esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Algorithmic Approach (Figure 1)
#### 2.2.4 Classification  
The model was tested using the cross-validation method [29]. A ten-fold cross-validation was used. The dataset was divided into training and validation data. The training data were used during model development, where the model was trained with nine folds and tested on the tenth set. This process was repeated ten times, with each fold being the test set once. The results were then averaged. The DEAP model, and the new SMCI model with adaptations, were both trained using the cross-validation method with the DEAP dataset for testing.  
The features extracted from each independent signal (PPG, GSR, and RESP) and the fused features were provided as inputs to nine different machine learning (ML) models that have previously shown success in research identified in the literature [20]. The models encompassed various types of trees (Decision, Random Forest, Gradient Boosting Machines, and AdaBoost), support vector machines (RBF, linear, poly, and sigmoid), and a naive Bayes model. Decision Trees classify data points using predefined rules. Each node in a Decision Tree represents a prediction based on measurable data features, with branches indicating possible outcomes. A Random Forest model combines multiple Decision Trees, each trained on random subsets of data and features, and makes predictions by averaging the outputs of all trees. In a Random Forest, all trees carry equal weight in the final prediction. Gradient Boosting Machines and AdaBoost are akin to Random Forests but differ in how trees contribute to predictions--each tree's impact is weighted, influencing the final prediction differently. Support Vector Machines (SVMs) seek an optimal hyperplane to separate data into classes. By mapping data onto a higher-dimensional space, SVMs identify a hyperplane maximizing margin between classes. Different kernel functions (RBF, linear, poly, and sigmoid) map data onto varied spaces. Naive Bayes is a probabilistic model using Bayes' theorem to predict class probabilities based on conditionally independent features. The ML model with the best performance accuracy was used as the robust DEAP model for future analyses and provided output pleasure, arousal, and dominance scores from the signals.
# A History of Channel Coding in Aeronautical Mobile Telemetry and Deep-Space Telemetry
## 4 Aeronautical Mobile Telemetry
### Channel Coding in the 21 st Century
#### 4.2.2 IRIG 106 LDPC Codes  
Low-density parity check (LDPC) codes were introduced in the IRIG 106 telemetry standard in 2015 [202]. The IRIG 106 LDPC codes are the family of nine AR4 JA LDPC codes adopted in the CCSDS standard [50] and described above in Section 3.10. The codes were designed for use with BPSK. Consequently, the LDPC codes are only defined for use with SOQPSK-TG in IRIG 106. The reason the AR4 JA code works with SOQPSK-TG is because SOQPSK-TG is a CPM with modulation index \(h=1/2\); CPMs with \(h=1/2\) often \"behave like QPSK\" which, in turn, \"behaves like BPSK\". The code parameters are listed in Table 2.  
Code performance, measured by the decoded bit error rate displays approximately 0.5 dB difference between \(k=1024\) and \(k=4096\). The rate-2/3 code is about 0.75 dB worse than the rate-1/2 code, and the rate-4/5 code is about 1 dB worse than the rate-2/3 code. See Figure D-10 of [163].  
The encoding procedure is illustrated in Figure 25. Because the data and the resulting codeword do not guarantee sufficient transitions to achieve symbol timing synchronization, the codeword is randomized. To aid in synchronization, an attached sync marker (ASM) is prepended to each codeword. The ASM is a 64-bit sequence for \(k=1024\) and a 256-bit sequence for \(k=4096\). As of this writing, the IRIG 106 LDPC codes have been used in a number of tests. See [203; 204; 205; 206; 2  
Figure 24: A block diagram of the EFTs transmitter.
## 2 Materials and Methods  
### Study Area  
El-Farafra Oasis (Figure 1) (26\({}^{\circ}\)00\"-27\({}^{\circ}\)30\" N, 26\({}^{\circ}\)30\"-29\({}^{\circ}\)00\" E) is one of the smallest oases excavated in the limestone plateau occupying the core region of Western Egypt [16; 20]. This natural depression has a total area of ~10,000 km\({}^{2}\), distanced ~650 km southwest of Cairo [21]. El-Farafra Oasis is characterized by a hot hyperarid desert climate with a mean annual air temperature of ~22 \({}^{\circ}\)C and average annual precipitation of less than 10 mm [16; 22]. The White Desert National Park (WDNP), also known as Sahara El-Beyda, is one of the main geographic attractions in the El-Farafra Oasis. It is located at a distance of ~45 km north of the oasis. This desert has a white creamy color and massive \"chalkycarbonate\" rocky formations that had been eroded, carved, and sculpted for millions of years by atmospheric agents and occasional sandstorms [20]. Groundwater in the El-Farafra Oasis and the WDNP is derived from bores discharging from the world's largest non-renewable groundwater resource, the Nubian Sandstone Aquifer System (NSAS), which is the only available water resource in this Saharan ecosystem [22; 23]. NSA is located in the eastern Sahara Desert, ca. 1600 km wide in both north-south and east-west directions. It extends for just over 2.6 Mkm2 between the north-western sector of Sudan, north-east of Chad, south-east of Libya, and most of Egypt, and the thickness of its permeable water-saturated sediments varies from hundreds of meters at its southern peripheries to several kilometers in its center and the north. This aquifer is comprised of wedge-shaped sandstone deposits spanning the early Paleozoic to Cretaceous age with the thin edge of the wedge outcropping to the south in the highlands of Sudan and Chad. Within Libya and Egypt, the aquifer is 1-2 km deep and confined beneath impermeable upper Cretaceous-Eocene beds [24]. Much of NSAs contains a large amount of high-quality groundwater, recharged during previous pluvial periods thousands to millions of years ago, with almost no current recharge [22; 23]. The over-use of the Nubian aquifer system (NSAS), as the world's largest aquifer, has already resulted in the abandonment of oases' settlements, where natural springs have been degenerated, and this subsequently will lead to oases' loss and severe environmental impacts [22]. A clear relationship between the ever-increasing number of wells and the dramatic decrease of active flowing springs has been observed (e.g., in oases of the Western Desert of Egypt) and is predicted to exacerbate due to the lack/non-compliance with sustainable exploitation plans [23; 25].
## 3 Results and Discussion
### Numerical Results  
#### 3.3.1 Numerical Validation  
This section compares experimental results with numerical predictions at 4 W heat load under NV for all PCMs. Figure 12 shows the experimental and numerical transient temperature for 35 min for salt hydrate (Figure 12A), paraffin (Figure 12B) and milk fat (Figure 12C).  
Figure 12A clearly shows a good agreement between the experimental and simulation results in the melting regimes. However, after melting, the curves deviated slightly which can be attributed to under-prediction of heat losses at higher temperatures. The disagreement observed is not potentially important as, for cooling applications, the PCMs will not generally be used after their phase change is completed.  
Figure 12B also shows good agreement between experimental and simulation results for paraffin wax with slightly lower melting point of actual PCM than applied in the model. Consequently, the paraffin wax starts melting earlier over a wider range in the experiment than predicted in the numerical simulation and causes a temperature variation by few degrees during the melting stage. In addition, a similar deviation as in Figure 12A was observed after the melting is completed. The temperature differences between experimental and simulation data for paraffin wax may stem from the bubble propagation and release observed at higher temperatures due to the natural convection currents [14].  
Figure 11: Transient cooling curve for milk fat represented by HS bottom surface temperature under NV and FV.  
For milk fat, Figure 12C depicts a larger deviation between experimental and simulation results at higher temperatures. The deviation may have resulted from the uncertainty in the convective heat loss coefficient and measured thermo-physical properties (especially melting point, heat capacity and heat of fusion) derived from DSC temperature hysteresis because of broader melting range and non-uniform composition of milk fat, which is difficult to model. In the DSC curve, the PCM showed more than one melting peak (representing at least two solidus and two liquidus temperatures) while the model can allow only one solidus and one liquidus temperature, which may have caused deviation as well. The problem can be handled for a milk fat sample of one melting peak and narrow melting range by refining butter, which is under consideration for future research.  
Generally, at lower temperatures, very good agreement is achieved between the experimental and numerical results, while at higher temperature, the deviation in all cases appears after PCM melting phase. The root-mean-square error (RMSE) and coefficient of determination (\(R^{2}\)) are computed to quantify the deviations between simulation and experimental results. The resulting error parameters for all PCMs indicate a very good agreement, with RMSE and \(R^{2}\) yielding 1.9 \({}^{\circ}\)C and 0.962, 2.5 \({}^{\circ}\)C and 0.972, and 2.2 \({}^{\circ}\)C and 0.981 for salt, paraffin, and milk fat, respectively.
## 6 Conclusions  
HCNs and IoT have been considered to form a new wireless network paradigm to evolve the mobile communication system toward 5G, while the CS game among densely-deployed sensor and mobile devices in HCNs became one of the key issues and challenges. For a CS game undergoing severe ICI, the congestion game model was found to be a suitable methodology for this study. In this article, we embedded ICI into the congestion game and theoretically identified the PSNE with three theorems. The closed-form expression for the ICI was calculated and helped validate the three theorems. We conclude that, on the one hand, the CS game among a large amount of densely-deployed devices can be considered from the statistical point of view by integrals, which made this game able to be solved with closed-form expressions. On the other hand, the PSNE of such a game may suddenly collapse to the macrocell under the impact of severe ICI, and the system performance, in terms of average spatial spectral efficiency, could be severely degraded by this collapse phenomenon. Therefore, this study should be quite helpful to design the future CS scheme for dense devices in an integrated environment with HCNs and IoT. Our future work involves the design of a CS scheme fitting for generalized scenarios, such as different traffic types, device types and user preferences, as well as device mobility. Meanwhile, we will theoretically consider a more generalized cell deployment, _i.e._, the irregular deployment of BSs with Voronoi cell boundaries.
## 3 SWIFTNET: Protocol for Fast-Reactive Monitoring  
SWIFTNET is an algorithm that combines the two well-known methods described above namely; a compressive sensing algorithm and a prediction algorithm together with an adaptive sampling strategy, which is designed to achieve fast-reactiveness and a prolonged network lifetime for WSNs. To meet the needs for an adaptive sampling strategy, we deviate from the traditional prediction approach with fixed sampling interval. Rather we allow the prediction algorithm to adjust the sampling interval depending on the incoming sensed data. With respect to event-detection applications, SWIFTNET is able to react quickly to varying changes in the measured values, limit the unnecessary data transmission, and yet provide accurate reconstruction of signals.  
In Figure 1, we show a schematic diagram of SWIFTNET in a WSN with star topology. The figure reveals how each node implement the SWIFTNET algorithm. For example, _Sensor node 1_ compresses the data traces and if necessary performs data prediction. The resulting vector is then transmitted to the _Sink node_ for decompression and processing. However, the key task in each node is how to simultaneously implement the data compression and prediction algorithms, while aggressively acquiring measurement samples in real-time. In the next section, we discuss in detail the theoretical features that underlie the SWIFTNET design solution, _i.e._, compressed sensing, data prediction, and adaptive sampling.
# Enzymatic Degumming of Soybean Oil for Raw Material Preparation in BioFuel Production
## 3 Results
### Effect of Enzyme Dose on Degumming Efficiency  
A study was conducted to determine the effect of the dosage of the enzyme complex Purifine(r) 3G on the efficiency of degumming of 1 kg of crude soybean oil. The dosage of the enzyme is an important factor that determines the efficiency of degumming and eco-nomic feasibility. Within the framework of the experiments, concentrations of 50, 100, 150, 200, and 250 ppm of the enzyme were tested. The level of phosphorus and free fatty acids in the final product was determined; the results are given in Table 2. Degumming was carried out for 3 h at fixed values of pH = 7.0; T = 60 \({}^{\circ}\)C; 2% H\({}_{2}\)O by weight of the oil.  
The results showed that at a dose of 100 ppm, the phosphorus level decreased from 421 to 9.5 ppm, and the FFA content acid value was 0.85 mg KOH/g oil. Increasing the dose to 150, 200, or 250 ppm had a slight effect on the phosphorus level (7.8-8.7 ppm), but was accompanied by an increase in the cost of the enzyme preparation and an increase in the level of free fatty acids. Reducing the enzyme dose to 50 ppm does not provide a phosphorus level of \(<\)10 ppm. Therefore, 100 ppm is the optimal dose that allows obtaining a phosphorus content of \(<\)10 ppm, which meets industrial standards and provides a balance between efficiency and costs.
###### Abstract  
Forest fires are among the most devastating natural disasters, causing significant environmental and economic damage. Effective early prediction mechanisms are critical for minimizing these impacts. In our previous work, we developed a smart and secure wireless sensor network (WSN) utilizing physical sensors to emulate forest fire dynamics and predict fire scenarios using machine learning. Building on this foundation, this study explores the integration of virtual sensors to enhance the prediction capabilities of the WSN. Virtual sensors were generated using polynomial regression models and incorporated into a supervector framework, effectively augmenting the data from physical sensors. The enhanced dataset was used to train a multi-layer perceptron neural network (MLP NN) to classify multiple fire scenarios, covering both early warning and advanced fire states. Our experimental results demonstrate that the addition of virtual sensors significantly improves the accuracy of fire scenario predictions, especially in complex situations like \"Fire with Thundering\" and \"Fire with Thundering\". The extended model's ability to predict early warning scenarios such as lightning and smoke is particularly promising for proactive fire management strategies. This paper highlights the potential of combining physical and virtual sensors in WSNs to achieve superior prediction accuracy and scalability of the field without any extra cost. Such findings pave the way for deploying scalable (cost-effective), intelligent monitoring systems capable of addressing the growing challenges of forest fire prevention and management. We obtained significant results in specific scenarios based on the number of virtual sensors added, while in some scenarios, the results were less promising compared to using only physical sensors. However, the integration of virtual sensors enables coverage of much larger areas, making it a highly promising approach despite these variations. Future work includes further optimization of the virtual sensor generation process and expanding the system's capability to handle large-scale forest environments. Moreover, utilizing virtual sensors will alleviate many challenges associated with the huge number of deployed physical sensors.  
Keywords:forest fire prediction; wireless sensor networks (WSN); virtual sensors; machine learning; early warning systems; fire scenario classification; data augmentation; environmental monitoring; supervector formation; smart sensing systems +
Footnote †: journal: Electronics
# Evaluation of the Bond Strength and Cytotoxicity of Alkasite Restorative Material
###### Abstract  
Cention N (CN; Ivoclar Vivadent, Schaan, Liechtenstein), advertised as an alkasite, is a bioactive bulk-fill resin-based composite (BF-RBC) with alkaline fillers. This study evaluated the resin-dentin micro-tensile bond strength (\(\mu\)TBS) and cytotoxicity of CN. Methods: Flat dentin surfaces were obtained, bonded with a universal adhesive, and randomly distributed into two groups. CN (group I) and a flowable BF-RBC, namely, Tetric N-Flow Bulk Fill, Ivoclar Vivadent, Schaan, Liechtenstein (group II), were used. After thermocycling, bonded samples were sectioned into micro-beams for \(\mu\)TBS evaluation. Resin-based composite (RBC) discs with a thickness of 2 and 4 mm were tested on human gingival fibroblast cells (HGFCs). Cytotoxicity was assessed by cell viability and growth using AlamarBlue(r) (Biosource, Camarillo, CA, USA) over a seven-day period. Independent t-test was utilized to statistically analyze \(\mu\)TBS data, while one- and two-way analysis of variance (ANOVA) and [PERSON]'s post-hoc tests were utilized to analyze the cell viability data. Results: There was no statistically significant difference (\(p>0.05\)) in the \(\mu\)TBS between the flowable BF-RBC and CN. For both materials, the HGFCs were viable, with constant growing over the seven-day period. Conclusion: CN provided a resin-dentin \(\mu\)TBS that was comparable to that provided by the flowable BF-RBC. Both materials showed acceptable cytotoxicity over the seven-day period at a thickness of both 2 and 4 mm.  
**Keywords:** alkasite; bulk-fill; resin-based composite; micro-tensile bond strength; dentin; cytotoxicity
# Adaptive BBU Migration Based on Deep Q-Learning for Cloud Radio Access Network
## 5 Simulation Results
### The Proposed Evaluation Results and Analysis
By learning from previous migration events, DQN minimized unnecessary migrations and improved the selection of target machines, resulting in fewer, but more efficient, migration events. The DQN-based migration scheme outperformed the traditional Kalman filter scheme in important performance aspects, including consumption of resources, efficiency in terms of energy, and migration times. With deep reinforcement learning, the system could dynamically adapt according to cloud usage behavior in terms of resources, and it could maintain a superior performance-energy consumption balancing act.
## 3 Numerical Result of Bandgap Characteristics
### Effect of the Initial Tensile Stress on the Bandgap Characteristics
Figure 13 presents the width of the first initial bandgap which varies in different configurations. It is clear that the initial stress reduces the bandgap width. Thus, an external extension or compression can be used to effectively modify the bandgaps in the structure, which can undergo large geometric deformations under tension or compression [32]. According to the elastic dynamic theory, when waves propagate in a two-dimensional uniform elastic medium (neglecting body force), Equation (1) can be established [11]. \[-\rho(\mathbf{r})\omega^{2}\mathbf{u}(\mathbf{r},t)=(\lambda(\mathbf{r})+\mu(\mathbf{r}))\
abla \
abla\cdot\mathbf{u}(\mathbf{r},t)+\mu\
abla^{2}\mathbf{u}(\mathbf{r},t) \tag{1}\]  
\(\mathbf{u}=\left\{u_{x},u_{y}\right\}^{T}\) is the displacement column vector, \(\
abla\cdot\mathbf{u}=\frac{\partial u_{x}}{\partial x}+\frac{\partial u_{y}}{ \partial y}\) is divergence, \(\mathbf{r}\) is the position coordinates of CCPM, \(\rho(\mathbf{r})\) is the material density, and \(\lambda(\mathbf{r})\) and \(\mu(\mathbf{r})\) are the Lame coefficients. In a unit cell of CCPM, Equation (1) can be discretized as follows:  
\[(\mathbf{K}-\omega^{2}\mathbf{M})\mathbf{U}=0 \tag{2}\]  
\(\mathbf{K}\) is the stiffness matrices; \(\mathbf{M}\) is the mass matrices. It shows that the \(\omega\) is related to the \(\mathbf{K}\) of CCPM. The stiffness of CCPM is not only affected by array configuration but also by initial stress. Thus, the influence of the initial stress generated by the structure on the bandgap at the same time as the configuration change cannot be ignored. Figure 12: Variation in the center frequency of the first initial bandgap.
## 5 Obstacles and Challenges in Evolving V2X Communication
### Use Case Specific Challenges
#### 5.4.3 Tunnels and Urban Canyons--Use Cases without GNSS  
Due to the specific channel access technology of C-V2X PC5, synchronization among the user equipment is required. In Mode 4/Mode 2 communication, it is solved by GNSS  
\begin{table}
\begin{tabular}{c c c} \hline
**Method** & **Severity/Importance** & **Solutions/Advantages** \\ \hline Passive attacks & Medium & Increase the No. of antennas, beam focusing \\ \hline Pilot contamination & High & Encrypted or/and scalable pilot set \\ \hline MCS saturation & Medium & Appropriate power control \\ \hline Uplink jamming & Critical & Allocating antennas to jam Eve, increase the \\  & & No. of antennas \\ \hline Leaky wave antennas & High & Unique properties, increased \\  & & secrecy capacity \\ \hline Physical layer key & High & Low need for computation resources \\ \hline \end{tabular}
\end{table}
Table 6: Summary of the flaws, available solutions, and severity of the problem (Severity/Importance (S/I): Critical (C), High (H), Medium (M), Low (L)).  
signals. Normally, depending on GNSS signal is not an issue since one of the most important pieces of information shared via V2X is the own position of the sender device, which comes from GNSS chips themselves. However, other positioning methods in the future might emerge, like ultra-wide band-based positioning, which could ensure the operation of V2X in such areas as well. However, V2X applications require relative position information. Relative position information could be valuable without a known absolute position as well. As we can see, dependency on GNSS signals might limit the usability of C-V2X technology in specific areas, like urban canyons or tunnels.
## 3 Methodology
### Power Flow Analysis  
Power distribution systems predominantly operate in a radial configuration, favored for its simplicity, cost-effectiveness, and ease of fault isolation [39; 40]. This structure, characterized by a single path from the source to each load, ensures efficient design and straightforward operation. Although some systems may incorporate limited meshing to meet specific operational needs, the radial layout remains the preferred choice due to its lower installation and maintenance costs, as well as the ease of integrating distributed energy resources (DERs). Radial configurations simplify fault detection and isolation, minimizing downtime and facilitating network management, which is crucial for maintaining stability. Additionally, the radial topology aligns well with typical urban and suburban network layouts, where loads are distributed across wide areas, making direct connections to substations more efficient. These advantages underscore why the radial structure is fundamental to power flow analysis and why it serves as the basis for the proposed optimization approach.
## 4 Results and Discussion
### Practical Applications of NAS in GANs  
NAS applied to GANs has opened exciting avenues for automatically discovering efficient, high-performing architectures. These NAS-GAN methods reduce the need for hand-crafted designs and allow for tailored models across various application domains. In the following, we discuss several practical applications along with published works that illustrate their real-world impact.