esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Analysis
### Sensitivity Analysis of Model Parameters  
In the training of neural networks, the training batch size has an impact on the performance of the model. The decrease in each gradient is determined by the overall sample data for each training batch. In the experiments, the accuracy of the model training results was further studied by varying the training batch size from 10 to 500, as shown in Figure 10, from which it can be concluded that the accuracy of the model training results did not decrease significantly when the training batch size was increased from 10 to 500. However, the accuracy was the highest when the training batch size was 10, and there was a significant convergence after 300 epoch.  
Figure 10: _Cont._  
Figure 9: Changing curve of loss and validation accuracy in GCN model training.  
With a learning rate of 0.005 and a training batch size of 10, keeping other conditions constant, this experiment investigated the effect of the number of layers of the model and the dimensionality of the shape-embedding vector on the performance of the model. From Figure 11, it can be concluded that the accuracy of the model classification could be improved by increasing the depth of the model and the dimension of the shape-embedding vector under certain conditions, and it can be seen from the figure that the highest accuracy can be obtained by choosing a model depth of 4 and an embedding vector dimension of 64 as parameters.  
After the experimental study to derive the hyperparameters when the model is optimal, the effect of model depth on model performance is further investigated. The optimal hyperparameters of the model were identified when the other hyperparameters of the model were certain, such as a training batch of 10, an embedding vector dimension of 64, a learning rate of 0.005, and a maximum of 350 iterations.
## 4 Current Approaches to Measure Resilience in Smart ICI
### Indicator-Based Approach
#### 4.1.2 The REWI Method  
The Resilience-Based Early Warning Indicator (REWI) method [38] encompasses three primary components: contributing success factors (CSF), general issues, and indicators. The central element, contributing success factors (CSF), consists of attributes of resilience used to derive the overall rating of a specific infrastructure. CSFs comprise various components or factors, such as the _Risk Understanding_ factor, which seeks to clarify questions such as how information and expertise about risk/hazards are obtained. _Anticipation_ is another CSF component that addresses expectations prior to a disruptive event. _Attention_ is a key factor determining which aspect of the infrastructure requires the most focus. _Response_ is a crucial factor concentrating on the actions to be taken during the event. _Robustness (of response)_ indicates the approach to ensure the accomplishment of response factors with minimal damage. _Resourcefulness/rapidity_ denotes indicators that ensure timely reactions to the event. _Decision support_ is another critical factor suggesting the need for an indicator that elucidates the trade-off between safety and production. _Redundancy_ is the final primary factor related to the CSFs indicators, which addresses strategies used to compensate for the unavailability of critical infrastructures.  
The second component of the \(REWI\) method involves common issues for each contributing success factor (CSF), ensuring that the objective of each CSF is met. For instance, the first CSF (Risk Understanding) has several issues to address, such as system knowledge and reporting of incidents, near-misses, and accidents. The third and final component of the \(REWI\) approach is the indicators, which are assigned to each general issue component and illustrate the process of assessing general issues.
## 3 Model Development
### Groundwater Hydrochemical Model
#### 3.2.2 Numerical Model and Boundary Conditions  
The model was three-dimensional and was developed using the finite difference code PHAST [28]. PHAST aims to simulate groundwater flow, solute transport, and the hydrogeochemical reactions. PHAST couples HST3D [29], which computes flow and solute transport processes, with PHARECQ [30], which simulates the hydrogeochemical reactions.  
Taking advantage of the symmetry of the problem, only one quarter of the system was modeled, allowing a significant reduction in computation time. As for the groundwater flow model, the 2-km long domain was discretized with irregular rectangular cells. The size of the cells was 10 m \(\times\) 10 m in the quarry and increased progressively by a factor of 1.05 towards the boundaries. The piezometric head was prescribed (Dirichlet BCs) at the outer boundaries of the model. The size of the modelled zone was chosen so that these limits were sufficiently far from the quarry to not influence the effects of pumping-discharge operations. This choice was based on the results of the groundwater flow model developed (Section 3.1). Note that the prescribed head at the outer boundaries was uniform, and thus, the regional piezometric gradient was not represented. The pumping and discharge flows were simulated by implementing a Neumann BC in the center of the quarry. The hydrochemical characteristics of the water released into the quarry during the discharge phases depended on the results of the routine relating to the upper reservoir and were readapted to each new pump-discharge cycle.
## 3 Proposed Joint Decoding (JD) Framework for Video
### Fusion Approach  
In the fusion process, the registered keyframes \(\text{F}^{\prime\prime}_{\text{K}}\) are down-sampled up to three levels using symlet 4-tap filter into their respective approximation (A) and detail (D) coefficient maps. Then, the point-to-point operations are performed to fuse the A and D coefficients in the two decomposition maps. The coefficients A and D are set as shown in Equations (7) and (8), respectively:  
\[\text{A}_{\text{Mean}}\text{(a,b)}=\text{(A}_{\text{K}}\text{ (a,b)}+\text{A}_{\text{NK}}\text{ (a,b))}/2 \tag{7}\]  
\[\text{D}_{\text{Mean}}\text{(a,b)}=\text{(D}_{\text{K}}\text{ (a,b)}+\text{D}_{\text{NK}}\text{ (a,b))}/2 \tag{8}\]  
The average magnitudes for each A and D coefficient having a similar coordinate of the two decomposition maps is calculated. The average value obtained then serves as the output for the fused map (FM). Once the fused map is generated, an inverse transformation is applied to generate the \(\text{F}_{\text{P}}\)'s predicted frames. The proposed frame estimation method predicts the object motions and produces predicted frames \(\text{F}_{\text{P}}\):  
\[\text{F}_{\text{P}}=\text{Transform}^{-1}\text{(FM)} \tag{9}\]  
The defined set of parameters for registration and fusion processes remained best when related to other sets of parameters for various video sequences that have been used.
## 3 Sensorless FOC with Luenberger-Type Back-EMF Observer
### Luenberger-Type Back-EMF Observer
This proves that such an observer is dependent on the machine parameters, where the uncertainty in \(L_{q}\) affects the steady-state accuracy of the observer. The block diagram of the back-EMF observer in the estimated rotational reference frame \(\gamma\delta\) is presented in Figure 11.  
It is evident in Figure 11 that the block diagram contains two PI controllers. The parameters of these controllers have to be properly calculated to allow correct control performance of the whole structure. Neglecting cross-coupling terms, the \(\gamma\)-axis and \(\delta\)-axis loops are identical to the current loop structure discussed in Section 3.1.1. This means that the same principle can be used to calculate the proportional and integral gains of the back-EMF controller with respect to \(d\)-axis inductance:  
\[ \begin{gathered} K_{P_{b}}=2\xi_{b}^{2}\omega_{0_{b}}L_{d}-R_{s}\\ \\ K_{l_{b}}=\omega_{0_{b}}^{2}L_{d}\end{gathered}  \tag{31}\]  
where \(\omega_{0_{b}}\) is the natural frequency of the back-EMF observer, and \(\xi_{b}\) is the damping factor of the back-EMF observer. The natural frequency of the back-EMF observer is usually set close to the current loop bandwidth, while the damping factor is in intervals of approximately 0.5-1.5. The lower damping factors lead to higher overshoot but faster response and vice versa.
# Fractional Order PI-Based Control Applied to the Traction System of an Electric Vehicle (EV)
## 2 Electric Car Model
### Transmission  
Equations (3) and (4) can be used to calculated angular velocity, torque and power of the transmission system (Figure 2).  
\[\tau_{t}(t) =F_{t}(t)r_{\omega},\quad\tau_{\omega}(t)=\frac{\tau_{t}(t)}{2} \tag{3}\] \[\omega_{\omega}(t) =\frac{v_{v}(t)}{r_{\omega}},\qquad p_{t}(t)=F_{t}(t)v_{v}(t),\]  
where \(\tau_{t}\) and \(\tau_{\omega}\) are the total traction torque and torque on each wheel, respectively, whose radius is \(r_{\omega}\). \(\omega_{\omega}\) is the angular velocity and \(p_{t}\) the tensile power. In addition, it is assumed that the transmission  
Figure 1: Diagram of forces acting upon the vehicle.  
efficiency is given by \(\eta_{TS}=0.95\) and there is a velocity reduction factor \(\gamma\). Consequently, the torque, angular velocity and power of the electric motor are given by:  
\[\begin{split}\tau_{m}(t)&=\frac{\tau_{t}}{\eta_{TS} \gamma}\quad\text{ if }p_{t}\geq 0\quad\text{and }\tau_{m}(t)\quad=\eta_{TS}\frac{\tau_{t}}{\gamma}\text{ if }\quad p_{t}<0\\ \omega_{m}&=\gamma\omega_{\omega}\\ p_{m}(t)&=\tau_{m}\omega_{m}.\end{split} \tag{4}\]  
**Remark 1**.: _With the information given above, it is clear that the moment of total inertia seen in the frame of reference of the engine is given by \(J_{t}=J_{M}+\frac{J_{t}}{\gamma^{2}}\), where \(J_{v}=Mr_{\omega}^{2}+k_{v}\) is the moment of inertia of the vehicle, with \(J_{\omega}\) the moment of inertia of the wheels._  
**Remark 2**.: _The nominal torque of the engine is 7.4 Nm, which is not enough to start the vehicle, so \(\gamma=6\) was chosen, enough for the \(EV\) to obtain a maximum speed of 30 km/h on flat terrain and a maximum speed of 15 km/h with \(5^{\circ}\) of slope._
## 5 Implementation and Experiment Results
### Phase 1: Continuous Authentication Phase  
The main idea of a continuous authentication system is to collect real-time user behavior information using mouse dynamics and use this information to continuously authenticate and reverify the user's identity. In particular, the behavior of the current user will be compared with the data stored in the system's database of the behavior of the genuine user. On the basis of the result of that comparison, the system either trusts the user and allows the user to continue working on the device, or the system logs the user out of the system and requires static authentication of the user in order for the user to continue working [37]. The dataset was separated into two parts: the first part (80% of the data) was used as a description of a genuine user in order to train the models; the second part (20% of the data) was used as actions of a user to be authenticated in order to test the model's performance. In this phase, experiments were conducted using each of the classifiers (  
Figure 11: Continuous authentication and anomaly detection models.  
nearest neighbor (KNN), decision tree (DT), random forest (RF), and convolutional neural network (CNN)) for all users with a single mouse movement action, a single point-and-click action, and a set of mouse movement and point-and-click actions. Comparing the results obtained from all the experiments, we found that Scenario A (a single mouse movement action) achieved the highest accuracies compared to Scenarios B and C, with Scenario A KNN ACC: 98.0%, DT ACC: 94.6%, RF ACC: 97.9%, and CNN ACC: 98.8%. It was also found that the CNN model obtained the highest accuracy, recall, precision, and F1-score, with CNN ACC: 98.8%, recall: 96.3%, precision: 97.9%, and F1-score: 95.5%. Tables 3-5 illustrate all the results that were achieved.
## 2 Materials and Methods
### Theoretical Methane Yield  
Theoretical methane yield was calculated based on the total COD of RF and CF samples. Theoretical calculation was performed as follows [21].  
a. Determine COD equivalent of methane:  
One mole of methane requires two moles of oxygen, meaning the chemical oxygen demand of methane is:  
\[\text{CH}_{4}+\text{ 2O}_{2}\rightarrow\text{ CO}_{2}+\text{ 2H}_{2}\text{O} \tag{7}\]  
\[\text{COD/mole CH}_{4}=2\times 32\text{ g O}_{2}\text{/mole}=64\text{ g O}_{2}\text{/mole}\]  
b. Determine the theoretical volume of methane based on g COD  
The volume of a mole of methane gas at standard conditions of 0 \({}^{\circ}\)C and one atm (atmospheric pressure) is 22.4 L. The theoretical volume of methane that can be obtained from a gram of COD is calculated as:  
\[\text{22.4 L CH}_{4}\text{/64 g COD}=0.35\text{ L CH}_{4}\text{/g COD}\]  
To calculate the theoretical methane yield at 35 \({}^{\circ}\)C, we used the ideal gas law:  
\[\frac{\text{P}_{1}\text{V}_{1}}{\text{T}_{1}}=\frac{\text{P}_{2}\text{V}_{2} }{\text{T}_{2}}=\text{Constant} \tag{8}\]  
\[\text{Yield at 35 }^{\circ}\text{C}\ =\ \frac{(1\text{ atm})(0.35\text{ LCH}_{4}\text{/g COD})(308.15\text{ K})}{(1\text{ atm})(298.15\text{ K})}=0.36\text{ LCH}_{4}\text{/g COD}\]  
c. Calculate theoretical methane yield of sample  
Theoretical methane yields of all samples were calculated from total COD, sample volume and the value for yield at 35 \({}^{\circ}\)C.  
\[\text{CH}_{4}\text{ yield of sample }(\text{L})=\ (\text{COD}_{\text{total}})( \text{V}_{\text{sample}})(0.36\text{ LCH}_{4}\text{/g COD}) \tag{9}\]d. Compare theoretical and experimental methane yield  
Experimental methane yield was corrected by subtracting the average volume of methane produced by blank parallels (V\({}_{\text{B1}}\), V\({}_{\text{B2}}\), V\({}_{\text{B3}}\)) from that of samples (V\({}_{\text{P1}}\), V\({}_{\text{P2}}\), V\({}_{\text{P3}}\)).  
\[\text{Experimental CH}_{4}\text{ yield }(\text{L})=\frac{\sum\left(\text{V}_{\text{P1}}+ \text{V}_{\text{P2}}+\text{V}_{\text{P3}}\right)}{\text{N}_{\text{sample}}} -\frac{\sum\left(\text{V}_{\text{B1}}+\text{V}_{\text{B2}}+\text{V}_{\text{B3 }}\right)}{\text{N}_{\text{blank}}} \tag{10}\]
# Biomechanical Characteristics for Identifying the Cutting Direction of Professional Soccer Players
## 3 Results  
### Distance at Which the Cutting Decision Was Made and Characteristics of Cutting Action  
See Figure 3 for the distance at which the cutting decision was made by the attacker and the height of the hip joints from the ground during the run-up and at landing. The study found that the attacker chose to cut 1.2 m from the defender, whether to the left or right. The forward-backward distance between the cutting foot and the defender was consistently 1.2 \(\pm\) 0.1 m (ES = 0.21, t = \(-\)0.978, \(p\) = 0.349); the horizontal distance was 2.2 \(\pm\) 4.1 cm when cutting to the right and 3.7 \(\pm\) 9.1 cm when cutting to the left (ES = 0.23, t = \(-\)0.495, \(p\) = 0.630). When a cutting direction was selected, the height of the hip joint was different: The height during run-up when cutting towards the right was significantly lower than that when cutting towards the left (0.92 \(\pm\) 0.07 m and 0.95 \(\pm\) 0.07 m, respectively, ES = 0.41, t = 2.471, \(p\) = 0.031). The landing height at the moment of cutting towards the right was also significantly lower than that when cutting towards the left (0.86 \(\pm\) 0.06 m and 0.89 \(\pm\) 0.06 m, respectively, ES = 0.50, t = 2.851, \(p\) = 0.016). However, there was no significant difference in the change of height from the run-up to landing in different cutting directions (ES = 0.04, t = 0.690, \(p\) = 0.504; 6.0 \(\pm\) 5.1 cm when cutting towards the right and 5.8 \(\pm\) 5.1 cm when cutting towards the left). The height of the hip joints normalized when using the ratio to body height. The average height during the run-up was 0.53 \(\pm\) 0.04 when cutting towards the right, and 0.55 \(\pm\) 0.03 when cutting towards the left (ES = 0.57, t = 2.471, \(p\) = 0.031). After normalization, the height of the hip joint at the moment of landing whencutting towards the right was about half of the body height, and the normalized value was 0.50 \(\pm\) 0.03, which was lower than the value of 0.51 \(\pm\) 0.03 when cutting towards the left (ES = 0.33, t = 2.471, \(p\) = 0.031).  
**Decision distance of cutting and hip height from the ground**  
There was no significant difference in the COM speed during run-up, when landing, and at the completion of cutting between different directions. The speed during run-up was 3.2 \(\pm\) 0.4 m/s when cutting to the right and 3.1 \(\pm\) 0.4 m/s when cutting to the left (ES = 0.03, t = \(-\)0.443, \(p\) = 0.666). The speed when landing was 3.1 \(\pm\) 0.3 m/s when cutting to the right and 3.0 \(\pm\) 0.3 m/s when cutting to the left (ES = 0.03, t = \(-\)0.796, \(p\) = 0.443). The average speed from the moment of landing to completion of the cutting action was 2.6 \(\pm\) 0.5 m/s when cutting to the right and 2.6 \(\pm\) 0.3 m/s when cutting to the left (ES= 0.00, t = \(-\)0.266, \(p\) = 0.795).
# Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues
## 4 FL-Aided Fuel Consumption Prediction
### Training and Deployment Considerations  
Evidently, previous analyses highlighted the performance-complexity trade-off, with the CLI presenting the highest accuracy and the highest complexity, whereas FL showed slightly lower accuracy with significantly lower complexity. One may say that for a completely accuracy-centric scheme, the CLI should be selected, whereas for a complexity-centric scheme, FL would be the best choice. In this selection equation, the training and deployment aspects of the schemes should be also taken into account. First, to make the CLI training feasible, the central server directly receives the data from all ships via an uplink communication protocol. Upon training finalization, the central model is transferred to all the other ships for future inference. As for FL training, ship-specific model updates are performed locally and only the model parameters are sent to the server for FedAvg, preserving the data privacy. All ships have finally the globally averaged model for potential inference purposes. Further, local model training is also the case for the LLMS and LLCI schemes, with the central server being used only for model, and not data, forwarding. Consequently, FL superiority is more pronounced when jointly considering its accuracy, complexity, feasibility, and privacy-preservation attributes.
## 3 Results
### Validation
#### 3.3.4 Measurement Setup for Measuring the Signal Propagation Delay  
To ensure that the real-time capability of the SuT is guaranteed and not compromised by the standardized I/O, the signal propagation time of each message is measured. The rate at which the ARINC 429 operates is 12,500 bits per second. With a message length of 32 bits, this results in a minimum signal propagation time of 32 (bits)/12,500 (bits/sec) = 2.56 ms. This is the minimum time required by an ARINC 429 message for transmission in the medium, according to the standard [43] (p. 45-14). The test bench, including the SFCC as the SuT, can handle delay times of 3.5 ms.  
Figure 13 shows the setup for the time measurement before and after the messages have passed the standardized I/O module. An ARINC 429 signal analyzer was used for the time measurement. The ARINC 429 messages were respectively recorded before and after  
Figure 12: Architecture of the flap load relief test scenario.  
passing the ARINC 429 I/O (e.g., \(t_{\text{ADIRU1}}-t_{\text{ADIRU1}^{\prime}}=t_{\text{I}}\)). The ARINC 429 analyzer records the messages and marks them with a timestamp. The time difference of a message from ingestion over, say, ADIRU1 and ADIRU1', determines the dwelling time of the message in the test bench. The delays of the proposed standardized I/O can be shown this way.
# Instantaneous Beam Prediction Scheme against Link Blockage in mmWave Communications
## 5 Simulation Results and Performance Evaluation
### Energy Consumption
The energy efficiency here is attributed to the reduced time using the RF chains. For example, the deep learning scheme requires 4 millijoules (mJ) to perform link recovery when using 64 beams. Meanwhile, the iterative, metaheuristics, beam aggregation and adjacent beam recovery methods require 60, 48, 9 and 8 mJ, respectively. Overall, the proposed scheme achieves 50% better energy efficiency and 25% reduced access times compared to the closest scheme, i.e., adjacent link recovery method [6]. Moreover, it outperforms the iterative scheme by 93%, the metaheuristic schemes in [3,4] by 91% and the beam aggregation method in [5] by 55%. Overall, the proposed LSTM-based beam prediction scheme presents key findings in mmWave communications. First, the near-instantaneous recovery times promote service continuity, enhanced QoS and saves in network resources since it does not occupy frequency or spatial resources to recover the defected beam. Moreover, the low power and energy consumption levels allow for realistic transceiver architecture at the MS with reduced heat exposure. Furthermore, the proposed scheme is implemented on a highly directional antennas at the MS and BS, without reliance on omni-directional modes, this magnifies the signal strength and channel capacity during the beam access and restoration phases.
# A Bayesian Network-Based Information Fusion Combined with DNNs for Robust Video Fire Detection
## 4 Experimental Results and Discussions  
Our method cannot use end-to-end training, because there are non-differentiable operations such as majority voting in the composite network. The Faster R-CNN, place classifier, and LSTM stages should be separately trained in the method. We used the libraries of 'Python 3.5', 'OpenCV 3.0', and 'TensorFlow 1.5' for designing deep learning models, and 'pgmpy' for constructing Bayesian net.