esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
#### PSI (Preference Selection Index) Technique  
PSI, one of the MCDM techniques, was first developed by [PERSON] and [PERSON] in 2010 and applied to a material selection problem [58]. PSI solves with a simple, easy, and systematic calculation. Due to this feature, it is a useful method for determining the importance levels of criteria in situations where decision-making is difficult and complex [59]. The PSI technique finds the variance values indicating the importance levels of each option by making variance-based calculations for each component calculates the preference index and makes the final ranking. The steps of the PSI technique are explained below in order [58].  
In this study, the importance levels of aroma compounds were determined using the PSI technique which has 4 steps by using Equations (1)-(7) [59].  
**Step 1.** Creating the decision matrix X: The matrix X in Equation (1), which consists of m rows and n columns, is created.  
\[\chi=\begin{bmatrix}x_{11}&x_{12}&\dots&x_{1n}\\ x_{21}&x_{22}&\dots&x_{2n}\\ \vdots&\vdots&\dots&\vdots\\ x_{m1}&x_{m2}&\dots&x_{mn}\end{bmatrix} \tag{1}\]  
**Step 2.** Normalizing the decision matrix. The normalized matrix is derived from decision matrix (1) using Equation (2) for maximization-oriented criteria and Equation (3) for minimization-oriented criteria.  
Figure 6: Multi-Criteria Decision Making process [1].  
\[R_{ij}=\frac{x_{ij}}{x_{j,max}};\;\forall\;i,\;j\;x_{j,max}=max\big{\{}x_{ij}\big{\}}; \;\forall\;i,j \tag{2}\]  
\[R_{ij}=\frac{x_{j,min}}{x_{ij}};\;\forall\;i,\;j\;x_{j,min}=min\big{\{}x_{ij} \big{\}};\;\forall\;i,j \tag{3}\]  
**Step 3.** Calculating the preference variance values (\(PV_{i}\)). These values are calculated using Equation (4). \(\overline{R_{J}}\) calculated using Equation (5) represents the arithmetic mean of each row of the normalized matrix.  
\[PV_{j}=\sum_{i=1}^{N}\big{[}R_{ij}-\overline{R_{j}}\big{]}^{2} \tag{4}\]  
\[\overline{R_{J}}=\frac{1}{N}\sum_{i=1}^{N}R_{ij} \tag{5}\]  
**Step 4.** Calculation of the general preference value (\(\psi_{j}\)). After the deviation in the preference values is found in Equation (6), the importance levels of the preference values are calculated in Equation (7).  
\[\varphi_{j}=\big{|}1-PV_{j}\big{|} \tag{6}\]  
\[\psi_{j=}\frac{\varnothing_{j}}{\sum_{i=1}^{M}\varnothing_{j}} \tag{7}\]  
The \(\psi_{j}\) values, calculated with the PSI technique, express the importance levels of aroma compounds.
## 3 Results
### Statistical Analysis of the Model
Moreover, the high value of the adjusted R\({}^{2}\) (96.04% and 97.93%) also indicates a significant correlation between the experimental and predicted runs by the software. These results can be explained by a similar study by [PERSON] et al. (2019) where the model was considered accurate when R\({}^{2}\) was close to one. Figures 2 and 3 show a linear curve for the experimental values in terms of the predicted ones. Next, the fitness of the models was also studied through a lack of fit test, where the values for all responses were not significant (\(p\)-value \(>\) 0.05), which showed the suitability of models selected to predict the responses ([PERSON] et al., 2019). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & \
\begin{tabular}{c} **Sum of** \\ **Square** \\ \end{tabular} &
\begin{tabular}{c} **Degrees of** \\ **Freedom (df)** \\ \end{tabular} & **Mean Square** & _F-Value_ & _p-Value_ & \\ \hline Model & 215.10 & 6 & 35.85 & 87.90 & \(<\)0.0001 & significant \\ Linear & 68.33 & 2 & 34.17 & 83.77 & 0.0001 & \\ mixture & 0.66 & 1 & 0.66 & 1.62 & 0.2585 & \\ \(x_{1}x_{3}\) & 57.30 & 1 & 57.30 & 140.51 & \(<\)0.0001 & \\ \(x_{2}x_{3}\) & 4.03 & 1 & 4.03 & 9.89 & 0.0255 & \\ \(x_{1}x_{2}x_{3}\) & 61.50 & 1 & 61.50 & 150.81 & \(<\)0.0001 & \\ Residual & 2.04 & 5 & 0.41 & & & \\ Lack of fit & 1.82 & 3 & 0.61 & 5.66 & 0.1539 & not significant \\ Pure error & 0.21 & 2 & 0.11 & & & \\ Cor total & 217.14 & 11 & & & & \\ R\({}^{2}\) & 0.9906 & & & & & \\ Adj. R\({}^{2}\) & 0.9793 & & & & & \\ CV & 2.37 & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Analysis of variance (ANOVA) for the total phenolic content activity.
## 3 Results and Discussion  
### Raw Forecast Performance  
The raw forecast performances at the 13 stations in the UCS area were similar. The data at station 51359 were used to analyze forecast performance as the correct data. As shown in Figure 4, the raw forecast performance for temperature and relative humidity varied temporally over the year. Generally, for temperature, the R-square values were lower in winter (December-January-February), with a mean of 0.65. In comparison, mean R-square values of temperature forecasts in summer was around 0.92. The larger RMSE values of around 5 \({}^{\circ}\)C indicated a more significant error in winter. The forecasts underestimated temperature during winter days, indicated by negative NME values of around \(-\)5 \({}^{\circ}\)C, and slightly overestimated temperature in summer days, with small positive values around 2 \({}^{\circ}\)C.  
For relative humidity, the smaller R-square values were associated with weaker correlations between forecasts and observations in winter. However, error levels indicated by RMSE values were lower during winter days (around 15%), whereas the RMSE values in summer and autumn days were generally higher than 20%. As for NME, it was positive during winter days (around 10%), indicating an overestimation of relative humidity. During summer and autumn days, WRF underestimated relative humidity at this station, with NMEs being around \(-\)25%.  
Figure 4: Forecast performance over time at station 51359: panels (**a**,**c**,**e**) are, respectively, referring to the R-square, RMSE, and NME values of temperature. The panels (**b**,**d**,**f**) are the metric values of relative humidity. The \(x\)-axis refers to observation time.  
As shown in Figure 4, the values of forecast correlations and error levels changed rapidly during seasonal transitions. This indicated that using the time-moving approach to train the model for model forecast post-correction could have a good performance when error levels change rapidly day by day. Therefore, the aforementioned first model, as described in the last section, was expected to have a better performance in post-correction.  
As shown in Figure 5, forecast metrics were evaluated at each forecast lead hour. For temperature, the R-square values decreased slightly from around 1 at the first lead hour to around 0.97 at the last lead hour. The R-square values linearly decreased in the first 40 lead hours before exhibiting periodic variations along the decreasing trend. The RMSE of the temperature forecast also increased from less than 3 \({}^{\circ}\)C to around 4.5 \({}^{\circ}\)C in the final lead hour. However, NME remained relatively stable for the first 40 lead hours before starting to fluctuate in a downward trajectory. As for relative humidity, it had a similar pattern to temperature, with a decreasing R-square, an increasing RMSE, and a fluctuating negative NME.
# Digital Mapping of Soil Organic Matter in Northern Iraq: Machine Learning Approach
## 3 Methodology
### LULC and Accuracy Assessment  
The main goal of LULC mapping is to delineate and demarcate appropriate regions for conducting soil sampling, with a specific emphasis on bare soil and cropland. To do so, a series of land surveys was conducted during a fortnight-long period spanning from 28 August to 10 September of the year 2021. A handheld GPS device was employed utilizing the same projection system as the study region to precisely delineate LULC features. The field survey data comprised seven different types of LULC: dense forest, sparse forest, cropland, built-up, soil, water bodies, and rock. The Landsat image acquired on 31 August 2021, was classified using an SVM classifier. This approach employs a statistical threshold for decision-making purposes to discriminate between visually similar signatures and identify a hyperplane that can effectively differentiate between two distinct categories. The classification process was executed using training data, which constituted 70% of the collected field data, resulting in the creation of a classified map.  
The precision of the LULC classification was evaluated by generating a confusion error matrix using validation data [32]. The validation dataset comprised 30% of the collected field data. The matrix facilitated a comparison between the predicted classification outcomes and actual outcomes derived from the validation dataset. The accuracy of the classified image was determined using various metrics, such as the overall accuracy, producer accuracy, user accuracy, and Kappa coefficient. The overall accuracy of the LULC classification is often expressed as a percentage, which is determined by dividing the total number of correctly categorized samples by the total number of examined samples. However, the use of the Kappa coefficient is favored over overall accuracy because of its ability to offer improved discrimination between different classes [33].
# Cloud Database Analysis of Instant Messaging Apps on Mobile Devices
## 3 Problem Solution
### Differential Equations for Databases  
The differential equation that governs the rate of growth of the database (Figure 14) will be presented.  
\[\frac{dN(t)}{dt}=rN(t)-c \tag{11}\]  
where:  
* \(N(t)\) denotes the quantity of database records (such as messages or users) at a given moment \(t\);
* \(r\) is the pace of increase (e.g., the number of new users joining over a specified time period);
* \(c\) represents a constant that denotes churn (e.g., users discontinuuing the service).  
The increase in the number of messages within a chat database can be described by the following differential equation:  
\[\frac{dM(t)}{dt}=\alpha M(t)-\beta \tag{12}\]  
where:  
* \(M(t)\) indicates the total number of messages over time \(t\);
* \(\alpha\) represents the rate at which new messages are generated;  
Figure 14: Growth of the database over time.  
\(\beta\) is the frequency of message removal or turnover.  
Figure 15 shows the increase in messages over time.  
User growth in a chat application can be represented by the following logistic growth equation:  
\[\frac{dU(t)}{dt}=rU(t)\left(1-\frac{U(t)}{K}\right) \tag{13}\]  
where:  
* \(U(t)\) represents the count of users at a given time \(t\);
* \(r\) represents the inherent growth rate;
* \(K\) refers to the system's maximum capacity.  
Figure 16 illustrates the logistic growth of users over a period of time.  
The frequency of message exchanges (new messages within a given timeframe) in the chat application can be represented as follows:  
\[\frac{dM(t)}{dt}=\alpha U(t)-\beta M(t) \tag{14}\]  
where:  
* \(M(t)\) represents the count of messages at a given time \(t\);  
Figure 16: User growth over time (logistic growth).  
Figure 15: Message growth over time.  
* \(\alpha\) is the rate of message generation proportional to the number of users;
* \(\beta\) is the degradation of the message (e.g., removal of outdated messages or dormancy of inactive users).  
Figure 17 illustrates the traffic of messages over time, considering the number of active users.  
The server load can be represented by the following:  
\[\frac{dL(t)}{dt}=\gamma M(t)-\delta L(t) \tag{15}\]  
The server load, determined by message traffic and data retention, is illustrated in Figure 18.  
Where:  
* \(L(t)\) is the load on the server at that moment \(t\);
* \(\gamma\) is the speed at which messages add to the load;
* \(\delta\) is the speed at which the system processes or purges.  
All three graphs overlaid are shown in Figure 19.  
Figure 17: Message traffic over time.  
Figure 18: Server load over time.  
The previous graph illustrates the relationship between user growth, message traffic and server load.
# A Study of 5G Edge-Central Core Network Split Options
## 7 Conclusions and Further Activities  
In the previous sections, we have presented a set of functional split options of the core network. As demonstrated, they provide highly distinct services in terms of operations' delay and manageability, making the choice of the appropriate option essential for the efficient deployment of the specific use cases. With the provided evaluation, this article underlines the potential to optimally address the different use cases. This potential must be proven towards different realistic deployment environments and further customized towards the use cases. We are currently engaged in such a refinement operation with some verticals including Industry 4.0, multimedia acquisition, and PPDR networks. It is expected that an additional set of communication requirements will be gathered from these activities would need a new stage of assessment of the proposed solutions.  
Furthermore, the network management solution presented here needs to be further refined in the direction of system runtime management where the different operations are highly time-constrained. For this, a new solution of optimized communication across the backhaul is need for monitoring for element management as well as for bulk transmissions of administrative policies.  
With the advent of 6G networks, it is foreseen that these initial beyond-5G core and network management options will become even more fluid, enabling a high level of dynamic functionality placement as well as a more morphing core network [17]. As the core network connectivity service characteristics as reflected in Table 6 remain the same, it is expected that also the split models will follow the same generic principles. Regardless, they will still have to be adapted to the fore coming 6G architecture as well as to the expected increase in the infrastructure capacity at the edge and the even more heterogeneous backhaul variation.  
Conceptualization, M.C. and T.M.; methodology, M.C. and P.C.; validation, M.C. and P.C.; formal analysis, P.C.; investigation, M.C.; writing--original draft preparation, M.C. and P.C.; writing--review and editing, M.C. and T.M.; visualization, M.C.; supervision, T.M. All authors have read and agreed to the published version of the manuscript.  
This work was partially funded by ESA ARTES AT project \"SATis5\" (Demonstrator for Satellite Terrestrial Integration in the 5G Context)--ESA/ESTEC Contract No.: 4000120663/17/NL/CLP and by EC H2020 5G-PPP ICT-17 5G-VINNI project.  
Data Availability Statement: Data can be provided by authors upon request. Data can be verified using a Fraunhofer FOKUS Open5 GCore testbed.  
The authors would like to thank [PERSON] from ESA/ESTEC for her valuable technical support in the SATis5 project. Furthermore, the authors would like to thank [PERSON] (SES), [PERSON] (iDirect), and our colleagues [PERSON], [PERSON] and [PERSON] for their support in the testbed development and assessment.  
The views expressed herein can in no way be taken to reflect the official opinion of the European Space Agency or of the European Commission.
## 5 Experimental Result and Analysis
### Analysis of the Regression Task  
To validate the effectiveness of the regression branch in modeling continuous embedding rates, we conducted a mean squared error (MSE) evaluation. This metric quantifies the model's ability to capture fine-grained embedding levels, which is essential for supporting multi-task learning and improving the discriminative representation shared with the classification task. The results are shown in Table 2.  
From Table 2, it can be seen that the regression task consistently achieves low mean squared error (MSE) values across multiple steganography methods, demonstrating the effectiveness of the proposed framework in estimating embedding rates. Notably, the model achieves an MSE of 0.0101 on the Miao (\(\eta=4\)) dataset, indicating high estimation accuracy under higher embedding rates. Even under lower embedding rates, such as Miao (\(\eta=1\)), the MSE remains relatively low at 0.0341, suggesting strong robustness. These results confirm that the regression branch successfully captures continuous variations in embedding rates, thereby enhancing the overall performance and generalization capability of the multi-task learning framework.
# Automated Clinical Impression Generation for Medical Signal Data Searches
## 4 Discussion
### Available Applications of Proposed Method  
The proposed method in this research, which aids the creation of indexing systems utilizing EEG signals for report generation, has various potential applications in the medical domain. Some of the available applications are as follows.  
1. Indexing and organization: The proposed method allows for the efficient indexing and organization of EEG signals in a dedicated database. Signals can be characterized with their respective impressions and stored in a term-based indexing system, making it easier to manage and access the data.
2. Efficient signal retrieval: Neurologists and researchers can use the system to retrieve specific EEG signals based on queries. For instance, using a query like \"eeg within normal limits in wakefulness and brief sleep for an adult of this age\", the system can locate relevant signals and their corresponding impressions, streamlining the retrieval process.
3. Report generation support: The system can be implemented in medical centers where EEG signals are collected from patients, but accompanying reports are pending neurologist review. By using the proposed method, the system can automatically generate impressions for the signals, facilitating the report generation process.  
Overall, the proposed method offers a promising solution to enhance EEG data utilization in clinical settings, making it more accessible and valuable for neurologists and researchers alike.
# Scenario-Based Emergency Material Scheduling Using V2X Communications
## SEMS Method
### SEMS Assumptions and Model
#### 3.4.1 SEMS Assumption and Notations  
We had the following assumptions for the SEMS model:  
(1) The locations of supply depots and disaster depots are known;  
(2) Emergency materials can only be delivered from supply depots to disaster depots;  
(3) The seriously damaged emergency materials stored in disaster depots cannot be used;  
(4) The amount of emergency materials stored at each supply depot is known;  
(5) The scheduling time depends on the transportation mode and distance between supply depots and disaster depots;  
(6) The mode selection is only made based on the transportation time, regardless of the weather and other conditions.  
Table 3 defines other notations used in the paper.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notations** & **Meaning** \\ \hline \(q_{ik}^{p}\) & The supply amount of material \(k\) stored in depot \(i\) in stage \(p\) \\ \(q_{ik}^{new}\) & The supply amount of material \(k\) generated in stage \(p\) \\ \(q_{ik}^{new}\) & The demand of material \(k\) generated in stage \(p\) \\ \(c_{ik}^{p}\) & The transportation cost for dispatching \(k\) using mode \(r\) in stage \(p\) \\ \(l_{ijr}\) & The distance from the supply depot \(i\) to \(j\) with mode \(r\) \\ \(C^{p}\) & The total emergency cost raised in stage \(p\) \\ \(l_{ijk}^{p}\) & Whether or not dispatching \(k\) from depot \(i\) to \(j\) by mode \(r\) in stage \(p\) \\ \(\theta^{p}\) & The saving index of an emergency material in stage \(p\), which changes with the stage advancement \\ \(x_{ipk}^{p}\) & The amount of material dispatching \(k\) from depot \(i\) to \(j\) by mode \(r\) in stage \(p\) \\ \(\theta_{ik}^{p}\) & The scenario coefficient of disaster depot \(j\) for material \(k\) in stage \(p\) \\ \(d_{jk}^{p}\) & The actual demand for material \(k\) in disaster depot \(j\) in stage \(p\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Definition of notations.  
Figure 2: SEMS network.
# Physical Layer Security Based on Non-Orthogonal Communication Technique with Coded FTN Signaling
###### Abstract  
In recent years, ensuring communication security at the physical layer has become increasingly important due to the transmission of sensitive information over various networks. Traditional approaches to physical layer security often rely on artificial noise generation, which may not offer robust solutions against advanced interception techniques. This study addresses these limitations by proposing a novel security technique based on non-orthogonal signaling using Faster-than-Nyquist (FTN) signaling. Unlike conventional FTN methods that utilize fixed symbol intervals, the proposed technique employs variable symbol intervals encoded as secure information, shared only with legitimate receivers. This encoding enables effective interference cancellation and symbol detection at the receiver, while preventing eavesdroppers from deciphering transmitted signals. The performance of the proposed technique was evaluated using the DVB-S2X system, a practical digital video broadcasting standard. Simulation results demonstrated that the proposed method maintains smooth communication with minimal performance degradation compared to traditional methods. Furthermore, eavesdroppers were unable to decode the transmitted signals, confirming the enhanced security. This research presents a new approach to physical layer security that does not depend on generating artificial noise, offering a path to more secure and efficient communication systems.  
physical layer security; FTN; non-orthogonal signaling; IDD; DVB-S2X  
3
## 3 Design Method of MCRWPT System Based on Simulation
### Design Circuit and Excitation Frequency
#### 3.2.2 Design Excitation Frequency
By increasing the excitation frequency, the transmission efficiency can be increased to 76.15%, corresponding to an output voltage of 21.57 V. The excitation frequency can be obtained based on the output voltage, as shown in Figure 11b. Decreasing the excitation frequency so that the excitation frequency is close to the resonant frequency, the TTE still increases to some extent. However, it is important to ensure that the excitation frequency is within the range calculated in the first step. As shown in Figure 11c, the TTE reaches its maximum when the output voltage is 21.57 V, and the output power of the MCRWPT system reaches a minimum of less than 10 W. The output power is positively correlated with the output voltage, and the excitation frequency is negatively correlated with the output voltage. Therefore, the output power can be improved by reducing the excitation frequency, but the TTE decreases. The last step is to optimize the TTE by changing the excitation frequency under the same output power condition. The TTE is the product of the MCRWPT system's efficiency and the DC-DC converter's efficiency. The efficiency of a DC-DC converter increases as the difference between the input voltage and the output voltage decreases. Figure 11a,b shows that as the excitation frequency increases, the output voltage decreases, and the efficiency of the DC-DC converter increases. The relationship between efficiency and output power is obtained under different excitation frequencies, as shown in Figure 11d. Under the same output power condition, the higher the excitation frequency, the higher the PTE. Compared with the other two excitation frequencies, the output efficiency is the highest when the excitation frequency is 160 kHz. Therefore, optimizing TTE is to increase the excitation frequency of the system under the condition of guaranteeing the output power. Figure 11: (**a**) Transmission efficiency changes with output voltage; (**b**) Excitation frequency changes with output voltage; (**c**) Output power (maximum) changes with the output voltage; (**d**) With different \(R_{L}\), output power changes with PTE.