esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dataset Establishment
### Data Preprocessing  
The 256 data sets obtained from this experiment were preprocessed to ensure that data quality met the requirements for modeling. The quantile-quantile plots of observed variables (see Figure 3) indicated a non-normal distribution, specifically, the expected normal values do not entirely fall within the 95% confidence interval. Thus, the interquartile range (\(IQR\)) method was intended for outlier detection. The \(IQR\)[42], defined as the difference between the 75 th percentile (\(Q3\)) and the 25 th percentile (\(Q1\)), serves as a measure of data dispersion. Outliers were identified as data points falling below (\(Q1-1.5\times IQR\)) or exceeding (\(Q3+1.5\times IQR\)). As demonstrated in Figure 4, no outliers were detected among the 256 data sets, confirming their suitability for subsequent analysis.  
Figure 2: Analysis of dominant input parameters based on the GRA algorithms.
## 2 Related Works
This can lead to integration issues and possible system failures such as:  
* _Capacity and latency issues._ In cases where data need to be transmitted at high speeds--for example, in train-to-train communication systems [14]--limited capacity or long delays can have a significant impact on the efficiency of e-service systems, in particular on fast cargo tracking and handling. * _Operational risk and adaptability issues._ The deployment of an integrated e-services system can be challenging for different modes of transport and geographical areas. The complexity of scaling these systems, especially in areas with different infrastructure capacities, can pose a risk to the overall functioning and reliability of the systems as highlighted in the study on ITSs carried out by [2]. * _Decision complexity_. Integration of multicritical decision support systems and developing decision-making skills, as explored by [5], can lead to complexity that can overwhelm users and lead to suboptimal decisions or delays. * _Data security risks can meet with cyber threats_. The integration of contextual data into e-services means that a large amount of sensitive information is collected, transmitted, and processed. This puts the system at risk of cyberattacks that can compromise the integrity and confidentiality of the data as suggested by [12; 13] discussed in the context of intelligent transport systems. Privacy issues arise when the collection of contextual data poses serious privacy concerns, including sensitive routes, transportation, and customer data. Unauthorized access by data subjects or data breaches can lead to serious legal consequences and reputational damage. We need the interoperable resources of ISs and the interoperability of different modes of transport. It is difficult to ensure interoperability between different modes of transport, such as rail and waterborne transport. Differences in standards, communication protocols, and data formats can lead to integration issues that can lead to gaps in activities and parentheses in data. The need for harmonization of international standards is very actual as well. Given the frequent cross-border multimodality, it is important to harmonize standards and rules across countries. Differences in these standards can hinder the seamless integration of contextual data as research in the field of logistics and international transport has shown. While the contextual integration of data into the e-service framework has great potential to improve the efficiency and sustainability of multimodal freight management, these risks need to be thoroughly addressed through strong technical, operational, and security measures. Successful integration depends on their ability to manage these risks through strategic planning, international cooperation, and continuous improvement of technical standards. Table 1 shows the comparison of the methods, which are indicated in the related research studies and grouped according to main features. This table indicates which method is used by researchers conducting studies on the topic of multimodal transportation.
## 5 Discussion
One interesting finding in this regard is that the largest difference between the potential- and current-based systems can be seen when using metal-plate electrodes. Only in this case, the full distortion effect of the highly conductive water surrounding the electrodes is noticeable. When using highly adhesive, foam-based Ag/AgCl electrodes, there is not a large difference in signal quality between the two systems. This is likely due to the high shielding factor of the foam material and the much higher conductivity of the electrode gel between the skin and the electrode surface. As the electrodes are covered by the gel on the one side and the connector and foam on the other side, the signal degrades over time only when the water washes away the electrode gel and the conductivity of the water is lower than that of the gel. We also did not evaluate how a high saline content of water would change the outcome of our physiological measurements. We do not expect different outcomes when using metal-plate electrodes, however, for gel-based adhesive electrodes it might make a difference. The results of the heart rate variability study endorse the physiological response to immersion showing an initial reflex bradycardia resulting from a restriction of blood flow to the peripheries and to venoconstriction due to hydrostatic forces, typically known as the mammalian diving reflex [29]. In general, immersion results in an increase of venous return, increased stroke volume and bradycardia as a modest increase in cardiac output [30]. It is expected that the effect of the mammalian diving reflex is amplified during full immersed conditions, since a high density of baro- and temperature receptors is located in the face. However, since it was not the direct aim of the HRV study to investigate the particular changes of the heart activity between submerged conditions, we do not discuss this further This has already been the topic of various previous research. For example, [PERSON] et al. evaluated the HRV changes during a challenging dive [31], [PERSON] et al. during face submersion in cold water [32], [PERSON] et al. during static and dynamic breath-hold dives [33] or [PERSON] et al. during exercises inside and outside the water [34]. Instead, the primary goal of this study, in the context of our developed sensor, was to demonstrate its applicability for such endeavors. We were successfully able to reproduce the characteristics in the HRV in dry and submerged conditions as reported by previous studies with potential-based ECG sensors [4]. This allows us to assume that our sensor can be used to perform accurate and repeatable measurements of the human ECG in dry and underwater conditions for physiological experiments or monitoring, in particular for the important evaluation of the HRV.
# Adaptive IDS for Cooperative Intelligent Transportation Systems Using Deep Belief Networks
## 6 Experimental Environment Setup
### Evaluation Metrics  
To evaluate the performance of the proposed IDS for cITS, this paper uses the accuracy, detection rate, and the false alarms rate as they are common metrics widely used by the extant research. Equations (3)-(6) are used to calculate the detection accuracy, detection rate, precision, false positive rate, and the F measure, respectively.  
\[\text{ACC}=\frac{TP+TN}{TP+TN+FP+FN} \tag{3}\]  
\[\text{DR}=\frac{TP}{TP+FN} \tag{4}\]\[\text{FPR}=\frac{FP}{FP+TN} \tag{5}\] \[\text{F1}=\frac{2\times\text{ Precision }\times\text{ Recall}}{\text{ Precision }+\text{ Recall}} \tag{6}\]  
where \(TP,TN,FP\), and \(FN\) denote the true positive, true negative, false positive, and false negative, respectively.
# Pairwise Likelihood Estimation of the 2 PL Model with Locally Dependent Item Responses
## 5 Discussion  
In this article, we propose a variant of PML estimation that allows the consistent estimation of item parameters in the 2 PL model despite the presence of local dependence. In the modified PML approach, the bivariate frequencies of items within the same testlet do not appear in the optimization function. As such, local dependence is essentially removed from the estimation. Of course, this change brings the disadvantage of efficiency losses compared with MML estimation if local independence holds. However, with large sample sizes, the bias outweighs the higher variance of PML estimation. Our proposed method had equal performance to the MML and PML estimation methods on a 2 PL normal copula model that additionally estimated local dependence parameters. This finding was obtained with an unstructured model of local dependence. If local dependence is summarized into a small(er) number of dependence parameters per testlet, the simultaneous estimation of the item parameters and dependence parameters might be more efficient than PML estimation with excluded item pairs of items referring to the same testlet. The PML estimation approach can be easily adapted to IRT models with polytomous item responses under local dependence. Similarly, multidimensional IRT models with a dependence structure can also be handled with the modified PML estimation. Importantly, if all items only load on one dimension, PML estimation solves the issue of the evaluation of high-dimensional integrals in MML estimation for multidimensional IRT models. This article did not discuss the estimation of standard errors. However, standard M-estimation theory can be used for statistical inference (see also [14, 41]). Future studies could evaluate the accuracy of standard error estimation under PML. In some applications, the standard errors of person parameter estimates \(\hat{\theta}\) of \(\theta\) are of interest [42, 43]. The M-estimation theory for a clustered data structure (i.e., cluster-robust standard errors) can be used to determine the standard errors in the case of local dependence. Alternatively, the resampling of items or testlets can also be used to compute standard errors [44]. This paper focuses on the estimation of the 2 PL model with local dependence. However, PML estimation for the Rasch model [45] with local dependence could also be carried out for smaller sample sizes [46]. Alternatively, prior distributions or regularized estimation can stabilize the estimation of the 2 PL model in smaller sample sizes [47, 48, 49]. In PML estimation, a prior distribution (or penalty terms) could also be included (see [50, 51] for a similar approach). Finally, previous research indicated that the effect of local dependence on the item parameters reduces with an increasing number of items for fixed lengths of testlets. In
# Empirical Remarks on the Translational Equivariance of Convolutional Layers
## 4 Experimental Results
### Experimental Setup  
We perform the comparative analysis for the four networks; the network trained with spatial images only, and three networks for the proposed fusion strategies. All the networks consist of convolutional layers followed by one fully connected layer and a softmax layer. For each network, we vary the number of convolutional layers by \(K\) times for deep architecture. For simplicity, we named the repeated \(K\) convolutional layers _Net-K_. For the maximum number of convolutional layers, \(K\) varies from 1 to 17 and the kernel size varies from \(3\times 3\) to \(37\times 37\). The number of nodes for the fully connected layer is 32, and 10 nodes for the softmax layer. Without max-pooling and preserving the size of feature maps, a convolutional layer of \(3\times 3\) kernel reduces the size of feature maps by 2 pixels, which results in 17 layers at maximum for \(38\times 38\) input images. We focused on the representation capability of convolutional layers, so we set the number of nodes of the fully connected layer mainly in charge of classification to 32, the minimum number that can obtain 99% training accuracy. Finally, the number of nodes of the softmax layer is set to 10 considering 10 numbers to be classified.  
We set training hyperparameters as follows: The RMSprop optimizer with an initial learning rate of 0.001 is used. The batch size is 5000, and the training epoch is 100. For a fair comparison, we fixed 100 epochs for all the experiments, where all the networks are sufficiently trained to achieve more than 99% training accuracy. We do not provide a loss-by-epoch graph since the MNIST dataset is well known for easy training, even by a simple network.  
Figure 4: The fusion strategy. (**a**) early fusion by concatenating raw image and its frequency image, (**b**) late fusion by concatenation, (**c**) late fusion by addition.
# Optimal Investment Timing and Scale Choice of Overseas Oil Projects: A Real Option Approach
## 3 Model Description
### Value of Oil Projects
#### 3.1.2 Revenue of Oil Project  
In the uncertain environment, fluctuating oil prices and the US dollar exchange rate directly affect the profit of overseas oil projects. Considering the cash inflow of overseas oil projects, the revenue mainly depends on the production, the fiscal regime of investee country, the oil price, and the US dollar exchange rate.  
\[R=\int_{0}^{T}e^{-rt}\cdot m(t)\cdot\theta(t)\mathrm{d}t=\int_{0}^{T}e^{-rt} \cdot m(t)\cdot\frac{\eta P(t)\cdot E(t)}{E_{0}}\mathrm{d}t \tag{3}\]  
where \(R\) denotes the revenue of overseas oil projects, and \(\theta(t)\) denotes the oil's unit revenue per barrel at time \(t\). \(\theta(t)\) depends on the after-tax return rate \(\eta\), oil price, and US dollar exchange rate. In order to simplify the analysis in this model, the after-tax return rate \(\eta\) is treated as a constant during the contract period. \(E_{0}\) denotes the benchmark of US dollar exchange rate. \(P(t)\) and \(E(t)\) denote the oil price and US dollar exchange rate at time \(t\), respectively.  
According to previous research [13; 21; 43; 44; 21], the oil price and US dollar exchange rate are assumed to be governed by the geometric Brownian motion, which indicates their variation and implies their influence on revenue.  
\[\mathrm{d}P=u_{P}P\mathrm{d}t+\sigma_{P}P\mathrm{d}z_{P} \tag{4}\]  
\[\mathrm{d}E=u_{E}E\mathrm{d}t+\sigma_{E}E\mathrm{d}z_{E} \tag{5}\]  
where \(u_{E}\) and \(u_{P}\) are the drift of oil price and US dollar exchange rate, \(\sigma_{P}\) and \(\sigma_{E}\) are the volatility of oil price and US dollar exchange rate. The \(\mathrm{d}z_{p}\) and \(\mathrm{d}z_{E}\) are independent increments of Wiener process.  
Therefore, the unit revenue is governed by the Geometric Brownian motion.  
\[\mathrm{d}\theta=u\theta\mathrm{d}t+\sigma\theta\mathrm{d}z \tag{6}\]  
\[u=u_{P}+u_{E} \tag{7}\]  
\[\sigma=\sqrt{\sigma_{P}^{2}+\sigma_{E}^{2}+2\rho_{E,P}\sigma_{P}\sigma_{E}} \tag{8}\]  
where \(u\) and \(\sigma\) are the drift and volatility of unit revenue, \(\rho_{P,E}\) is the correlation coefficient between oil price and exchange rate, and \(\mathrm{d}z\) is the independent increment of a Wiener process.  
Therefore, the revenue of each phase can be discounted to that of the beginning. Considering the uncertainties, the revenue function of overseas oil projects can be written as Equation (9):  
\[R(s;\theta)=E\bigg{[}\int_{T_{0}}^{T_{3}}m(t)\cdot\theta(t)\cdot e^{-rt}\mathrm{d }t|\theta(0)=\theta\bigg{]}=R_{1}+R_{2}+R_{3}=s\cdot\theta\cdot\xi \tag{9}\]  
\[\xi=\bigg{\{}(\frac{a\cdot OIP}{T_{1}})\bigg{[}\frac{e^{-(r-u)T_{0}}-e^{-(r-u)T _{1}}}{(r-u)^{2}}+\frac{T_{0}e^{-(r-u)T_{0}}-T_{1}e^{-(r-u)T_{1}}}{r-u}\bigg{]} \bigg{\}}+\bigg{\{}\frac{a\cdot OIP}{r-u}\Big{[}e^{-(r-u)T_{1}}-e^{-(r-u)T_{2}} \Big{]}\bigg{\}} \tag{10}\]  
where \(R_{1}\), \(R_{2}\), and \(R_{3}\) denote the revenue of build-up, plateau production, and decline phase, respectively. \(\zeta\) denotes the expression of discounted revenue coefficient.
## 5 Experiments
### Effectiveness and Efficiency  
In this section, we aim to evaluate the efficiency and effectiveness of the proposed method in optimizing QoS values of service composition. We conducted two types of experiments. The first type consisted of 60 subtasks divided into four groups with 100, 150, 200, and 250 candidate cloud services for each subtask, respectively, while the second type had four groups with 20, 40, 60, and 80 subtasks and a fixed number of 200 candidate services for each subtask. Experimental results are presented in Figures 8 and 9.  
As shown in Figure 8, the SkyDRL method achieved an average QoS value of 39.1, which is slightly lower than PD-DQN and SFGA. This can be attributed to the fact that SkyDRL aims to balance both the QoS optimization and energy consumption of service composition, resulting in a trade-off between the two metrics. However, SkyDRL still performed well in optimizing the overall QoS of service composition, indicating its effectiveness in solving service composition problems. Similarly, Figure 9 shows that the SkyDRL method obtained an average QoS value of 30.795, while PD-DQN, Q-learning with skyline, SFGA, and MAACS achieved averages of 31.86, 25.21, 31.42, and 30.76, respectively. These results demonstrate that SkyDRL is effective in optimizing QoS values of service  
Figure 7: Energy consumption for PD-DQN, Q-learning with skyline, SFGA, MAACS, and SkyDRL, with 60 subtasks but different numbers of candidate services for each subtask.  
composition for varying problem scales. Although SkyDRL's QoS performance is not the best among the evaluated methods, it is important to note that SkyDRL also reduces energy consumption in addition to QoS optimization. Therefore, a certain decrease in QoS values for service composition could be a reasonable trade-off. Overall, the experimental results confirm that SkyDRL is an effective method for solving service composition problems with good performance in optimizing QoS and reducing energy consumption.
# A Review of Seatbelt Technologies and Their Role in Vehicle Safety
## 4 Effectiveness, Challenges, and Limitations of Seatbelt Technologies
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **Retractor** & **Pretensioner** & **Load Limiter** & **Adaptive Seatbelt** \\ \hline Response Time & Fast (milliseconds) & Instant (milliseconds) & \
\begin{tabular}{c} Gradual, during \\ sustained force \\ \end{tabular} &
\begin{tabular}{c} Continuous; capable of \\ pre-emptive and real-time \\ response \\ \end{tabular} \\ \hline Cited Papers & [123; 124; 125; 126; 127; 128; 129; 124; 125; 126; 127] & [29; 30; 31; 32; 33; 34; 36; 37; 128; 129; 130; 131; 132] & [38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
Their advantages include enhancing restraint efficiency and improving comfort; however, disadvantages involve added mechanical complexity, increased costs, and limited effectiveness during high-speed or side-impact events. The next generation of safety systems includes adaptive or smart seatbelts and biometric health-monitoring seatbelts. Adaptive seatbelts adjust tension dynamically based on occupant characteristics and crash dynamics, improving both comfort and protection through integration with AI and in-cabin sensors [143]. Their advantages lie in personalizing restraint systems for individual passengers; however, disadvantages include high development costs, the need for extensive sensor networks, and limited real-world deployment. Biometric seatbelts offer additional benefits by monitoring vital signs such as heart rate and respiration, enhancing health monitoring during driving. While promising for detecting medical emergencies or drowsy driving, their disadvantages include being in early research stages, raising privacy concerns, and requiring regular maintenance and calibration. Although not yet a core crash safety feature, these technologies show potential for integration into future preventive safety systems. An important limitation of this review is the potential for selection bias stemming from the restriction to English-language publications. This decision was made in recognition of English being the predominant language of scientific communication, which facilitates broader dissemination and accessibility of findings within the global research community. However, this language restriction may have led to the exclusion of relevant studies published in other languages, potentially limiting the cultural and contextual diversity of the evidence base. Additionally, the review excluded grey literature and did not apply a formal quality assessment of the included studies, which may affect the comprehensiveness and reliability of the conclusions drawn.
# DMRVR: Dynamic Milk-Run Vehicle Routing Solution Using Fog-Based Vehicular Ad Hoc Networks
## 3 DMRVR Solution  
An OEM company, its orders, its suppliers, a logistics operator, and its pickup vehicles are the elements that composed a Milk-run operation. The OEM company requires that the route's total  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Paper**} & \multicolumn{1}{c}{**Vehicular Communication**} & \multicolumn{1}{c}{**Traffic Jams**} & \multicolumn{1}{c}{**Suppliers**} & \multicolumn{1}{c}{**Routing Algorithm**} & \multicolumn{1}{c}{**Simulation Environment**} \\ \hline [PERSON] et al. [12] & 802.11p & - & - & - & OPNET \\ [PERSON] et al. [19] & 802.11p & No & No & TS-PSO & NS-2.34, SUMO \\ [PERSON] et al. [11] & - & Yes & No & GA, 3-OPT & Simulator in Java \\ [PERSON] et al. [20] & 2 g, 3 g, 4 g & - & - & - & - \\ [PERSON] et al. [5] & - & Yes & No & GMM, MDP & Matlab 7 \\ [PERSON] et al. [10] & - & No & Yes & SA & Simulator in Pascal \\ [PERSON] et al. [44] & 802.11p & Yes & - & - & NS-3 and Matlab \\ [PERSON] et al. [45] & VCC & No & - & Parallel GA & C++ \\ DMRVR solution & 802.11p & Yes & Yes & GA, 3-OPT & OMNeT++, SUMO, Artery \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative of related works with the DMRVR solution.  
operation time to deliver/pick up the goods is performed within a Just-in-Time (JIT) limit. Problems with the supplier production line or traffic jam delays can occur during the route, causing tardiness. In this case, the logistics operator will send auxiliary vehicles to cover the pickup locations removed from the initial route. The process is over when the last pickup vehicle drops the products at the OEM company. The treatment of supplier's manufacturing delays [46], together with the delays generated by adverse traffic conditions and by road disturbances (road maintenance, accidents, external effects such as weather conditions, etc.), must be treated in accordance with a decision process, formalized through appropriate algorithms to be discussed next.