esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Model Results  
### Hyperparameter Tuning Results for Optimal Model  
The hyperparameter tuning procedure, which combined a grid search and k-fold cross-validation, was employed to determine the optimal model parameters for the MARS models, aiming for the lowest RMSE. According to Figure 5, the performance of the MARS model was more sensitive to the 'nprune' parameter than the interaction degree, highlighting the crucial role of 'nprune' in achieving top-tier model accuracy.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Statistics** & \(C_{G}\) **(\%)** & \(C_{S}\) **(\%)** & \(C_{F}\) **(\%)** & \(LL\) **(\%)** & \(PL\) **(\%)** & \(E\) **(kJ/m3)** & \(w_{opt}\) **(\%)** & \(\rho_{\text{dmax}}\) **(Mg/m3)** \\ \hline Standard deviation & 14.566 & 23.388 & 30.016 & 164.218 & 7.405 & 735.435 & 5.964 & 0.199 \\ Mean & 7.468 & 29.448 & 63.088 & 108.727 & 22.005 & 894.066 & 17.512 & 1.751 \\ Median & 0.000 & 27.000 & 70.000 & 40.650 & 20.150 & 593.000 & 17.000 & 1.750 \\ Maximum & 67.100 & 89.000 & 100.000 & 608.000 & 48.300 & 2755.000 & 43.700 & 2.330 \\ Minimum & 0.000 & 0.000 & 8.600 & 16.000 & 6.100 & 155.000 & 5.300 & 1.090 \\ Kurtosis & 3.482 & \(-0.471\) & \(-1.250\) & 3.264 & 0.748 & 2.266 & 2.862 & 0.977 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model variable descriptive statistics.  
Figure 4: Relative frequency distribution of input and output variables.  
As detailed in Figure 5, a rigorous five-fold cross-validation process pinpointed 'degree = 2' and 'nprune = 13' as the prime settings for the \(w_{opt}\) MARS model. Leveraging these parameters, the model showcased exemplary performance across the 131 training datasets, registering an average RMSE. On the other hand, for the \(p_{\text{dmax}}\) MARS model, the most suitable hyperparameters were 'nprune = 11' and 'degree = 2'. With these settings, the model attained an optimal predictive capacity, reflected by an average RMSE of 0.064 and an R\({}^{2}\) of 0.889, specifically for predicting the maximum dry density in compacted soil.
## 4 Results and Discussion
As can be seen from the comparison chart, Figure 7a shows that for the same target, the confidence degree of the original YOLOv5 has been greatly improved, from 0.40 to 0.73. In Figure 7b, the original YOLOv5 incorrectly recognizes the container ship as a general cargo ship, while the improved algorithm recognizes it correctly. Compared to the original algorithm, the improved YOLOv5 has improved in accuracy. In Figure 7c, there is a missed detection when there are multiple targets in the image, and some of them are small. The original YOLOv5 misses the fishing boat while the improved YOLOv5 recognizes the fishing boat accurately. The improved YOLOv5 shows improved recall compared to the original algorithm. In Figure 7d, there is a situation where there is more overlap between the two ships. When facing overlapping targets, the original YOLOv5 is lower than the improved YOLOv5 in confidence. The confi-dence of original YOLOv5 are 0.56 and 0.83, and the confidence of the improved YOLOv5 are increased to 0.73 and 0.85. The improved YOLOv5 possesses stronger recognition ability when facing overlapping targets. Based on the above situation, the improved YOLOv5 algorithm's ability to detect overlapping targets and various types of ships was significantly improved, and the error rate was reduced. The improved YOLOv5 algorithm has a better recognition effect for false identification, missed identification, and overlapping targets. The experiments show that each module improves the performance of YOLOv5 under this dataset in terms of precision, recall, mAP50, and mAP50:95. Finally, in terms of precision, the improved YOLOv5 improves the performance over the original YOLOv5 by 1.4%, and in terms of recall by 2.6%, in terms of mAP50 by 1.2%, and in terms of mAP50:95 by 3.1%, with smaller model parameters and faster detection speeds, which can meet the needs of sea vessel detection. We used a public dataset in this experiment. Due to limitations in the dataset, there are few images relating to bad weather in it. However, we believe that the receptive field enhancement module and the coordinate attention mechanism can improve the detection ability of YOLOv5 under bad weather. Figure 7: Comparison of YOLOv5 and improved YOLOv5; (a) mAP0.5; (b)mAP0.5:0.95; (c) precision; (d) recall.
# AODV-EOCW: An Energy-Optimized Combined Weighting AODV Protocol for Mobile Ad Hoc Networks
## 3 AODV-EOCW Protocol
### Objective Weighting  
This is different from AHP, which relies on subjective experience. EWM is an objective weighting method. Entropy is used in information theory to measure the degree of disorder in a system and is an abstraction of the information uncertainty. The entropy weight method determines the weight by calculating the entropy value. The smaller the uncertainty of the measurement is, the less information is reflected, and the lower the entropy weight is in the evaluation system.  
Since the entropy weight is completely composed of objective information, it has strong objectivity. In engineering, the entropy weight value is generally also called a correction coefficient, and the objectivity of the weights of metrics can be improved by modifying the weights according to their entropy weights.  
The general entropy weight solution process is as follows:  
1. Build an evaluation matrix. The matrix is constructed according to different schemes and index numbers \(n\), where \(X=(X_{ij})\), in which \(i=1,2,\cdots m\), and \(j=1,2,\cdots,n\). In the study, \(m\) is the number of different links, and \(n\) is the number of metrics that the route is calculated on, which is \(n=3\).
2. Data normalization. The data are preprocessed according to the method shown in Equation (4). The purpose of this step is to eliminate the incommensurability between indicators: \[Y_{ij}=\frac{x_{ij}-min(x_{j})}{max(x_{j})-min(x_{j})}\] (4) in which \(Y_{ij}\) is the positive value of the \(i\)-th scheme on the \(j\)-th evaluation index.
3. Calculate the index entropy value. The entropy value of the \(j\)-th index is shown in Equation (5). \[H_{j}=\sum_{i=1}^{m}Y_{ij}ln\frac{Y_{ij}}{ln\,m}\] (5)
4. Calculate the index deviation. According to the value of each measurement index, that is, \(H_{j}\), the degree of deviation \(d_{j}\) of the index \(j\) can be calculated according to Equation (6). \[d_{j}=1-H_{j}\] (6)Calculate the entropy weight. According to the number of index deviations, the entropy weight \(\mu_{j}\) of the \(j\)-th index can be calculated according to Equation (7):  
\[\mu_{j}=\frac{d_{j}}{\sum_{j=1}^{m}d_{j}} \tag{7}\]  
where \(0\leq\mu_{j}\leq 1\), \(\sum_{j=1}^{m}\mu_{j}=1\).
## 3 Related Work
Existing studies on vehicular ad hoc networks (VANETs) and V2X communication systems often employ blockchain for data integrity and identity authentication [22]. However, they rely on consensus mechanisms that are not optimized for real-time environments. For instance, most use traditional consensus protocols that introduce high latency, which is unsuitable for time-sensitive vehicular applications. The proposed system overcomes this limitation by utilizing a Practical Byzantine Fault Tolerance (PBFT) consensus mechanism. PBFT is specifically designed for environments with a moderate number of participants, ensuring both fast consensus and resilience to malicious nodes, a critical requirement for vehicular networks. Another gap lies in the lack of comprehensive testing under real-world conditions. While existing studies often demonstrate the effectiveness of blockchain-based systems under idealized settings, they do not account for the impact of vehicle mobility, network jitter, or latency variability on system performance. This research fills this void by simulating dynamic network conditions and analyzing their effects on Sybil attack detection, DoS mitigation, and consensus latency. These evaluations provide valuable insights into the robustness and scalability of the proposed system in practical environments. In summary, the proposed framework builds on existing research by combining decentralized consensus mechanisms, AI-driven decision-making, and modular architecture to address the challenges of scalability, real-time processing, and adaptability. By bridging these gaps, this work advances the field of vehicular communication systems, paving the way for secure and efficient deployment in smart city environments.
# Mesoporous Carbon as Galvanic-Corrosion Activator Accelerates Fe Degradation
## 3 Results and Discussion
### Degradation Behavior
Thus, it seems that the Fe/1.5 MC presented a more suitable degradation rate as bone implants. In this work, the electrochemical tests and immersion tests indicated that MC enhanced the degradation rate of Fe. As compared with the electrochemical test, the immersion test with a long period was able to more accurately characterize its degradation behavior [38]. The corrosion mechanism of the Fe/MC composite in body fluid was shown in Figure 8c-e. At the initial corrosion stage, the grain boundaries encountered galvanic corrosion due to the potential difference between grains and grain boundaries [39]. The Fe matrix was gradually dissolved into Fe ions by  
\[\mathrm{Fe}\rightarrow\mathrm{Fe}^{2+}+2\mathrm{e}^{-} \tag{3}\]  
Electrons raised from the dissolution of Fe matrix moved to MC, where cathodic reaction occurred as follows  
\[2\mathrm{H}_{2}\mathrm{O}+\mathrm{O}_{2}+4\mathrm{e}^{-}\mathrm{\to 4 OH} \tag{4}\]  
Figure 8: (**a**) Fe ion concentrations released from the Fe/MC composites after immersion for 7, 14, 21, and 28 d, n = 3, * \(p<0.05\); (**b**) Calculated degradation rate basing on immersion tests; (**c**) Initial corrosion stage caused by galvanic corrosion; (**d**) Local enlarged drawing of electron transfer between galvanic couple; (**e**) Corrosion products precipitated on surface. Significantly, MC possessed a mesoporous structure with a large specific surface area, as presented in Figure 8d, which considerably increased the area ratio between the cathode and anode. In this condition, the consumption of anode was accelerated. In addition, MC also exhibited excellent conductivity, which reduced the charge transfer resistance and consequently increased the corrosion current [40]. Due to the enhanced galvanic corrosion, a large amount of corrosion product covered the matrix (Figure 8e), which was generated by the following equation [41]:  
\[\text{Fe}^{2+}+2\text{OH}^{\text{T}}\rightarrow\text{Fe(OH)}_{2} \tag{5}\]  
\[4\text{Fe(OH)}_{2}+\text{O}_{2}+\text{H}_{2}\text{O}\to 4\text{Fe(OH)}_{3} \tag{6}\]  
\[\text{Fe(OH)}_{2}+2\text{Fe(OH)}_{3}\rightarrow\text{Fe}_{3}\text{O}_{4}+4 \text{H}_{2}\text{O} \tag{7}\]  
Nevertheless, the aggressive chloride ion in the physiological environment was able to break through this corrosion product layer, making the corrosion of the iron matrix continue until the whole matrix was disintegrated [42].
# LPBERT: A Protein-Protein Interaction Prediction Method Based on a Pre-Trained Language Model
## 3 Results
### Comparative Experiment
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F1-Score** & **MCC** \\ \hline PIPR & 90.29 \(\pm\) 1.08 & 92.01 \(\pm\) 1.84 & 88.31 \(\pm\) 0.32 & 92.29 \(\pm\) 1.90 & 90.11 \(\pm\) 1.02 & 80.68 \(\pm\) 2.21 \\ RAPPPID & 91.25 \(\pm\) 0.27 & 93.85 \(\pm\) 0.51 & 88.28 \(\pm\) 0.26 & 94.21 \(\pm\) 0.51 & 90.98 \(\pm\) 0.26 & 82.64 \(\pm\) 0.57 \\ DeepTrio & 92.77 \(\pm\) 0.63 & 93.70 \(\pm\) 2.01 & 91.77 \(\pm\) 1.35 & 93.76 \(\pm\) 2.21 & 92.70 \(\pm\) 0.55 & 85.60 \(\pm\) 1.27 \\ LPBERT & **97.94 \(\pm\) 0.16** & **98.60 \(\pm\) 0.42** & **97.27 \(\pm\) 0.46** & **98.61 \(\pm\) 0.43** & **97.93 \(\pm\) 0.16** & **95.89 \(\pm\) 0.31** \\ \hline \hline \end{tabular}  
This table reports the mean and standard deviation of the three test results. The best values are in bold.  
\end{table} 
Table 5: Comparison of each method on the BioGRID _S. cerevisiae_ dataset (%). LPBERT's accuracy fell short by 0.65% compared with SDNN-PPI and by 0.56% compared with DeepCF-PPI. When analyzing this result, we think there may have been two reasons. First, although [PERSON] et al. pre-trained ProteinBERT on a large-scale protein dataset, it was mainly tested on tasks such as protein structure or post-translational modifications and did not directly involve PPI prediction tasks. Therefore, ProteinBERT may not have fully captured the feature paradigm related to PPI. Fine-tuning ProteinBERT for PPI tasks may be an effective strategy to improve performance. Second, after using ProteinBERT to extract feature representations of protein sequences, the GCT and LCR in the deep feature extraction module may not fully express features that are beneficial to the PPI task, resulting in limitations to the performance of LPBERT. We compared LPBERT with two other protein-language-model-based PPI prediction methods: xCAPT5 and MPB-PPI. As shown in Table 7, LPBERT outperformed MPB-PPI across all the evaluation metrics on multiple datasets. When compared with xCAPT5 on the Pan dataset, LPBERT's accuracy was 1.04% lower, with similar patterns observed across the other metrics. Although LPBERT showed a slightly lower performance than xCAPT5, it offered significant advantages in computational efficiency. xCAPT5 is recommended on platforms with 80 GB GPU memory and 120 GB RAM, whereas LPBERT works efficiently with just 18 GB GPU memory and 3 GB RAM.
# Machine Learning Techniques in Structural Wind Engineering: A State-of-the-Art Review
## Abbreviations  
\begin{tabular}{l l}
**Nomenclature** & \\ \(x\) & Machine learning input variable \\ \(y\) & Machine learning output \\ \(h\) & Neural network hidden layer \\ \(x_{j}^{h}\) & Input for a generic neuron \\ \(w_{ij}^{h}\) & Weight of a generic connection between two nodes \\ \(b_{j}^{h}\) & Bias of a generic neuron \\ \(y_{i}^{h}\) & Output for a generic neuron \\ \(f(u)\) & Transfer function \\ \(u_{ij}\) & Value of membership function \\ \(m_{ij}\) & Mean of the Gaussian function \\ \(\sigma_{ij}\) & Standard deviation of the Gaussian function \\ \(L_{1}\) & LASSO regularization \\ \(L_{2}\) & Ridge regularization \\ \(p_{i}\) & Predicted output \\ \(m_{i}\) & Measured output \\ \(S_{i}\) & Normalized measure for error \\ \(\theta\) & Wind direction \\ \(\beta\) & Roof slope \\ \(D\)/\(B\) & Side ratio \\ \(x\), \(y\), \(z\) & Pressure taps coordinates \\ \(Re\) & Reynolds number \\ \(Ti\) & Turbulence intensity \\ \(Sx\), \(Sy\) & Interfering building location \\ \(R\)/\(D\) & Curvature ratio \\ \(d\)/\(b\) & Side ratio without curvature \\ \(D\)/\(H\) & Height ratio \\ \(h\) & Building height \\ \(Sc\) & Scruton number \\ \(M\) & Mass ratio \\ \(L\) & Distance between the centerline of the cylinders \\ \(U\) & Reduced velocity \\ \(H_{1}\) & Flutter Derivatives (vertical motion) \\ \(A_{2}\) & Flutter Derivatives (torisonal motion) \\ \(mi\), \(ni\) & Vertex coordinates \\ \(L\) & Length of the building \\ \(Vb\) & Wind velocity \\ \(TC\) & Terrain category \\ \(\overline{Cp}\) & Mean pressure coefficient \\ \(\widetilde{C_{p}}\) & Peak pressure coefficient \\ \(\widetilde{C_{p}}\) & Root mean square pressure coefficient \\ \(\varphi\) & The angle measured horizontally with respect to wind direction \\ \(\Pi\) & The angle measured vertically with respect to the vertical axis of \\ \(\Pi\) & the dome to the ring beam. \\ \(C_{A}\) & Neighboring area density \\
**Abbreviations** & \\ ABLWT & Atmospheric boundary layer wind tunnel \\ AIC & Akaike information criterion \\ ANN & Artificial neural network \\ CFD & Computational fluid dynamics \\ CNN & Convolutional neural networks \\ DL & Deep learning \\ DNN & Deep neural network \\ DT & Decision tree regression \\ E\({}_{f}\) & Coefficient of efficiency \\ FFNN & Feed-forward neural network \\ \end{tabular}
## 7 Applications of the Typical Medium DCA to Systems with Disorder
### Results for Models with More Realistic Parameters
#### 7.2.2 Multiple Orbitals  
The multi-orbital TMDCA with the Ansatz defined in Equations (61) and (63) has been tested for a 3D Anderson model with two degenerate bands (denoted by a and b), so that both nearest-neighbor hopping and disorder potential in this case are \(2\times 2\) matrices in the band basis given by  
\[\underline{t_{ij}}=\underline{t}=\left(\begin{array}{cc}t^{aa}&t^{ab}\\ t^{ba}&t^{bb}\end{array}\right), \tag{108}\]  
and  
\[\underline{V_{i}}=\left(\begin{array}{cc}V_{i}^{aa}&V_{i}^{ab}\\ V_{i}^{ba}&V_{i}^{bb}\end{array}\right), \tag{109}\]  
respectively. The intra-band hopping is set as \(t^{aa}=t^{bb}=1\), with finite inter-band hopping \(t^{ab}\). The local inter-band disorder \(V_{i}^{ab}\) is set to be zero considering the two bands orthogonal to each other so that the randomness only comes from the local intra-band disorder potential \(V_{i}^{aa(bb)}\) that follow independent binary probability distribution functions with equal strength, \(V^{aa}=V^{bb}\) and impurity concentration \(x=0.5\). As shown in Figure 35, in this two-band system the TMDCA again captures localization, where the TDOS at the band center gradually decreases as the disorder strength increases, and eventually vanishes at the critical point. The critical disorder strength reaches convergence within our numerical precision for a cluster size of roughly \(N_{c}=98\).  
To demonstrate the effect of inter-band hopping in this two-band model, the evolution of the mobility edge as a function of \(t^{ab}\) with a fixed disorder strength is also studied and shown in Figure 36. The dome-like shape around the band center reflects the delocalization effect of the inter-band hopping which is again in excellent agreement with results from the TMM method.  
Figure 35: The TDOS at the band center (\(\omega=0\)) vs. \(V^{aa}=V^{bb}\) in the a-b two-orbital model with increasing cluster size, for \(t^{aa}=t^{bb}=1.0\), \(t^{ab}=0.3\), \(V^{ab}=0.0\). For \(N_{c}=1\), the critical disorder strength is 0.65 and as \(N_{c}\) increases, it increases and converges to 0.74 for \(N_{c}=98\). Reprint from [12].  
To further benchmark the method, the calculated ADOS and TDOS using the DCA and TMDCA are also compared with those calculated using the KPM which is shown in Figure 37. As shown in the plot, a nice agreement between the (TM)DCA and KPM are achieved.
###### Abstract  
The increasing penetration rate of grid connected renewable energy power generation reduces the primary frequency regulation capability of the system and poses a challenge to the security and stability of the power grid. In this paper, a distributed photovoltaic (PV) storage virtual synchronous generator system is constructed, which realizes the external characteristics of synchronous generator/motor. For this kind of input/output bidirectional devices (e.g., renewable power generation/storage combined systems, pumped storage power stations, battery energy storage systems, and vehicle-to-grid electric vehicles), a synthesis analysis method for system power-frequency considering source-load static frequency characteristics (S-L analysis method) is proposed in order to depict the system's power balance dynamic adjustment process visually. Simultaneously, an inertia matching method is proposed to solve the problem of inertia matching in the power grid. Through the simulation experiment in MATLAB, the feasibility of the distributed PV storage synchronous virtual machine system is verified as well as the effectiveness of S-L analysis method and inertia matching method.  
frequency regulation; PV storage; virtual synchronous generator; S-L analysis method; inertia matching; input/output bidirectional devices; synchronous machine grid equivalent model
# KACFormer: A Novel Domain Generalization Model for Cross-Individual Bearing Fault Diagnosis
## 3 Experiments
### Contrast Experiment  
To validate the advancement of our proposed KACFormer model, we conducted a comprehensive comparative experiment involving both traditional classic models and modern advanced models, ranging from CNN-based methods to contemporary Transformers. The compared models include classic CNN architecture as well as frameworks that share a similar CNN-Transformer structure to our approach. Below are detailed descriptions of the contrasted models:  
1. WDCNN. Pioneering work in the field of fault diagnosis, serving as the baseline model [6].
2. CNN-LSTM. A fault diagnosis model utilizing the CNN module and the LSTM module with 100 neurons set for calculation [30].
3. Convformer. A network designed to extract robust features by integrating both global and local information, with the goal of enhancing the end-to-end fault diagnostic  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Blocks** & **Layers** & **Parameters** & **OUTPUT SHAPE** \\ \hline Input signal & - & - & 1 \(\times\) 2048 \\ \hline \multirow{3}{*}{KAConv Block1} & KAConv & channel = 16, k = 64, s = 16, p = 1 & 16 \(\times\) 61 \\  & Activation & SiLU & 16 \(\times\) 61 \\  & MaxPool1D & channel = 16, k = 2, s = 2, p = 0 & 16 \(\times\) 30 \\ \hline \multirow{3}{*}{KAConv Block2} & KAConv & channel = 32, k = 2, s = 2, p = 1 & 32 \(\times\) 16 \\  & Activation & SiLU & 32 \(\times\) 16 \\  & MaxPool1D & channel = 32, k = 2, s = 2, p = 0 & 32 \(\times\) 8 \\ \hline \multirow{3}{*}{CMKAA Transformer} & CMKAA & h = 32, embed dim = 8, dropout = 0.1 & 32 \(\times\) 8 \\  & Flatten & - & 1 \(\times\) 256 \\  & KFF & n = 32, activation = SiLU & 1 \(\times\) 256 \\  & Layer norm & - & 1 \(\times\) 256 \\  & Drop out & p = 0.1 & 1 \(\times\) 256 \\ \hline \multirow{3}{*}{MLP classifier} & Fc layer1 & n = 100, activation = ReLU & 1 \(\times\) 100 \\  & Fc layer2 & n = 3, activation = ReLU & 1 \(\times\) 3 \\ \cline{1-1}  & Softmax & - & 1 \(\times\) 3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameters of the selected model.  
performance of gearboxes under high levels of noise [21]. To better adapt to the mission, the small version of the Conformer is utilized.
* Liconvformer. A novel advanced lightweight fault diagnosis model with separable multiscale convolution and broadcast Self-attention [22].
* Clformer. A transformer based on convolutional embedding and Self-Attention mechanisms [31].
* MobileNetV2. This neural network enhances the state-of-the-art performance of mobile models across various tasks, benchmarks, and a wide range of model sizes [32].
* ResNet18. ResNet18 is a convolutional neural network architecture known for its use of residual connections, enabling efficient training of deeper models while maintaining strong performance in image classification and other computer vision tasks [33].
* KACFormer. The proposed model.
# Benefits of Probiotics--Biodetoxification
## 2. Probiotics--Biodetoxification of Food
### The Role of Probiotics in Food Biodetoxification
#### 2.2.3 Pesticides  
Pesticides are widespread, harmful contaminants of food products that generally appear in the food chain via plants. Their toxic effect particularly jeopardizes human endocrine, reproductive, and neurological functions. There are approximately 500 pesticide compounds registered and used worldwide. According to the purpose of use, several groups can be distinguished: fungicides, insecticides, herbicides, and pesticides [40]. Probiotics, via binding or enzymatic degradation (carboxylesterases, phosphatases, phosphodriesterases, hydrolases), have been shown to be effective in elimination of pesticides and amelioration of their toxicity [41]. Probiotic strains (_L. acidophilus_ LA-5, _Bif. antimalis_ subsp. _lactis_ BB-12, _L acidophilus_, _L. delbrueckii_ subsp. _bulgaricus_, _L. plantarum_, _L. rhamns_, _L. casei_, _S. thermophilus_, _Bif. bifidum_) displayed successful reduction of organochlorine pesticides in dairy products [42; 43]. Probiotic strains also reduce pesticides in vegetable products (kimchi, sauerkraut, pickles), grains, flours, sourdough, meat, meat products, wines, and fruit juices [40].