esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Modelling Methodology, Simulation Mechanism, and Control Strategy
### Modelling Methodology
#### 3.1.1 Photovoltaic System Model  
As shown in Figure 4, the PV system model in study mainly includes the circuit of the PV array and maximum power point tracking (MPPT) charger that consists of MPPT control and a boost DC/DC converter. For the circuit of the PV array, the single-diode equivalent current source model, which describes the v-i characteristic of Equations (1) to (6) of the solar cell, is used. In Equations  
Figure 3: Detail circuit assembly of the C-\(u\)grids in study.  
Figure 2: Illustration of C-\(u\)grid layout in the study. BES: battery energy storage.  
(1) to (6), i is the output current of the PV, i\({}_{\text{PH}}\) is the current generated by the incident light, i\({}_{0}\) is the reverse saturation or the leakage current of diodes, q is the electron charge, v is the output voltage of the PV, k is Boltzmann constant, T is the temperature in Kelvin, and A is the ideal factor of the PV (generally A = 1-1.5). The series resistance R\({}_{\text{S}}\) and the parallel resistance R\({}_{\text{P}}\) represent the effect on PV conversion efficiency, i\({}_{\text{SCr}}\) is the short-circuit current under the standard testing condition (STC; usually as 1000 W/m\({}^{2}\), 25 \({}^{\circ}\)C with AM 1.5 G solar spectral), \(\lambda\) is current solar insolation, i\({}_{\text{rs}}\) is reverse saturation current, T\({}_{\text{r}}\) and T are the temperatures under STC and current, respectively, N\({}_{\text{s}}\) and N\({}_{\text{p}}\) are series and parallel numbers of solar cells, and K\({}_{\text{i}}\) and K\({}_{\text{v}}\) are temperature coefficients of short-circuit current and open-circuit voltage, respectively. The required parameters in the model can be obtained from the PV module product datasheets, and the calculation procedures for R\({}_{\text{S}}\) and R\({}_{\text{P}}\) are concluded in [16] and [17]. A boost type DC/DC converter circuit is used to raise the voltage from PV system. In addition, the output performance of the PV system may often be affected by the ambient temperature and sunlight insolation. To ensure the PV output power always stays at a maximum power point, the perturbation and observation (P&O) method is implemented in the PV system [18].  
\[\text{i}=\text{i}_{\text{PH}}-\text{i}_{0}\left[\exp(\frac{\text{q}( \text{v}+\text{i}\text{R}_{\text{s}})}{\text{KTA}})-1\right]-\frac{\text{v}+ \text{i}\text{R}_{\text{s}}}{\text{R}_{\text{P}}} \tag{1}\] \[\text{i}_{\text{PH}}=\left[\text{i}_{\text{SCr}}+\text{K}_{\text {i}}(\text{T}-298)\right]*\lambda/1000\] (2) \[\text{i}_{0}=\text{i}_{\text{rs}}\left[\frac{\text{T}}{\text{T}_ {\text{r}}}\right]^{3}\exp\left[\frac{\text{q}*\text{E}_{\text{g}}}{\text{Bk }}(\frac{1}{\text{T}_{\text{r}}}-\frac{1}{\text{T}})\right]\] (3) \[\text{i}_{\text{rs}}=\text{i}_{\text{SCr}}/[\exp(\text{q}_{\text {VOC}}/\text{N}_{\text{S}}\text{kAT})-1]\] (4) \[\text{i}_{\text{PH,m}}=\text{i}_{\text{PH}}\text{N}_{\text{P}}\] (5) \[\text{i}_{0,m}=\text{i}_{0}\text{N}_{\text{P}} \tag{6}\]
# AIS Data Vulnerability Indicated by a Spoofing Case-Study
## 4 Case Study on Spoofing Large Amounts of AIS Fake Targets Nearby Elba Island and Its Impact on VDL
We can assume that the spoofer transmitted all consecutive MMSI numbers, namely 3500 in the first broadcast and the same number in the second broadcast. The third broadcast was aborted immediately after it started for some inexplicable reason. A total of 5241 AIS messages or 3741 different MMSIs (ships) were received. During the second transmission, a large number of AIS messages number 5 were also received, also containing the identification, dimensions and type of the ship. All ships were identified as passenger ships (AIS type 60) with a length of 90 m and a width of 24 m, without draft and destination information. The names and call signs of the vessels were generic, namely VESS0 and CAL, adding the last three digits of MMSI (starting from 000 up to 999). The following figures (Figures 6 and 7) show the spatial distribution of received AIS messages (for the 1 st and 2 nd burst), depicting real targets in the area, a cloud of artificial targets, and marked spoofed targets of particular interest. Right at the beginning of the first spoofing, two particularly interesting AIS targets also appear at the edge of the generated cloud of artificial targets. The first with the invalid MMSI number 99999999, which is quite common around AIS and often associated with navy vessels. The second vessel is a Belize-registered bulk carrier (MMSI 312320000) that has been scrapped since 2016. Individual fishing vessels are known to take on the identity of scrapped vessels. Figure 5: AIS bursting—MMSI selection and temporal occurrence. Figure 6: Spatial and temporal distribution of the received AIS signals during the 1 st burst. Also, right at the beginning of the second spoofing, particularly interesting ships appear at the edge of the generated cloud. This time, one of the generic artificial ships emerges from the cloud and appears very close to the aforementioned ships from the first burst. In addition, one of the ships appears as a coastal station with the invalid MMSI number 4803509, which was generated by truncating the record from the generic MMSI data of the artificial ship's MID 24480******* by the first two digits. Thus one of the AIS targets, probably 244803509, is converted to an AIS BS signal. Among other bursts, the US-registered vessel MMSI 367309390 also appears on the south side. This vessel is also fake. Among the many artificial AIS targets, the vessel MMSI 244805000 stands out; the number of a fishing vessel that has been spoofed several times in the past, this vessel is placed directly in front of the real vessel Crudemed. This vessel was also transmitting static data, like many others among the second burst. The speed of the ship is 40 knots. Figure 7 shows the position of this ship and its data with blue color text.
# HFDF-EffNetV2: A Lightweight, Noise-Robust Model for Fault Diagnosis in Rolling Bearings
## 2 Materials and Methods
### Mixed Local Channel Attention Mechanism  
Although attention mechanisms like squeeze-and-excitation (SE) and the convolutional block attention module have shown good performance in many tasks, the mixed local channel attention (MLCA) mechanism excels at capturing high-level feature representations. It offers better adaptability and enables more detailed channelwise feature learning across multiple levels, particularly when handling complex features [31]. It applies two steps of average pooling (AP) to transform the input feature vectors into a shape of \((1\times{\rm C}\times{\rm ks}\times{\rm ks})\), with the first branch capturing global information and the second branch capturing local spatial information. After one-dimensional convolution, the two one-dimensional vectors are restored to their original resolution through unaverage pooling (UNAP) and the information is then fused to achieve hybrid attention. As shown in Figure 2, Conv1d denotes one-dimensional convolution, and the kernel size k of the one-dimensional convolution in MLCA is proportional to the channel C, indicating that only the relationship between each channel and its k neighboring channels are considered when capturing local cross-channel interaction information. The value of k is calculated as in Equation (3).  
Figure 2: MLCA mechanism structure.  
\[\text{k}=\Phi(\text{C})\ =\left|\frac{\log_{2}(\text{C})}{\gamma}+\frac{\text{d}}{ \gamma}\right|_{\text{ odd}} \tag{3}\]  
where C is the number of channels; k is convolutional kernel size; \(\gamma\) and d are hyperparameters, with the default value set to 2; the function odd ensures that k is odd by adding 1 if k is even.
# A New Framework of Human Interaction Recognition Based on Multiple Stage Probability Fusion
## 4 Piecewise Fusion Recognition Algorithm  
### Recognition Based Hidden Markov Model  
Hidden Markov model (HMM) can be used to model the motion of small changes in time and space scales, so it is the most widely used method for human action recognition. In order to avoid the problem that the HMM's recognition results could be considered poor because of an incomplete training sample, the proposed method will preserve the probability similarity statistics of the test image sequences at various stages. The final recognition result is obtained by weighted fusion of the similar probability of each stage. The calculation process of the probability similarity statistical graph is shown in Figure 6.  
The aim of the HMM recognition algorithm for action recognition is to determine the topological structure of the graph model and the calculation of the probability similarity and the training problem of setting the optimal parameters of the model [23]. On the basis of the construction of the HMM topology, the full connected topology of the continuous HMM is utilized to model the action. The probability of generating an observation symbol from each state can be computed by the Gaussian probability-density function as Equation (4):  
\[b_{i}(o_{t})=b_{(u_{i},\sum_{i})}(o_{t})=\frac{1}{\sqrt{2p^{d}}\sqrt{|\sum_{i} |}}e^{-\frac{1}{2}(o_{t}-u_{i})^{T}\sum_{i}^{-1}(o_{t}-u_{i})} \tag{4}\]  
where \(u_{i}\), \(\sum_{i}\) is respectively the mean and covariance matrix of observations in cluster \(i\); \(d\) is the dimension of observation symbol \(o_{t}\); \((o_{t}-u_{i})^{T}\) is the transpose of matrix \((o_{t}-u_{i})\); \(\sum_{i}^{-1}\) is the inverse of matrix \(\sum_{i}\).  
Figure 6: The calculation process of the probability similarity statistics graph with the Hidden Markov model (HMM) algorithm.  
In our method, one interaction sequence is segmented into three stages in time domain. At the start stage and the end stage, the two persons involved in the interaction are segmented into two separate objects, so two HMMs are trained to model the objects' motions in those stages. In the middle stage, the two persons involved in the interaction are segmented as an integrated object, so only one HMM is trained to model the object's motion in this stage. In general, one class of interaction can be represented by five HMMs.  
The essence of the HMMs training problem with the given structure is to maximize the observation probability by adjusting the model parameter for the observation sequence. The Baum-Welch algorithm could easily allow us to optimally adapt model parameters to observe the training data [24; 25]. However, this is dependent on the choice of initial parameters. If the improper initial parameters are chosen, it can lead the procedure to the Local minimum so that the best action model cannot be obtained. Thus, the result of the \(k\)-means algorithm is taken as the initial input of the Baum-Welch algorithm.
# SDR-Enabled Multichannel Real-Time Measurement System for In Situ EMF Exposure Evaluation
###### Abstract  
The spatial and temporal variability of the signals emitted by modern communication devices produced a paradigm shift in approaching the human exposure to electromagnetic fields (EMF). This inherent variability requires in situ, agile EMF measurement solutions capable of performing real-time isotropic measurements. The aim of this paper is to describe a new real-time, highly flexible multichannel EMF measurement system that consists of a sensor connected to state-of-the-art software-defined radio (SDR) equipment. In this paper an electric field sensor is proposed, but we also provide information on the extension of the probe to electric and magnetic fields. In the receiver section, the proposed solution is compared in terms of performances (sensitivity and accuracy), costs, and requirements, with standard solutions based on spectrum analyzers or a digital oscilloscope. Finally, the proposed solution was tested considering the signals emitted in various operating scenarios by a mobile device operating in the LTE-A and IEEE 802.11 ax mobile communication standards. The results confirm the versatility and efficiency of the proposed solution for in situ EMF measurements of signals emitted by the new generation communication devices.  
in situ exposure evaluation; multichannel EMF measurements; software defined radio; spectrum analyzer; digital oscilloscope 2022112670. [[https://doi.org/10.3390/electronics11172670](https://doi.org/10.3390/electronics11172670)]([https://doi.org/10.3390/electronics11172670](https://doi.org/10.3390/electronics11172670))
###### Abstract  
Cellular materials have been used in many applications such as insulation, packaging, and protective gear. Expanded polystyrene has been widely used as energy-absorbing liner in helmets due to its excellent cost-benefit relation. This synthetic material can absorb reasonable amounts of energy via permanent deformation. However, in real-world accidents, helmets may be subjected to multi-impact scenarios. Additionally, oil-derived plastic is presently a major source of societal concern regarding pollution and waste. As a sustainable alternative, cork is a natural cellular material with great crashworthiness properties and it has the remarkable capacity to recover after compression, due to its viscoelastic behavior, which is a desired characteristic in multi-impact applications. Therefore, the main goal is to analyze the applicability of agglomerated cork as padding material in safety helmets. First, a finite element model of a motorcycle helmet available on the market was developed to assess its safety performance and to establish a direct comparison between expanded polystyrene and cork agglomerates as liners. Secondly, a new helmet model with a generic geometry was developed to assess the applicability of agglomerated cork as liner for different types of helmets, based on the head injury risk predictions by the finite element head model, YEt Another Head Model (YEAHM), developed by the authors. Several versions of helmet liners were created by varying its thickness and removing sections of material. In other words, this generic helmet was optimized by carrying out a parametric study, and by comparing its performance under double impacts. The results from these tests indicate that agglomerated cork liners are an excellent alternative to the synthetic ones. Thus, agglomerated cork can be employed in protective gear, improving its overall performance and capacity to withstand multi-impacts.  
agglomerated cork; helmets; finite element head model; brain injuries; head trauma; cellular materials; protective devices; passive safety; traumatic brain injury; sustainable solutions Article
## 3 Human Genetic Diversity
### MeHg Exposure and Human Genetic Diversity
Moreover, individuals carrying at least one T allele for GCLC (rs17883901) also had significantly higher MeHgP. As recent findings suggest, apart from being associated with hair mercury level, the SNPs in glutathione-related genes can influence the impact of methylmercury exposure on early child neurodevelopment. [PERSON] and colleagues [66] analyzed GSH-related gene variability in mothers with a diet rich in fish coming from the population of Seychellois. Genotypes of these mothers were analyzed in association with maternal hair and blood Hg, cord blood Hg, and children's mental and motor development, as expressed by the Mental Developmental Index (MDI) and the Psychomotor Developmental Index (PDI), respectively. The authors genotyped SNPs within three genes: GCLC, whose function have been described above; GCLM, encoding the modifier subunit of the GCL (Figure 1a), and GSTP1, which encodes a glutathione S-transferase (Figure 1b). What they found is that individuals with GCLC rs761142 TT genotype showed higher mean maternal hair Hg than AG and GG. Moreover, individuals carrying the combination of GCLC rs761142-TT and GCLM rs41303970-CC genotypes showed higher hair Hg than G plus T carriers. Finally, increasing Hg in maternal and cord blood was associated with lower PDI among GCLC rs761142 TT carriers, while increasing Hg in hair was associated with lower MDI among GSTP1 rs1695 GG carriers. Another recent study carried out on children from Valentia [67] showed that hair Hg levels were associated with worse neurobehavioral development, and that several SNPs located in the GSTP1 (rs1695) and BDNF (rs1519480, rs7934165, rs7103411) genes modified the association between Hg levels in children's hair samples and two indexes of neurobehavioral function. The brain-derived neurotrophic factor (BDNF), in particular, is a protein that promotes neural survival in adult brains, and is poorly expressed in several diseases, such as Alzheimer's and Parkinson's. Two recent reviews [39; 72] focusing on this topic collectively listed thirty-two genes whose variation is related to Hg body burden and susceptibility to Hg toxicity, and, in particular, twelve of these genes are related to hair Hg level and/or to MeHg exposure outcomes.
# Enhanced YOLO Network for Improving the Efficiency of Traffic Sign Detection
## 4 Experiments and Discussion
### Analysis of Ablation Experiments
#### 4.3.3 Improved Anchor Frame Clustering Algorithm Effectiveness Analysis  
The experimental comparison findings with and without the clustering approach suggested in this paper are displayed in Table 5. The clustering algorithm used before  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **P (\%)** & **R (\%)** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS]:0.95 (\%)** & **FPS (Hz)** \\ \hline YOLOv5s & 90.01 & 83.68 & 87.88 & 71.53 & 100 \\ YOLOv5s+ & 91.43 & 87.6 & 92.65 & 75.25 & 97 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experiments conducted before and after the multi-scale feature fusion module was improved are compared.  
Figure 6: Confusion matrix.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **P (\%)** & **R (\%)** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS]:0.95 (\%)** & **FPS (Hz)** \\ \hline Data amplification & 66.24 & 54.11 & 60.12 & 47.32 & 106 \\ After data amplification & 90.01 & 83.68 & 87.88 & 71.53 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experiments conducted before and after dataset augmentation and expansion are compared.  
improvement is indicated by YOLOv5s+ in Table 5, whereas the clustering algorithm suggested in this research is indicated by YOLOv5s+_K. From Table 5, it can be found that using the improved clustering algorithm improves the overall detection performance. Among them, the accuracy P is improved by about 1.2%, which shows the effectiveness of the improved clustering algorithm. This is primarily because the upgraded K-means clustering method can generate more appropriate initial candidate frames for traffic signs based on the TT100K dataset, which allows the network model to extract road traffic sign information more effectively.
# Analysis of Hydrogen Filling of 175 Liter Tank for Large-Sized Hydrogen Vehicle
## 2 Numerical Implementation  
### Governing Equation  
The governing equations for the filling phenomenon of CGH\({}_{2}\) are the equations for conservation of mass and momentum in unsteady relations. The governing equations are considered with the Reynolds density-averaged variables in Cartesian tensor notation [16]. The conservation equations of mass and momentum for the filling phenomenon of hydrogen gas are described as follows:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial}{\partial x_{i}}(\rho u_{i})=0, \tag{1}\]\[\frac{\partial}{\partial t}(\rho u_{i})+\frac{\partial}{\partial x_{j}}\big{(}\rho u _{i}u_{j}\big{)}=-\frac{\partial p}{\partial x_{i}}+\frac{\partial}{\partial x_{j }}\Bigg{[}u\Bigg{(}\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u_{j}}{ \partial x_{i}}-\frac{2}{3}\delta_{ij}\frac{\partial u_{i}}{\partial x_{l}} \Bigg{)}\Bigg{]}+\frac{\partial\Big{(}-\rho\overline{u_{i}^{\prime}u_{j}^{ \prime}}\Big{)}}{\partial x_{j}}, \tag{2}\]  
\[\frac{\partial}{\partial t}(\rho E)+\frac{\partial}{\partial x_{i}}\big{[}u_{ i}(\rho E+p)\big{]}=\frac{\partial}{\partial x_{j}}\Bigg{[}\Big{(}k_{h}+\frac{C_{p}\mu_{t}}{ Pr_{t}}\Big{)}\frac{\partial T}{\partial x_{j}}+u_{i}\big{(}\tau_{ij}\big{)} \big{]} \tag{3}\]  
where \(\rho\), \(p\), \(t\), \(\delta_{ij}\), \(E\), \(k_{h}\), \(C_{p}\), \(\mu_{t}\), \(Pr_{t}\), \(T\), and \(\big{(}\tau_{ij}\big{)}_{eff}\) are the density, pressure, time, Kronecker delta tensor, total energy, thermal conductivity, specific heat, turbulence viscosity, turbulent Prandtl number, and deviatoric stress tensor respectively. Here total energy and deviatoric stress tensor are expressed as follows:  
\[E=h-\frac{p}{\rho}+\frac{u^{2}}{2}, \tag{4}\]  
\[\big{(}\tau_{ij}\big{)}_{eff}=\mu_{eff}\Bigg{(}\frac{\partial u_{j}}{\partial x _{i}}+\frac{\partial u_{i}}{\partial x_{j}}\Bigg{)}-\frac{2}{3}\mu_{eff} \frac{\partial u_{i}}{\partial x_{i}}\delta_{ij} \tag{5}\]  
where \(\mu_{eff}\) represents the effective viscosity. The energy equation is coupled to the conservation of mass and momentum equation. The superscripts and--denote the turbulent fluctuating component and Reynolds time-averaged component, respectively.
## 5 Experimental Research
### Energy Saving  
During the cycle operation of the electric loader, the kinetic energy, braking energy, recovery energy, and energy efficiency are shown in Table 4. Here, \(E_{\text{fde}}\) and \(E_{\text{wde}}\) are the energy consumed by rolling resistance and wind resistance during braking, respectively. \(T_{\text{r}}\) and \(n_{\text{r}}\) are the regenerative braking torque and speed of the motor, respectively.  
The braking energy recovery effect is calculated according to Table 4. The calculation results under four cycle working conditions are shown in Table 5. In one cycle, the vehicle average energy consumption under cycle conditions is 2773.163 kJ. In the four cycles, it can be seen that the braking recovery rate \(\eta_{\text{k}}\) is all above 42.50%, and the brake energy recovery efficiency \(\eta_{\text{bd}}\) is generally above 60.78%, reaching a maximum of 71.64%. The regenerative braking energy recovery is stable. Compared with the loader without regenerative braking, the maximum energy saving can be 7.58% under the whole vehicle energy consumption in a cycle condition. Namely, when the loader completes every 100 cycles, the regenerative energy can enable the loader to complete an additional 7.58 working cycles. This can improve the economy of the vehicle. Using the electric loader with the proposed control strategy for a long time can increase the driving range and extend the battery lifetime. And the purpose of saving energy and protecting the environment can be achieved  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Explanation** & **Calculation Formula** \\ \hline Vehicle available energy, namely kinetic energy reduction \(E_{\text{k}}\) & The amount of kinetic energy lost by vehicle during braking. & \(E_{\text{k}}=\frac{1}{2}m(v_{1}^{2}-v_{2}^{2})\) \\  & Theoretically, the maximum recovered braking energy \(E_{\text{tb}}\) & \\ Maximum theoretical braking energy \(E_{\text{tb}}\) & vehicle kinetic energy reduction minus the energy consumed by the driving resistance. & \(E_{\text{tb}}=E_{\text{k}}-E_{\text{fde}}-E_{\text{wde}}\) \\  & & \\ Motor braking energy \(E_{\text{mb}}\) & Energy that can be recovered by motor braking. & \(E_{\text{mb}}=\frac{\int T_{\text{r}}n,\text{d}t}{950\text{vol}}\) \\ Vehicle energy consumption under cycle conditions \(E_{\text{ec}}\) & It corresponds to the net discharge energy of the power battery under the cycle condition. & \(E_{\text{ec}}=\frac{\int\text{U}\text{1}\text{1}\text{0}00}\) \\ Brake recovery rate \(\eta_{\text{k}}\) & The percentage of motor braking energy \(E_{\text{mb}}\) in vehicle available energy \(E_{\text{k}}\) during braking. & \(\eta_{\text{k}}=\frac{E_{\text{mb}}}{E_{\text{k}}}\) \\ Braking energy recovery efficiency \(\eta_{\text{bd}}\) & The ratio of the motor braking energy \(E_{\text{mb}}\) to the maximum theoretical braking energy \(E_{\text{tb}}\). & \(\eta_{\text{bd}}=\frac{E_{\text{mb}}}{E_{\text{tb}}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Energy flow in an electric loader.  
Figure 14: Torque distribution test curves under high speed and emergency braking (12 m/s).