esa-sceva/satcom-chunk-collection huggingface dataset:
## Glossary  
\begin{tabular}{p{28.5 pt} p{284.5 pt}} BCSS & Between-Clusters Sum of Squares: A measure of the variance between different cluster centroids, used to evaluate clustering quality. \\ BERT & Bidirectional Encoder Representations from Transformers: A pre-trained deep learning model designed for natural language processing tasks. \\ CH & Calinski-Harabasz Index: A clustering validation metric that measures the ratio of between-cluster variance to within-cluster variance. \\ CLB-BER & CLUBS-BERT: The proposed model combining improved CLUBS clustering with BERT for electricity consumption behavior classification. \\ CLUBS & CLUustering Big data by Sampling: A non-parametric clustering algorithm designed for large datasets using hierarchical strategies. \\ DistilBERT & Distilled BERT: A smaller, faster variant of BERT that retains most of BERT's performance while reducing computational requirements. \\ DTW & Dynamic Time Warping: An algorithm for measuring similarity between temporal sequences that may vary in speed or length. \\ ECDW & Euclidean-Cosine Dynamic Windowing: The proposed method combining Euclidean distance and cosine similarity with dynamic windowing for time-series comparison. \\ LLM & Large Language Models: Deep learning models with billions of parameters trained on vast amounts of text data. \\ LSTM & Long Short-Term Memory: A type of recurrent neural network architecture capable of learning long-term dependencies. \\ MHA & Multi-Head Attention: An attention mechanism that allows the model to attend to information from different representation subspaces. \\ MLM & Masked Language Model: A pre-training task where certain tokens in the input are masked and the model learns to predict them. \\ NFS & Network File System: A distributed file system protocol allowing file access over a network. \\ PCA & Principal Component Analysis: A dimensionality reduction technique that transforms data to lower dimensions while preserving variance. \\ PNN & Probabilistic Neural Network: A neural network used for classification and pattern recognition problems. \\ PSO-K & Particle Swarm Optimization K-means: An optimization algorithm that combines particle swarm optimization with K-means clustering. \\ Redis & Remote Dictionary Server: An in-memory data structure store used as a database, cache, and message broker. \\ WOSS & Within-Cluster Sum of Squares: A measure of the variance within each cluster, used to evaluate clustering compactness. \\ \end{tabular}
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 7 Efficiency and Security Analysis
### Security Proof  
As previously mentioned, Sillcom is based on a similar outsourcing model to three computational servers [57]. We perform a simple security analysis of Sillcom based on the outsourcing model defined in [58]. Sillcom instantiates the outsourcing scheme of [58] with secure three-party computation, where the distance computation part is based on three-party RSS sharing, and the remaining three computational modules adopt an asymmetric structure. The computing party \(P_{2}\) ceases to participate in subsequent computations after completing the sharing conversion of the distance computation results. Below, we first provide the security proof for the sharing conversion performed by the \(\Pi_{\text{SC}}\) algorithm.  
Theorem 1: _The \(\Pi_{SC}\) protocol is secure in the presence of semi-honest adversaries._  
Proof: Let \(\mathcal{S}\) be a simulator and \(\mathcal{A}\) be any probability polynomial time (PPT) adversary, we now prove that the outputs of the two games \(Real_{\mathcal{A}}(\lambda)\) and \(Idea_{\mathcal{A}\mathcal{S}}(\lambda)\) are computationally indistinguishable. First, assume the adversary controls \(P_{2}\). In this case, \(\mathcal{A}\) simply distributes the computation result and does not receive any further response from the other two computing servers, so the security is not compromised. Now consider the adversary controls \(P_{0}\) or \(P_{1}\). For example, when \(P_{1}\) is corrupted, in the real view, the adversary \(\mathcal{A}\) receives a message vector from \(P_{2}\). In the ideal view, the simulator \(S\) interacts with \(\mathcal{A}\) and simulates exact transcripts for interactions between \(\mathcal{A}\) and \(P_{2}\), generating the correct distribution of outputs to enable security against semi-honest adversaries. Since the honest \(P_{2}\) locally generates a random permutation \(\pi\) and a random value vector, and the distance 3P-ASS shares computed by RSS are also random for \(P_{1}\), no PPT adversary can distinguish the output of \(Real_{\mathcal{A}}(\lambda)\) and \(Idea_{\mathcal{A}\mathcal{S}}(\lambda)\).  
Theorem 2: _Sillcom is secure against a semi-honest adversary in the \((\Pi_{SED},\Pi_{SC},\Pi_{Shuffle},\)\(\Pi_{rank})-hybrid model._  
Proof: Similar security analysis of \(\Pi_{\text{Shuffle}}\) and \(\Pi_{\text{rank}}\) are presented in prior [46; 49] and \(\Pi_{\text{SED}}\) in [59], rely on the result of [60], Sillcom is secure under concurrent general composition.
## 3 Results
### Datasets  
The datasets utilized in this research include FGG and YR to evaluate the performance of our fire detection model. FGG contains 31 videos captured from indoor and outdoor environments, with 17 videos having normal scenes and 14 having fire scenes. The dataset includes 62,690 frames, with the details in [2]. On the other hand, YR is a small-scale dataset consisting of 2000 images of normal scenes, with 1000 images belonging to the fire scenes and 1000 images belonging to the normal scenes. The dataset presents a challenge due to objects like colored fire, i.e., light and sun. The datasets are divided into three sets for testing, validation, and training, consisting of 20%, 10%, and 70% of the data, respectively, as performed by previous studies [66].
# Compp-DDPG: Computation Offloading with Privacy Preservation in a Vehicular Edge Network
## 3 Model of VEN with Privacy Preservation
### Scenario Description
Other vehicular entertainment tasks provide value-added services, which can tolerate the completion delay exceeding the maximum deadline but with some degradation of data availability. In this work, the ICV tasks are assigned to be executed locally, offloaded to another ICV with spare computational resources, or offloaded to the MEC server. ICVs with available computing capacity for task offloading are regarded as the vehicular edge computing (VEC). The offloading decision of ICV-\(i\) is defined as \(\mathcal{X}=\{x_{i}|x_{i}\in\{0,\,1,\,2\},i\in N\}\). The ICV-\(i\) chooses the local execution strategy of task \(T_{i}\) when \(x_{i}=0\). The offloading decision of calculating the task \(T_{i}\) on VEC is selected when \(x_{i}=1\). Task \(T_{i}\) is offloaded and processed on the MEC server when \(x_{i}=2\). In addition, the delay-sensitive vehicular tasks are not suitable for the situation of the binary offloading [31]. Partial offloading is introduced in this work to make better usage of the computational resources, which means that the vehicular task \(T_{i}\) can be executed separately onboard and edge computing as two portions. The offloading ratio \(\lambda_{i}(0\leq\lambda_{i}\leq 1)\) is depicted as the offloading part ratio to the entire vehicular task \(T_{i}\). ICV-\(i\) computes \(\lambda_{i}D_{i}^{in}\) locally and offloads the rest \((1-\lambda_{i})\)\(D_{i}^{in}\) to the VEC or MEC server [32]. For the limited computing and communication capacity of the ICV, each VEC is considered to only be able to execute one offloaded task from another ICV. The main notation is shown in Table 1.
## 3 Materials and Methods
### Data Preprocessing and Augmentation  
To improve the dataset quality and enhance model performance, several preprocessing techniques [18] were applied:  
* Handling missing values: missing data points were handled using forward and backward filling interpolation to ensure continuity in sensor readings.
* Rate of change: first-order differences for sensor values to detect abrupt fluctuations.
- Rolling window statistics: Short-term variability indicators such as standard deviation over a 5 time step window [20]. A rolling window approach was applied to compute short-term fluctuations. The standard deviation over a window of \(N=5\) time steps was computed as:  
\[\sigma_{t}=\sqrt{\frac{1}{N}\sum_{i=t-N}^{t}(X_{i}-\bar{X})^{2}} \tag{2}\]  
where \(\bar{X}\) is the mean over the window. These features helped capture sensor variability over time.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **Count** \\ \hline Total sensor nodes & 15 \\ Total data points & 14,776 \\ Sampling interval & 10 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of dataset statistics.  
Z-score normalization: Standardization of sensor readings to account for scale differences. To normalize the feature values and make them comparable across sensors, we applied Z-score normalization: \[Z_{i}=\frac{X_{i}-\mu}{\sigma}\] (3) where \(\mu\) and \(\sigma\) represent the mean and standard deviation, respectively.
* Normal data: Regular environmental conditions with no detected anomalies.
- Malfunctions: Sensor faults or hardware failures, detected using reconstruction error from the Transformer Autoencoder and further validated by Isolation Forest.
- Intrusions: external interferences or false data injections, identified based on Isolation Forest classification and XGBoost refinement.
* Min-max scaling: finally, all features were rescaled to the range \([0,1]\) using min-max normalization, ensuring that no feature dominated the model's learning process.
## 3 Sensitivity Analysis
### Sensitivity of Buckling Constraint Functions  
For convenience of calculation, this paper introduces the auxiliary variable \(\mu=1/\lambda\). Then, the eigenvalue equation of the linear buckling analysis is:  
\[(\mathbf{K}_{G}+\mu_{i}\mathbf{K})\mathbf{\varphi}_{i}=0 \tag{22}\]  
Referring to the paper by [PERSON] et al. [41], the sensitivity of a single critical buckling factor to design variables can be derived from the eigenvalue equation:  
\[\frac{\partial\mu_{i}}{\partial\hat{x}_{e}}=-\mathbf{\varphi}_{i}^{T}(\frac{ \partial\mathbf{K}_{G}}{\partial\hat{x}_{e}}-\mu_{i}\frac{\partial\mathbf{K}}{ \partial\hat{x}_{e}})\mathbf{\varphi}_{i}+\mathbf{v}^{T}\frac{\partial\mathbf{K}}{ \partial\hat{x}_{e}}\mathbf{u}_{0} \tag{23}\]  
where \(\mathbf{v}^{T}\) is the adjoint displacement vector, which can be solved with the following adjoint equation:  
\[\mathbf{K}\mathbf{v}=\mathbf{\varphi}_{i}^{T}\frac{\partial\mathbf{K}_{G}}{\partial U}\mathbf{ \varphi}_{i}=\left\{\begin{aligned} \mathbf{\varphi}_{i}^{T}\frac{\partial\mathbf{K}_{G}}{ \partial u_{1}}\mathbf{\varphi}_{i}\\ \vdots\\ \mathbf{\varphi}_{i}^{T}\frac{\partial\mathbf{K}_{G}}{\partial u_{i}}\mathbf{ \varphi}_{i}\end{aligned}\right\} \tag{24}\]  
where \(n\) is the total number of degrees of freedom in the structural system, and the sensitivity of the geometric stiffness matrix to the design variables is:  
\[\frac{\partial\mathbf{K}_{G}}{\partial\hat{x}_{e}}=\frac{\partial\hat{E}_{G}}{ \partial\hat{x}_{e}}\mathbf{K}_{G0} \tag{25}\]  
where \(\mathbf{K}_{G0}\) is the geometric stiffness matrix and \(\mathbf{K}_{G}\) is defined as \(K_{G}=E_{G}(x)K_{G0}\), and the sensitivity of the geometric stiffness matrix to the element node displacement is:  
\[\begin{array}{l}\frac{\partial\mathbf{K}_{G}}{\partial u_{i}}(\mathbf{u})=\frac{ \partial}{\partial u_{i}}\int\limits_{\Omega}\mathbf{g}^{T}\mathbf{S}gd\Omega_{e}= \int\limits_{\Omega_{e}}\mathbf{g}^{T}(\mathbf{I}_{2}\otimes\frac{\partial\mathbf{e}_{e}} {\partial u_{i}})\mathbf{g}d\Omega_{e}\\ =\int\limits_{\Omega_{e}}\mathbf{g}^{T}(\mathbf{I}_{2}\otimes\mathbf{D}\mathbf{B} \frac{\partial\mathbf{u}_{i}}{\partial u_{i}})\mathbf{g}d\Omega_{e}=\int\limits_{ \Omega_{e}}\mathbf{g}^{T}(\mathbf{I}_{2}\otimes\mathbf{D}\mathbf{B}\delta_{e,i})\mathbf{g}d\Omega _{e}\end{array} \tag{26}\]  
If \(\mathbf{u}_{i}\) is the nodal displacement component of the \(i\)-th element, then it takes a value of \(1\); otherwise, it takes a value of \(0\). The rest of the vectors or matrices have already been described above and will not be repeated here.  
Referring to the paper of [PERSON] et al. [13], the first 12-order buckling factors of the structure are aggregated by the K-S aggregation function. The sensitivity of the buckling factor to the design variables after aggregation is:  
\[\frac{\partial J^{KS}[\mu_{i}]}{\partial\hat{x}_{e}}(\hat{\pi})=\frac{\sum\limits \limits_{i=1}^{q}e^{\rho(\mu_{i}(\hat{x})-\mu_{1}(\hat{x}))}\frac{\partial\mu_{ i}}{\partial\hat{x}_{e}}(\hat{\pi})}{\sum\limits_{i=1}^{q}e^{\rho(\mu_{i}( \hat{x})-\mu_{1}(\hat{x}))}} \tag{27}\]
# Deep Learning Methods for Bug Bite Classification: An End-to-End System
## 2 Materials and Methods
### CNN Variants Used for Bug Bite Classification
#### 2.6.2 Classifier Fusion  
Classifier fusion [48] represents a methodology for combining the inference capabilities of multiple classifiers with the goal of achieving an improved accuracy by using one of many possible fusion methods. This subsection presents the fusion algorithm used in this study. To implement the fusion, we have used four different types of deep neural networks as base models: MobileNet-v2, VGG16, Inception-v3, and DenseNet169, customized for the specific problem of bug bite classification. Multiple classifiers were generated from each of the four base types, with subtly different model architectures, and the best performing model from each type was chosen for the classifier fusion. As will be described in more detail in the Section 3, each of these models has the power to perform better in the classification of bug bites of different classes. We expected that with the fusion of the different models, we will take advantage of the powerful capabilities for correct classification that each model exhibits for certain classes, which would lead to an improved fused model.  
The implementation of the classifier fusion is given in Figure 6. The fusion was implemented as a variant of ensemble learning [49] called model stacking. In the ensemble architecture, we used each of the chosen models and concatenated their outputs in one output vector, which was then fed to a fully connected layer with 128 neurons with a ReLU activation function.  
The purpose of the fully connected layer is to learn the impact each model should have when predicting each of the 9 target labels. Some models will be given greater weights for predicting a value for some of the labels, and other models will have a greater influence for other labels. Finally, a softmax layer with 9 neurons was added to the output of the fully connected layer in order to produce a probability distribution over each of the target labels (Table 8).  
The experimental results have shown a substantial improvement in the accuracy of the stacked neural network ensemble model compared to each of the individual models. The confusion matrices and evaluation metrics are outlined in Section 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer** & **Output Shape** & **Number of Parameters** \\ \hline Input Layer & (N, 224, 224, 3) & 0 \\ DenseNet169 Layers & (N, 7, 7, 1664) & 0 \\ GlobalAveragePooling2D & (N, 1664) & 0 \\ BatchNormalization & (N, 1664) & 6656 \\ Dropout & (N, 1664) & 0 \\ Dense with Dropout & (N, 2048) & 3,409,920 \\ Dense with Dropout & (N, 512) & 1,049,088 \\ Dense & (N, 128) & 65 664 \\ Dense—Output & (N, 9) & 1161 \\ \hline Total parameters: 17,175,369 & & \\ Trainable parameters: 4,529,161 & & \\ Non-trainable parameters: 12,646,208 & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of the custom layers added to the pre-trained DenseNet model.
###### Abstract  
To address the freezing damage to tunnel lining caused by frost heaving of the surrounding rock in water-rich tunnels in cold regions, a numerical thermo-mechanical coupling model for tunnel-surrounding rock that considers the anisotropy of frost heave deformation was established by examining overall frost heaves in a freeze-thaw cycle. Using a COMSOL Multiphysics 6.0 platform and the sequential coupling method, the temperature field evolution of tunnel-surrounding rock, freezing cycle development, and distribution characteristics of the frost heaving force of a tunnel lining under different minimum temperatures, numbers of negative temperature days, frost heave ratios, and anisotropy coefficients of frost heave deformation were systematically simulated. The results revealed that the response of the temperature field of tunnel-surrounding rock to the external temperature varies spatially with time lags, the shallow surface temperatures and the area around the lining fluctuate with the climate, and the temperature of the deep surrounding rock is dominated by the geothermal gradient. The extent of the freezing cycle and the frost heaving force increase significantly when lowering the minimum temperature. The maximum frost heaving force usually occurs in the region of the side wall and the spring line, and tensile stress is prone to be generated at the spring line; the influence of slight fluctuations in the minimum temperature or the short shift in the coldest day on the frost heaving force is limited. A substantial increase in frost heaving force is observed with higher frost heave ratios; for example, an increase from 0.25% to 2.0% results in a 116% rise at the sidewall. Although the increase in the anisotropy coefficient of frost heave deformation does not change the overall distribution pattern of frost heaving force, it can exacerbate the directional concentration of frost heave strain, which can increase the frost heaving force at the periphery of the top arch of the lining. This study revealed the distribution pattern and key influencing factors of the freezing cycle and frost heaving force for tunnels, providing a theoretical basis and data reference for the frost resistance design of tunnels in cold regions.  
numerical simulation; coupled analysis; orthotropic anisotropy; frost heaving Article
## 2 Materials and Methods
### Texture Synthesis of RSSI Based on GAN Network  
In a typical GAN network [14], the generator network maps randomly generated noise data, such as Gaussian noise, to fake samples. The discriminator network must receive real data samples (to discriminate between data samples) or fake samples. The generator is then trained to cheat the discriminator. Simultaneously, the discriminator also needs to be trained to be able to overcome the generator. Generally, the competition between the generator and the discriminator enables the generator to generate enough samples to deceive the discriminator, and the discriminator can better distinguish between true samples and constructed samples. The min-max objective function of the game between the generator \(G\) and the discriminator \(D\) is given by  
\[\underset{G}{minmax}_{D}E_{x\sim P_{F}}[\log(D(x))]-E_{\widetilde{X}\sim P_{g}}[ \log(D(\widetilde{x}))], \tag{10}\]  
where \(P_{r}\), \(x\) and \(P_{g}\) are the real data distribution, real sample and model distribution, respectively. Fake samples can be represented as \(\widetilde{x}=G(z)\), \(Z=P(z)\), where the input \(z\) of the generator is sampled from a simple noise distribution, such as a spherical Gaussian distribution. If the optimization and training of the discriminator are after the generator updates the parameters, then the minimization of the \(JS\) divergence between \(P_{r}\) and \(P_{g}\) is the minimization value function. \(JS\) divergence is an important indicator of the similarity between two probability distributions based on Kullback-Leibler (\(KL\)) divergence. \(JS\big{(}P_{r},P_{g}\big{)}\) in GAN can be calculated as follows.  
\[JS\big{(}P_{r},P_{g}\big{)}=KL(P_{r},P_{m})+KL\big{(}P_{g},P_{m}\big{)} \tag{11}\]  
where \(KL\big{(}P_{g},P_{m}\big{)}\) is defined as \(\int\log\Big{(}\frac{P_{g}(x)}{P_{g}(x)}\Big{)}P_{r}(x)d\mu(x)\) Both \(P_{r}\) and \(P_{g}\) are assumed to be absolutely continuous and admit densities.  
Based on the work of [PERSON] et al. and [PERSON] et al. on texture synthesis, we used a GAN to synthesize the texture of each element extracted. The texture synthesis technology in this section is based on the work of [PERSON] and [PERSON]. We sampled the surface texture of rivers, forests, grasslands, and oceans in RSSIs and selected a big number of typical surface texture samples as the input of texture synthesis. When performing texture synthesis, we selected a total of 100 texture samples from five categories, including rivers, lakes, seas, meadows, and forests, representing the typical characteristics of the corresponding category. These five classes of samples were taken from rivers, lakes, ocean forests, and grasslands. Figure 2 is an example of our selection of samples.
## 3 Results
### Brevinin-2 Family Peptides Used for Anti-Infective Therapy  
To verify the anti-infective therapeutic potential of Brevinin-2 family peptides, a screening in 24-well plates was performed with MRSA-C. _elegans_. Thirteen peptides were screened and demonstrated increased survival rates of MRSA-infected nematodes (Figure 4). The adapted standard to classify the therapy peptides refers to the \"Anti-infective screening of Brevinin-2 family\". Four positive peptides contributed to a survival rate of more than 60%. Brevinin-2, Brevinin-2-OA3, Brevinin-2 ISb, and Brevinin-2 TSa increased the survival rate after 96 h compared to other peptides. The living worms were fed on _E. coli_ OP50 during the assay. Four of the antimicrobial peptides identified above affected the nematode survival according to the standard for <60% survival rate. The following experiments were conducted using only these four antimicrobial Brevinin-2 peptides.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Name** & **MIC on APD (_S. aureus_)** & **MIC (MRSA)** & **MBC (MRSA)** \\ \hline
1. & Brevinin-2 & 8 \(\upmu\)M & 9.6 \(\pm\) 1.3 \(\upmu\)M & 23.4 \(\pm\) 4.2 \(\upmu\)M \\
2. & Brevinin-2 DYc/RNa & 37.5 \(\upmu\)M & 32.3 \(\pm\) 2.7 \(\upmu\)M & 82.5 \(\pm\) 12.4 \(\upmu\)M \\
3. & Brevinin-2 HS2 & 19 \(\upmu\)M & 21.5 \(\pm\) 1.1 \(\upmu\)M & 39.7 \(\pm\) 5.7 \(\upmu\)M \\
4. & Brevinin-2 ISb & 6.3–25 \(\upmu\)M & 8.7 \(\pm\) 0.9 \(\upmu\)M & 33.8 \(\pm\) 5.4 \(\upmu\)M \\
5. & Brevinin-2 LF2 & 25 \(\upmu\)M & 23.4 \(\pm\) 2.1 \(\upmu\)M & 61.5 \(\pm\) 12.6 \(\upmu\)M \\
6. & Brevinin-2-OA3 & 3–13 \(\upmu\)M & 6.7 \(\pm\) 0.7 \(\upmu\)M & 31.5 \(\pm\) 6.2 \(\upmu\)M \\
7. & Brevinin-2-OR5 & 6.6–13.2 \(\upmu\)M & 8.6 \(\pm\) 1.6 \(\upmu\)M & 43.3 \(\pm\) 8.6 \(\upmu\)M \\
8. & Brevinin-2-OW2 & 3.3–6.6 \(\upmu\)M & 4.9 \(\pm\) 1.2 \(\upmu\)M & 28.7 \(\pm\) 3.9 \(\upmu\)M \\
9. & Brevinin-2 RE1 & 20–50 \(\upmu\)M & 35.9 \(\pm\) 4.4 \(\upmu\)M & 103.8 \(\pm\) 14.6 \(\upmu\)M \\
10. & Brevinin-2-related peptide & 25 \(\upmu\)M & 27.8 \(\pm\) 2.6 \(\upmu\)M & 58.6 \(\pm\) 13.4 \(\upmu\)M \\
11. & Brevinin-2 SKA & 50 \(\upmu\)M & 62.3 \(\pm\) 6.5 \(\upmu\)M & 196.3 \(\pm\) 26.6 \(\upmu\)M \\
12. & Brevinin-2 TP1 & 100 \(\upmu\)M & 89.7 \(\pm\) 11.7 \(\upmu\)M & 345.7 \(\pm\) 64.3 \(\upmu\)M \\
13. & Brevinin-2 TSa & \(\geq\)25 \(\upmu\)M (MRSA) & 31.2 \(\pm\) 6.1 \(\upmu\)M & 78.4 \(\pm\) 12.5 \(\upmu\)M \\ \hline \hline \end{tabular}
\end{table}
Table 3: In vitro minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values effective against _Staphylococcus aureus_ strain ATCC33591 (MRSA).