}}\big{|}AD_{1,j}\big{|}\cos \big{(}\delta_{1,\textit{ls}}-\delta_{j,\textit{ls}}-v_{1j}\big{)}\\ &\forall\textit{ls}\in N_{\textit{ls}},\forall\textit{i}\in N_{ \textit{y}}\\ & QU_{i,\textit{ls}}=\sum\limits_{j=1}^{N_{\textit{t}}}\big{|}V_ {1,\textit{ls}}\big{|}V_{j,\textit{ls}}\big{|}AD_{1,j}\big{|}\sin\big{(} \delta_{1,\textit{ls}}-\delta_{j,\textit{ls}}-v_{1j}\big{)}\\ &\forall\textit{ls}\in N_{\textit{ls}},\forall\textit{i}\in N_{ \textit{y}}\\ & PG_{b,\textit{ls}}-PD_{b,\textit{ls}}=\sum\limits_{j=1}^{N_{ \textit{l}}}\big{|}V_{b,\textit{j,ls}}\big{|}V_{j,\textit{ls}}\big{|}AD_{b,j} \big{|}\cos\big{(}\delta_{b,\textit{j,ls}}-\delta_{j,\textit{i,ls}}-v_{bj} \big{)}\\ &\forall\textit{b}\in N_{\textit{b}},\forall\textit{ls}\in N_{ \textit{ls}},\forall\textit{i}\in N_{\textit{y}}\\ & QG_{b,\textit{ls}}-QD_{b,\textit{l},\textit{ls}}=\sum\limits_{ j=1}^{N_{\textit{l}}}\big{|}V_{b,\textit{j,ls}}\big{|}V_{j,\textit{ls}}\big{|}AD_{b,j} \big{|}\sin\big{(}\delta_{b,\textit{i,ls}}-\delta_{j,\textit{i,ls}}-v_{bj} \big{)}\\ &\forall\textit{b}\in N_{\textit{b}},\forall\textit{ls}\in N_{ \textit{ls}},\forall\textit{i}\in N_{\textit{y}}\\ \end{split} \tag{13}\]  
where Equations (10) and (11) demonstrate the constraints of power balance in the reference bus. Equations (12) and (13) illustrate the constraints of power balance for different load buses (from 1 to \(N\)).
# Rainy Environment Identification Based on Channel State Information for Autonomous Vehicles
## 4 Vehicular Environment Identification Methodology  
In this paper, the vehicle environment identification process is considered a multi-classification problem, and the computed CSI values, including a dataset of 128 samples, are input features for the proposed CNN model. The CSI values obtained by precisely capturing the subtle changes in the wireless channel provide rich environmental information for the CNN model. After deep feature extracting and learning by the CNN model, different categories of vehicular environments can be effectively distinguished. By optimizing the structure and parameters of the CNN model, the accuracy of environment identification based on CSI values can be further improved. The specific process flow of vehicle environment identification is shown in Figure 3.
## 3 Proposed Method
### Adaptable Behavioural Layer
#### 3.3.3 Retrieval-Augmented Generation (RAG)  
The RAG architecture combines document retrieval with generative sentiment analysis to improve the context relevance of output generated by the model. In this framework, relevant documents are first identified from a vector database and then provided to the LLMs for sentiment classification, which is particularly valuable for domain-specific tasks like analyzing financial reviews, as shown in Algorithm 5.  
The retrieval process begins by encoding both the query and the document corpus into dense embeddings using a pre-trained embedding model (e.g., all-MiniLM-L6-v2). FAISS is then used to efficiently search the vector database, retrieving the top \(k\) relevant documents for each query. The selection of \(k\) is optimised for each dataset, where an ideal balance between retrieval speed and contextual relevance was found at \(k=5\) based on validation tests. Additionally, cosine similarity was chosen as the distance metric in the FAISS index to improve matching accuracy.  
RAG enhances the generative performance of LLMs by integrating these retrieved documents into the generation process. The system consists of two main components: a retriever and a generator. The retriever uses FAISS to find the most relevant documents for a query, and the generator then utilises the contextual information from these documents to generate a sentiment-annotated response. To control the relevance and specificity of generated responses, we set the LLMs' decoding parameters to a beam width of 3, a temperature of 0.7, and top-\(k\) sampling at 40.  
In RAG, the probability of generating a response \(r\) given a query \(q\) is defined as  
\[P(r\mid q)=\sum_{d\in D_{k}}P(r\mid q,d)P(d\mid q) \tag{7}\]  
This equation sums over the top-\(k\) retrieved documents \(D_{k}\), where \(P(r\mid q,d)\) represents the probability of generating the response given the query and a specific document and \(P(d\mid q)\) denotes the probability of retrieving the document given the query. The weighted approach allows the model to produce more relevant responses, enhancing its ability to manage complex or specialised data contexts [112]. This combined retrieval-generation approach enables the model to leverage domain-specific knowledge effectively, making RAG well suited for sentiment analysis tasks that rely on context-specific information.