esa-sceva/satcom-chunk-collection huggingface dataset:
# Oblique Wide-Angle Multi-Sector Metamaterial Absorber for Space Applications
## 2 Design
### Sectorial Absorber Concept and Objective  
For satellites, the emitting source is positioned close to the platform at a position which is known in advance (Figure 1). The purpose of the absorber is to limit multipath effects which could lead to interferences with other devices positioned on the platform. The close position of the source provides an angle of incidence which is oblique and that covers a large angular range. In this scenario, the common way to design absorbers with optimization at normal incidence is not appropriate. Thus, this article focuses on oblique incidence with \(\theta\) varying from 35\({}^{\circ}\) to 65\({}^{\circ}\). For this range, absorption of 13 dB in the frequency band [2 GHz, 2.3 GHz] is set as objective.  
This kind of performances cannot be achieved with a single low mass planar absorber. The absorbing surface is thus divided into different regions with respect to the incoming angle, as represented in Figure 1. For each region, the dimensions of the unit cell are optimized separately for a specific and narrower range of angle of incidence. The design of each sector thus becomes relatively simple and can be addressed using low mass structures and well-known FSS patterns like rectangular patches. The final step is to join and combine these different sectors together to increase the absorbing range of incidence of the whole structure.
# Damage Evolution Analysis in a \"Spaghetti\" Bridge Model Using the Acoustic Emission Technique
## 4 Results
### Frequency Fluctuations during the Damage Process  
The AE data set was divided into five intervals to evaluate frequency fluctuations, as shown in Figure 13. For each interval, the results from computing the Power Spectral Density (PSD) were plotted in the logarithmic scale, with a linear fit being applied to the region where the frequency distribution approximates a straight line in the bi-log dominium, i.e., in the range from \(10^{3}\) to \(10^{4}\) Hz. The angular coefficient for the resulting fitting line is the parameter \(\gamma\) that is used to evaluate frequency fluctuation. Frequencies that are below 1 kHz cannot be reliably traced to the damage process because of interference with the structure's natural vibration modes. The marked attenuation for frequencies that are above 30 kHz is due to the anti-alias filtering embedded in the signals' electronic conditioning apparatus.  
Still, in Figure 13, the variation of \(\gamma\) is compared to load distribution and the cumulative number of events. The decrement of \(\gamma\) (and also of \(\epsilon\), as already shown in Figure 12), means that a wide band of frequencies is activated at each event, which agrees with the conclusions by [PERSON] [6] regarding unstable physical phenomena.  
Figure 13: Evolution of \(\gamma\) throughout the AE test.
# BLE Signal Reception and Localization Performance with Varying Receiver and Beacon Setups
## Appendix A  
In the appendix, signal maps are presented, with each figure corresponding to an individual gateway receiver. The figures depict normalized signal values received from five distinct BLE devices, ranging from 0 to 2 in height on the Z axis. The first column displays signal measurements at an orientation of \(0^{\circ}\), while the second column illustrates measurements at an orientation of \(180^{\circ}\).
## 5 Discussion and Future Work
### Optimal Risk Prediction Model
#### 5.2.2 Comparison with Other Study  
This section shows a performance comparison of the proposed model with existing research in predicting building fire risk. Table 10 shows the performance test of the model proposed in this study with the results obtained by existing research. Most of the existing studies only report accuracy results, and less for F1-score and AUC (including auroc and auroc). According to Table 10, the proposed model outperforms existing related studies. In addition to gradient boosting incorporating k-fold cross-validation, the top three top models reported in Section 5.2.1 have achieved over 92% accuracy and over 81% F1-score, and over 90% AUC score. This performance test demonstrates that the prediction model proposed in this study is acceptable in terms of risk prediction compared to existing research on building fire risk prediction.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **ML Algorithm Used** & **Accuracy** & **Recall** & **F1-Score** & **Auroc** & **Auroc** \\ \hline [PERSON] et al. [11] & Deep Neural & 75.1\% & — & — & — & — \\  & Network & & & & & \\  & TrAdaBoost (a typical & & & & \\ [PERSON] et al. [12] & transfer learning & 89.0\% & **88.0\%** & — & 89.0\% & — \\  & method) & & & & & \\  & SVM & 78.0\% & — & — & — & — \\  & [PERSON] et al. [24] & K-nearest Neighbor & 92.4\% & — & — & — & — \\  & [PERSON] et al. [25] & AdaBoost (CART) & 71.0\% & — & 69.0\% & — & — \\  & XGBoost (Test with balanced data) & 91.0\% & — & — & — & — \\  & [PERSON] et al. [27] & Logistic Regression & — & 80.3\% & 78.3\% & — & — \\  & [PERSON] et al. [28] & Random Forest & — & — & — & 91.0\% & — \\  & [PERSON] et al. [29] & Neural Networks & — & 55.8\% & 40.0\% & 76.3\% & — \\  & [PERSON] et al. [30] & Random Forest & 91.2\% & — & — & — & — \\  & [PERSON] et al. [31] & Neural Networks & 89.1\% & 59.3\% & 70.1\% & — & — \\  & Gradient boosting & & & & & \\  & with RFE-Top20 & & **93.2\%** & 84.3\% & **81.9\%** & **95.9\%** & **88.8\%** \\ Proposed model & features using K-fold & & & & & \\  & Cross-Validation & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of the performance achieved by the proposed model with existing research.  
Figure 13: Precision-recall curves for six machine learning algorithms using REF-Top20 by k-fold cross-validation.
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 5 Experimental Results and Discussion
### Experimental Results  
The experiment was performed on six standard 512 \(\times\) 512 grayscale images with 256 gray levels. The images are titled Baboon, Lena, Peppers, Airplane, House, and Lake. First, compression and encryption of these images were performed with IWT-based SPIHT. Then, this bit stream was further compressed along with maximum confusion diffusion operations via Kd-tree. Finally, an additional level of security was achieved via encryption through a varying parameter logistic map and modified quadratic chaotic maps. Experimental results demonstrate that our method provides maximum compression with a high level of security, which are the two core objectives in this research. Figure 7 shows Lena's encrypted results for the wavelet coefficients under various sizes of diffusion.  
Figure 6: Reverse encryption and compression process.  
Figure 7: Experimental results. (**a**) Lena original grayscale image; (**b**) Lena reconstructed image; (**c**) Reconstructed with an incorrect key value.
# SDN-Based Routing Framework for Elephant and Mice Flows Using Unsupervised Machine Learning
## 4 Results and Analysis of Experiment
### Experimental Design
#### 4.1.4 Identifying Flow  
One of the serious problems that affects the quality of service for mice flows is a slow-down transfer caused by elephant flows through a network, which leads to a degradation of network performance. The identification mechanism was proposed to distinguish the flows as elephants and mice by leveraging some of the parameters clarified in Section 4.1.3. Each elephant or mice flow is defined based on the threshold values marked on pre-decided features using the thresholding method [32]. The final step in this phase includes representative flow extraction for the elephant and mice flows in each cluster that has resulted from the clustering process. It is interesting in this step that, in some clusters, one or two representative flows can be extracted for elephant or mice flows.
## 2 Research Methodology
### Content Transfer Protocols  
The adapted methodology for this case study was started by investigating the recent protocols that are recommended for this system. It was found that there was an issue regarding the arrival time, which included the delay of video streaming segments. Therefore, depending on the investigation results, three protocols have been proposed to solve this delay issue. These protocols are FASP, File Catalyst protocol (FCP), and Signiant. They are considered as the most adequate protocols for delivering adaptive video streaming segments. These protocols have been selected based on delay time (protocols with a minimum arrival time). In Figure 2, the proposed protocols between the original and edge servers in a cloud-CDN are described.
## 5 Performance Evaluation
### Simulation Results
In this phase, the UAV BS is located in the above-mentioned simulated scenario and an arbitrary uniform distribution for the ground users is considered. Figure 3: \(Q\)-learning algorithm convergence in terms of rewards for different set of weights for 2000 episodes. The testing phase is conducted performing a certain number of runs, to average the results with regard to the specified parameters. Figure 4 shows the single UAV BS position optimization for different set of weights and the results are the normalized version of the average values between 0 and 1. The goodness of considering a trade-off between coverage and energy consumption, achieved by means of the two different weights, is mostly visible in two of the five experiments. In particular, for the \(w_{1}=\)0.2 and \(w_{2}=\)0.8, the best average energy consumption is achieved, whereas for the weights \(w_{1}=\)0.0 and \(w_{2}=\)1.0, best overall coverage, and rewards are obtained. In other words, the proper performances of the above-mentioned trade-off can be observed from the results in Figure 4. Effectively, when energy consumption is not prioritized, the UAV BS finds the optimum position in fewer episodes but at the expense of a higher energy consumption, conversely in the remaining cases. Therefore, the designed weighting mechanism works well, as the performance of the \(Q\)-learning algorithm is deeply affected by the numerical values of the weights. However, these results does not only affirm that the weighting mechanism works, but also gives superiority to the proposed approach, as it can converge to a solution according to the requirements of the network operators.
# An Implementation of the HDBSCAN* Clustering Algorithm
## 2 Background and Literature Review
### Hdbscan  
More recently, the HDBSCAN algorithm [3] was introduced. It stands for Hierarchical DBSCAN. The asterisk suffix indicates an improvement the same authors of [3] make to DBSCAN. These authors extend their work in [8] by introducing a complete framework for cluster analysis and outlier detection which includes an enhanced explanation of the HDBSCAN algorithm. HDBSCAN improves on DBSCAN by establishing a hierarchical representation of the clusters. The hierarchy derived from the execution of the algorithm can be used very effectively for cluster extraction and outlier detection. HDBSCAN overcomes the limitations of DBSCAN by identifying clusters of any density. Although there are several benefits provided by the HDBSCAN algorithm, there is one drawback that should be considered. The algorithm has an overall asymptotic complexity of \(O(n^{2})\). Furthermore, there are multiple sub-steps of the algorithm, which themselves have a complexity of \(O(n^{2})\). This complexity can have a significant impact on the algorithm execution time for large datasets. There are implementations of the K-Means algorithm mentioned earlier, which have a complexity of \(O(n)\). Table 1 summarizes the strengths and weaknesses of the DBSCAN and HDBSCAN algorithms.
# Cloud-Edge Collaborative Model Adaptation Based on Deep Q-Network and Transfer Feature Extraction
## 3 Model Framework
### Reinforcement Learning Decision and Strategy Mechanism
#### 3.1.1 State Vector Construction  
For each target category \(c\in C\), a state vector \(S_{c}\) is constructed, which includes the following three components:  
\[Sc=\left(acc_{c}^{edge},acc_{c}^{cloud},\Delta_{c}\right) \tag{4}\]  
\(acc_{c}^{edge}\): The detection accuracy rate of the edge-side model for category \(c\), calculated as \(acc_{c}^{edge}=\frac{N_{total,c}^{edge}}{N_{sensors\ (satellites)}}\).  
\(acc_{c}^{cloud}\): The detection accuracy rate of the cloud-side fusion model for category \(c\), calculated as \(acc_{c}^{cloud}=\frac{N_{total,c}^{total}}{N_{sensors\ (satellites)}}\).  
\(\Delta c=acc_{c}^{cloud}-acc_{c}^{edge}\) is the difference between the cloud-side and edge-side accuracies, used as an immediate reward signal in reinforcement learning. This reward \(r_{c}=\Delta_{c}\) reflects the accuracy improvement brought by the migration operation at the edge, which serves as the core feedback signal for Q network optimization.  
\(N_{sensors\ (satellites)}\): Total number of sensors (satellites).  
\(N_{total,c}^{edge}\) : The total number of sensors (satellites) in a real aircraft that are determined to be of category \(c\) by the edge model.  
\(N_{total,c}^{cloud}\): The total number of sensors (satellites) in a real aircraft that are determined to be of category \(c\) by the cloud model fusion.
## 4 Findings
### Providing Security Metadata
To monitor device authenticity, experts suggested the inclusion of information on either industry or government maintained standards for various devices in the IoT-Health network (for example, The Therapeutic Goods Administration (TGA), Health Insurance Portability and Accountability Act (HIPAA), U.S. Food and Drug Administration (FDA)), as included in the last row of the table.
## 5 Factors Affecting the Anaerobic Digestion Process and Biogas Production
### Co-Digestion of Two Substrates  
In a biogas production process, anaerobic microorganisms have different requirements of organic and micronutrients for their growth and degradation of substrates. These nutritional requirements of microorganisms are usually not satisfied by the digestion of single substrates. As a result, a combination of two or more substrates can be co-digested. The suitability of substrates for biogas production is determined by their primary nutritional composition, including carbohydrates, proteins, and lipids [61]. This nutritional composition greatly influences biogas yield and methane content produced. Suppose a substrate has an imbalance in carbon to nitrogen ratio, such as animal manure. It can be co-digested with a carbon-rich substrate to reimburse for the imbalance, thus, obtaining improved process stability and biogas production [49]. Thus, co-digestion of sugar industry wastewater and Tunisian green macroalgae has been conducted to enhance biogas and methane production [110]. Further, [PERSON] et al. optimized biogas production through co-digestion of the organic part of municipal solid waste and chicken manure [111]. Other examples of substrates used in co-digestion are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Feedstock 1** & **Feedstock 2** & **Temperature (\({}^{\circ}\)C)** & **Optimal pH** & **HRT (Days)** & **Biogas/Methane Yield (L)** & **Reference** \\ \hline Fruit and vegetable waste & Sewage sludge & 20–30 & 4.1 & 105 & 331 & [112] \\ \hline Leather flashing (LF) & MSW & - & 6.5 & 30–35 & 6.518 & [113] \\ \hline Taihu algae & Kitchen waste & 35 & - & 1 & 0.388.6 & [114] \\ \hline Horse dump & Cow dung & 28–33 & - & 30 & 0.360 & [115] \\ \hline Dairy manure & Food waste & 35 & - & 20–30 & 0.311 & [116] \\ \hline Whole stillage & Cattle manure & 37 & 5.9–6.6 & 640 & 0.310 & [117] \\ \hline Coffee-pulp & Cow dung & 35 & 7.0 & 240 & - & [118] \\ \hline Food waste & Straw & 35 & 7.0–7.5 & - & 0.580 & [119] \\ \hline Municipal wastewater & Poultry waste & 35 & 7.3 & 34 & 0.88 & [120] \\ \hline Fruit vegetable waste & Sugarane & - & 3.9–7.0 & 30 & 2.600 & [121] \\ \hline Water hyacinth & Sugar mill & \multirow{2}{*}{30, 40} & \multirow{2}{*}{6.4–8.8} & \multirow{2}{*}{15} & \multirow{2}{*}{6.771} & \multirow{2}{*}{[122]} \\ effluent & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Previously reported studies on biogas and methane production through a co-digestion of different types of feedstocks at diverse operating parameters.  
The above table clearly shows that not only in SA but around the globe too there has been a lack of co-digesting sugarcane process effluent and municipal solid waste for biogas production. This shows that there is a gap in the research regarding the use of these two substrates as co-substrates, both locally and all around the world.
# Single-Stage Step-Down Power Factor Corrector without Full-Bridge Rectifier
## 5 Experimental Results
### Measured Curves of THD, PF, and Efficiency  
Figures 35 and 36 show the curves of THD and PF versus output power at different input voltages, respectively. From Figure 35, it can be seen that under the same output power, the lower the input voltage, the higher the THD. This is because there is more distortion current created from the lower input voltage. From Figure 36, it can be seen that under the same output power, the lower the input voltage, the lower the PF. This is because there is more difference in phase between the input voltage and the fundamental input current. From this figure, it can be seen that the PF can be kept above 0.88. And, the maximum PF is 0.95. Figure 37 shows the curve of efficiency versus output power at different input voltages. From this figure, it can be seen that the efficiency can be maintained above 95%. And, the maximum efficiency is 96.8% and can be achieved at the lowest input voltage under the minimum output power. In addition, the power losses at load under input voltages of 90 V, 110 V, and 130 V are 4.342 W, 4.064 W, and 3.817 W, respectively.  
Figure 34: Output voltage ripple under 100% load at input voltages of (**a**) 90 V; (**b**) 110 V; (**c**) 130 V.
# Pharmaceuticals in Coastal Waters: An UHPLC-TOF-MS Multi-Residue Approach
## 2 Materials and Methods
### Validation Procedure  
Given the lack of specific legislation setting maximum residue levels (MRL) for pharmaceuticals in environmental waters, with the exception of those included in the Water Framework Directive [15], validation followed the Commission Regulation 2021/808 on the performance of analytical methods for residues of pharmacologically active substances used in food-producing animals and on the interpretation of results [16]. The optimized method was validated quantitatively with the following parameters: specificity, selectivity, precision and linearity. However, since the WFD sets the maximum acceptable method detection limit, the limit of detection (LOD) and limit of quantification (LOQ) were calculated instead of the decision limit (CC\({}_{\alpha}\)) and the detection capability (CC\({}_{\beta}\)).  
Identification criteria were based on the accurate mass measurement of the base ion with an error \(<\) 5 ppm (\(\Delta\)m), a maximum variation of 2.5% in the relative retention time (RRT) of the analyte compared with the standard and an isotope ratio difference lower than 10% when compared with the theoretical pattern. Figure 1 presents the example of the identification of oxytetracycline. The following equations were used to calculate the \(\Delta\)m and RRT [17].  
Equation (1): relative retention time (RRT)  
\[\text{RRT}=\frac{\text{RT}_{\text{analyte}}}{\text{RT}_{\text{internal standard}}} \tag{1}\]  
where RT\({}_{\text{analyte}}\) is the retention time of the analyte, and RT\({}_{\text{internal standard}}\) is the retention time of the internal standard.  
Equation (2): Deviation of RRT (\(\Delta\)RRT)  
\[\Delta\text{RRT}\left(\%\right)=\left(\frac{\text{RRT}_{\text{spiked samples}}-\text{RRT}_{\text{standard}}}{\text{RRT}_{\text{standard}}}\right) \times 100 \tag{2}\]  
Equation (3): Deviation of exact mass (\(\Delta\)m)  
\[\Delta\text{m (ppm)}=\left(\frac{\text{Exact mass}-\text{Detected mass}}{ \text{Exact mass}}\right)\times 10^{6} \tag{3}\]  
The isotope ratio difference by the overlap of spectrums is automatically calculated by the software PeakViewTM 2.2 and LibraryViewTM 2.2.  
Validation procedure and quality control samples (spiked samples) were performed using coastal water samples, collected and previously analyzed to prove they were blank samples. For the selectivity and specificity study, 20 blank coastal water samples were analyzed to assess if any interference could compromise the accurate identification of the target compounds. Additionally, the same samples were spiked with all compounds to verify the detection capability through the identification criteria, which also allowed the evaluation of precision (repeatability and reproducibility) in terms of relative standard deviation (RSD) and the recovery. The linearity was evaluated in a concentration range between 0.8 and 6 ng/L,and the LOD and LOQ were estimated in accordance with the observed signal-to-noise ratio in the spiked samples and blank samples, respectively 3:1 and 10:1.
## 5 Discussion
### Composition in Cathode Deposit  
As represented in Table 1, Nb could be easily suppressed at the Zr-2.5 Nb anode; however, Fe and Ni were deposited at the cathode against expectations. This trend was consistent across all tests. Since the standard reduction potential of Nb, Fe, Co, and Ni was \(-\)1.61, \(-\)1.473, \(-\)1.279, and \(-\)1.299 V (vs. Cl\({}_{2}\)/Cl\({}^{-}\)), respectively, the oxidation tendency of Nb was stronger than that of other alloying elements [16; 26]. The composition of Nb in the cathode deposit was much higher than that of the others as well. This could cause the difference of anodic equilibrium potential between Nb and alloying elements to become wider. Therefore, if a very small amount of Nb was dissolved from the anode and deposited on the cathode, it was reasonable to consider that Fe, Co, and Ni were not dissolved from the anode at all. The deposited content of Fe and Ni could be regarded as background impurities in the initial LiCl-KCl salt.  
The concentrations of Nb were analyzed to be much lower than in the literature [12]. They were involved with a low applied current density on the anode specimen. Hence, the anodic potential could be maintained below the redox potential of Nb during the electrorefining. Unlike a lab-scale experiment, a pilot- or commercial-scale process desires a high current density which results in a high throughput, and that would induce the anode potential rise. An enhanced mass transfer using an electrolyte mixing system, such as rotating electrodes, stirrs, and baffles, can mitigate the potential elevation at the anode. Additional investigations with experimental and computational approaches are required to quantify the stirring effects on the potential change.
# An Overview of Indoor Positioning and Mapping Technology Standards
## 4 Our Research on IPMN Standards
### Multi-Source Fusion Positioning Data Interface  
This kind of standard specifies the data interface among positioning engines. Usually, there are two kinds of positioning engines, one is the main engine, which integrates positioning results from different technologies, and the others are called sub-engines. Each sub-engine involves typically an individual technology, as illustrated in Figure 10.  
The protocol for information exchange between two engines is based on request/response mechanism, but not specified with concrete protocols in this standard; however, both the sensor data and the positioning results are specified in detail.  
Related standards specified the data structure, format, type, and etc. of individual positioning technologies, such as GB/T 38627-2020 [102], specify the magnetic positioning data interface. Other indoor positioning standards, such as visual positioning data interface, protocols for information superposition based on ubiquitous location, and so on are planned or are under development.