esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Way Linear Probing Revisited
## 6 Simulation Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\(n\)} & \multirow{2}{*}{\(\alpha\)} & \multicolumn{2}{c}{LocallyLinear} & \multicolumn{2}{c}{WalkFirst} & \multicolumn{2}{c}{DecideFirst} \\ \cline{3-8}  & & **Avg** & **Max** & **Avg** & **Max** & **Avg** & **Max** \\ \hline \multirow{2}{*}{\(2^{8}\)} & 0.4 & **1.75** & **4.34** & 1.65 & 4.70 & 1.63 & 4.81 \\ \cline{2-8}  & 0.9 & **12.18** & **33.35** & 12.54 & 34.40 & 13.48 & 47.76 \\ \cline{2-8}  & 0.4 & **1.62** & **6.06** & 1.68 & 6.32 & 1.68 & 6.82 \\ \cline{2-8}  & 0.9 & **12.42** & **48.76** & 12.78 & 51.80 & 13.45 & 94.98 \\ \hline \multirow{2}{*}{\(2^{16}\)} & 0.4 & **1.62** & **7.14** & 1.68 & 7.31 & 1.68 & 8.92 \\ \cline{2-8}  & 0.9 & **12.66** & **59.61** & 12.98 & 62.24 & 13.53 & 125.40 \\ \cline{2-8}  & 0.4 & **1.65** & **8.25** & 1.71 & 8.50 & 1.71 & 10.76 \\ \cline{2-8}  & 0.9 & **12.83** & **67.23** & 13.11 & 69.45 & 13.62 & 145.30 \\ \cline{2-8}  & 0.4 & **1.62** & **8.90** & 1.71 & 8.95 & 1.71 & 11.46 \\ \cline{2-8}  & 0.9 & **12.72** & **65.58** & 13.19 & 73.22 & 13.66 & 164.45 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The average and the maximum cluster sizes averaged over \(10\) iterations each consisting of \(100\) simulations of the algorithms. The best performances are drawn in boldface. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\(n\)} & \multirow{2}{*}{\(\alpha\)} & \multicolumn{2}{c}{LocallyLinear} & \multicolumn{2}{c}{WalkFirst} & \multicolumn{2}{c}{DecideFirst} \\ \cline{3-8}  & & **Avg** & **Max** & **Avg** & **Max** & **Avg** & **Max** \\ \hline \multirow{2}{*}{\(2^{8}\)} & 0.4 & **1.73** & **4.73** & 1.78 & 5.32 & 1.75 & 5.26 \\ \cline{2-8}  & 0.9 & **4.76** & **36.23** & 4.76 & 43.98 & 5.06 & 59.69 \\ \hline \multirow{2}{*}{\(2^{12}\)} & 0.4 & **1.74** & **6.25** & 1.80 & 7.86 & 1.78 & 7.88 \\ \cline{2-8}  & 0.9 & **4.76** & **47.66** & 4.80 & 67.04 & 4.94 & 108.97 \\ \hline \multirow{2}{*}{\(2^{16}\)} & 0.4 & **1.76** & **7.93** & 1.80 & 9.84 & 1.78 & 10.08 \\ \cline{2-8}  & 0.9 & **4.78** & **56.40** & 4.89 & 89.77 & 5.18 & 137.51 \\ \cline{2-8}  & 0.4 & **1.76** & **8.42** & 1.81 & 12.08 & 1.79 & 12.39 \\ \cline{2-8}  & 0.9 & **4.77** & **65.07** & 4.98 & 108.24 & 5.26 & 162.04 \\ \hline \multirow{2}{*}{\(2^{22}\)} & 0.4 & **1.76** & **9.18** & 1.81 & 12.88 & 1.79 & 13.37 \\ \cline{2-8}  & 0.9 & **4.80** & **71.69** & 5.04 & 118.06 & 5.32 & 181.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The average and the maximum successful search time averaged over \(10\) iterations each consisting of \(100\) simulations of the algorithms. The best performances are drawn in boldface. algorithm. On the other hand, algorithms WalkFirst and LocallyLinear are superior to the others in worst-case performance. It is worth noting that surprisingly, the worst-case successful search time of SmallCluster is very close to the one achieved by WalkFirst and better than that of DecideFirst, although, it appears that the difference becomes larger as \(n\) increases.
# Anomaly Detection of CAN Bus Messages Using a Deep Neural Network for Autonomous Vehicles
###### Abstract  
The in-vehicle controller area network (CAN) bus is one of the essential components for autonomous vehicles, and its safety will be one of the greatest challenges in the field of intelligent vehicles in the future. In this paper, we propose a novel system that uses a deep neural network (DNN) to detect anomalous CAN bus messages. We treat anomaly detection as a cross-domain modelling problem, in which three CAN bus data packets as a group are directly imported into the DNN architecture for parallel training with shared weights. After that, three data packets are represented as three independent feature vectors, which corresponds to three different types of data sequences, namely anchor, positive and negative. The proposed DNN architecture is an embedded triplet loss network that optimizes the distance between the anchor example and the positive example, makes it smaller than the distance between the anchor example and the negative example, and realizes the similarity calculation of samples, which were originally used in face detection. Compared to traditional anomaly detection methods, the proposed method to learn the parameters with shared-weight could improve detection efficiency and detection accuracy. The whole detection system is composed of the front-end and the back-end, which correspond to deep network and triplet loss network, respectively, and are trainable in an end-to-end fashion. Experimental results demonstrate that the proposed technology can make real-time responses to anomalies and attacks to the CAN bus, and significantly improve the detection ratio. To the best of our knowledge, the proposed method is the first used for anomaly detection in the in-vehicle CAN bus.  
CAN bus; anomaly detection; DNN; autonomous vehicle; triplet loss network Article
## 3 Stochastic Load Conditions
### Wind Environments
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Wind Direction** & **0–3** & **3–6** & **6–9** & **9–12** & **12–15** & **15–18** & **18–21** & **Sum** \\ \hline N(0\({}^{\circ}\)) & 0.688 & 2.103 & 2.574 & 0.476 & 0.284 & 0.000 & 0.000 & 6.125 \\ NNE (22.5\({}^{\circ}\)) & 1.220 & 3.514 & 5.694 & 1.326 & 0.424 & 0.240 & 0.005 & 12.423 \\ NE (45\({}^{\circ}\)) & 0.728 & 2.604 & 3.136 & 0.857 & 0.356 & 0.144 & 0.000 & 7.825 \\ ENE (67.5\({}^{\circ}\)) & 0.496 & 0.876 & 1.324 & 0.759 & 0.208 & 0.000 & 0.000 & 3.663 \\ E (90\({}^{\circ}\)) & 0.356 & 1.285 & 1.098 & 0.662 & 0.444 & 0.000 & 0.000 & 3.844 \\ ESE (112.5\({}^{\circ}\)) & 0.248 & 1.027 & 1.324 & 0.891 & 0.252 & 0.000 & 0.000 & 3.742 \\ SE (135\({}^{\circ}\)) & 0.416 & 0.851 & 1.024 & 1.021 & 0.356 & 0.000 & 0.000 & 3.668 \\ SSE (157.5\({}^{\circ}\)) & 3.496 & 2.604 & 0.752 & 0.775 & 0.314 & 0.096 & 0.000 & 8.037 \\ S (180\({}^{\circ}\)) & 0.840 & 3.347 & 2.724 & 1.326 & 0.216 & 0.096 & 0.000 & 8.549 \\ SSW (202.5\({}^{\circ}\)) & 0.916 & 4.624 & 6.563 & 1.376 & 0.483 & 0.360 & 0.005 & 14.327 \\ SW (225\({}^{\circ}\)) & 1.204 & 2.679 & 2.620 & 0.958 & 0.346 & 0.000 & 0.000 & 7.806 \\ WSW (247.5\({}^{\circ}\)) & 0.416 & 1.878 & 1.936 & 0.837 & 0.194 & 0.000 & 0.000 & 5.261 \\ W (270\({}^{\circ}\)) & 0.388 & 1.327 & 1.352 & 0.705 & 0.420 & 0.000 & 0.000 & 4.192 \\ WNW (292.5\({}^{\circ}\)) & 0.296 & 1.895 & 0.612 & 0.583 & 0.225 & 0.000 & 0.000 & 3.610 \\ NW (315\({}^{\circ}\)) & 0.208 & 1.436 & 0.483 & 0.485 & 0.264 & 0.072 & 0.000 & 2.947 \\ NNW (337.5\({}^{\circ}\)) & 0.452 & 1.269 & 1.575 & 0.416 & 0.208 & 0.060 & 0.000 & 3.980 \\
**Sum** & 12.368 & 33.317 & 34.791 & 13.451 & 4.994 & 1.068 & 0.010 & 100.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Relative frequency of mean wind speed and direction (%).
## 3 Materials and Methods: Thermodynamic Model and Simulation
### Model of the SOFC
#### 3.2.1 Fuel and Oxidant Utilization  
The usage of ammonia can be determined in terms of the actual provide and consumption of the ammonia, or its hydrogen counterpart, such that [21; 22; 24; 29]  
\[U_{f\lambda}=\frac{(Fuel)_{consumed}}{(Fuel)_{supplied}}=\frac{(H_{2})_{consumed }}{(H_{2})_{supplied}} \tag{21}\]  
The air utilization can be calculated as:  
\[U_{f\gamma}=\frac{(Air)_{consumed}}{(Air)_{supplied}}=\frac{(O_{2})_{consumed }}{(O_{2})_{supplied}} \tag{22}\]  
A list of operating values for the proposed system at the designated operating point is presented in Table 2.  
Through the component stack, the net power output of the SOFC system can be calculated as [30; 31; 32]:  
\[W_{stack}=i\cdot a\cdot V_{c}\eta_{DA} \tag{23}\]  
where \(i,\ a,V_{c}\), and \(\eta_{DA}\) denote the current density (A/m\({}^{2}\)), surface area (m\({}^{2}\)), actual voltage of the stack (V), and efficiency of the inverter, respectively [19; 31].  
\[V_{c}=V_{R}-V_{loss} \tag{24}\]  
where \(V_{R}\) denotes the cell ideal reversible voltage and,  
\[V_{loss}=V_{ohm}+V_{act}+V_{con} \tag{25}\]  
In this expression, \(V_{ohm},\ V_{act}\), and \(V_{con}\) indicate the ohmic losses (V), activation losses (V), and concentration losses (V), respectively.  
\[V_{ohm}=V_{ohm,a}+V_{ohm,c}+V_{ohm,e}+V_{ohm,int} \tag{26}\]  
\[V_{ohm,a}=\frac{i\rho_{a}\ (A\cdot\!\pi\cdot\!D_{m})}{8\cdot t_{a}} \tag{27}\]  
\[V_{ohm,c}=\frac{i\rho_{c}\ (A\cdot\!\pi\cdot\!D_{m})^{2}}{8\cdot t_{c}}\ A\cdot(A+2 (1-A-B)) \tag{28}\]  
\[V_{ohm,e}=i\rho_{a}t_{e} \tag{29}\]  
\[V_{ohm,int}=i\cdot\rho_{int}\cdot\!\pi\cdot D_{m}\frac{t_{int}}{w_{int}} \tag{30}\]  
\[V_{act}=\frac{2 RT}{F\cdot n_{e}}\ Arcsinh\left(\frac{i}{2i_{0,k}}\right) \tag{31}\]  
\begin{table}
\begin{tabular}{l c} \hline \hline  & **Parameters** & **Value** \\ \hline Ambient temperature & 303 K \\ Ambient pressure & 100 kPa \\ Utilization factor (\(u\)) & 0.8 \\ Excess air factor (\(\lambda\)) & 1.4 \\ SOFC efficiency & 56.8\% \\ Total power required by propulsion plant of target vessel at maximum load [29] & 3800 kW \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selected operating point.  
\[V_{con}=\frac{RT}{2F}\ ln\left(\frac{1-\frac{i}{t_{L}t_{B}}}{1+\frac{i}{t_{L}O_{2}} }\right)+\frac{RT}{2F}\ ln\left(\frac{1}{1-\frac{i}{t_{L}O_{2}}}\right) \tag{32}\]  
In addition, the actual voltage of the stack can also be defined based on the I-V curve [8; 11; 13; 33; 34; 11]  
The cell efficiency may be estimated as [17]:  
\[\eta_{cell}=\frac{V_{c}}{1.25} \tag{33}\]  
Otherwise, thermodynamically, the energy efficiency of the fuel cell can be calculated as:  
\[\eta_{en,\ SOFC}=\frac{\dot{W}_{elect,SOFC}}{\dot{m}_{ammonia}h_{ammonia}+ \dot{m}_{air}h_{air}-\dot{m}_{7}h_{7}} \tag{34}\]  
Or [31; 35]:  
\[\eta_{en,SOFC}=\frac{\dot{W}_{SOFC}}{\dot{m}_{NH_{3}}LHV_{NH_{3}}} \tag{35}\]  
where \(\dot{m}_{NH_{3}}\) denotes the mass flow rate of NH\({}_{3}\) (kg/h) and \(LHV_{NH_{3}}\) is the low heating value of NH\({}_{3}\) (KJ/kg).
## 6 Algorithm Design for Improved QoS
### An Optimized Participation Strategy at the Network Edge  
For any smartphone \(n\) that aims to optimize (8), \(U_{n}(\mathbf{w}_{R_{n}})\) is required to be maximized within a limited number of rounds due to (3). As (8) is equivalent to (9), smartphone \(n\) can target maximizing the valuation of its data contribution within \(T_{n}\).  
In DML with synchronized model aggregation, being denied access to the up-to-date global model even once can be catastrophic. Local training starting from a stale model results in a reduced contribution to the overall model utility [42]. Therefore, any smartphone \(n\) should make sure that the accumulated valuation is large enough to access the up-to-date global model; otherwise, \(\sum_{r\in\mathcal{R}}V(a_{n,r}d_{n,r})\) will be reduced, since local training based on a stale model is inevitable. As time is limited, every participation opportunity is valuable. Therefore, an optimized strategy is to contribute in every round to accumulate valuations for better model rewards: that is,  
\[a^{*}_{n,r}=1,\forall n\in\mathcal{N},r\in\mathcal{R},r\leq R_{n,\max}. \tag{25}\]  
Private data of any smartphone \(n\) are limited. An optimized approach to data usage in DML is to reuse each instance as frequently as possible to maximize its value. However, the reuse of instances might lead to model overfitting on \(\mathbf{d}_{n}\) and further reduce the valuation calculated by other smartphones. Therefore, we can use the proposed curriculum data scheduling in Section 5.3 for greater data valuation while not reducing global model utility. More formally, the optimized data scheduling for any smartphone \(n\) is described as  
\[\mathbf{d}^{*}_{n,r}=\{(\mathbf{x}_{n,i},y_{n,i})\in\mathbf{\hat{d}}_{n,r}|W^{\text{start}}_ {n,r}\leq i\leq W^{\text{end}}_{n,r}\},r\in\mathcal{R},r\leq R_{n,\max}. \tag{26}\]  
Intuitively, the proposed moving window ensures that easier instances are only used in the early rounds to help the global model converge fast. In contrast, more difficult instances are used in later rounds to push the near-converged model to better performance.
# The Relationship between Radon and Geology: Sources, Transport and Indoor Accumulation
## 2 The Origin of Radon
### Accumulation of Radon in Indoor Environments
#### 2.3.1 Radon Diffusion from the Soil
The lowest mean concentration was detected in basic rock areas (83 Bq\(\cdot\)m\({}^{-3}\)), while sites on the alluvial plain had a mean of 66 Bq\(\cdot\)m\({}^{-3}\), despite constituting the largest share of the sample size at 24%. Although sites on marine formations accounted for a significant 12% of the sites, they exhibited a moderate mean concentration (92 Bq\(\cdot\)m\({}^{-3}\)). These findings reveal the direct influence of geological classes on indoor radon levels, underscoring the importance of considering local geology in risk assessments for radon exposure. The results raise additional questions about the mechanisms leading to the observed variability and stress the need for further investigations to develop comprehensive radon mitigation strategies. [PERSON] et al. (2021) investigated the epidemiological implications of radon and thoron exposure in Ireland, delineating the geographical distribution of these elements, their decay products, and their correlation with lung cancer incidence [74]. The study identifies a particular concern in County Carlow, which records the highest mean indoor radon level. They emphasize the scarcity of studies addressing dose coefficients for thoron and its decay products, despite findings indicating potential thoron release in certain areas. The persistence of thoron decay products in indoor air and their associated radiation exposure suggest that further research is needed. The paper emphasizes the necessity of including thoron and its decay products in the analysis of priority areas due to potential adverse health effects, especially in rural areas where housing lacks concrete foundations. The study highlights the complexity of the radon and thoron potential landscape, noting areas of high radon but low thoron potential. [PERSON] et al. (2021) suggest further testing and monitoring to improve the accuracy of radon potentials. The research presents a comprehensive analysis of radon/thoron exhalation rates, natural radioactivity levels, and related risks across multiple geological formations and soil types in Ireland, underlining the geology's role as a controlling factor and the importance of thoron progeny measurements for effective dose estimates. The authors also propose using airborne estimated values as a promising tool for predicting radon/thoron potentials, offering a more nuanced, geologically-informed understanding of radiation exposure risks.
# A Hybrid Deep Learning Model Based on FFT-STL Decomposition for Ocean Wave Height Prediction
## 3 Experimental Results and Analysis
### Data Analysis and Modeling
This layer effectively captures the basic features based on the local pattern and time dependency of the data, and outputs a standardized feature map, laying the foundation for subsequent processing. The second-layer module constructs a deep convolution operation unit based on 10,304 trainable parameters. Through the stacking and nonlinear transformation of multiple layers of convolution kernels, it completes the abstraction and extraction of high-order features, and finally outputs a 64-channel feature tensor, which greatly enhances the richness and hierarchy of data feature expression. The third layer innovatively introduces the gated activation unit architecture, which integrates 8320 parameters to construct a dynamic gating mechanism. Through adaptive weight adjustment, intelligent screening of interdisciplinary features and nonlinear fusion of cross-channel information are achieved, which effectively suppresses the risk of overfitting while improving the model's expression ability. To adapt to the input requirements of the fully connected layer, the fourth layer adopts a flattening operation module. This module compresses the three-dimensional feature tensor into a two-dimensional feature matrix, eliminates spatial dimension redundancy and optimizes computational efficiency while retaining the core features of the data. The fully connected layer is configured as the output module at the end of the network. The nonlinear mapping relationship is constructed through 65 parameters to accurately map the abstract feature space with the prediction target, and finally output the prediction result. The modules are connected through feature dimension matching and calculation logic to form a complete end-to-end data prediction system, taking into account the depth of feature extraction and the efficiency of prediction tasks. During the training process, the loss function showed a steady downward trend, as shown in Figure 10. The training loss dropped from about 0.02% to below 0.0050%. At the same time, the validation loss always fluctuated below the 0.0025% threshold. Figure 11 shows that the model's predictions on the test data are almost exactly the same as the actual observations, indicating a high accuracy in capturing the remainder. The final prediction is obtained by summing the three predicted components and applying an exponential transformation. A comparison between the predicted and actual values is illustrated in Figure 5:0 The evaluation metrics indicate that the model achieves an MSE of 0.0087, RMSE of 0.0935, and MAE of 0.0783, demonstrating its predictive accuracy.
## 3 Results  
### Definition of Metrics  
To compare the accuracy of the previously presented algorithms, the following metrics were used: Precision, F1-score, Recall (Sensitivity), and Cohen's Kappa coefficient. These metrics were calculated after the algorithms evaluated the test dataset using the confusion matrix. This matrix displays the number of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) for each class.  
Precision is a metric that calculates the proportion of correct predictions in relation to the total number of samples [25]. It is useful for evaluating the overall performance of a classifier. The formula is as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{4}\]  
Recall is a metric that measures the proportion of positive instances that were correctly identified [25]. It is useful for evaluating the classifier's ability to find all relevant samples of a specific class. The formula is as follows:  
\[Recall=\frac{TP}{TP+FN} \tag{5}\]  
F1-score is a measure that combines Precision and Recall into a single value. It provides a balanced measure between the classifier's precision and recall capabilities [25]. It is particularly useful when the dataset is imbalanced in terms of classes. The formula is as follows:  
\[F1-Score=\frac{TP}{TP+\frac{1}{2}(FP+FN)} \tag{6}\]  
[PERSON]'s Kappa coefficient is a measure that expresses the level of agreement between two annotators in a classification problem [26]. It is defined as follows:  
\[\kappa=\frac{2\times(TP\times TN-FN\times FP)}{(TP+FP)\times(FP+TN)+(TP+FN) \times(FN+TN)} \tag{7}\]  
In our multiclass classification study, the evaluation metrics commonly used for binary classification problems were adapted for application in a multiclass context. To assess the performance of our classification model, we used the macro-averaged technique [27].  
The choice of this approach was based on the need to treat all classes equally during evaluation, regardless of their size or data distribution. First, the metrics were calculated in a binary manner for each class individually, and then these metrics were averaged to obtain an overall evaluation of the model.  
It is important to note that results from models trained on balanced and imbalanced datasets are not directly comparable due to differences in data distribution and model behavior. In addition to evaluating the performance, this study highlights how data balancing influences a model's ability to generalize and accurately classify instances. By presenting the results separately, we aim to demonstrate the impact of data distribution on model performance and provide insights into the advantages and limitations of using balanced datasets.
###### Abstract  
In a household setting, a wheelchair-mounted robotic arm (WMRA) can be useful for assisting elderly and disabled individuals. However, the current WMRA can only perform movement and grasping tasks through joystick remote control. This method results in low efficiency due to poor coordination between the mobile platform and the robotic arm as well as the numerous operational steps required. To improve the efficiency and success rate of the robot in task execution, this paper proposes a parking location optimization method that combines the occupied grid map (OGM) and the inverse reachability map (IRM). Firstly, the SLAM algorithm is used to collect environment information, which is then stored in the form of an occupied grid map. The robotic arm workspace is then gridded, and the inverse reachability map is calculated based on the grasping pose of the target object. Finally, the optimal position of the mobile platform is obtained by comparing the optimal location point in the inverse reachability map and the obstacle information in the occupied grid map. This process achieves base placement optimization based on the grasping pose. The experimental results demonstrate that this method reduces the user operation time by 97.31% and overall task completion time by 40.57% when executing household environment tasks compared with the joystick control, increasing the range of executable tasks compared with the algorithm of the EL-E robot and reducing task completion time by 23.48% for the same task. This paper presents a parking location optimization method that can improve the grasping efficiency of the robotic arm and achieve parking location position selection for the WMRA in a household environment.  
wheelchair-mounted robotic arm; optimization of base placement; inverse reachability map; simultaneous localization and mapping Article