esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Comparative Studies
#### 4.4.2 Performance of Popular Classifiers  
The maximum accuracy was obtained for the total dataset using the combination of both the statistical-based and text-based features. To prove the effectiveness of the proposed approach, this feature set was fed to different machine learning algorithms such as support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), naive Bayes multinomial (NBM), and rule-based (PART). The results obtained are shown in Figure 15. Comparisons were done keeping the number of dropouts (0.5) and epochs (150) the same for the classification algorithms considered here as those for which the maximum accuracy had been obtained for the CNN.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Parameters**} & \multicolumn{2}{c}{**Accuracy (in \%)**} \\ \cline{3-4}  & & **English Dataset** & **Bangla Dataset** \\ \hline Inception & 26,524,202 & 84.36 & 77.58 \\ MobileNet & 7,425,994 & 82.96 & 81.58 \\ Proposed & 3,974,318 & 93.52 & 93.58 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Results obtained for the other two CNN models.  
Figure 14: The Heat map obtained on the Bangla dataset and English datasets. The heat maps illustrate the model in which the datasets obtained maximum accuracies (for the English dataset it was the Inception model whereas for the Bangla dataset it was the MobileNet model).  
The parameters used for the SVM were: type of SVM, nu-SVC; type of kernel, sigmoid; degree in kernel function, 3; gamma in kernel function, 1/k; coef0 in kernel function, 0; nu of nu-SVC, 0.5.  
The parameters used for the RF were: number of iterations, 1000; minimum number of instances per leaf, 1; minimum variance for split, 1e-3; maximum depth of the tree, 0; and batch size, 100.  
The parameters used for the MLP were: number of iterations, 1000; batch size, 100; number of hidden units, 17; learning rate, 0.3; momentum, 0.2; loss function, squared error; activation function, approximate sigmoid.  
The default parameters were considered for the NBM model as we used the tokenizer and stopwords removal separately. Furthermore, default values for the stemmer and classifier capabilities were chosen for the experiment.  
The parameters used for PART were: confidence threshold for pruning, 0.50; minimum number of objects per leaf, 3; the unpruned decision list was generated; reduced error pruning; and number of folds for reduced error pruning, 4. One fold was used as a pruning set.
# Cost Analysis and Optimization of Modern Power System Operations
## 5 Sensitivity Analysis  
To evaluate the robustness of the optimization results, a sensitivity analysis was conducted on two key parameters: energy storage efficiency and wind curtailment penalty (VWC).  
Storage Efficiency: As shown in Figure 13, increasing storage efficiency from 80% to 100% significantly reduces operating costs, indicating that storage system performance has a strong influence on economic outcomes.  
Wind Curtailment Penalty: Figure 14 demonstrates that increasing the penalty for curtailed wind energy encourages the system to utilize more renewable power, leading to lower overall operating costs. This suggests that properly pricing curtailment can incentivize greater renewable integration.  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Scenario** & **Operating** & **Energy Storage** & **Avoided Emissions** & **Flexibility Level** \\  & **Cost (S)** & **System Usage** & **Avoided Emissions** & **Flexibility Level** \\ \hline
**Base Scenario** & 485,240 & None & Baseline & Low \\ \hline
**Reserve Constraint** & & & & \\
**(Spinning Reserve)** & 210,900 & None (Spinning Reserves maintained) & Improved reliability (not quantified) & Medium (Spinning Reserve) \\ \hline
**Renewables + Storage** & 223,360 & Battery Energy Storage & Reduced (due to renewables) & High (Storage + Renewables) \\ \hline
**Demand Flexibility** & 209,650 & Demand-side flexibility & Reduced (due to demand flexibility) & Highest (Demand Flexibility) \\ \hline \end{tabular}
\end{table}
Table 2: Summary of results of case analysis.  
Figure 14: The variation in wind curtailment penalty vs. operating cost.  
Figure 13: The variation in storage efficiency vs. operating cost.  
These results underscore the importance of accurate parameter selection and the economic value of enhancing storage performance and curtailment handling strategies.
# Visuo-Haptic Simulations to Understand the Dependence of Electric Forces on Distance
## 4 Data Analysis and Results
### Perception Questionnaires  
As mentioned before, at the end of the haptic interaction session, all students were asked to fill out an exit perception questionnaire about their experience when working with the visuo-haptic simulators, on a Likert scale, with 1 = Totally disagreement, 2 = Disagreement, 3 = Neutral, 4 = Agreement, and 5 = Total agreement. In Figures 11-13 below, results derived from the perception questionnaire, indicated as percentage of the total number of answers for the whole student sample (N = 111), for questions QP1 (Figure 11), QP2 (Figure 12), and QP3 (Figure 13) of Table 4, are presented. The results for each type of simulation (point change, line change, and plane charge) are also indicated.  
As it can be seen from Figure 11, most students consider that the haptic environments helped them to better understand the dependence of the electrical force with distance for different charges distributions (about 95%). Likewise, most of the students consider that the visuo-haptic simulators have a simple and intuitive manipulation (Figure 12) and that the visualization was appropriate and attractive (about 93%, see Figure 13). Derived from our exit survey (see Table 4), Figure 14 shows as percentage of the total sample, the students' degree of agreement about how much the use of the visuo-haptic simulators helped them to better understand the nature of electrostatic forces, with 97% of \"Agreement\" and about 75% of \"Total agreement\".  
Related to this fact, Figure 15 shows as percentage of the total sample, that more than 97% of the students would recommend using haptic simulators to study other themes of Electricity and Magnetism curricula, or other Physics courses.  
The last question of the exit survey was an open question in which the students could freely comment on their perception of the haptic devices and their overall experience with the haptic session. The students expressed very optimistic comments and in Table 10 we summarize representative comments.  
Figure 14: Percentage distribution of the students that consider that the use of the visuo-haptic simulators helped them to better understand the nature of the electrostatic forces, for each of the three visuo-haptic simulators.  
Figure 13: Percentage distribution of student answers given to perception question _QP3_ “Appropriate and attractive visualization”, for each of the three visuo-haptic simulators.  
As it can be seen from Table 10, most of the students liked very much the haptic activity and consider that the haptic devices helped them to better understand the theoretical concepts discussed in class.
## 6 Discussion and Limitations  
The issue of EVCS security is a genuine industrial concern. If exploited by malicious actors and state-sponsored attack groups, a cyberattack on the EVCS may have disastrous effects. Due to the limited number of EVCSs already deployed and the growing number of Electric Vehicles with a short battery range, any outage in a single EVCS has the potential to disrupt the travel plans of EV users. In addition, an entire power grid can be shut down by a cyberattack, which can have a direct impact on the economy. To limit this danger, a precise and effective intrusion detection system is required. In this study, the use of machine learning to construct the IDS engine is explored. To accurately evaluate the suggested IDS, a dataset that accurately reflects the actual traffic and assaults may be required. This study evaluates two machine learning (ML)-based intrusion detection system (IDS) classifier methods using the IoT-23 dataset, which is comprised of native IoT network traffic. Each  
Figure 7: Precision, Recall, and F-1 Score for the Decision Table versus the Filtered Classifier.  
classifier's theory of operation is based on a distinct set of premises. We observed from the findings that the filtered classifiers perform exceptionally well in terms of accuracy and other metrics on the testing data. Therefore, it can be utilized to protect the EVCS network against DDoS attacks.  
However, the following limitations are present in our research:  
1. Although Deep Learning (DL) is widely employed in a variety of application domains, such as image pre-processing and language translation, it is beyond the scope of this work.
2. Without installing a physical EVCS system, we trained and assessed the ML algorithms offline via virtual simulation. However, it is crucial to understand how this IDS handles intrusions in real-time by detecting internet threats.
3. When conducting an intrinsic evaluation, we should compare different datasets. In this study, however, we solely used the IoT-23 dataset to train and assess various ML algorithms. We intend to compile our own dataset from an actual EVCS system and evaluate multiple datasets to develop a viable intrusion detection algorithm.
# Deep Learning-Based Imagery Style Evaluation for Cross-Category Industrial Product Forms
## 5 Feature Visualization of the Imagery Style Evaluation Method  
### Visualization Technique  
To visually demonstrate the effectiveness of the ISE method, the regions of interest in the NTTSSD paradigms are illustrated by the feature maps of each layer and the Gradient-weighted Class Activation Mapping (Grad-CAM) technique. The implementation process of the Grad-CAM technique [74] is shown in Figure 15. The original image is firstly forward-propagated through the network to obtain the feature maps A. Then, by backpropagating the prediction value \(y^{c}\) of category \(c\), the gradient information A' of the feature maps A is obtained. Next, the gradients A' are averaged over w and h to determine the importance of each channel within the feature maps A. Finally, the weighted sum is computed and passed through ReLU to obtain Grad-CAM.  
The calculation formulas for Grad-CAM are as follows:  
\[L_{Grad-CAM}^{c}=ReLU\left(\sum_{k}a_{k}^{c}A^{k}\right) \tag{21}\]  
\[a_{k}^{c}=\frac{1}{Z}{\sum_{i}{\sum_{j}{\frac{\partial{y^{c}}}{\partial{A_{ij} ^{k}}}}}} \tag{22}\]  
where \(A\) represents the last convolutional output feature maps, \(k\) represents the Kth channel in feature maps \(A\), \(c\) denotes category c, \(A^{k}\) represents the data of channel \(k\) in feature map \(A\), \(a_{k}^{c}\) represents the weight associated with \(A^{k}\), \(y^{c}\) represents the score predicted by the network for the category, \(A_{ij}^{k}\) represents the data of feature map \(A\) in channel k at the position of coordinate (i,j), and \(Z\) equals the product of the width and height (w \(\times\) h) of the feature map.
## 2 Background and Motivation
### DRAM PUF Technologies  
In this section, we explain the existing DRAM-based PUFs and their strategies to enhance robustness and reliability.  
A variety of works have discussed the concept of using DRAM as the major hardware of most modern systems to organize an intrinsic PUF. DRAM cells do not have zero values at the start-up time and the capacitor in each cell is initialized to a random value caused by manufacturing variations, which is utilized to provide device signatures and configure a DRAM PUF [22]. The requirement of power cycles and a period of time before response generation to extract unbiased signatures are the most important challenges of this technique. Retention-based PUF is another well-studied mechanism, which generates DRAM PUF-based random and unique patterns by preventing the refresh operation for a period of time (waiting time). Retention-based PUFs require a long period of time to extract sufficient failure. This method exploits the pre-selection of blocks and helper data algorithms to extract the robust responses for key generation and authentication purposes. Therefore, retention-based PUFs include significant time, hardware and storage overheads.  
Figure 1: DRAM organization [12]. (**a**) DRAM cell. (**b**) DRAM subarray. (**c**) DRAM device.  
Figure 2: DRAM timing at read operation [13].  
Latency-Based DRAM PUFs  
As mentioned in the DRAM organization, there are specified timing constraints to schedule the DRAM operations correctly. Altering these parameters can affect the reliability of DRAM information and result in data leakage. Latency-based structures benefit this feature to construct a PUF. The \(\text{t}_{\text{CCD}}\)-based PUF is formed by reducing the minimum time period required to activate rows to be accessed [12]. This structure applies a filtering mechanism to eliminate unstable bits in different iterations to enhance the PUF's robustness and repeatability. A separate DRAM rank is needed to count and store the latency failures of each iteration. The evaluation time of PUF responses is noticeably increased due to the filtering phase. However, this mechanism is not adequate, and ECC approaches are still required to perform a reliable PUF.  
Another technique is proposed in [13] that is based on \(\text{t}_{\text{RP}}\)-reduction and disrupts precharge procedure to obtain erroneous data. The \(\text{t}_{\text{RP}}\)-based technique categorizes the cells on the basis of their dependency on input patterns and measurements. Then, only the independent cells are qualified to be used. Next, the specific selection algorithm is designed to choose the acceptable cells and improve the robustness of PUF. In such a scenario, the CRP space is noticeably contracted, and the effects of environmental variations are not considered.
###### Abstract  
Pulse rate variability (PRV), measured from pulsatile signals such as the photoplethysmogram (PPG), has been largely used in recent years as a surrogate of heart rate variability (HRV), which is measured from electrocardiograms (ECG). However, different studies have shown that PRV does not always replicate HRV as there are multiple factors that could affect their relationship, such as respiration and pulse transit time. In this study, an in-vitro model was developed for the simulation of the upper-circulatory system, and PPG signals were acquired from it when haemodynamic changes were induced. PRV was obtained from these signals and time-domain, frequency-domain and non-linear indices were extracted. Factorial analyses were performed to understand the effects of changing blood pressure and flow on PRV indices in the absence of HRV. Results showed that PRV indices are affected by these haemodynamic changes and that these may explain some of the differences between HRV and PRV. Future studies should aim to replicate these results in healthy volunteers and patients, as well as to include the HRV information in the in-vitro model for a more profound understanding of these differences.  
pulse rate variability; photoplethysmography; haemodynamics; in-vitro cardiovascular models; vascular tissue phantoms Article
## 4 Results and Discussion
### Partial Charge Distribution  
The partial charge (PC) \(\Delta Q\) on each atom is defined as the deviation of the effective charge \(Q_{\alpha}^{*}\) (Equation (1)) from the neutral charge \(Q^{0}\) on the same atom, or \(\Delta Q=Q^{0}-Q_{\alpha}^{*}\). We have calculated the PC for every atom in all five crystals as shown in Figure 4. As expected P, Mg, K, Cu, and H have positive PC whereas O and N atoms have negative PC and their values are very similar. Minor variations of the PC for P, H, or O only reflect their locations in the structural units of H\({}_{2}\)P\({}_{2}\)O\({}_{7}\) or H\({}_{2}\)O. The minor variations can also originate from the different crystal symmetry, triclinic vs orthorhombic. Thus, the PC for each atom in the crystals shown in Figure 4 are not very insightful. On the other hand, grouping the PC for atoms in each of the three structural unit will be quite revealing. They are listed in Table 2. It is noted that there are considerable differences in the PC of the three structural units between the five crystals including the two water molecules (2:H\({}_{2}\)O). This indicates that there could be subtle difference in inter molecular interaction and corresponding reactivity.
## 4 OTA Control Methods
### Physical methods  
In the European Union, dilution with non-contaminated foodstuffs is forbidden. Initially, it was suggested that in order to avoid OTA, the external layers in contact with the producing fungi should be eliminated [257]. However, in the case of grapes this operation is complex and has been rejected. It has also been proposed to eliminate the products colonized by fungi from the manufacturing process. However, fungus absence does not imply the mycotoxin is gone and the confirmation night not be fully controlled [125]. Thermal treatments do not completely eliminate OTA [258]. Afreezing (-20 \({}^{\circ}\)C), defrost (26 \({}^{\circ}\)C) process and UV and gamma treatments are able to diminish the producing fungal conidia; however, only gamma radiation can destroy the OTA [259-261].
## 3 Results
### The Field Tests  
To verify the practical application capabilities of the dual receivers tracking system, we further implemented a series of field tests. As shown in Figure 12, a PIG with the ELF transmitter was pulled forward at a constant speed through the pulling system. The length, wall thickness, and diameter of the oil and gas pipeline are 84 m, 12 mm and 813 mm, respectively. The geometrical and electromagnetic parameters of the transmitter and receivers are the same as those in Section 3.2. The coordinate system is established with the Tracking Receiver 1 as the origin to realize the 4D tracking of the PIG. In this experiment, DOF \(x\) represents the height distance between the transmitter and the receiver, and the base height of the pipe is \(w_{0}=800\) mm, so \(w_{1}=1187.5\) mm. DOF \(y\) represents the horizontal distance, while \(d_{1}=1906.5\) mm. DOF \(z\) describes the tracking of the PIG along the pipe axis. Since the PIG with the ELF transmitter is located inside the pipeline, its actual position in the \(z\)-axis direction cannot be directly given. As shown in Figure 12a, the time of the PIG and dual receivers tracking system is synchronized with the GPS time, and the midpoint of the pipe support base is selected as the reference pipe feature point. The time when PIG passes through the selected reference pipe feature point is used as the true value of the coordinate \(z\). Therefore, the tracking error of DOF \(z\) can be expressed by marking the time error.  
The tracking errors of the experiments are shown in Table 6. In the table, it can be found that the tracking of the DOF \(x\), \(y\), \(z\) and pitch angle are all achieved. The error of \(-1.407^{\circ}\) in orientation and the error within 10 cm in locating are at least 50% smaller than \(6^{\circ}\) and 20 cm in [30] and 40 cm in [27].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DOF** & **Pitch Angle** & \(x\) & \(y\) & \(z\) \\ \hline MFSI method & \(-1.407^{\circ}\) & \(112.91\) (cm) & \(182.26\) (cm) & \(13.57^{.}57.848\) \\ True value & \(0^{\circ}\) & \(118.75\) (cm) & \(190.65\) (cm) & \(13.57^{.}57.853\) \\ Error & \(-1.407^{\circ}\) & \(-5.84\) (cm) & \(-8.39\) (cm) & \(-4.82\) (cm) 1  \\ Error (\%) & - & \(-4.92\) & \(-4.40\) & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: The tracking errors of the dual receivers tracking system and MFSI algorithm in the field tests.  
Figure 12: The dual receivers are applied in field tests: (**a**) the dual receivers tracking system, (**b**) a PIG with an extremely low-frequency (ELF) transmitter in the pipeline.
# A Cluster-Based Optimal Computation Offloading Decision Mechanism Using RL in the IIoT Field
###### Abstract
The research uses a technique of partial offloading rather than the full offloading of tasks. In [14], the aim is to maximize the utility function by calculating revenues and costs using energy consumption and task execution delay. Since it considered the mobility of users, migration was also considered. The aim of [15] is to improve the efficiency of energy consumption and the cost of offloading. The consumption of computing resources of a MECS were used as the offloading cost so that tasks were not concentrated on a specific MECS in a smart home environment. In [16], the focus was on formulating the offloading assignment problem to minimize the total system cost within delay constraints of each application. It heuristically solves the problem by using the dependence between tasks. In summary, except for [12], which deals with the IIoT field, all studies relate to the IoT field and vehicle field. However, due to the characteristics of the IIoT environment, which requires low device mobility, latency constraint, and energy efficiency, the proposed techniques in IoT and vehicle environments are not suitable. We propose an algorithm that determines the optimal offloading strategy of tasks to minimize the task execution delay and energy consumption of devices to satisfy energy efficiency and delay constraint, which are important requirements in the IIoT field. However, it is not easy to manage because of the difficulty in predicting how many tasks will occur and the load situation of MECSs and devices. In addition, devices have limited computational power and battery life. To solve this problem, Reinforcement Learning (RL) is adopted while using the MEC paradigm and D2D communication. RL follows a Markov decision process (MDP) that models a decision-making problem and learns by mapping the state to action to maximize reward value. Among RL algorithms, Q-learning is model-free RL that deals with the MDP problem in which the probability of state transition is unknown. Therefore, in our problem of finding an optimal offloading strategy by reflecting only the current state, using Q-learning to make optimal decisions is suitable without requiring prior knowledge. Since Q-learning requires a relatively low computational complexity compared to other reinforcement learning techniques, it is suitable for application to IIoT devices that do not have high computing capabilities. Nevertheless, since Q-learning stores and maintains the Q-value for the state-action pair in the Q-table, as the Q-table increases, increasing the time complexity to search for the Q-table and the space complexity to maintain it is inevitable [17]. Therefore, we adopt device clustering to manage devices with low complexity, then perform Q-learning to obtain an optimal offloading strategy in the IIoT field.