esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Demonstration and Validation
### Results Analysis
Finally, since they represented the same practical situation, the KPIs for the twenty intermediate products out of sixty were calculated and compared with the values obtained for the last twenty products out of forty in the corresponding test cases. The mismatches derived from this confrontation indicated that the manufacturing unit will often originate different results for the same test case. The most likely reason is the casual network instability, which may be exacerbated by the affluence of communications between agents depending on the amount of products being processed at a certain time. Such unsteady nature of the physical demonstrator partially explains the inaccuracies in the model's predictions.
## 5 Simulation and Analysis
### BER Performance  
To thoroughly evaluate the effectiveness of the proposed method for LDACS, we conducted BER simulations and contrasted its performance with that of other algorithms.  
Figure 10: Output SINR of the adaptive beamformer for (**a**) 100–200 snapshots and (**b**) 100–1000 snapshots.  
Figure 9: Output SINR versus DOA error angles.  
BER quantifies the ratio of error bits to total transmitted bits, serving as a direct indicator of a system's reliability and communication quality, making it a vital metric for a comprehensive evaluation of communication systems.  
Figure 11 simulates the BER variation with SNR for various algorithms. Using the curve, which lacks interference suppression, as a baseline, it is clear that all simulated algorithms demonstrate improvements in BER to varying extents across different SNR levels. Notably, as shown in Figure 11, the proposed method consistently outperforms the other methods across a broad range of SNR values. Furthermore, the figure reveals a significant improvement in BER performance at lower SNRs, highlighting the proposed method's enhanced capability to maintain signal integrity in challenging noise environments. These results demonstrate that the proposed method not only improves signal detection and suppression but also significantly improves the overall reliability and quality of LDACS communications.
# A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
## 4 A Novel Battery SoC Estimation Using a Data-Driven PHM Approach
### Cross-Validation for Model Selection  
The final step after the model optimization is to perform a cross-validation (CV) which evaluates the risk of the selected estimator. Data are split into a training sample \(D_{train}\) and a validation sample \(D_{test}\), then a CV algorithm will select the minimum loss.  
Two CV techniques are identified in the literature [32]: subject-wise and record-wise. The first segregates data for training and validation (also known as holdout), ensuring they are independent. The record-wise technique splits the dataset randomly, where the same data can be shared between training and validation.  
A record-wise (k-fold) CV technique is selected herein, which divides the dataset into \(k\) blocks (folds). One of the k blocks, where \(k=10\), is used as the validation set, while the remaining \(k-1\) are used as the training set. This is an exhaustive process, repeated \(k\) times. MSE (or \(L2\) loss) and the \(R^{2}\) (R-squared), given by (32) are employed as scoring metrics.  
\[R^{2}=1-\frac{SS_{res}}{SS_{tot}}=1-\frac{\sum_{k=1}^{n}(y_{k}-\bar{y}_{k})^{2 }}{\sum_{i=k}^{n}(y_{k}-\bar{y}_{k})^{2}} \tag{32}\]
## 3 Results and Discussion
### Antimicrobial Activity
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Seeds and**} & \multicolumn{4}{c}{**Gram-Positive Bacteria (G+)**} & \multicolumn{4}{c}{**Gram-Negative Bacteria (G–)**} & \multirow{2}{*}{**Yeast**} \\ \cline{2-2} \cline{4-9}  & _B. subtilis_ & _S. aureus_ & _E. hirnea_ & _E. faecalis_ & _P. aeruginosa_ & _E. coli_ & _P. mirabilis_ & _V. harveyi_ & _C. albicans_ \\ \hline Black cumin & - & - & - & - & - & - & - & - & - \\ Milk thistle & 1.56 \(\pm\) 0.02 & - & - & 6.83 \(\pm\) 0.63 & - & - & 3.85 \(\pm\) 0.76 & - & - \\ Fenugreek & 3.05 \(\pm\) 0.64 & - & - & - & - & - & 3.75 \(\pm\) 0.68 & 3.73 \(\pm\) 0.53 & 0.28 \(\pm\) 0.06 \\ Almond & 3.42 \(\pm\) 0.77 & - & - & - & - & - & 2.89 \(\pm\) 0.49 & - \\ White & 2.98 \(\pm\) 0.01 & - & - & - & - & - & 0.53 \(\pm\) 0.01 & 1.25 \(\pm\) 0.01 \\ White & - & - & - & - & 6.95 \(\pm\) 1.38 & - & 3.09 \(\pm\) 0.01 & - & - \\ mustard & - & - & - & - & - & 2.71 \(\pm\) 0.01 & - & - \\ Star anise & 8.89 \(\pm\) 0.03 & - & - & - & - & - & 2.71 \(\pm\) 0.01 & - & - \\ Eggfruit & - & - & - & - & - & - & 4.18 \(\pm\) 0.26 & 3.05 \(\pm\) 0.50 & - \\ Spurulina & - & - & - & - & - & - & - & - & - \\ Chlorella & - & - & - & - & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Zones of inhibition (mm) of the growth of microorganisms in the analyzed plant materials.
# The Role of HNO2 in the Generation of Plasma-Activated Water by Air Transient Spark Discharge
###### Abstract
Electric fields, temperature gradients, electrohydrodynamic spray of liquids, charging of water droplets, ionic wind, chemical reactions among various species, and other phenomena make solvation of reactive species from NTP to water very complicated process that is still not completely understood [11; 21]. The plasma-liquid interface surface area is a key parameter maximizing the contact between the plasma and the treated water solution, thus determining the obtained plasma chemical effects [23]. The transformation of bulk water into fine droplets results in an increase of this interfacial surface area and thus accelerates the transport of reactive plasma species into the water. The electrohydrodynamic spray of liquids, here simply called electrospray (ES), is a simple process to produce microdroplets from bulk liquid by a strong electric field. Despite the pioneering experimental studies describing several ES modes were conducted more than a century ago [24; 25], due to a high application potential of ES in many areas [26; 27; 28; 29; 30; 31], the research of ES continues [32; 33; 34], and several reviews were published recently focused on different aspects of ES [35; 36; 37]. The idea of using ES microdroplets to increase plasma-water interface area has been adopted by several research groups [21; 38; 39; 40], as well as in our previous works [15; 41]. For instance, TS was also used to prepare PAW by the electrospray of fine aerosol droplets directly through the active plasma zone, which resulted in a very efficient transfer of gaseous RONS into water. The PAW generated by TS contains besides H\({}_{2}\)O\({}_{2(aq)}\) high concentration of NO\({}_{2}^{-}\)(aq) and NO\({}_{3}^{-}\)(aq) [15]. This was attributed to the fact that the dominant gas phase species in air TS are NO and NO\({}_{2}\). In this paper we focus on the possible role of gas phase nitrova acid (HNO\({}_{2}\)) on the PAW generation by TS. Even with much lower gas phase concentration than NO\({}_{2}\) and NO, HNO\({}_{2}\) may influence PAW generation significantly since the \(k_{H}\) of HNO\({}_{2}\) (50 mol kg\({}^{-1}\) atm\({}^{-1}\) at 298.15 K [42]) is four and five orders of magnitude higher then \(k_{H}\) of NO\({}_{2}\) (0.007 mol kg\({}^{-1}\) atm\({}^{-1}\) at 295 K [43]) and NO (0.0018 mol kg\({}^{-1}\) atm\({}^{-1}\) at 298.15 K [44]),respectively. In our previous work, we were able to detect small amounts of HNO\({}_{2}\) in the gas phase using the Fourier transform infrared (FT-IR) spectroscopic technique [45]. A new diagnostic technique (direct UV-Vis absorption spectroscopy) allowed us to perform a more precise HNO\({}_{2}\) diagnosis in the TS-treated air when installed much closer to the plasma reactor. Moreover, the experiments where PAW is prepared by the direct ES of microdroplets directly through the discharge zone in a common one-stage system are compared to the experiment with a two-stage system, with one reactor for the TS air treatment followed by the ES of water microdroplets in the second reactor.
# Cooperative Terrestrial-Underwater FSO System: Design and Performance Analysis
## 6 Concluding Remarks  
In this work, a new cooperative system that extends optical-wireless networks from terrestrial to underwater environments is proposed and analyzed. The proposed FSO-TU system consists of two links, a terrestrial link and an underwater link, both joined through a relay node operating in an amplify and forward scheme. In order to analyze the performance of the proposed system, a new closed-form expression for the average BER is derived. This novel expression includes the effect of the main phenomena that degrade the signal quality on both links, some of which have not previously been considered. In particular, the derived expression considers, for the terrestrial link, the effect of path loss, pointing errors and atmospheric turbulence and, for the underwater link, the effect of path loss and oceanic turbulence. The Gamma-Gamma and Weibull statistical distributions are used to model atmospheric and oceanic turbulence, respectively. The analysis of BER performance under different channel conditions and different practical scenarios shows that pointing errors such as atmospheric and oceanic turbulence can seriously degrade the performance of the hybrid system. In addition, ocean turbulence causes the occurrence of a BER floor that limits system performance in some scenarios. It should be noted that the analytical expression of the BER proposed here provides a simple and efficient procedure to estimate the behavior of the system under main channel degradation factors in real scenarios, thus constituting a valuable tool for the design of these systems.  
[PERSON] and [PERSON] defined the scope of the review manuscript. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] compiled the necessary information to elaborate this review manuscript. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] derived the expressions and obtained the results. [PERSON], [PERSON], [PERSON] and [PERSON] wrote the article. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
Figure 5: Average bit error probability of the cooperative FSO-TU system as a function of the SINR for different ocean turbulence conditions and for different ocean link depths in clear ocean water. Favorable conditions are assumed for the atmospheric section defined by weak turbulence conditions, a jitter with \(\sigma_{p}=0.1\) m and \(h_{l}=1\).  
**Funding:** This research was supported by the Spanish Ministerio de Ciencia, Innovacion y Universidades (PID2019-107792 GB-I00).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Depth Segmentation Approach for Egocentric 3D Human Pose Estimation with a Fisheye Camera
## 1 Introduction  
Recently, more people have begun playing virtual reality (VR) games while wearing head-mounted displays (HMDs). During gameplay, it is important to track the user's pose in order to receive user inputs or to synchronize the virtual representation of the user, an avatar, according to the user's pose. Researchers [1] have estimated the full-body pose using the inverse kinematics (IK) method with only a few limb joint positions, but this approach often yields inaccurate results and can lead to a negative user experience [1; 2].  
Many researchers attempted to track the full-body pose. The vision tracking approach is promising because it could be achieved with one or a few cameras, so it is cheap and lightweight, with a high level of compatibility and accuracy [3; 4; 5]. In general, there are two types of vision tracking approaches: outside-in and inside-out [6; 7; 8; 9; 10]. The outside-in tracking approach usually takes full-body images from one or more external cameras [11; 12; 13]. However, this outside-in tracking approach necessitates a camera setup process and requires space and money to accommodate the cameras [14; 15]. To solve these problems, researchers [8; 10] have recently attempted an inside-out tracking approach, where images are captured from cameras mounted on an HMD or other body-worn devices. This approach supports high portability. However, most inside-out approaches rely solely on 2D information, such as heatmaps, and perform 3D lifting to estimate the 3D human pose.  
In this paper, we propose a new inside-out approach for 3D pose estimation using a fisheye camera mounted on an HMD. Our approach regresses depth maps and segmentation maps in addition to the heatmaps. However, regressing all three in a single model is challenging due to optimization difficulties and a high risk of overfitting. To solve these issues, we used two separate backbone networks: one dedicated to heatmap regression and the other dedicated to the regression of depth maps and segmentation maps. Since depthmaps and segmentation maps have similar visual characteristics, we regress them to the same backbone. By integrating information from the segmented depth map (SegDepth) and the heatmap, our method incorporates depth information more effectively than previous approaches that lifted 2D pose into 3D pose. This results in improved accuracy for 3D pose estimation, especially when dealing with depth ambiguity. In evaluations, integrating the SegDepth module into existing architectures (xR-EgoPose [8] and Mo2 Cap2 [10]) improves their performance. These results demonstrate the generality and adaptability of the SegDepth module.  
In this paper, we propose the following:  
* A SegDepth module to effectively extract depth features from highly distorted images.
* Architecture that uses the features of depth map, segmentation map, and heatmap for 3D pose estimation.
* A new end-to-end framework for 3D pose estimation using images from a single fisheye camera mounted on an HMD.
# Security Awareness in Software-Defined Multi-Domain 5G Networks
## 5 Implementation
### Security Inferencing and Anomaly Detection  
The platform for security inferencing is Apache Spark [56]. Spark is a general engine for cluster-based data processing originating from UC Berkeley. It provides specialized data processing libraries (including Structured Query Language (SQL) and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming), which may be combined to create parallelized applications. Spark is designed to be fast, and by supporting mainstream languages (Java, Scala, Python and R languages) it is easily accessible for developers. It can be run as a stand-alone mode or, e.g., within Hadoop--which is a framework for distributed storage (using Hadoop Distributed File System, HDFS) and processing (using MapReduce) of large sets of (history) data.  
The _Monitoring Enabler_ is a Python application that subscribes and processes event information in order to, for example, detect anomalies or occurring attack patterns or to generate security status information for other security/trust components. Inferencing information users at the end of the chain, the _Trust Metric Enabler_ and _Adaptive Security Controller_ are then expected to infer knowledge on slice's security situation, i.e., provide trust metrics or initiate some security responses.  
The anomaly detection mechanism integrated into the current implementation is streaming-\(k\)-means clustering [57; 58] which is an unsupervised machine-learning algorithm. Streaming-\(k\)-means is an adaptation of a K-means algorithm [59; 60], which partitions a set of data points (feature vectors) into \(k\) number of clusters. The original \(k\)-means algorithm learns a clustering model during a training period. Anomalies are then found by calculating the distance of new data points from the cluster centre points and determining whether the distance is larger than the predefined threshold. Streaming-\(k\)-meansprovides support for forgetfulness; the algorithm can learn over time how the clustered data changes. The algorithm is therefore suitable for dynamic environments where devices are connecting and disconnecting to the network and initiating new connections (even in normal non-attack situations). A challenge for the algorithm is to detect attacks that are gradually strengthening or which are variations of previously seen attacks (if those have not been excluded from the clustering model).
# Characterization and Separation of Traditional and Bio-Plastics by Hyperspectral Devices
###### Abstract  
Nowadays, bio-plastics can contaminate conventional plastics sent to recycling. Furthermore, the low volume of bio-plastics currently in use has discourage the development of new technologies for their identification and separation. Technologies based on hyperspectral data detection may be profitably employed to separate the bio-plastics from traditional ones and to increase the quality of recycled products. In fact, sensing devices make it possible to accomplish the essential requirement of a mechanical recycling technology, i.e., end products which comply with specific standards determined by industrial applications. This paper presents the results of the hyperspectral analysis conducted on two different plastic polymers (PolyEthylene Terephthalate and PolyStyrene) and one bio-based and biodegradable plastic material (PolyLactic Acid) in different phases of their life cycle (primary raw materials and urban waste). The reflectance analysis is focused on the near-infrared region (900-1700 nm) and data are detected with a linear-spectrometer apparatus and a spectroradiometer. A rapid and reliable identification of three investigated polymers is achieved by using simple two near-infrared wavelength operators employing key wavelengths.  
plastics; PET; PS; PLA; imaging; separation; spectroscopy; NIR Article
# A New Cascade-Correlation Growing Deep Learning Neural Network Algorithm
## 4 Experimental Results
### Two Spirals Classification Problem  
As shown in Figure 6, the two-spiral problem consists of two interconnected spirals, which has been commonly used to test the network algorithms. In this experiment, we built and trained CCG-DLNN, which can correctly make a classification and make a good generalization of test results. The training set size is 200, 50% of which will generate a \(-1\) and leave 1 remaining. The size of the test data set is 100, which is generated from equal space points in space \([-6,6]\times[-6,6]\). The maximum number of installed hidden neurons is 22. Figure 7 displayed the results obtained from the three separate neural architectures shown in Table 1.  
Figure 6: Training sample of two-spiral problem.  
The proposed algorithm CCG-DLNN has the accuracy of 99.50% after adding two layers with total 22 neurons, However, the growing step at the GP-DLNN algorithm gives an accuracy of 92.23% after adding three layers with a total 70 neurons. The CCNN algorithm performed 87.21% accuracy after adding 10 neurons at 10 layers.
# The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review
## 4 Critical Applications
### Disaster Response and Recovery
#### 4.3.4 Long-Term Resilience Planning
This hybrid approach allows for accurate modeling and adaptation to dynamic changes in the behavior of distributed grid entities. The framework also integrates non-intrusive load monitoring (NILM) techniques to disaggregate the overall energy consumption into individual appliance models. By connecting the hybrid Digital Twins with AI-driven recommender systems, the framework can provide personalized energy efficiency recommendations to end-users based on near-real-time deviations in their consumption patterns. The study demonstrates the potential of the proposed approach to achieve a 36.8% reduction in the net energy consumption through the implementation of the recommendations. However,the scalability and computational complexity challenges associated with large-scale deployments of the framework are not extensively discussed. Additionally, the integration challenges with existing smart grid infrastructure and potential conflicts with established standards and protocols are not thoroughly addressed. Furthermore, the impact of the data quality, availability, and labeling efforts required for training the machine learning models is not comprehensively analyzed. To synthesize the research papers discussed above, Table 4 presents a quantitative analysis of the research focus distribution across the reviewed papers.
## 3 Results and Discussion  
### Temperature Profile  
Figure 2 displays the temperature distribution along the length of the HP for pure pentane and GNP-n-pentane nanofluid at q\({}^{\prime\prime}\) = 5 kW/m\({}^{2}\). As shown, the temperature in the evaporator is high, while gradually reduces in the adiabatic and reaches the lowest at the condenser. For pentane, the temperature of the evaporator is relatively close to the boiling temperature of pentane, hence, there is a potential for incipience of the boiling heat transfer in the evaporator section. If boiling hat transfer occurs in the evaporator, due to the formation of the bubbles, the HTC considerably increases, which can be attributed to the bubble interactions and the local agitation due to the bubble movements close to the walls of the evaporator. Interestingly, for GNP-pentane nanofluid, it can be seen that along the length of the HP, temperature decreases compared to pure n-pentane. This is because; more heat is absorbed by the working fluid due to the increase in the conduction heat transfer and thermal conductivity of the working fluid. For example, in the evaporator region, the average temperature of HP for pentane was 78 \({}^{\circ}\)C, while for nanofluids at wt.% = 0.1, 0.2 and 0.3, it was 75 \({}^{\circ}\)C, 72 \({}^{\circ}\)C and 69.5 \({}^{\circ}\)C, respectively.  
This is because; the GNPs absorb the thermal energy using conduction mechanism and transfer it to the cold region of the working fluid using conduction-conduction and conduction-convection mechanisms. Hence, the heat-transfer mechanism is improved in the evaporator, which in turn improves the total HTC of the HP.  
As can also be seen in Figure 3, the thermal resistance of the HP decreases by increasing the applied heat flux to the evaporator section. This can be ascribed to the increment in the HTC of the HP, which is an intensified at large heat flux value. Also, the thermal resistance showed an asymptotic behavior meaning that at high heat flux conditions, the thermal resistance of the HP is minimized. With adding more nanomaterials to the working fluids, the thermal resistance of the HP is more suppressed. Furthermore, the thermal resistance of the HP at heat fluxes \(>\)25 kW/m\({}^{2}\) is almost constant showing the stability of the working fluid inside the studied system.  
The thermal resistance strongly depends on the temperature difference between the adiabatic and the evaporator section. Initially, for small applied heat, the convective heat transfer is the dominant mechanism of heat transfer, hence, with an increase in the applied heat, the temperature of the working fluid increases reaching the boiling point of the working fluid. Then, with an increase in the applied, only the bubble formation can potentially increase, and temperature of the working fluid remains  
Figure 3: Variation of the thermal resistance with the applied heat flux for n-pentane and nanofluids.  
Figure 2: The temperature distribution of the HP for pentane and GNP-pentane nanofluids.  
constant. Hence, for high applied heat to the evaporator, the thermal resistance or thermal performance off the HP remains unchanged.