esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
The Sub-Sieve AutoSizer and Laser Particle Size Analyzer were used to test six commercial powders classified as \(-\)325 mesh (\(-\)45 \(\upmu\)m). Comparison of results acquired from both instruments has led to the following conclusions:  
* For well-dispersed spherical powders, the volume/surface mean diameters and specific surface areas estimated by either technique differ by less than the measurements-related uncertainty limit (<10%).
* For irregularly shaped and/or firmly aggregated powders, the estimation of specific surface area and particle size/size distribution should be analyzed separately by the SAS and LPSA instruments, respectively.
* The LPSA is a more versatile instrument than the SAS. The mathematical algorithms used to convert the scattered light signals obtained from individual particles are capable of calculating a variety of parameters characterizing both individual particles and the sample as a whole.
* The imaging capabilities of scanning electron microscopy (SEM) make it a useful tool for qualitative interpretation of data acquired by both the LPSA and the SAS.
* The SAS quantitative assessments supported by SEM imaging may provide some insight into the internal structure (porosity) of the powder.  
There is no universal technique for measuring particle size and surface area. Therefore, the selection of a specific method has to be based on powder characteristics and an application process that must be controlled.  
Conceptualization, J.K.; methodology, J.K. and D.T.; formal analysis, J.K. and D.T.; investigation, J.K. and D.T.; resources, J.K. and D.T.; writing--original draft preparation, J.K.; writing--review and editing, J.K. and D.T.; visualization, J.K. and D.T.; supervision, J.K. and D.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported by AGH University of Krakow through contract 16.16.110.663.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors gratefully acknowledge [PERSON] of AGH University of Krakow for his able assistance with SEM documentation.  
The authors declare no conflicts of interest.
## 2 Context of the Experiments
### SIGE for the Managers of the EVCI and for the EV Users  
SIGE offers a web user interface for the managers of the EVCI. It allows the operators not only to know the state of all the charging stations, but also to act on them. For example, the operator can stop the current charging session or restart it. The operator can also unlock a plug or set the maximum charging power. The web interface also provides statistics by user, by area, by charging station and by charging point.  
The EV user must authenticate himself using the RFID badge issued by the CEA in order to be able to connect his vehicle to one of the charging points. This badge is first presented to the RFID reader of the Diva. The latter reads the ID number stored in the badge and the Diva station queries SIGE. If the badge ID is in the database and if the user participates in the experiments, SIGE sends a text message inviting him to fill in his forecast departure time (further noted \(\mathrm{t}_{\mathrm{dep}}\)) and the SOC of his car at the start of the charging process (further noted \(\mathrm{SOC}_{\mathrm{init}}\)). Without a response from the user, SIGE considers default values (i.e., \(\mathrm{t}_{\mathrm{dep}}\) = 5:00 p.m. and \(\mathrm{SOC}_{\mathrm{init}}\) = 25%). At the same time, SIGE sends the authorization of charge to the charging station while the user plugs his EV to the Diva terminal connector. The charging station then communicates with the vehicle via the cable, by carrier current, in accordance with the IEC 61851 standard, an international standard for electric vehicle conductive charging systems. Then, SIGE sends a charging test profile to the Diva constituting of two phases. The first phase, called the \"discovering phase\", involves charging the EV battery at its maximum power during a short period. This phase enables SIGE to estimate the maximum charging power of the EV, noted \(\mathrm{P}_{\mathrm{Max}}\), but it also confirms to the user that his vehicle is electrically connected. Afterwards, in a second phase, SIGE controls the charge of the EV according to given objectives (see Section 2.7). When the charge is finished, SIGE sends a text message to the user, inviting him to disconnect his vehicle and to clear the parking place.
# The Color Mix Problem
## 3 Hybrid Genetic Algorithm for the Solution of the Color Mix Problem  
The principle of the used algorithm is similar to that of the hybrid genetic algorithm [15] (for more complete information on the functioning of genetic algorithms, the reader is referred to [16]), where global explorative/diversified search of the genetic operators is in teamwork with the local exploitative/intensified search. The exploitative search, in particular, is performed by the iterated hierarchical tabu search procedure (see Section 3.4.1).  
In our algorithm, the permutation elements \(p(0),\ p(1),\ \ldots,\ p(n-1)\) are simply linked to the genes of individuals' chromosomes. No encoding is required. Meanwhile, the objective function value of the given solution, \(z(p)\), is associated with the fitness of individuals.  
The basic components of our hybrid genetic algorithm are as follows: (1) construction of initial population; (2) parent selection for crossover procedure; (3) crossover procedure; (4) improvement of the offspring by the HITS algorithm; (5) population replacement. The top-level pseudocode of the genetic algorithm is provided in Algorithm 1.  
```
// top-level pseudocode of the hybrid genetic algorithm // input: \(B\), \(n\), \(m\), \(m_{1}\) // output: \(P\) // parameters: \(G\), \(PS\), \(DT\), \(idle\_gen\_limit\) begin \(P\leftarrow\textbf{Initial\_Population\_Construction}(PS)\); \(i\gets 0\); \(i^{\prime}\gets 0\);  do \(p^{\prime},p^{\prime\prime}\leftarrow\textbf{Selection}(P)\); \(p^{o},c_{1},c_{2},c_{3}\leftarrow\textbf{Greedy\_Crossover}(B,n,m,m_{1},p^{ \prime},p^{\prime\prime})\); \(p^{k},z^{k}\gets k\_\textbf{Level\_Hierarchical\_Iterated\_Tabu\_Search}(B,n,m,m_{1},p^{ \circ},z(p^{\circ}),c_{1},c_{2},c_{3})\); \(j\gets 0\); \(include\_solution\leftarrow\text{TRUE}\); do if\(\big{(}\big{(}\textbf{Distance}\Big{(}p(j),p^{k}\Big{)}<DT\vee\ z^{k}=z(P(j))\big{)}\ \wedge\ z^{k}\geq z(P(0))\big{)}\)\(include\_solution\leftarrow\text{FALSE}\) endif; \(j\gets j+1\) while(\(j\
eq PS\)); if(\(include\_solution\)) if(\(z(P(0))>z^{k}\))\(p(0)\gets p^{k}\); elseif(\(z(P(PS-1))>z\)) \(P(\textbf{Upper\_Index}(P\ ))\gets p^{k}\) endif; \(i^{\prime}\gets i\); elseif(\(i-i^{\prime}>idle\_gen\_limit\ \wedge\ i<G-idle\_gen\_limit\)) \(P\leftarrow\textbf{Population\_Restart}(PS)\); \(i^{\prime}\gets i\) endif; \(i\gets i+1\) while(\(i\
eq G\)) endif
```  
**Algorithm 1** Hybrid_Genetic_Algorithm  
Notes. 1. \(G\), \(PS\) denote the total number of generations and the population size, respectively. 2. The distance (**Distance**) is calculated using the Equations (6) and (7). 3. \(DT\) denotes the distance threshold between solutions. 4. \(idle\_gen\_limit\) denotes the limit of idle generations (we used \(idle\_gen\_limit=\max\{3,\ 0.05G\}\)). 5. The mutation process is embedded into the \(k\)-level hierarchical iterated tabu search algorithm. 6. The population restart (**Population_Restart**) is performed by applying the \"clone\" of the initial population construction procedure.
## 4 Electric and Magnetic Field Determination in the Vicinity of the Antennas Analyzed  
The values of the electric and magnetic fields were determined for both the initial and optimized antenna in order to see if the limits imposed by the standards in force are  
Figure 20: SAR distribution on YZ transversal plane for a distance of 4 cm between antenna and tissues: (a) initial antenna; (b) optimized antenna.  
\begin{table}
\begin{tabular}{c c c} \hline
**R (m)** & \(P_{d}\) **for the Initial Antenna (W/m\({}^{2}\))** & \(P_{d}\) **for the Optimized Antenna (W/m\({}^{2}\))** \\ \hline
0.01 & 2937.754647 & 3851.540616 \\
0.05 & 117.5101859 & 154.0616246 \\
0.1 & 29.37754647 & 38.51540616 \\
0.5 & 1.175101859 & 1.540616246 \\
1 & 0.293775465 & 0.385154062 \\ \hline \end{tabular}
\end{table}
Table 8: Radiated power density for the initial and optimized antennas.  
in accordance. According to the ICNIRP standard, we can determine the limits for EMF exposure up to 2 GHz, but we considered the same calculation formulas for 2.4 GHz also, considering that the values for the electric field and magnetic field are calculated from the frequency at which the antenna is functioning.  
Thus, for an exposure over 30 min, Formulas (2) and (3) are applied, while for an exposure between 6 and 30 min, Formulas (4) and (5) are used.  
\[E=1.375f_{M}{}^{0.5} \tag{2}\]  
\[H=0.0037f_{M}{}^{0.5} \tag{3}\]  
\[E=4.72\,f_{M}{}^{0.43} \tag{4}\]  
\[H=0.0123\,f_{M}{}^{0.43} \tag{5}\]  
where \(f_{M}\) is the frequency in MHz for which the antenna is designed.  
The maximal values accepted for the frequency of 2.4 GHz are calculated and are presented in Table 9.  
The electric and magnetic field for the antennas placed directly on the tissue are represented in Figures 21 and 22, considering the legend of the representation between 0 and the maximum values of the electric and magnetic field that are calculated according to the standard ICNIRP for the 2.4 GHz. For both structures, the electric and magnetic field are exceeded in all tissue layers considered.  
When the tissues are placed at a distance of 4 cm from the antennas (Figures 23 and 24), the values of the electric and magnetic field are exceeding the standard limits for a wider area around the antenna than in the previous case. However, if we consider the tissue layers, it can be stated that the values for the electric and magnetic field have lower values in the tissues if the antenna is placed further from them, as expected; thus, a distance larger than 4 cm from the antenna is needed.  
Figure 21: Electric field distribution on YZ transversal plane: (**a**) initial antenna; (**b**) optimized antenna.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Exposure over 30 min**} & \multicolumn{2}{c}{**Exposure between 6 and 30 min**} \\ \hline
**E** & **H** & **E** & **H** \\ \hline
67.36 & 0.181 & 28.411 & 0.349 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The maximal values accepted for the frequency of 2.4 GHz for the electric and magnetic field.
# Research on Multi-Port Ship Traffic Prediction Method Based on Spatiotemporal Graph Neural Networks
###### Abstract  
The intelligent maritime transportation system has emerged as a pivotal component in port management, owing to the rapid advancements in artificial intelligence and big data technology. Its essence lies in the application of digital modeling techniques, which leverage extensive ship data to facilitate efficient operations. In this regard, effective modeling and accurate prediction of the fluctuation patterns of ship traffic in multiple port regions will provide data support for trade analysis, port construction planning, and traffic safety management. In order to better express the potential interdependencies between ports, inspired by graph neural networks, this paper proposes a data-driven approach to construct a multi-port network and designs a spatiotemporal graph neural network model. The model incorporates graph attention networks and a dilated causal convolutional architecture to capture the temporal and spatial dimensions of traffic variation patterns. It also employs a gated-mechanism-based spatiotemporal bi-dimensional feature fusion strategy to handle the potential unequal relationships between the two dimensions of features. Compared to existing methods for port traffic prediction, this model fully considers the network characteristics of the overall port and fills the research gap in multi-port scenarios. In the experiments, real port ship traffic datasets were constructed using data from the Automatic Identification System (AIS) and port geographical information data for model validation. The results demonstrate that the model exhibits outstanding robustness and performs well in predicting traffic in multiple sub-regional port clusters.  
spatiotemporal graph neural network; traffic flow prediction; ship big data; AIS; port traffic prediction +
Footnote †: journal: Marine Science and Engineering  
0000-0001-9993-3227-3327][PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Research on Multi-Port Ship Traffic Prediction Method Based on Spatiotemporal Graph Neural Networks. _J. Mar. Sci. Eng._**2023**, _11_, 1379. [[https://doi.org/10.3390/jmse11071379](https://doi.org/10.3390/jmse11071379)]([https://doi.org/10.3390/jmse11071379](https://doi.org/10.3390/jmse11071379))
# Formation of Conductive Networks in Polysulfone Filled with Graphite-Derived Materials
## 4 Summary and Conclusions  
Bipolar cells are a crucial component of polymer electrolyte membrane fuel cell stacks. A wide range of conductive materials for bipolar plate manufacture metal, graphite, and composite have been produced. Polymer composites filled with carbon particles are thought to be the most promising composite bipolar plate contender. Carbon/polymer composites outperform pure metal and graphite in terms of cost, weight, corrosion resistance, and machinability.  
Our research aims to generate high filled composites with high density and low porosity in order to attain high conductivity characteristics in composites. Several forms of graphite were employed as fillers, including AG, NG, and CB, to study the effect of filler type, size of the particles, and aspect ratio on the conductive properties and mechanical behavior of composites. In comparison to AG and CB, utilizing NG as a filler resulted in a denser and lower porosity composite. To create a better conductive structure, we suggested using CB particles as a second filler with NG and AG. Depending on the properties of the composite filled with CB, we found that using 2 and 4 wt. % of CB with NG as second filler will enhance the composite material conductivity. So, as a result we could achieve the goal we wanted and we enhanced the thermal and electrical conductivity of the composite by producing a more perfect structure, to decrease material porosity and to increase material density. Adding graphite type fillers to the polymer did not improve the mechanical  
\begin{table}
\begin{tabular}{c c c} \hline
**Filler Content wt. \%** & **Electrical Conductivity S/cm** & **Thermal Conductivity W/m K** \\ \hline
0 wt. \% & \(1\times 10^{-11}\) & 0.05 \\
2 wt. \% & 0.0408 \(\pm\) 0.01 & 0.22 \(\pm\) 0.1 \\
4 wt. \% & 0.0549 \(\pm\) 0.01 & 0.24 \(\pm\) 0.1 \\
6 wt. \% & 0.0648 \(\pm\) 0.01 & 0.26 \(\pm\) 0.1 \\
8 wt. \% & 0.09 \(\pm\) 0.02 & 0.28 \(\pm\) 0.1 \\ \hline \end{tabular}
\end{table}
Table 5: Thermal and electrical conductivity of PSU-based composites filled with CB.  
properties of the composite as well as using CB as filler; however, we noticed that the flexural strength of the composite decreased compared to unfilled polysulfone. This can be explained by the difference in properties between the filler and the polymer, as we know that the graphite material was considered as a fragility material, which led to a decrease in the flexural strength of the composite.  
Conceptualization, A.A.S.; investigation, H.M.; writing--original draft preparation, H.M.; writing--review and editing, V.V.T.; supervision, A.A.S.; project administration, V.V.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author in accordance with established practice and university requirements.  
The authors declare no conflicts of interest.
# Deep Learning-Based Classification of High-Resolution Satellite Images for Mangrove Mapping
## 4 Discussion
### Spatiotemporal Evolution of Mangroves in Shuidong Bay--A Case Study  
Here we applied the attention U2-Net model to map the mangroves in Shuidong Bay as an example, which is a coastal area in Maoming City, Guangdong Province, China. We used 2 m resolution satellite images from 2015 to 2018 to analyze the spatial distribution and temporal evolution of mangroves in this area. The mangrove maps obtained from the model are shown in Figure 7, where the mangroves are marked in red and yellow. It can be seen that the mangroves are mainly distributed along the coastline of Shuidong Bay, and some of them are located in the estuaries of rivers. By using high-resolution satellite images, we can identify a large number of small mangrove patches that may be overlooked by other methods. These small patches are important for maintaining the biodiversity and ecosystem functions of mangroves. Figure 8 also shows the changes in the mangrove area over the four years. We can see that the mangrove forest expanded steadily from 2015 to 2018, with an average growth rate of over 10 \(\mathrm{hm^{2}}\) per year. The total area of mangroves increased by 17% in four years, reaching approximately 270 \(\mathrm{hm^{2}}\) in 2018. This indicates that the mangrove protection and management policy implemented by the local government in Maoming is effective, and that the mangrove restoration and conservation efforts have achieved positive results. The attention U2-Net model can provide useful information for monitoring and evaluating the status and trends of mangroves in Shuidong Bay, and it can also be applied to other regions with similar environmental conditions. This proves the important application value of this model for the long-term dynamic monitoring of mangroves. The research results are expected to provide useful feedback  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
information for government administrators, scientists, and other stakeholders related to mangrove dynamics.  
Figure 7: Distribution of mangroves in Shuidong Bay from 2015 to 2018.
## 4 Development and Validation of the Platform
### Platform Performance Results  
A series of tests were conducted to validate the deployment of the hyper-distributed IoT-Edge-Cloud platform to ensure a comprehensive characterization. The platform architecture is consistently similar across all proposed sites; therefore, testing was performed exclusively at Edge Site 1. Nonetheless, the results can be extrapolated and scaled to Edge Site 2 due to these similarities.  
To thoroughly validate the platform's performance, tests were divided into two main sets. The first set focused specifically on evaluating the 5G core network, including its handling of control- and user-plane latencies as well as peak data rates under various load scenarios, as shown in Table 1. The second set assessed the complete platform at Edge Site 1, as illustrated in Table 2, encompassing both the 5G core and the radio access network (RAN),to provide a holistic view of the system's performance, considering real-world conditions across the entire network infrastructure.
## 2 System Model
### AF Protocol  
The AF relaying protocols amplifies the received signal before transmitting it to the users. In this regard, the variable gain is defined as \(G=\sqrt{\frac{P_{R}}{\|\mathbf{h}_{SR}\|_{F}^{2}P_{S}+N_{0}}}\). In the second phase, with the help of Equation (1) the received signal can be expressed as \[y_{D_{i}}^{AF} =\left\|{\bf{h}}_{i}{\bf{w}}_{i}\right\|Gy_{R}+n_{D_{i}} \tag{9}\] \[=\left\|{\bf{h}}_{i}{\bf{w}}_{i}\right\|G\sqrt{P_{S}}{\bf{h}}_{SR}^ {+}{\bf{w}}_{SR}\left(\sqrt{\Xi_{1}}x_{1}+\sqrt{\Xi_{2}}x_{2}\right)\] \[+\left\|{\bf{h}}_{i}{\bf{w}}_{i}\right\|Gn_{R}+n_{D_{i}}.\]  
The instantaneous received SINR for \(D_{1}\) then becomes  
\[\Gamma_{D_{1}\to x_{1}}^{AF} =\frac{G^{2}P_{S}{\left\|{\bf{h}}_{1}\right\|}^{2}{\left\|{\bf{h} }_{SR}\right\|}^{2}{\Xi_{1}}}{G^{2}P_{S}{\left\|{\bf{h}}_{1}\right\|}^{2}{\left\| {\bf{h}}_{SR}\right\|}^{2}{\Xi_{2}}+{\left\|{\bf{h}}_{1}\right\|}^{2}G^{2}N_{ 0}+N_{0}} \tag{10}\] \[\simeq\frac{\phi{\gamma_{SR}}{\left\|{\bf{h}}_{1}\right\|}^{2}{ \Xi_{1}}}{\phi{\gamma_{SR}}{\left\|{\bf{h}}_{1}\right\|}^{2}{\Xi_{2}}+{\left\| {\bf{h}}_{1}\right\|}^{2}\phi+1}.\]  
According to the NOMA principle, the approximate SINR expressions for \(D_{1}\) and \(D_{2}\) are expressed as  
\[\Gamma_{D_{2}\to x_{1}}^{AF}\simeq\frac{\phi{\gamma_{SR}}{\left\|{\bf{h}}_{2} \right\|}^{2}{\Xi_{1}}}{\phi{\gamma_{SR}}{\left\|{\bf{h}}_{2}\right\|}^{2}{ \[PERSON]_{2}}+{\left\|{\bf{h}}_{2}\right\|}^{2}\phi+1}, \tag{11}\]  
and  
\[\Gamma_{D_{2}\to x_{2}}^{AF}\simeq\frac{\phi{\left\|{\bf{h}}_{2}\right\|}^{2} {\tilde{\gamma}_{SR}}{\Xi_{2}}}{{\left\|{\bf{h}}_{2}\right\|}^{2}\phi+1}. \tag{12}\]
# A Deep Learning Approach with Feature Derivation and Selection for Overdue Repayment Forecasting
## 4 Experiments
### Baselines  
In order to comprehensively measure the performance of our proposed AD-MBLSTM model, we use baseline models, including multiple general machine learning models and basic neural networks, that have been utilized frequently in previous work for comparison. We divide the input data into dynamic and static features in AD-MBLSTM; however, this operation does not suit the baseline models, so we simply implement feature derivation and selection on the inputs and afterwards train the processed data using baselines with the same parameters.  
* Logistic regression (LR) is a fundamental and commonly used classification approach based on sigmoid function and the maximum likelihood method. The LR model does not need to assume the prior distribution of input data. Not only can the classification label be determined, but the predicted probability can also be obtained. and so the threshold can be adjusted according to demand and label distribution.
* XGBoost [46] achieved state-of-the-art performance in large numbers of machine learning tasks as soon as it was proposed. The overall idea of XGBoost is to constantly add new decision trees to improve the performance of a system. Newly supplied trees can make up for the shortcomings of the previous weak classifiers to compensate for prediction residuals.
* The factorization machine (FM) [47] interactively combines input features in pairs, which allows training to be performed on each pair of potential features. There is a similarity between FM and SVM in the formula. However, in contrast to SVM, all interactions between features are considered via factorized parameters in FM, and so FM works well even in problems with huge sparsity.
* DNN extracts derived and selected features directly and connects them to the output layer.  
* In multi-BiRNN, we consider each variable of the input record as a dynamic variable and feed input data into a two-layer bidirectional RNN. We choose this structure due to its similarity to the LSTM layer in AD-BLSTM, thus facilitating comparison.
* Multi-BiLSTM has the same structure as multi-BiRNN except that the RNN is replaced with LSTM.