esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
With the rapid development of sensor and computing technologies, personal vehicles are now capable of gathering large amounts of information on vehicle status and the roadway environment, as well as making proximity estimates and predicting potential driving events. Recent advances in vehicle automation have envisioned future driving without the need for drivers to attend to the road. Ford previously announced delivery of a high production fully automated vehicle by 2021 [1]. Volvo, Nissan, Honda, Toyota, and BMW have all promised similar timelines [2, 3, 4, 5]. Federal and state legislation is also responding to this rapid technological change [6, 7]. These vehicles will be fully equipped with information systems for navigation, communication, and entertainment, which will continuously capture much of the driver's attention inside the vehicle. As a result, there is a potential shift in information communication from driver-roadway interaction to driver and in-vehicle display interaction. In this case, drivers may be less likely to notice road signs and rather increasingly rely on a GPS device to notify them of a speed limit change or beginning of a school zone. While in-vehicle notification of such information can overcome issues with road signs, such as poor visibility due to weather and maintenance problems, it poses significant human factors challenges on how to effectively deliver signage information while minimizing potential distraction and keeping the driver aware of the road environment.  
Despite decades of research on in-vehicle notification designs (see [7] for a complete review), most studies have focused on presenting information that is critical for driving safety, such as collision warnings and lane departure warnings. In contrast, how to effectively present information that is non-safety critical and secondary to driving but important for a trip (e.g., specific roadside services), remains unexplored, especially in the context of highly automated driving. As driver attention could differ based on the relevance or importance of a piece of information, results of studies that have focused on the impact of using in-vehicle information systems on manual driving [8; 9; 10] or the effectiveness of collision warning designs [11; 12; 13] may be informative but may not necessarily generalize to non-safety critical notifications under automated driving. If a display notification is not directly relevant to a primary driving task, drivers may exhibit lower levels of attention, which may be compounded by degraded alertness during automated driving [14; 15; 16].  
This literature review provides an overview of the current understanding of driver interaction with road signage and automated driving technologies as well as factors that affect driver performance, including characteristics of the driver, the environment, vehicle automation, and the in-vehicle display. We identify research gaps that need to be addressed to inform the design of in-vehicle messaging of road signage information under highly automated driving conditions and propose a framework to guide further empirical investigations.
## 3 System Model
### Transmission Service Model and Transmission Model  
In the scenario outlined in this paper, observation satellites are tasked with continuously monitoring the ground and collecting data transmission services at the outset of system operations. Denote by \(D^{o}_{n}\) the data collected by the observation satellite \(OS_{n}\). Assume that, following data collection, an observation satellite \(OS_{n}\) is required to relay the data to the ground station through relay satellites in the following time slots. Define \(\gamma^{o}_{n,k,t}\in\{0,1\}\) as the selection variable for satellite transmission relays concerning observation satellites. A value of \(\gamma^{o}_{n,k,t}=1\) indicates that the observation satellite \(OS_{n}\) sends data to relay satellite \(RS_{k}\) in time slot \(t\), whereas \(\gamma^{o}_{n,k,t}=0\) indicates no transmission. Let \(\gamma^{r}_{n,k,t^{\prime},t}\in\{0,1\}\) represent the transmission selection variable between relay satellites. If \(\gamma^{r}_{n,k,t^{\prime},t}=1\), relay satellite \(RS_{k}\) forwards data \(D^{o}_{n}\) to relay satellite \(RS_{k^{\prime}}\) in time slot \(t\); otherwise, \(\gamma^{r}_{n,k,k^{\prime},t}=0\). Additionally, let \(\gamma^{g}_{n,k,t}\in\{0,1\}\) be the variable for selecting the ground station transmission from relay satellites. If \(\gamma^{g}_{n,k,t}=1\), the relay satellite \(RS_{k}\) transmits data \(D^{o}_{n}\) to the ground station in the time slot \(t\); if not, then \(\gamma^{g}_{n,k,t}=0\).
# Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response
## 6 Discussion, Limitations, and Future Work
### Technical Limitations
#### 6.3.2 Coping with Detection Errors  
The primary objective of this study is to improve the efficiency of incident handling by reducing false positives in the escalation set. Therefore, performance evaluation in the experiment focused on the positive class, i.e., critical alerts that serve as the escalation set in incident response. However, it is crucial to note that eliminating all false positives may not always be feasible and could even lead to an increased risk of false negatives, which could compromise the security of the network infrastructure. In such scenarios, we recommend setting a high threshold value for the expected recall rate, such as 99%, and opting for a detection model that meets this requirement while having the fewest number of false positives.  
In addition, incident response is primarily a manual process conducted by security operators, who can help identify false positives by manually reviewing alerts selected by the SIEM system. Integrating feedback from the security operations can refine the algorithm and reduce false positives over time. To achieve this, it is essential for the security operators to have a clear understanding of the SIEM system's mechanism and provide valuable feedback for performance improvement.
## 3 Background Knowledge
### Transformer Architecture  
Transformer [20] is a state-of-the-art technique in natural language processing. It was designed to deal with sequence-to-sequence problems, such as machine translation, text classification, and sentiment analysis.  
The Transformer model proposes an architecture based on a self-attention mechanism and does not use Recurrent Neural Networks architecture. However, it provides a much better performance than other Sequence-to-sequence models in long sequence processing. Moreover, the transformer architecture processes the input sequences in parallel, which improves the training process time.  
Some of important features of Transformer are:  
* Encoder: The original encoder of the Transformer architecture has six identical layers, and each layer is constructed with a multi-head self-attention mechanism and a fully connected feed-forward network mechanism. Both of these mechanisms consist of a residual connection and a normalization layer. In our work, we only used four layers.  
Figure 3: DoH tunneling attack scheme.  
* Decoder: The original decoder also includes six identical layers, each consisting of an additional sublayer. The additional sublayer performs multi-head attention over the output of the encoder stack.
* Attention Mechanism: The attention component of the network maps the critical and relevant elements from the input sequence and assigns higher weights to these elements, which enhances the accuracy of the output prediction.
* Scaled Dot-Product Attention: Scaled dot-product attention is an attention mechanism that calculates the output as a weighted sum of the values, where the weight assigned to each value is determined by the dot-product of the query with all the keys: \[\text{Attention}(Q,K,V)=\text{softmax}\left(\frac{QK^{T}}{\sqrt{d_{k}}}\right)V\] (1)
* Multi-Head Attention: The architecture of multi-head attention is shown in Figure 4. Instead of only computing the attention once, the multi-head attention mechanism operates through the scaled dot-product attention multiple times in parallel. The separated attention outputs are concatenated and linearly transformed into the expected dimensions. The multi-headed-attention matrix for input matrices (\(Q\), \(K\), \(V\)) is calculated as: \[\text{MultiHead}(Q,K,V)=\text{Concat }(\text{head}_{1},\dots,\text{ head}_{\text{h}})W^{\text{O}}\] (2) where \(\text{head}_{\text{i}}=\text{ Attention}\left(QW_{i}^{Q},KW_{i}^{K},VW_{i}^{V}\right)\) where \(W_{i}^{Q},W_{i}^{K},W_{i}^{V}\), and \(W^{O}\) are parameter matrices to be learned.
## 3 Results and Discussion
### Model Robustness and Generalization  
Previous studies often develop prediction models using the data from a single region for both the training and validation, which limits their applicability to other regions. This study aimed to assess the robustness and generalizability of the prediction models across regions. The models were developed using 65 samples from Heyuan and 306 samples from the Pearl River Delta. When applied to the Heyuan data, the models performed poorly, while the models for the Pearl River Delta performed well (Table 5, Figure 9).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & **Validation Sample Size** & **R\({}^{2}\)** & **NME** & **MRE (\%)** & **RMSE** \\ \hline Artificial Neural & & & & & \\ Network & 65 & 0.58 & 0.02 & 11.93 & 0.07 \\ Random Forest & 65 & 0.79 & 0.04 & 8.28 & 0.05 \\ \hline \end{tabular}
\end{table}
Table 4: Accuracy of the model for Heyuan.  
Figure 8: Predicted \(\mathrm{BAF_{Zn}}\) and measured \(\mathrm{BAF_{Zn}}\) for (**a**) ANN and (**b**) RF in Heyuan.  
The sample-to-feature ratio (SFR) for Heyuan was 7.43, which is less than the ideal threshold of 10, leading to a weaker model performance. In contrast, the SFR for the Pearl River Delta was 34.71, ensuring better model stability. Combining the data from both regions improved the SFR and allowed the model to perform well across the study area, demonstrating its robustness and generalizability.  
Finally, to further validate the models' generalization ability, the models were tested on 65 samples from Zhaoqing, collected in 2016. The results showed that the models performed  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Region** & **Model** &
\begin{tabular}{c} **Validation** \\ **Sample Size** \\ \end{tabular} & **R2** & **NME** & **MRE (\%)** & **RMSE** \\ \hline \multirow{4}{*}{Heyuan} & Artificial & \multirow{2}{*}{13} & \multirow{2}{*}{0.34} & \multirow{2}{*}{\(-\)0.03} & \multirow{2}{*}{18.47} & \multirow{2}{*}{0.13} \\  & Neural & Network & & & & \\ \cline{2-5}  & Random & & & & & \\ \cline{2-5}  & Forest & \multirow{2}{*}{13} & \multirow{2}{*}{0.55} & \multirow{2}{*}{\(-\)0.07} & \multirow{2}{*}{16.30} & \multirow{2}{*}{0.11} \\ \hline \multirow{4}{*}{Pearl River Delta} & Artificial & \multirow{2}{*}{63} & \multirow{2}{*}{0.84} & \multirow{2}{*}{\(-\)0.003} & \multirow{2}{*}{15.09} & \multirow{2}{*}{0.07} \\  & Neural & Network & & & & \\ \cline{2-5}  & Random & & & & & \\ \cline{2-5}  & Forest & \multirow{2}{*}{64} & \multirow{2}{*}{0.86} & \multirow{2}{*}{0.003} & \multirow{2}{*}{13.25} & \multirow{2}{*}{0.07} \\ \cline{2-5}  & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Predicting accuracy comparison for Heyuan and Pearl River Delta areas.  
well, with the RF model slightly outperforming the ANN model (Table 6, Figure 10). This indicates that the prediction models have good generalizability within the study area, although further research is needed to refine the prediction methods for different regions.
## 3 Proposed Quality of Service Routing Protocol (QoSRP) in UWSNs
### Underwater Acoustic Channel Detection (UWCD) Algorithm
#### 3.2.3 User Selection in Cooperative Mechanism
The fusion center periodically computes the detection probability of each \(\mathcal{SU}\) based on its measurements in different rounds and updates its table. Thus, a fewer \(\mathcal{SU}_{3}\) with the highest detection probability sense and provide the most accurate results with reduced sensing overhead in UWSNs.
# Plant-Inspired Soft Growing Robots: A Control Approach Using Nonlinear Model Predictive Techniques
## 4 Nonlinear Model Predictive Growth Control
### Control Objective  
The main intention of the proposed MPC-based growth control scheme is to confirm the vine robot's stabilization performance over a desired reference state \(\mathbf{x}_{r}=[x_{r},\,y_{r},\,z_{r},\,s_{r},\,\theta_{r},\,\phi_{r}]^{T}\) specified in task and joint spaces. The proposed controller has to take into account the irreversible growth of the robot and the actuators' limit constraints while finding the optimal control actions. The objective function \(J\) is selected such that the tracking performance is enhanced, and the control effort over a prediction horizon \(N\) is minimized as follows:  
\[\begin{split} J(k)=&\sum_{j=1}^{N}\mathbf{e}_{(k+j)}^{T} \,\mathbf{Q}\,\mathbf{e}_{(k+j)}\\ &+\sum_{j=1}^{N}\Delta\mathbf{u}_{(k+j-1)}^{T}\,\mathbf{R}\,\Delta\mathbf{u}_{ (k+j-1)}\end{split} \tag{10}\]  
The tracking error is represented as \(\mathbf{e}=\mathbf{x}-\mathbf{x}_{r}\), while \(\Delta\mathbf{u}\) represents the control increment. The objective function's weighting matrices, \(\mathbf{Q}\geq\ 0\) and \(\mathbf{R}\geq 0\), are assumed to be fixed over the prediction horizon \(N\).
## 3 Results and Discussion
### Comparison of Manually and Automatically Performed Scratching  
In order to evaluate the capabilities and limitations of the scratching performances, the scratch border roughness is interpreted as a function of its respective operating procedure. For manually, as well as automatically performed scratch assays, the impacting forces and the border roughness values are depicted (Figure 3).  
Figure 2: 3D model of the constructed apparatus consisting of two main components: while the sample translation system (front) ensures track accuracy, the actual scratching system (back/top) enables different angles and forces for the scratching blade (depicted in red). The parameters under investigation are highlighted.  
It shows that manually and automatically performed roughness values vary depending on their operational mode and especially on the method used. While the manually performed values range between 30 and 170 \(\upmu\)m, the mechanically performed scratches enable values of 20 to 70 \(\upmu\)m. Concerning the force accuracy, the manually performed assays vary by roughly 0.1 to 0.15 N per scratch and exceed 0.2 N per scratch for the usually favoured pipette tip technique. Mechanically performed scratch assays undergo a variation of less than 0.1 N/scratch, even with the directional mode not being differentiated.  
When taking a closer look at the manually performed scratch techniques, the force deviation depends on the tool used. While the rigid and barely deformable pipette tip shows a large force deviation due to the operator guiding it, the rubber spatula and cell scraper perform with little deviation. Both these elastic tools show a more rigid and therefore inconsistent force application in push direction; however, in pull direction deviations can be smoothed out more easily. Concerning the automated scratching, the deviation in force results from the simultaneous consideration of push and pull assays.  
At this point, it can be stated that the conventionally used pipette tip provides the scratching border with the least roughness values of all manual techniques investigated.
## 3 Results  
The final study sample used for our analysis included 69 NCCLs associated with DH and belonging to 23 patients, 15 of which were females and 8 of which males, with a mean age of 41.4 years. All patients completed the study. The NCCLs selected for analysis included those that did not meet the exclusion criteria, reported in the Material and Methods section, and that showed a VAS score between 6 and 9 in three different quadrants of the mouth for each patient. Finally, thanks to randomization software, the 69 NCCLs were assigned to the three study groups, each consisting of 23 NCCLs and treated with a different laser therapy. In the same patient, one lesion per selected quadrant was included in a different study group.  
The values of mean and standard error of the VAS scores for each study group are shown in Table 2 for each time point of the therapeutic protocol: baseline (t0), at treatment completion (t1), at 15 days (t2), and at 3 months (t3).  
Figure 2: Flow diagram showing the study project.  
A significant reduction of the mean VAS score was estimated for each study group at t1, t2, and t3 compared with the mean VAS score assessed at t0 (\(p\)-value of Wilcoxon test < 0.0001 for each tested pair and for each study group).  
No significant difference in the mean of VAS scores at t0 was appraised among the three study groups (\(p\)-value of the one-way ANOVA test: 0.9846; mean values are shown in Table 2).  
The comparison of the means of the VAS scores at t1, t2, and t3 between each pair of study groups reveals the following: a statistically significant difference for LLLT vs. LHLT at each tested point of the laser treatment timeline; for the HPLT vs. LHLT comparison, the difference is extremely significant only at t2 and t3; for LLLT vs. HPLT, a significant difference was observed only at t1 (the \(p\)-values are shown in Table 3).  
An improvement of DH symptomatology was registered for all study groups. The HPLT and LHLT present the best results, 59% and 60%, respectively, immediately after the laser treatment (t1), while the improvement of DH was 49% for LLLT. On the other hand, LLLT and LHLT reveal a similar trend in the reduction of improvements, which was more gradual over time than the HPLT, which showed a rapid decrease at t2 (59% at t1 vs. 39% at t2), but shows unvaried values from t2 to t3 (Table 2).
# Borehole Trajectory Optimization Design Based on the SAC Algorithm with a Self-Attention Mechanism
## 2 Materials and Methods
### Self-Attention Mechanism  
The self-attention mechanism is a special kind of attention mechanism. Query, Key, and Value matrices come from the same sequence data. Its network structure is shown in Figure 2.  
\[\text{Self-Attention}(X)=\text{softmax}\Bigg{(}\frac{XW^{Q}(XW^{K})^{\top}}{ \sqrt{d_{k}}}\Bigg{)}(XW^{V}) \tag{8}\]  
where \(W^{Q}\in\mathbb{R}^{d_{x}\times d_{k}}\), \(W^{K}\in\mathbb{R}^{d_{x}\times d_{k}}\), and \(W^{V}\in\mathbb{R}^{d_{x}\times d_{k}}\) are the parameter matrices.  
To further enhance model representation, multiple attentional modes are learned at the same time, i.e., multi-head attentional mechanisms:  
\[\text{MultiHead}(X)=\text{Concat}(\text{head}_{1},\dots,\text{head}_{h})W^{O} \tag{9}\]  
Each head is calculated separately:  
\[\text{head}_{i}=\text{Self-Attention}(XW^{Q}_{i},XW^{K}_{i},XW^{V}_{i}) \tag{10}\]  
This figure illustrates the multi-head self-attention structure. The left \"Scaled Dot-Product Attention\" is the basic attention unit, with inputs as Query (Q), Key (K), and  
Figure 2: Attention mechanism.  
Value (V) matrices (all derived from the same sequence data) and outputs as weighted features. \"Multi-Head Attention\" enhances model representation by concatenating features from multiple parallel attention units (heads); colors (red, blue) distinguish computations of different heads.
## 4 Simulation and Analysis
### Altering the Base Station Position  
In this scenario, the base station position is changed and the network performance in our proposed protocol is observed in comparison with the other protocols. BS positions _sink.x_ and _sink.y_ are changed from 50 to 25, without modifying other parameters. These changes affect dead node performance, packet delivery rate and average power  
Figure 5: Packets delivery ratio to BS.  
Figure 6: Nodes energy consumption.  
consumption. Simulation outputs show that these parameters display better results in the proposed EZ-SEP compared to the parent protocols, as demonstrated in Table 3(a). When the data routing is started, Z-SEP shows better performance in node life as compared to the proposed EZ-SEP, but the EZ-SEP algorithm improves after fa ew rounds and shows better results after 3000 rounds and this remains true till the end. The SEP and LEACH protocols show the worst results in comparison with the Z-SEP protocol and EZ-SEP. In addition, EZ-SEP is better in packet delivery ratio and transmits more packets compared to the three existing protocols. Furthermore, the LEACH and SEP algorithms demonstrate higher energy consumption compared to the other protocols. Nodes' residual energy is around 0.1 J in the proposed EZ-SEP at round 2000, while in Z-SEP it is almost 0. LEACH is the most energy-consuming protocol of all. Thus, our proposed protocol increases the service lifetime and improve the stability period of the sensor network, since it consumes less energy than other ones.
## 4 Conclusions  
The aim of the present study was to investigate the ability of the support vector machine (SVM) to predict the effect of horizontal screen diameters on hydraulic parameters of vertical drops. A total of 164 experimental datapoints was obtained and three statistical parameters, namely RMSE, \(\mathrm{R}^{2}\) and KGE, were used to evaluate the accuracy of the models. The present study used two scenarios, which included the relative energy dissipation (\(\Delta\mathrm{E}/\mathrm{E}_{0}\)) and the relative pool depth (\(\mathrm{y}_{\mathrm{p}}/\mathrm{H}\)), which were entered in the support vector machine network as dimensionless parameters. To obtain the best SVM model for parameters \(\Delta\mathrm{E}/\mathrm{E}_{0}\) and \(\mathrm{y}_{\mathrm{p}}/\mathrm{H}\), input configurations for relative energy dissipation and relative pool depth were introduced into the SVM based models, respectively. The results show that there is a good correlation between the values of \(\Delta\mathrm{E}/\mathrm{E}_{0}\) and \(\mathrm{y}_{\mathrm{p}}/\mathrm{H}\) obtained by the SVM model and the experimental values of \(\Delta\mathrm{E}/\mathrm{E}_{0}\) and \(\mathrm{y}_{\mathrm{p}}/\mathrm{H}\) with input parameters. The relative diameter of the screen (D/H) and the critical relative depth (\(\mathrm{y}_{\mathrm{c}}/\mathrm{H}\)) were found to be the best combination for predicting hydraulic performance. The results of the sensitivity analysis show that the critical relative depth parameter (\(\mathrm{y}_{\mathrm{c}}/\mathrm{H}\)) is the most effective parameter for predicting the dependent parameters of the present study.  
Conceptualization, R.D., E.A., A.G., J.A. and M.B.; methodology, E.A., A.G. and M.B.; software, E.A. and A.G.; validation, R.D., E.A., A.G., J.A. and M.B.; formal analysis, R.D., E.A., A.G., J.A. and M.B.; resources, R.D. and A.G.; data curation, R.D., E.A., A.G., J.A. and M.B.; writing--original draft preparation, R.D., E.A., A.G., J.A. and M.B.; writing--review and editing, R.D., E.A., A.G., J.A. and M.B.; supervision, R.D., A.G. and J.A.; project administration, R.D., A.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
# Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-Band OpenMote Hardware
## 3 TSCH Energy Model
### TSCH Time Slots
#### 3.1.4 Time Slot RxIdle  
When the transmitter has no data to send, the slot that could have been a TxDataRxAck becomes a Sleep slot. However, on the receiver side, a different type of slot is needed to represent the behavior of the node: the RxIdle slot occurs when the receiver expects data, but does not receive anything. The states of RxIdle are shown in Table 5. The behavior of RxIdle is not an error; it simply means that a slot was reserved, but the transmitter did not have any data to send at that moment.