esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optimization of the POD-P Parameters by NBA  
To ensure the optimal performance of the POD-P controller, accurate tuning of the parameters (KPOD, Tw, Tb1, Ta1, Tb2, and Ta2) is crucial. Proper tuning directly impacts the system's ability to effectively damp oscillations and maintain stability in the presence of fluctuating renewable generation. This article employs the NBA to optimize these parameters, enhancing the damping ratio of the electromechanical mode. By improving the damping ratio, the system's stability is reinforced, ensuring robust performance under perturbances. The NBA is chosen due to its proven efficiency and superior convergence properties, as demonstrated in [47]. This includes its ability to handle complex, multi-modal objective functions and success in tuning controllers within high-renewable-penetration systems. Specifically, it has been shown to outperform traditional optimization methods, such as PSO, by achieving faster convergence and better computational efficiency under similar scenarios. Its robustness, particularly in systems with varying dynamic conditions, such as those in PV plants integrated into modern grids, makes it a reliable choice [47].  
The NBA, an enhanced version of the original Bat Algorithm (BA) developed by [PERSON] in 2010 [48], is inspired by the echolocation behavior of bats. In this algorithm, bats navigate by emitting sound waves and analyzing returning echoes, a process simulated to explore and exploit the solution space effectively. Introduced by [PERSON] in 2015 [49], the NBA incorporates adaptive Doppler compensation, stochastic habitat selection, and inertia weight adjustments to improve search efficiency. This algorithm is particularly effective for dynamic systems characterized by high renewable penetration.  
In the NBA, each bat represents a candidate solution characterized by a position, velocity, and frequency, which are updated iteratively to improve the objective function. For the POD controller, the system's damping ratio is chosen as the objective function [50]. The limit used in this objective function has been selected according to Spanish operating procedures [51], which establish a damping ratio threshold of 5% to define acceptable system stability, with higher damping ratios indicating greater stability. The mathematical formulation of the algorithm is thoroughly explained in [47] and [49].  
The POD-P parameters, KPOD, Tw, Tb1, Ta1, Tb2, and Ta2, are constrained within a range based on the operational limits specified in [16]. The NBA iteratively refines these parameters, balancing exploration (searching the solution space) and exploitation (refining promising solutions). This study runs the NBA with 30 bats and 50 iterations. As a result, the POD-P controller achieves an optimal configuration, producing a corrective signal, dP_POD, that dynamically adjusts the PPC's active power setpoint, effectively damping oscillations and maintaining stability. The optimized parameters for each POD-P controller are determined using a base scenario and remain fixed throughout all subsequent simulations conducted in this research.
## 2 Reed-Solomon Codes  
Reed-Solomon codes are linear and cyclic block-based FECs [18]. This group of FECs similarly adds redundant or parity information to the original message during the encoding. This, accordingly, provides RS Codes the capability to detect and recover the possibly corrupted data through the decoding steps.  
A primitive \((n,k)\) RS Code has a data word length of \(k\) symbols and a code word length of \(n\) symbols [8]. These symbols are defined over a finite field \(GF(q^{m})\), with a symbol size of \(m\) bits and a prime base of \(q\)[19]. In this paper, the \(q\) value is set to 2 as all data words are transmitted in the binary format [20]. Based on the imposed redundancy, the correction capability of primitive RS Codes is bounded to \(s\) symbol errors, where \(n-k=2s\). This indicates that up to \(2s\) symbols errors can be detected by RS Codes.  
In this paper, the code word, \(C(x)\), will be presented in the symbol format or its binary equivalent, as shown in Equation (1).  
\[C(x)=[D_{1},\ldots,D_{i},P_{1},\ldots,P_{j}], \tag{1}\]  
Here, data word symbols and parity symbols are denoted with \(D_{i}\) and \(P_{j}\), respectively, where \(1\leq i\leq k\) and \(1\leq j\leq 2s\).  
Note that the same encoding and decoding steps, which were previously employed in [20], will be used within this paper.
## 4 Validation and Analysis
### Data Preparation  
It is important to point out that cutting ability evaluation cannot be directly calculated based on the monitoring data from the Jilin water transport tunnel according to Equation (3). However, to validate the classification performance of the proposed model, the datasets were divided into two groups. Only short pieces of data before disc cutter replacements were classified as \"replacement needed\" data. The data of working days on which no disc cutter replacements occurred were randomly selected as \"no replacement needed\" data. More specifically, the \"replacement needed\" data were constructed from the data before replacements in a total of 278 cases, and the \"no replacement needed\" data were  
Figure 5: Number of disc cutter replacements at each position.  
constructed from the data of a total of 416 no-disc-cutter-replacement days. The detailed data preprocessing procedures are shown below.
# A Robust Sharding-Enabled Blockchain with Efficient Hashgraph Mechanism for MANETs
## 5 Asynchronous Intra-Sharding and Inter-Sharding Consensus
### Principle of Tree-Assisted Inter-Sharding Consensus  
The basic requirement of inter-sharding consensus is to guarantee the consistency and atomicity of a cross-shard transaction. The principle of the tree-assisted inter-sharding consensus is that cross-shard transactions are updated first and then confirmed so that eventual consistency is achieved. Since a tree is proposed to include the cross-shard transaction and its related transactions, the atomicity of the cross-shard transaction can also be guaranteed. The details of this principle are described as follows.  
* A cross-shard transaction is generated and updated to the graph of the local shard and sent to involved shards.
* Transactions related to the cross-shard transaction are generated and updated by involved shards in parallel. They refer to the cross-shard transaction and are sent to other involved shards, constructing the tree globally.
* When the cross-shard transaction is used, the number of child nodes of the cross-shard transaction and their validity are checked. Only if the cross-shard transaction is validated by all involved shards is it confirmed. Otherwise, if a given time is expired, the cross-shard transaction is revoked by a newly updated transaction, and the event containing the cross-shard transaction is determined to be a False Event and added into the blacklist.  
Thus, the efficiency of updating valid cross-shard transactions is improved. Its update time is only related to the delay of consensus in a local shard. The inter-sharding consensus is suitable to the MANET, since the communication between shards is opportunistic. In addition, the generation of cross-shard transactions is accountable.
# Building Penetration Losses at 3.5 GHz: Dependence on Polarization and Incidence Angle
## 3 Experimental Set-Up
### Building Facades  
Four different types of building facades were considered for this study. Two of them, shown in Figures 8 and 9, are 30 cm width and are made of two brick walls separated by  
Figure 6: Results of a S\({}_{21}\) measurement as a function of frequency: (a) amplitude; (b) phase.  
Figure 7: Identification of the different propagation contributions in the delay domain.  
an air chamber. The brick walls are made of hollow clay bricks glued by mortar. Small stones cover the exterior surface, made by concrete, providing a rough surface. The inner face is plastered and painted by plastic paint. A schematic representation can be seen in Figure 10. For the facade with a window's case, about half of the energy of the antenna is transmitted through the window and the other half across the facade. The windows are made of a single-glass pane within an aluminium frame.  
Figure 8: Building facade with windows.  
Figure 9: Building facade with no windows.  
The third case analyzed (see Figure 11) corresponds to a 15 cm facade made of a thin brick wall covered with plastic paint on both sides. The last facade analyzed was fully made of double glass pane with an air gap in the middle and aluminium frames, as we can see in Figure 12.  
Figure 11: 15 cm facade made of brick.  
Figure 10: Building facade schematic.  
Measurements were taken at up to six spots placed with regular spacing along the facades for each type of facade. A total of ten frequency sweeps were performed with the VNA for each location and incident angle.
# Carbon Nano-Onion Peroxidase Composite Biosensor for Electrochemical Detection of 2,4-D and 2,4,5-T
## 4 Conclusions  
We explored the possibility of using CNO-modified electrodes for the construction of an amperometric enzyme biosensor for phenoxy herbicides based on HRP immobilized on CNOs and further cross-linked with a cyclodextrin polymer. Deposition of this composite on screen-printed electrodes allowed the development of highly sensitive biosensors for 2,4-D and 2,4,5-T on soil and water samples based on the inhibition of HRP activity. The incorporation of CNOs had a positive effect in enhancing the sensitivity due to the large surface area combined with the enhanced electron transfer properties of these nanoparticles.  
Figure 8: Storage stability and reuse cycles (inset) of SPE/CD/CNO/HRP and SPE/CD/HRP biosensors.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Herbicide** & **Sample** & **Method** & **Concentration** \\ \hline \multirow{3}{*}{2,4-D} & \multirow{3}{*}{Soil treated with 2,4-D} & SPE/CD/CNO/HRP biosensor & (11 \(\pm\) 3) mg/kg \\  & & ELISA & (9.6 \(\pm\) 0.5) mg/kg \\ \hline \multirow{3}{*}{2,4,5-T} & River water spiked with & SPE/CD/CNO/HRP biosensor & 3.0 uM (added) \\  & 2,4,5-T & & (2.85 \(\pm\) 0.05) uM \\ \cline{1-1}  & & & (found) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analysis of soil and spiked river water samples.  
CNOs also proved beneficial to improve their stability and repeatability. It could thus be expected that this strategy can be further extended to develop portable pesticide detection systems for in-field agricultural or environmental applications.  
V.S.: investigation, data curation. A.F.: conceptualization, writing--review and editing, supervision. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministerio de Economia y Competitividad, Spain (Grant BIO2012-30936 to A.F.) and Generalitat de Catalunya (pre-doctoral scholarship 2015 FI_B00523 to V.S.).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
# An Automatic 3D Point Cloud Registration Method Based on Biological Vision
## 2 Cone Vertex Detection
### Cone Vertex Recognition  
The Harris operator can be used to detect corners in all simulated images \(I^{u}(u=1,2,\)\(\ldots,3s-1,3s)\). \(\overline{Q}_{v}^{u}(u=1,2,\ldots,3s-1,3s)(v=1,2,\ldots,r_{u}-1,r_{u})\) represents the detected \(v\)-th corner in image \(I^{u}\). Its pixel coordinates are notated as \(\overline{q}_{v}^{u}\), where \(r_{u}\) is the number of all detected corners in image \(I^{u}\). Then, its corresponding point \(\overline{q}_{v}^{u}\) in 2D space can be calculated according to Equation (12) as:  
\[\widetilde{q}_{v}^{u}=\overline{q}_{v}^{u}-I_{c}^{u}+\widetilde{p}_{c}^{u}. \tag{12}\]  
The closest point \(\widetilde{p}_{I\overline{q}}^{u}\) can be searched for \(\widetilde{q}_{v}^{u}\) in the 2D point cloud.  
\[h_{v}^{u}=\underset{i=1,2,\ldots,n-1,n}{\min}\,dis(\widetilde{q}_{v}^{u}, \widetilde{p}_{i}^{u}), \tag{13}\]  
where \(h_{v}^{u}\) is the index of \(\widetilde{p}_{I\overline{q}}^{u}\), in 2D point clouds.  
Since the corresponding relationship of the points remains unchanged during the projection process from a 3D point cloud to a 2D point cloud, the corresponding point in the 3D point cloud of \(\overline{q}_{v}^{u}\) can be written as \(P_{I\overline{q}}^{1}\).  
The clustering of \(\left\{P_{I\overline{q}_{v}^{u}}^{1}\right\}\) is executed on the basis of Euclidean distance according to the following steps:  
(a) \(\eta\) is notated as the number of categories.  
\[\eta=\sum_{u=1}^{3s}r_{u}+1 \tag{14}\]  
\(r_{u}\) is the number of all detected corners in image \(I^{u}\), 3\(s\) is the number of all simulated images.  
(b) \(d_{ij}\) represents the distance between the centers of \(C_{i}\) and \(C_{j}\). \(C_{i}\) and \(C_{j}\) is the \(i\)-th and \(j\)-th category. If the minimum of \(\left\{d_{ij}\right\}\) is smaller than the distance threshold \(\lambda\), cluster \(\left\{P_{I\overline{q}_{v}^{u}}^{1}\right\}\) into \(\eta\)-1 categories and \(\eta=\eta\)-1.  
3. Repeat step (b) until the minimum of \(\left\{d_{ij}\right\}\) is equal to or greater than \(\lambda\).
4. The coordinate of each clustering center can be obtained by calculating the mean value of members in its corresponding category.  
\(\psi_{i}\) represents the number of members of \(C_{i}\). Since the cone vertex is a robust feature point, it should be observed from the most viewpoints. Thus, all values of \(C_{i}\) that satisfy \(\psi_{i}\leq\kappa\) should be deleted, as shown in Figure 4c, where \(\kappa\) is a threshold set for the number of observations. Figure 4d shows the rough localization result of a cone vertex. The detected cone vertex is notated as \(\chi_{i}(i=1,2,\ldots,\rho-1,\rho)\), where \(\rho\) is the number of all detected cone vertices.
## 2 Materials and Methods
### Sample Preparation  
All the mineral powders were dried in an oven at 250 \({}^{\circ}\)C to constant weight for 24 h. In this procedure, almost all of the water was removed. Then, the mineral powders were mixed at the fixed proportions based on the dry weight. Eight clay-quartz mixtures were prepared in the laboratory. All the mixtures consisted of montmorillonite and quartz or kaolinite and quartz. The clay mineral (montmorillonite or kaolinite) contents in the mixtures increased from 40% to 100% with the increment of 20%. The mixtures were named after the kind of clay mineral and clay mineral content. As an example, K60 consisted of kaolinite and quartz and the kaolinite fraction was 60%. Next, distilled water was added to the mixtures until the moisture content reached the plastic limit. Afterward, all the eight mixtures were put into plastic sealing bags for two days to reach a full hydration state. Following the hydration period, the mixtures were put into the hollow cylinder with the inner diameter and height of 31.9 mm and 80 mm respectively, and a jack was used to shape the mixtures under static pressure. All the formed mixtures had reached the maximum dry density at the plastic limit. Finally, all the mixtures were saturated with distilled water by the vacuum method. The mixtures were taken  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Property**} & \multicolumn{3}{c}{**Materials**} \\ \cline{2-4}  & **Quartz** & **Kaolinite** & **Montmorillonite** \\ \hline
**Aterberg limits** & & & \\ Plastic limit (\%) & – & 32.00 & 61.34 \\ Liquid limit (\%) & – & 68.99 & 178.65 \\ Plasticity index & – & 36.99 & 117.31 \\
**Grain size distribution** & & & \\ Clay (\%;\(<\)0.005 mm) & 1.63 & 70.23 & 84.10 \\ Silt (\%; 0.005–0.075 mm) & 4.15 & 29.77 & 15.90 \\ Sand (\%; \(>\)0.075 mm) & 94.22 & 0 & 0 \\
**Chemical composition** & & & \\ SiO\({}_{2}\) (\%) & 98.85 & 57.82 & 66.63 \\ Al\({}_{2}\)O\({}_{3}\) (\%) & 0.57 & 35.24 & 16.07 \\ K\({}_{2}\)O (\%) & – & 3.89 & – \\ Fe\({}_{2}\)O\({}_{3}\) (\%) & 0.08 & 1.86 & 6.50 \\ Ca\(\Delta\)O (\%) & 0.18 & – & 3.99 \\ MgO (\%) & – & 0.37 & 5.32 \\
**Specific gravity** & 2.68 & 2.76 & 2.61 \\
**Classification** & – & CH & CH \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic properties of quartz, kaolinite, and montmorillonite used in this study.  
out from distilled water once the weight has reached maximum limit. NMR and evaporation tests were carried out on the eight mixtures after surface water was erased.
## 2 Materials and Methods
### Hypotheses and Variable Definition  
The key variables were as follows:  
* Independent: virtual-physical interaction intensity (X\({}_{1}\)), privacy budget hierarchy (X\({}_{2}\)), and assessment dimensionality (X\({}_{3}\));
* Mediating: spatial perception (M\({}_{1}\)) and collaborative awareness (M\({}_{2}\));
* Dependent: tactical execution accuracy (TEA) (Y\({}_{1}\)), decision time (Y\({}_{2}\)), and tactical creativity (Y\({}_{3}\)). Our hypotheses were as follows:
* H\({}_{1}\): X\({}_{1}\) positively influences M\({}_{1}\) (\(\beta_{1}\) = 0.68, \(p\) < 0.01), enhancing Y\({}_{1}\) and reducing Y\({}_{2}\).
* H\({}_{2}\): X\({}_{2}\) improves model generalization, indirectly boosting M\({}_{2}\) (\(\beta_{2}\) = 0.42, \(p\) < 0.05), thereby elevating Y\({}_{2}\) and Y\({}_{3}\).
* H\({}_{3}\): X\({}_{3}\) positively moderates outcome sustainability: the3D assessment groups show 15% lower skill decay than 1D groups (\(\Delta\) = 0.15, \(p\) = 0.013).  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Objective** & **Method** & **Tools/Parameters** & **Rationale** \\ \hline Pedagogical effect comparison & Mixed-design ANOVA & SPSS27, [PERSON]’s test (W = 0.62, \(p\) < 0.05), with Greenhouse-Geisser correction (\(\epsilon\) = 0.85) & Handles within/between-group interactions \\ \hline Privacy–performance tradeoff & Federated learning evaluation & PySyft, \(\epsilon\) = 0.3/0.8 differential privacy & Quantifies accuracy loss vs. communication cost \\ \hline Cognitive load mechanisms & Grounded theory coding & NVivo12, [PERSON]’s \(\kappa\) = 0.82 interaction patterns & Reveals teacher–student–AI interaction patterns \\ \hline Long-term skill transfer & ANCOVA & Baseline-adjusted R\({}^{2}\) = 0.67 & Controls individual differences \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overview of data analysis methods.  
Figure 2: Data analysis method flowchart.  
Experimental validation: we compared high/low feedback frequencies (X\({}_{1}\)), \(\epsilon=0.3/0.8\) privacy configurations (X\({}_{2}\)), and 1D/3D assessment modes (X\({}_{3}\)) to test theoretical pathways.
# Does Nanosilver Have a Pronounced Toxic Effect on Humans?
## 3 Experimental Research Conducted by the Authors of the Review
### Supplementary Experiments with Microorganisms  
Since there is a significant body of literature on the bactericidal and/or bacteriostatic effect of silver [2], in particular, silver ions (Ag\({}^{+}\)), as well as the study of the toxicity of AgNPs for animals, in 2010-2011, research was conducted to elucidate the toxic effect of uncoated AgNPs on Gram-negative and Gram-positive bacteria, and on yeast-like fungi [62]. The effect of concentrated solutions of AgNPs (50 or 100 mg/L) or drugs based on Ag\({}^{+}\) (in the form of nitrate or proteinase) on the growth of microorganisms in Petri dishes was assessed. According to the obtained results, AgNPs did not affect the growth of microorganisms, as opposed to the pronounced antiseptic effect of solutions of known drugs based on Ag\({}^{+}\) and antibiotics. When studying the effect of AgNPs coated with polyethylene glycol at a concentration range of 0.025-0.5 \(\upmu\)M, it was shown that NPs stimulated spirulina biomass, and the content of proteins, carbohydrates, and auxiliary pigments was slightly influenced by the presence of NPs in the cultivation medium [63].  
These results, despite the fact that they contrast with the common descriptions of AgNPs as an antibacterial agent, can be easily explained. Silver itself is practically insoluble in water and does not create a toxic level of Ag\({}^{+}\) in solutions. The formation of Ag\({}^{+}\) is possible only from a silver salt, for example, from AgNO\({}_{3}\) used in medicine (silver nitrate, Stilus Lapidis). At the same time, both Ag\({}^{+}\) and the acidic residue of the NO\({}_{3}\)- solution act as antiseptics, which was demonstrated in the described experiments when nitrates of other metals were used.
# Time Series Anomaly Detection Using Signal Processing and Deep Learning
## 4 Experiment and Results
### Discussion  
The experimental results highlight that the FNN is particularly well suited for time series data that possess underlying signal structures. This strength is further enhanced through the use of bandpass filtering, which refines the input to focus on relevant frequency components, reducing noise. FNNs are designed to capture complex temporal relationships while preserving the continuous nature of time series data throughout the network, unlike traditional architectures that may ignore time or treat time steps independently. As demonstrated in the prior literature, this inherent ability to model time series data enables FNNs to consistently achieve superior performance across a variety of time series tasks. Moreover, the FNN Autoencoder not only learns a compact feature representation but also maintains the temporal continuity of the data in the latent space, leading to more effective anomaly detection. Given these properties, FNN Autoencoders exhibit strong  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **MLP (No BP)** & **MLP (BP)** & **LSTM (No BP)** & **LSTM (BP)** & **FNN (No BP)** & **FNN (BP)** \\ \hline MSL & 0.871 & 0.863 & 0.886 & 0.863 & 0.849 & 0.846 \\ SMAP & 0.700 & 0.706 & 0.699 & 0.706 & 0.698 & 0.706 \\ SMD & 0.709 & 0.758 & 0.765 & 0.789 & 0.792 & **0.816** \\ PSM & 0.936 & 0.977 & 0.962 & 0.980 & 0.945 & **0.987** \\ SWAN & 0.791 & 0.795 & 0.789 & 0.793 & 0.801 & **0.819** \\ SWAT & 0.755 & 0.901 & 0.821 & 0.910 & 0.878 & **0.927** \\ WADI & 0.797 & 0.799 & 0.826 & 0.875 & 0.838 & **0.893** \\ \hline \hline \end{tabular}
\end{table}
Table 2: F1 score results comparing different methods.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **MLP (No BP)** & **MLP (BP)** & **LSTM (No BP)** & **LSTM (BP)** & **FNN (No BP)** & **FNN (BP)** \\ \hline MSL & 0.930 & 0.928 & 0.940 & 0.934 & 0.925 & 0.922 \\ SMAP & 0.850 & 0.855 & 0.852 & 0.857 & 0.848 & 0.849 \\ SMD & 0.840 & 0.852 & 0.875 & 0.875 & 0.893 & **0.904** \\ PSM & 0.956 & 0.977 & 0.978 & 0.987 & 0.981 & **0.995** \\ SWAN & 0.861 & 0.865 & 0.860 & 0.865 & 0.870 & **0.881** \\ SWAT & 0.837 & 0.949 & 0.892 & 0.958 & 0.931 & **0.962** \\ WADI & 0.890 & 0.895 & 0.915 & 0.944 & 0.925 & **0.958** \\ \hline \hline \end{tabular}
\end{table}
Table 3: AUC results comparing different methods.  
results in detecting anomalies, particularly in datasets where the time series behavior is structured and continuous.  
However, as with many deep learning methods, FNNs require a sufficient amount of data to train effectively and are computationally intensive compared to simpler models. This introduces a trade-off between model performance and resource requirements. Several steps can be taken to mitigate these limitations, such as employing dimensionality reduction techniques, more efficient network architectures, or transfer learning approaches where feasible.