esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment
### Baseline Models  
To assess the predictive performance of CeT, we select seven classic prediction methods to establish baseline models. These models are trained on the same dataset as CeT and compared against it during testing, providing an intuitive assessment of CeT's predictive capabilities. To ensure a comprehensive evaluation, the selected methods span statistical, machine learning, and deep learning methods.  
* Historical Average (HA): As a fundamental statistical method, HA computes the average traffic states over the previous four signal phases to generate predictions. This method serves as a baseline to evaluate the performance of more sophisticated methods, providing insights into the benefits of leveraging historical patterns.
* Autoregressive Integrated and Moving Average (ARIMA) [18]: ARIMA is a widely adopted statistical model for time series forecasting. It captures linear dependencies in historical traffic data through its auto-regressive, differencing, and moving average components. Its inclusion ensures a comparison with traditional time series analysis techniques.
* Support Vector Regression (SVR) [37]: SVR is a robust machine learning method that constructs a hyperplane in kernel space to model nonlinear relationships in traffic data. It represents a classical approach to regression tasks and provides a baseline for evaluating the effectiveness of more advanced predictive techniques.
* Random Forest (RF) [38]: Implemented using Scikit-learn, RF is an ensemble learning method that combines 10 decision trees to improve predictive accuracy while controlling overfitting. This method is included as a representative of ensemble methods, which are widely recognized for their performance in traffic prediction tasks.
* Feedforward Neural Network (FNN) [25]: FNN is a deep learning model structured as an MLP with one hidden layer consisting of 256 neurons. It processes historical cell space states to predict future traffic states, serving as a baseline for evaluating the performance of more complex neural network architectures.
* Adaptive Graph Convolutional Recurrent Network (AGCRN) [31]: AGCRN enhances GCNs with node adaptive parameters to learn spatial dependencies and employs RNNs to capture temporal correlations. This method is included to represent advanced spatial-temporal modeling techniques.
* Graph WaveNet (GWN) [32]: GWN is an advanced deep learning method that combines 1-dimensional dilated convolutions to capture long-term temporal correlations and constructs an adaptive dependency matrix through learnable node embeddings to model spatial dependencies. It serves as a cutting-edge baseline for evaluating CeT's performance against highly optimized architectures.  
By including methods from diverse methodological groups, we ensure a thorough and balanced evaluation of CeT's predictive performance. Each model is chosen to represent a distinct class of approaches, enabling a comprehensive comparison across statistical, machine learning, and deep learning paradigms.
###### Abstract  
It is a common practice that one converter controls DC voltage and the other controls power in two-terminal voltage source converter (VSC)-based high voltage DC (HVDC) systems for AC gird interconnection. The maximum transmission power from a DC-voltage-controlled converter to a power-controlled converter is less than that of the opposite transmission direction. In order to increase the transmission power from a DC-voltage-controlled converter to a power-controlled converter, an improved virtual impedance control strategy is proposed in this paper. Based on the proposed control strategy, the DC impedance model of the VSC-HVDC system is built, including the output impedance of two converters and DC cable impedance. The stability of the system with an improved virtual impedance control is analyzed in Nyquist stability criterion. The proposed control strategy can improve the transmission capacity of the system by changing the DC output impedance of the DC voltage-controlled converter. The effectiveness of the proposed control strategy is verified by simulation. The simulation results show that the proposed control strategy has better dynamic performance than traditional control strategies.  
VSC-HVDC; DC-side oscillation; virtual impedance; impedance-based Nyquist stability criterion +
Footnote †: journal: Applied Sciences
###### Abstract  
Electrospun nanofibrous membranes have gained considerable attention in bone tissue engineering due to their ability to mimic the extracellular matrix and provide a suitable environment for cell attachment and proliferation. This study investigates the fabrication, characterization, and biocompatibility of poly(L-lactic acid) (PLA)-based membranes enhanced with periclase (MgO) and gold nanoparticles (AuNPs). The membranes were fabricated using an optimized electrospinningning process and subsequently characterized using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), Fourier-transform infrared spectroscopy (FT-IR), and contact angle measurements. Additionally, in vitro biodegradation studies in simulated body fluid (SBF) and cytocompatibility tests with osteoblast-like cells were conducted. The results demonstrated that the incorporation of MgO and AuNPs significantly influenced the structural and chemical properties of the membranes, improving their wettability and bioactivity. SEM imaging confirmed uniform fiber morphology with well-distributed nanoparticles. FT-IR spectroscopy indicated successful integration of bioactive components into the PLA matrix. Cytocompatibility assays showed that modified membranes promoted higher osteoblast adhesion and proliferation compared to pristine PLA membranes. Furthermore, biodegradation studies revealed a controlled degradation rate suitable for guided bone regeneration applications. These findings suggest that electrospun PLA membranes enriched with MgO and AuNPs present a promising biomaterial for GBR applications, offering improved bioactivity, mechanical stability, and biocompatibility.  
compposites; biomaterials; nanoparticles; guided bone regeneration +
Footnote †: journal: Applied sciences  
Article
# Energy Consumption Analysis of ISO/IEC 29192-2 Standard Lightweight Ciphers
## 3 ISO/IEC 29192-2 Standard Lightweight Ciphers
### LEA Algorithm  
LEA offers 128, 192, and 256-bit key variants with 128-bit block length. The algorithm encrypts 24 rounds for 128-bit key lengths, 28 rounds for 192-bit key lengths, and 32 rounds for 256-bit key lengths. Only 32-bit ARX (bitwise add, rotate, XOR) operations were used in the LEA encryption algorithm. This provides high-speed encryption and decryption for 32-bit and 64-bit systems. The key scheduling stage also uses ARX operations. The LEA encryption structure is given in Figure 1c. The internal structure of LEA is designed to maximize performance and simplicity by relying exclusively on ARX operations. Each round in LEA consists of three primary steps: modular addition, cyclic rotation, and XOR operations. Modular addition introduces non-linearity and ensures diffusion, while cyclic rotation changes the bit positions to prevent direct relationships between input and output. The XOR operation adds randomness and strengthens the algorithm against cryptanalytic attacks. LEA's key schedule is equally efficient, using simple ARX operations to generate round keys dynamically, ensuring minimal overhead during key derivation. The absence of S-boxes and reliance on ARX operations not only simplifies the implementation of LEA but also optimizes it for both software and hardware environments. This design enables LEA to achieve high throughput and low energy consumption, which makes it particularly suitable for IoT devices and other resource-constrained platforms.
## 4 Results and Discussion
### Brittleness Index of the Stabilized Soil  
The parameters involved in determining the computation of the brittleness index (IB) are total and elastic strain (Equation (1)) from the stress-strain graph (Figure 4). This computation is based on the study done by [PERSON] et al. [32].  
\[\text{I}_{\text{B}}=\text{(Elastic Strain)}/\text{(Total Strain)} \tag{1}\]  
Traditionally stabilized soil shows brittle behavior, which is often not very desirable for the structures constructed over such soils [33]. Thus, an attempt was made to understand the effect of PG on the \(\text{I}_{\text{B}}\) of the lime-stabilized soil. Figure 5 shows the variation in \(\text{I}_{\text{B}}\) values of the different stabilized soil combinations.  
Figure 4: Computation of the brittleness index from a stress-strain curve.  
Figure 3: Influence of PG on the residual strength of lime-stabilized soil.  
The I\({}_{\rm B}\) values range between 0 to 1. I\({}_{\rm B}\) values of pure lime-stabilized soil combinations range from 0.5 to 0.93. Without any addition of PG, the I\({}_{\rm B}\) values for L1, L2 and L3 are 0.5, 0.93 and 0.93, respectively. When the lime is added beyond the ICL, I\({}_{\rm B}\) values computed from the stress-strain curve did not show any difference. The I\({}_{\rm B}\) values for LP1, LP2 and LP3 are 0.54, 0.83 and 0.875, respectively. For LP1, the I\({}_{\rm B}\) value is higher than L1. This may be due to PG augmenting the supply of the cations required for stabilization below the ICL content. However, the pattern differs for the increased percentage of 5.5% and 7% lime, which is the ICL and the OLC; the I\({}_{\rm B}\) values reduced with the addition of PG (Figure 5). This proves that with the addition of PG at and beyond the ICL combination, the I\({}_{\rm B}\) value reduces, making the soil less brittle. It can also be noted that the maximum benefit of PG on the desirable improvement of brittle behavior is at ICL rather than OLC. Thus, it can be concluded that the addition of PG to the lime-stabilization of an expansive soil improves the brittle behavior desirably when minimum lime content is available.
# 6G Cognitive Information Theory: A Mailbox Perspective
## 6 Data Life Cycle Extensions for Information Value
### Data Fusion for Data Analysis  
For IoT applications, the best manifestation of service intelligence is the ability to analyze and process massive amounts of heterogeneous data. As an information-processing technology, the function of data-fusion technology in the data-analysis stage is embodied in the use of computers to automatically analyze and synthesize certain observation information obtained as per time sequence under certain criteria to complete the required decision-making and evaluation tasks. Therefore, data fusion is an indispensable and effective means for data analysis and intelligent decision-making in heterogeneous environments.  
With the development of 5G technology, edge intelligence (edge cloud and terminal intelligence) has become an important evolutionary direction to improve service quality. The real-time analysis and mining of data are pushed in the direction of depth by edge intelligence and cloud intelligence. Therefore, research on data analysis and fusion can be summarized in four aspects: (1) what to fuse (data type and fusion type), (2) where to fuse (fusion location), (3) when to fuse (fusion target), and (4) how to fuse (fusion technologies). As shown in Figure 7, we review existing data-fusion technologies according to the above four aspects: (1) data type (structured data, unstructured data, and semi-structured data), (2) fusion target (improving data quality, multi-source data integration, in-depth information mining, and enhancing decision making and evaluation), (3) fusion technologies (the statistical method and the deep-learning method in machine learning), (4) fusion location (terminal fusion and analysis, edge cloud fusion and analysis, cloud fusion and analysis), (5) fusion type (data in data out, data in feature out, feature in feature out, feature in decision out, and decision in decision out).
## 3 Results and Discussion  
### Effect of NTP on Viability of HaCaT Cells  
HaCaT cells were stimulated with NTP at an interval of 5 s, and the cell viability was measured. Compared with the untreated cell group, the survival rate of HaCaT cells treated for 10 s and 15 s significantly increased (both \(p<0.05\)), and the cell survival rate of the 15 s treatment group reached the peak (Figure 1A) (\(p<0.001\)). However, when the plasma treatment time was increased to 20 s and 25 s, the cell survival rate decreased sharply, which was significantly lower than that of the control group. The results showed that low-dose NTP promoted HaCaT cell viability, while high-dose NTP inhibited it.  
As an important cellular component in the wound healing process, the enhancement of the cell viability of HaCaT cells was undoubtedly beneficial to wound healing. In addition to promoting the viability of cells related to wound healing, such as fibroblasts [38] and vascular endothelial cells [19], low-dose NTP has also been reported to promote the viability of HaCaT cells [34], but the mechanism of its action has not been elucidated. Furthermore, both polysaccharide from Gracilaria lemaneiformis [39] and NTP-like low-intensity pulsed ultrasound [40] promoted wound healing by promoting HaCaT cell viability. In this study, low-dose NTP treatment (10 s and 15 s treatment groups) promoted cell viability, while high-dose NTP treatment (20 s and 25 s treatment groups) inhibited it compared with control group, which was similar to the study of [PERSON] et al. [34].
## 3 Results
### Sensory Analysis of the Tested Rice Drinks
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Sensory Distinctions** & **RB\_1** & **RB\_2** & **RB\_3** & **RB\_4** & **RB\_5** & **RB\_6** & **RB\_7** & **RB\_8** & **RB\_9** & **RB\_10** \\ \hline Colour & 4.65 & 2.24 & 4.76 & 5.30 & 6.54 & 3.53 & **4.78** & 3.39 & 1.91 & 1.16 \\ S\textless{} sweet & 1.29 & 2.69 & 1.98 & 2.53 & 1.87 & 2.51 & 1.65 & 2.00 & 3.34 & 2.43 \\ S\textless{} milky & 0.99 & 2.10 & 1.7 & 2.80 & 1.22 & 2.03 & 0.76 & 1.62 & 2.41 & 1.81 \\ S\textless{} vanilla & 0.81 & 1.84 & 1.67 & 1.96 & 2.15 & 1.31 & 0.63 & 0.98 & 1.76 & 1.56 \\ S\textless{} rice & 1.87 & 4.56 & 2.16 & 2.71 & 2.25 & 3.36 & 1.84 & 2.97 & 3.99 & 2.56 \\ S\textless{} fatty & 1.17 & 2.37 & 1.37 & 2.36 & 1.55 & 2.01 & 1.16 & 1.22 & 2.14 & 1.50 \\ S\textless{} other (caramel) & 0.00 & 0.40 & 0.00 & 0.00 & 0.38 & 0.27 & 0.44 & 0.85 & 0.21 & 0.00 \\ Overall smell intensity & 2.74 & 4.73 & 2.63 & 3.73 & 3.65 & 3.96 & 2.71 & 3.38 & 4.60 & 3.76 \\ Density & 2.57 & 1.54 & 1.78 & 3.09 & 2.46 & 2.23 & 2.01 & 1.63 & 2.71 & 1.01 \\ Viscosity & 2.61 & 1.81 & 1.73 & 2.90 & 2.51 & 2.40 & 2.46 & 2.31 & 2.79 & 1.07 \\ T\textless{} sweetness & 2.82 & 5.74 & 4.55 & 4.99 & 3.44 & 5.86 & 4.52 & 5.82 & 5.13 & 2.79 \\ F\textless{} milky & 3.3 & 2.81 & 3.47 & 4.24 & 2.53 & 3.24 & 2.43 & 3.47 & 4.01 & 1.89 \\ F\textless{} rice & 3.17 & 4.69 & 3.66 & 4.36 & 3.98 & 3.53 & 3.11 & 3.94 & 5.00 & 3.23 \\ F\textless{} fatty & 2.87 & 2.84 & 2.42 & 3.16 & 3.05 & 2.97 & 2.47 & 2.51 & 2.80 & 1.50 \\ F\textless{} other (caramel) & 0.69 & 1.71 & 2.16 & 1.27 & 1.95 & 1.53 & 1.61 & 0.48 & 0.77 & 2.40 \\ \textless{}Body” & 4.13 & 3.74 & 3.5 & 5.79 & 3.88 & 5.06 & 4.00 & 4.38 & 5.71 & 2.70 \\ Overall sensory quality & 4.92 & 4.83 & 4.75 & 5.54 & 4.38 & 5.37 & 4.53 & 4.74 & 5.60 & 3.97 \\ \hline \hline \end{tabular}  
* S\textless{}—smell; T\textless{}—teste; F\textless{}—flavour.  
\end{table} 
Table 5: Sensory evaluation of the tested rice drinks. A limitation of the research conducted in this study is the evaluation of selected quality characteristics of only one type of vegetable beverages, i.e., rice beverages. It would be very valuable to carry out comparative studies on a wider group of vegetable beverages obtained from different raw materials, which the authors of this work are currently doing.
## 5 Conclusions  
In the last few years, diagnostic computer systems based on image processing have been extensively used. This can help radiologists, and minimize the time of diagnosis. Several breast cancer detection and classification algorithms have been presented to enhance medical image analysis. These algorithms have various deficiency, i.e., false diagnosis and prediction; the tumor appears in a area with significantly lower contrast, high complexity of memory, computationally complex approach and require more treatment time to identify the accurate tumor, and current deep learning algorithm need extensive training data to overcome the problems of overfitting and high computational cost.  
This article intended to resolve the aforementioned issues by the proposed TTCNN-based classification approach for breast cancer detection and classification. Firstly, the source mammogram images are pre-processed by employing the modification to the legacy CLAHE approach. Then, by using TTCNN architecture, the breast cancer regions that are malignant and benign are classified from the mammogram images and the EL examines the texture features and extracts the general information of shape, limit the size of the output vector, and refines the model's overall learning ability. Deep features are extracted from eight state-of-the-art DCNN models and optimal layers for feature extraction are determined by monitoring fluctuations in classification performance using a succession of experiments to select the best deep features. Afterwards, the features are fused using the CSID fusion algorithm, and for the robust feature selection an ECfA approach is used.  
The proposed approach was applied to DDSM, INbreast, and MIAS datasets, and obtained an accuracy of 99.08%, 96.82%, and 96.57%, respectively. In addition, the proposed approach is visually gratifying, provides better results, and is more capable in the detection and classification of breast mammograms and outperforms other systems. Breast images are accurately detected and classified in less computation time and give pleasant results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Database** & **Time of Training/Epoch (s)** & **Time of Testing/Image (s)** \\ \hline DDSM & 102 & 1.2 \\ \hline INbreast & 31 & 1.7 \\ \hline MIAS & 27 & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Computational time for breast cancer classification.  
In the future, this work will be explored for other application areas of biomedical imaging such as brain tumor detection.  
Conceptualization: S.M.; methodology: S.M. and R.D.; software: S.M.; validation: S.M.; formal analysis: S.M. and R.D.; investigation: S.M., R.D. and R.M.; data curation: S.M. and R.D.; writing--original draft preparation: S.M.; writing--review & editing, R.D. and R.M.; supervision: R.D.; funding acquisition: R.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data and program codes are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization
## 3 Methodology
### Power Optimization Process
#### 3.3.4 Decoding of Voltage  
The decoding process is necessary in order to obtain a continuous value for the dependent variable (\(V\)), which is also required for the calculation of the objective function. The phenotype or the voltage's (\(V\)) decoded value (\(V_{dec}\)) can be retrieved using a set of equations [33].  
\[precision=\frac{(ub-lb)}{2^{l}-1} \tag{23}\]  
The above (23) computes the precision of each bit that is used in (24) to eventually calculate the phenotype or decoded value of (\(V\)).  
\[V_{dec}=\left[\sum_{n=0}^{l-1}\left(Bit\mid_{position=n}*2^{n}\right)\right] \cdot precision+lb \tag{24}\]  
Substituting (\(ub\), \(lb\), \(l\))) in (23), the precision is  
\[precision =\frac{(20-0)}{2^{8}-1}\] \[=7.84\cdot 10^{-2}\]  
For the chromosome in Figure 4, the phenotype can be calculated according to (24), as follows  
\[V_{gen}=V_{dec} =\left(2^{3}+2^{5}\right)\cdot precision+lb\] \[=\left(40\right)\cdot 7.84\cdot 10^{-2}+0\] \[=3.136\]
# Using Ground-Based Passive Reflectors for Improving UAV Landing
## 6 Conclusions  
The conducted studies of the problem solving the effectiveness of a UAV landing using ground-based reflectors, and its onboard radar, allowed to draw the following main conclusions:  
* Dissimilar to the modern aircraft landing systems, this system did not require the presence of radio engineering devices and a developed infrastructure in the runway area, that makes such systems especially relevant when it is necessary to deploy them quickly, or operate UAVs in poorly developed territories;
* The reflectors' locations in the runway area should be carried out taking into account the estimated landing point and the UAV's approach trajectory;
* To ensure errors in estimating the UAV's location in the horizontal plane, it was enough to provide a base on the side reflectors of about 50 m;
* All reflectors' locations in the runway plane did not allow to accurately estimate the UAV's true height, which was especially critical at the final stage of the UAV's landing;
* Lifting one of the reflectors to a height of up to 20 m did not allow to obtain a significant gain in errors in determining the location and height of the UAV;
* In order to improve the accuracy of the UAV's height estimation, it was necessary to use additional algorithms for processing received radio signals and the results of primary measurements.  
Conceptualization, D.Y. and B.T.; methodology, D.Y. and B.T.; software, D.Y. and E.E.; validation, T.S. and E.E.; formal analysis, D.Y. and B.T.; investigation, T.S. and E.E.; resources, E.E.; data curation, D.Y. and E.E.; writing--original draft preparation, D.Y. and E.E.; writing--review and editing, D.Y., T.S., E.E. and B.T.; visualization, E.E.; supervision, D.Y. and B.T.; project administration, T.S.; funding acquisition, D.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the state assignment of the Ministry of Science and Higher Education of the Russian Federation, project no. FSFF-2020-0015.  
Not applicable.
## 4 Experiments and Analysis
### Visualization Results  
Several qualitative results are shown in Figure 10. We selected some representative images for the presentation of the test results. Figure 10 shows (a) a clear image (source domain) and (b) a foggy image (target domain), both detected with the YOLOv5 model. (c) Our proposed method (CAST-YOLO) was used for detection in foggy images.  
Figure 10: Several visualization results in the target domain.  
Figure 9: Scale shift for foggy weather adaptive detection. The results are from Cityscapes\(\rightarrow\)Foggy Cityscapes. The scale of the image is fixed in the source domain, and we resize the different scales of image in the target domain, as shown in the X-axis.  
Although YOLOv5 achieved excellent detection performance on the source domain, there were significant missing or false detections in the target domain for foggy weather adaptive detection. However, our method alleviated the limitations of YOLOv5 in foggy weather adaptive detection, which improved the classification and localization accuracy.
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 2 Cooperative HSTN
### HSTN Relay Network  
To enhance the performance of HSTNs, satellite signals may be relayed independently towards users [54]. Thus, the users of an HSTN can receive signals from both the satellites and the relays to take advantage of this spatial diversity from hybrid systems and to overcome the masking effect in satellite networks. This leads to a HSTN relay network, as shown in Figure 4. Many HSTN relay networks have been proposed in recent years using AF, FDF, and SDF for outage, error, and ergodic capacity analysis in the presence of co-channel interference (CCI), multi-antenna, mobile nodes, shadowing, CSI, and other conditions.