esa-sceva/satcom-chunk-collection huggingface dataset:
# Knowledge Interpolated Conditional Variational Auto-Encoder for Knowledge Grounded Dialogues
## 4 Knowledge Interpolated Conditional Variational Auto-Encoder (KIV)  
To address the representation entangled issue of the standard CVAE, we introduce our KIV model to explicitly disentangle the latent variable \(z\) by incorporating two new variables \(z_{k}\) and \(z_{c}\). Using two independent variational encoders, KIV initially encodes the given context and knowledge separately. Upon acquiring the disentangled latent variables \(z_{k}\) and \(z_{c}\), we employ linear interpolation to blend \(z_{k}\) and \(z_{c}\). This approach allows for smooth transitions between latent variables in an interpretable and controllable way. Specifically, the latent variables \(z_{k}\) and \(z_{c}\) in our model are linearly interpolated using a weight factor \(\lambda\in(0,1)\), thereby constructing a 1-simplex as follows:  
\[z=\lambda z_{k}+(1-\lambda)z_{c}. \tag{2}\]  
In this study, we denote the interpolation weight corresponding to knowledge as \(\lambda\), and the interpolation weight of context is given by \(1-\lambda\). The interpolation weight \(\lambda\) signifies the knowledge specificity and relevance of the response, thus facilitating better interpretation and connection of empirical findings. By incorporating the interpolated latent variable into Equation (1) and applying the convex property of the KL-divergence, we can deduce a new ELBO as the training objective:  
\[\mathcal{L}_{\text{KIV}} = -\lambda\text{KL}(q_{\phi}(z_{k}|y,k)||p_{\theta}(z_{k}|k)) \tag{3}\] \[-(1-\lambda)\text{KL}(q_{\phi}(z_{c}|y,c)||p_{\theta}(z_{c}|c))\] \[+\mathbb{E}_{z_{c}\sim q_{c},z_{k}\sim q_{k}}\log p_{\theta}(y|c, k,z)\]  
A detailed derivation showing that \(\mathcal{L}_{KIV}\) still serves as a valid lower bound of \(\log p(y|c,k)\) is provided in Appendix A.  
Figure 1: Comparison of graphical models: (**a**) the Vanilla CVAE; (**b**) KIV\({}_{\text{d}}\), the KIV model with a deterministic interpolation weight; (**c**) KIV\({}_{\text{p}}\), the KIV model with a probabilistic weight, in which the weight \(\lambda\) is a latent variable sampled from the Logistic-Normal distribution conditioned on \(c\) and \(k\).  
As illustrated in Figure 2, the proposed KIV model is composed of context/knowledge encoders and a response decoder, the specifics of which are expanded upon in the following section. The interpolation methods will be introduced in the subsequent section.
## 5 Experimental Evaluation
### Comparison Study and Discussion
#### 5.2.2 Task Type Description  
When the number of subtasks changes, the scale of the problem changes, and the algorithm becomes more demanding. In order to compare the performance of each algorithm when the scale of the problem increases, a scenario with the number of subtasks \(N=30\), a density of 0.6, and a width of 0.6 was generated for experimentation. This application of width and density appeared in the training dataset, but the number of tasks increased, and, although the characteristics were the same, the problem was more complex. As shown in Figure 7, in terms of latency, the average latency of the DRL algorithm was only improved by 20 s after 20 iterations, indicating that the algorithm was still exploring the environment, with more strategies for poorer performance, not yet finding a better strategy, whereas MTD3 CO surpassed DRL after two iterations, indicating that it successfully used meta-strategies to guide the learning and could adapt well to the increased complexity of the problem.
## 4 Results and Discussion
### Effect of the Number of Training Samples  
This section studies the effect of the number of training samples on the performance of the crack location detection network. In this section, the performance of the crack location detection model based on the convolutional neural network algorithm is analyzed in the case of 100, 300, 500 and 700 training samples, respectively. The corresponding results are shown in Figure 13. The figure shows that when the signal-to-noise ratio is equal to 10 dB, that is, the signal power is much larger than the noise power, the accuracy of the 4 training samples is 100%, and the accuracy is high. The more training samples there are, the higher the accuracy of the fault detection network is. With the increase of noise power, that is, with the decrease of the signal-to-noise ratio, the accuracy will decrease obviously when the number of training samples is 100. The average accuracy can maintain a high value in a wide range of the signal-to-noise ratio when the number of training samples is 700. Even when the signal-to-noise ratio is \(-4\) dB, the accuracy is still greater than 98%, indicating that when the number of samples is high, the fault detection network has low requirements for the cleanliness of the signal, which is suitable for the application in the actual industrial scene. The above analysis shows that the number of training samples plays a decisive role in the recognition rate and anti-noise ability of the fault detection network. It also shows that when the sample size is not enough, the strategy of overlapping sampling for data enhancement is effective.  
Figure 14 shows the confusion matrix of the crack location detection model established in this paper when the number of training samples is 300 and 500, and the value of the signal-to-noise ratio is 0 dB. It can be seen from Figure 14a that when the number of training samples is 300, the accuracy of the healthy rotor (Case 0), the double-disk rotor right crack (Case 2) and the single-disk rotor right crack (Case 4) are higher, which are 100%, 100% and 99%, respectively.  
Figure 12: Confusion matrix under different SNR: (**a**) SNR \(=-6\); (**b**) SNR \(=-4\).  
In addition, only 1% of the samples in Case 4 are misclassified as Case 0. In the samples of the left crack of the double-disk rotor (Case 1), 33% and 19% of the samples are detected as Case 0 and Case 2. In the samples of the left crack of the single-disk rotor (Case 3), 49% and 5% of the samples are detected as Case 0 and Case 2. In Case 1 and Case 3, samples are easily misclassified as healthy rotors. Figure 14b shows that when the number of training samples is 500, only 7% of the samples which are the left crack (Case 1) of the double disc rotor are misclassified as the right crack (Case 2) of the double disc rotor, while the accuracy of other cases is 100%. The accuracy of Case 1 and Case 3 is the lowest under the above two training samples. This shows that the reduction of the number of training samples makes the above crack conditions easily misclassified as other conditions.
## 2 Related Work
### Evaluating LLMs in Legal Tasks  
Due to the high complexity and accuracy requirements in the legal domain, a careful evaluation of LLMs on a wide range of legal tasks becomes essential. Recent advances in legal benchmarks, such as LawBench [16], LegalBench [17], and LAIW [18], have provided frameworks for assessing LLM performance on tasks such as legal knowledge recall and logical reasoning across different case types. LawBench and LAIW focus on general legal reasoning tasks, such as retrieving case law, applying legal principles, and assessing logical consistency across cases. These benchmarks are valuable for understanding how well LLMs perform when retrieving legal knowledge and reasoning about legal knowledge. However, they lack the specificity required for regulatory compliance tasks such as GDPR documentation. For example, GDPR compliance requires the creation of structured documents with precise formatting and terminology, i.e., requirements not assessed by these benchmarks. In addition, regulatory compliance requires sensitivity to evolving legal standards and a robust handling of sensitive data, whereas both aspects are not covered in current evaluation frameworks.  
Another limitation of existing benchmarks is their emphasis on static datasets, which do not reflect the dynamic nature of legal and regulatory environments. For compliance-sensitive tasks, such as GDPR documentation, LLMs must generate outputs that conform to predefined legal standards while taking updates to the law into account as well. Current benchmarks do not assess the adaptability of the LLMs to such changes or their ability to manage jurisdiction-specific requirements, which are critical to ensuring legal accuracy and compliance.  
In addition, current benchmarks do not evaluate the structured output generation required for GDPR documentation. Compliance tasks involve generating documents such as data processing agreements or records of processing activities, which both require linguistic accuracy and adherence to regulatory formats. These tasks differ significantly from traditional legal tasks like case retrieval or logical reasoning, highlighting the need for tailored benchmarks that test LLMs on their ability to generate structured, regulatory-compliant output.  
This study addresses these gaps by introducing benchmarks tailored to GDPR documentation tasks. The proposed benchmarks evaluate the ability of LLMs to generate structured outputs that meet compliance requirements, with a focus on accuracy, regulatory adherence, and integration into dynamic legal environments. Thereby, this study advances the evaluation of LLMs for regulatory compliance tasks, providing a framework that bridges the gap between general legal reasoning and compliance-specific applications.  
Building on the evaluation of LLMs, their use in the legal domain poses unique challenges. Complex legal language, jurisdictional differences, and evolving laws highlight key limitations. The next section explores these challenges and their implications for compliance and ethics.
## 4 Results and Discussion
### Experimental Results of 1D Raw Data
#### 4.2.3 Results of All Models with 1D Raw Data  
The experimental findings of our study are displayed in Table 6 and Figure 5. We evaluated the results of various ML models and DL models. The results show that the top-performing ML models, RF, SVM, ET, and GB, achieve higher accuracy than the DL  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline RF & 0.9242 & 0.9308 & 0.9242 & 0.9169 \\ SVM & 0.9091 & 0.9058 & 0.9091 & 0.9061 \\ ET & 0.9091 & 0.9077 & 0.9091 & 0.9025 \\ GB & 0.9091 & 0.9183 & 0.9091 & 0.8979 \\ AdaBoost & 0.8485 & 0.8408 & 0.8485 & 0.8436 \\ DT & 0.8333 & 0.8208 & 0.8333 & 0.8248 \\ LR & 0.7727 & 0.8378 & 0.7727 & 0.7914 \\ SGD & 0.6970 & 0.8142 & 0.6970 & 0.7269 \\ KNN & 0.6061 & 0.7488 & 0.6061 & 0.6461 \\ NB & 0.5455 & 0.7501 & 0.5455 & 0.5901 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of ML models using 1D raw data.  
Figure 4: Performance of ML models on 1D raw data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline
1D CNN & 0.8491 & 0.8491 & 0.8491 & 0.8491 \\ MobileNet & 0.8208 & 0.8255 & 0.8208 & 0.8213 \\ EfficientNetB4 & 0.8302 & 0.8335 & 0.8302 & 0.8307 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of All Models with 1D Raw Datamodels. The best-performing ML model, RF, obtains an accuracy of 0.9242, while the most efficient DL model, 1D CNN, achieves an accuracy of 0.8491. The RF model is a suitable choice for this type of data, achieving high accuracy. The results indicate that ensemble methods, such as RF, ET, and GB, outperform single models, such as DT, LR, and SGD. Tree-based models, such as RF, ET, and GB, perform well on the dataset, achieving high accuracy.
## 3 Model
### GCN Layer  
GCN is a simple and effective graph-based convolutional neural network that learns information for graph nodes containing all neighboring nodes and its own nodes, as shown in Figure 6. GCN acts directly on the graph, and its inputs are the graph structure and the feature representation of the nodes in the graph. The model proposed in this paper learns the dependency information in the input sentence dependency relation tree by a graph convolutional neural network.  
Figure 4: Two pruning operation diagrams: top is local subtree pruning; bottom is shortest-path tree pruning. SBV, subject–predicate relation; ATT, definitive–medial relation; VOB, verb-object relation. The Chinese sentence in the figure means “The most common cause of liver cirrhosis is chronic hepatitis C”. “\(\overrightarrow{\text{\#}}\)\(\overrightarrow{\text{\#}}\)\(\overleftarrow{\text{\#}}\  
Firstly, the sentences are preprocessed, and the feature vectors obtained by encoding the word segmentation through the BiLSTM layer are used as nodes in the graph. The relations between different nodes in the results of the dependency analysis are then used as edges that constitute the graph structure of the graph convolutional neural network. To reduce the effect of noise in the sentences, the model uses a pruning strategy on the dependency tree and transforms the pruned dependency graph into the adjacency matrix \(A\).  
Based on the adjacency matrix \(A\), for each node \(v_{i}\in V\), the GCN at layer \(l\) learns the node information on the dependency relation tree and calculates the output of node \(v_{i}\) at layer \(l\) as \(h_{v_{i}}^{(l)}\). The specific calculation is shown in Equations (4) and (5):  
\[h_{v_{i}}^{(0)}=x_{v_{i}}\quad v_{i}\in V \tag{4}\]  
\[h_{v_{i}}^{(l)}=f(\sum_{j=1}^{n}a_{ij}W^{l}h_{v_{j}}^{-1}+b^{(l)})\quad v_{i}, v_{j}\in V \tag{5}\]  
where \(h_{v_{i}}^{(0)}\) represents the initial embedding of node \(v_{i}\), \(x_{v_{i}}\) represents the original feature of node \(v_{i}\), \(h_{v_{j}}^{l-1}\) represents the hidden state of node \(v_{j}\) after \(l-1\) layer graph convolutional neural network, \(w^{l}\) represents the weight matrix, \(b^{(l)}\) represents the bias, \(a_{i,j}\) represents the corresponding elements of node \(i\) and node \(j\) in the adjacency matrix, \(f(.)\) represents a nonlinear function and is the ReLU function in this model, and \(h_{v_{i}}^{(l)}\) represents the hidden state of node \(v_{i}\) after the \(l\) layer convolutional neural network. For each layer of GCN, functions \(f(.)\), matrix \(W^{l}\), and matrix \(b^{(l)}\) are shared on all nodes, which makes the number of parameters on the model irrelevant to the graph, enabling the GCN model to be well-extended.
## 4 Life Cycle Analysis (LCA) Comparison between Batteries and PHES
### LCA of PHES  
Conducting a comprehensive Life Cycle Analysis (LCA) of pumped hydroelectric storage (PHES) entails an exhaustive examination of the environmental impacts associated with the entire operational life cycle of a pumped storage facility, as outlined by the International Renewable Energy Agency (2019) [32]. This entails assessing the environmental footprint encompassing infrastructure construction, ongoing maintenance and operation, and eventual decommissioning or repurposing. Initial environmental impacts are influenced by resource extraction and processing for reservoir construction, as well as the production and transportation of turbines and generators. The LCA also accounts for the continuous operation of the facility, including energy inputs for pumping water to the upper reservoir and generating electricity during peak demand periods. Moreover, end-of-life considerations involve decommissioning and potential site repurposing, adding another layer to the sustainability evaluation. While the construction phase may pose notable environmental impacts, LCA reveals that PHES offers several environmental benefits. Despite the initial construction phase's impacts, the long operational lifespan of PHES facilities allows for significant energy storage and grid stabilization, thereby reducing reliance on fossil-fuel-based power plants. The overall reliability and productivity of the grid hinge on the ability to store excess electricity during low-demand periods and release it during times of peak demand. Through LCA, a comprehensive understanding of PHES's environmental effects and trade-offs is achieved, enabling the development of plans to optimize its sustainability and establish it as a pivotal component in the transition to a more sustainable energy future. Our proposed work, Figure 11, derived completely by our team, depicts the design of a rectangular water tank at level 6. The tank, measuring 10.18 m wide, 0.306 m high, and 4.5 m long, is designed to withstand a load of 3 kN/m\({}^{2}\). Calculations show that the tank can handle the expected load of 137.34 kN. The accompanying top-down view likely indicates the building where the tank will be located. This approach allows for a thorough examination of different designs and their respective efficiencies, facilitating a more informed decision on the most suitable turbine for the MLCP implementation.
## 3 Results
### Experimental and Numerical Model Results  
The temperature of the mold's contact points with the glycol-water mixture is considered as a constant that is equal to temperature of the glycol-water mixture. The top surface of the ice cream boundary layer is considered to be where the convection heat transfer is happening, and the heat transfer convection coefficient is considered as 10 W/(m\({}^{2}\) K). Ambient temperature is considered constant.  
During experimental data recording, every second, the center point temperature of ice cream was recorded. Numerical simulation results were also captured every second, and numerical simulation was carried out for each specific heat correlation, which is given in Equations (10), (14), (15), and (24). Experimental results and numerical results are compared in Figure 7.  
As is evident from Figure 7, the proposed formulation exhibits a favorable agreement with experimental results and correlations found in the existing literature. Consequently, the decision was made to proceed with a parametric analysis to explore additional factors influencing freezing performance. Conducting experimental studies for each parameter is impractical due to the considerable time and effort involved. Therefore, employing parametric numerical studies allows for a systematic investigation into the primary parameters affecting the study's outcomes. Glycol-water mixture temperature, ice cream initial temperature, and air volume inside ice cream are the parameters of the freezing process. For each parameter, three levels were defined. For the glycol-water mixture temperature, \(-\)34, \(-\)37, and \(-\)40 \({}^{\circ}\)C are defined. Air amounts according to ice cream volume are considered to be 30, 50, and 80%. Initial ice cream temperature is considered as \(-\)1, 1, and 4 \({}^{\circ}\)C for the numerical analysis. The elapsed time until the ice cream center point reaches \(-\)18 \({}^{\circ}\)C was determined for each case, and the effects on the freezing time were discussed. Parametric study results can be found in Table 4. It is found that glycol-water mixture temperature is  
Figure 6: Thermal conductivity results according to literature correlations.  
the most effective parameter in the freezing process and the least effective parameter is the amount of air inside the ice cream.
# DSVN: A Flexible and Secure Data-Sharing Model for VANET Based on Blockchain
###### Abstract  
Vehicular Ad Hoc Network (VANET) is an important part of the modern intelligent transportation system, which can provide vehicle communication at a certain distance. More importantly, VANET can provide route planning and autonomous driving for drivers by analyzing data. However, VANET's data privacy and security are a huge challenge when serving drivers. In this paper, we propose a VANET data-sharing model (DSVN) that combines ciphertext-based attribute encryption (CP-ABE), blockchain, and InterPlanetary File System (IPFS). DSVN uses an outsourced and revocable ciphertext policy attribute-based encryption (ORCP-ABE) scheme, which is improved based on CP-ABE. ORCP-ABE uses key encryption key (KEK) trees to manage user attribute groups and revoke user-level attributes. It eliminates redundant attributes in the access policy by attribute-weighted access trees. Moreover, DSVN has no single point of failure. We demonstrate the indistinguishability under the chosen-plaintext attack (IND-CPA) security of DSVN by a game based on the computational Diffie-Hellman (CDH) assumption. Experimental results show that DSVN can store and share data with low overhead. Additionally, it can revoke attributes of users safely.  
VANET; data sharing; CP-ABE; blockchain; attribute revocation Article
## 3 PR Calculation in a GNSS Receiver  
From the theory, we know that a typical GNSS receiver computes the user's position from the estimated PR \(\rho_{i}\) (distance between the \(i\)-th satellite and the receiver). Considering that both satellites and receiver's clocks are affected by independent errors, we call _system time_ the reference time frame where satellites and the receiver's clocks are referred. Before introducing the equation, we define some notations [17]:  
* \(T_{t}\) is the system time at which the signal left the satellite
* \(T_{RX}\) is the system time at which the signal reached the user receiver
* \(\delta t\) is the offset of the satellite clock from system time (written in the navigation message)\(t_{RX}\) is the offset of the receiver clock from system time
* \(T_{t}+\delta t\) is the satellite clock reading at the time that the signal left the satellite
* \(T_{RX}+t_{RX}\) is the user receiver clock reading at the time the signal reached the user receiver
* \(c\) is the speed of light  
A generic \(\rho\) for a single satellite is given by taking into account all the clock errors  
\[\begin{split}\rho&=c\left(T_{RX}+t_{RX}\right)-c \left(T_{t}+\delta t\right)\\ &=c\left(T_{RX}-T_{t}\right)+c\left(t_{RX}-\delta t\right)\\ &=r+c\left(t_{RX}-\delta t\right)\end{split} \tag{2}\]  
where \(r\) is the geometric range and the second term \(c\left(t_{RX}-\delta t\right)\) is the residual distance due to the not perfect synchronisation between satellite and receiver clocks. To calculate this, the time of flight of the signals is measured and the clock corrections will be made after data demodulation and Position, Velocity and Time (PVT) computation.
# Preparation and Characterization of Gold Nanorods Coated with Gellan Gum and Lipoic Acid
## 2 Materials and Methods
### Evaluation of Coated Gold Nanorods Hyperthermia  
AuNRs/LA, AuNRs/LA,GG, and AuNRs/GG-LA samples at different Au concentrations (10, 20, 35 \(\upmu\)g\(\cdot\)mL\({}^{-1}\)) were placed in a 24-well plate and treated with a diode laser (GBox 15A/B by GIGA Laser) with the fixed wavelength of 810 nm, setting the laser power of \(3.5\times 10^{-3}\) W/mm\({}^{3}\), \(7\times 10^{-3}\) W/mm\({}^{3}\), and \(14\times 10^{-3}\) W/mm\({}^{3}\). A fiber optic temperature detector (\(\pm\)1 \({}^{\circ}\)C sensitivity) was used to record temperatures at preset laser exposure time ranging from 0 to 300 s, and hyperthermia profiles were thus obtained plotting temperatures versus exposure time.  
Infrared-camera hyperthermia studies were performed using a FlirT250 Infrared Camera with a resolution of \(240\times 180\) pixels, a sensitivity of 80 mK NETD/0.08 \({}^{\circ}\)C and a measurable temperature range of \(-20\)\({}^{\circ}\)C : 50 \({}^{\circ}\)C. In particular, dispersions of AuNRs/LA, AuNRs/LA,GG, and AuNRs/GG-LA at gold concentrations of 2.5 and 5 \(\upmu\)g\(\cdot\)mL\({}^{-1}\) were placed in 24-well plates and treated for 100 s with the aforementioned diode laser, setting the power of \(10\times 10^{-3}\) W/mm\({}^{3}\). At time intervals of 20 s, the IR images were acquired, and the temperatures were recorded. The same experiment was performed on an equal amount of ultrapure water, used as negative control.