esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Setup
### Evaluation Metrics  
The pre-trained model utilizes two datasets, N-zhvoice and NR-zhvoice, each comprising 1,090,000 unlabeled samples. Approximately 5% of these data are randomly selected and labeled for use in subsequent speaker verification tasks. Of the labeled data, 1\(\sim\)1.5% is designated as the test set, with the remainder allocated for training purposes. The evaluation criteria for the speaker verification tasks encompass the Equal Error Rate (EER) and the Minimum Detection Cost Function (MinDCF). Associated metrics include the False Acceptance Rate (\(FAR\)), the False Rejection Rate (\(FRR\)), and the threshold settings. The EER is defined as the point at which the FAR and FRR are equal, reflecting a balance between the two types of errors at different threshold settings. The formula for computing MinDCF is provided in Equation (17).  
\[DCF=C_{FRR}\times FRR\times PT+C_{FAR}\times FAR\times PI \tag{17}\]  
The costs associated with false rejection (\(C_{FRR}\)) and false acceptance (\(C_{FAR}\)) are both set as 1, while the prior probabilities for the real speaker (\(PT\)) and the impostor speaker (\(PI\)) are assigned as 0.01 and 0.99, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{Room/m} & L (length) & \(u(5,10)\) & & L & L/2+\(u(-0.2,0.2)\) \\  & W (width) & \(u(5,10)\) & Source/m & W & W/2 + \(u(-0.2,0.2)\) \\  & H (height) & \(u(3,4)\) & & H & \(u(0.9,1.8)\) \\ \hline \multirow{3}{*}{\(T_{60}\)} & low & \(u(0.1,0.5)\) & & L & L/2 + \(u(-1.6,-0.8)\) or \(u(0.8,1.6)\) \\  & middle & \(u(0.5,1)\) & Microphone/m & W & W/2 + \(u(-1.6,-0.8)\) or \(u(0.8,1.6)\) \\ \cline{1-1}  & high & \(u(1,1.5)\) & & H & \(u(5,10)\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reverb configuration (\(u\): uniform distribution).  
The metrics are computed through the following steps: (1) acoustic features, extracted either by a MFCC extractor or a pre-trained model, are input into a speaker recognition model to derive classification features; (2) the cosine similarity between the classification features for each audio is calculated and designated as the target score; (3) the target label and the corresponding target score are compiled into a score list; (4) the threshold is incremented from 0 to 0.99 in steps of 0.01. For each threshold, the \(FRR\) and \(FAR\) are determined. Subsequently, the EER and MinDCF values are computed based on these rates.
# Jamming and Spoofing Techniques for Drone Neutralization: An Experimental Study
## 2 Background
### UAV Communication Standards  
Several technologies can facilitate communication between a ground station and a drone, with Wi-Fi being a prominent option due to its widespread use. As the IEEE 802.11 standards outlined, Wi-Fi operates in the 2.4 GHz and 5 GHz bands. The 2.4 GHz band supports the 802.11 b/g/n standards, whereas the 5 GHz band is used by 802.11 a/n/ac devices [6; 7; 8].  
IEEE 802.11 ac (Wi-Fi 5) operates between 5180 MHz and 5825 MHz, while Wi-Fi 6 (IEEE 802.11 ax) improves on Wi-Fi 5, working in both the 2.4 GHz and 5 GHz bands [9]. Wi-Fi 6E extends into the 6 GHz band, from 5.925 to 7.125 GHz, offering 1200 MHz of spectrum bandwidth for enhanced channel capacity. Wi-Fi's advantages include its unlicensed operation, but its limitations are in range and reliance on Line-of-Sight (LoS) connections. Cellular networks like Long-Term Evolution (LTE) and 4G overcome these issues, extending the communication range beyond LoS [10; 11].
# Explainability and Interpretability in Concept and Data Drift: A Systematic Literature Review
## 6 Taxonomy
### Rule/Logical Pattern Mining
#### 6.5.3 Multidimensional Class Evaluation  
Consisting of nine papers, this group primarily addresses general concept drift, with isolated references to incremental concept drift and data drift, as can be seen from Table A22. This distribution indicates that rule-based methods naturally detect drift as changes in underlying patterns rather than explicitly distinguishing between specific drift subtypes. The application contexts are primarily data mining, process mining, cybersecurity, finance, and aviation. These contexts share structured, interpretable data environments, indicating that rule-based explanations are particularly effective when clear, actionable patterns must be communicated directly to users or stakeholders. Regarding scope, the class is fairly balanced, although a slight preference for global explanations is observable. This highlights rule-based approaches' effectiveness in capturing broad behavioral shifts through explicit, interpretable rules, but with retained capability for instance-level precision when needed. The primary audience is data scientists across all papers, consistent with the methodological complexity of generating, interpreting, and maintaining rule sets. While rules themselves are inherently interpretable, their generation and validation generally demand specialized analytical knowledge. Notably, this category explicitly specifies interpretability-related metrics in one paper, referring to \"faithfulness and monotonicity.\" The presence of these metrics, although limited, signals an awareness of the need for quantitative validation of interpretability. The learning mode predominantly features online learning, appropriate for real-time rule updates reflecting changes in streaming data. Offline and hybrid methods appear less frequently, suggesting that rule-based approaches are more naturally suited to dynamic environments where rules evolve incrementally.
###### Abstract  
This study proposes metal-assisted chemical etching (MAE) as a facile method to fabricate silicon nanowire (SiNW) array structures, with high optical confinement for thin crystalline silicon solar cells. Conventional SiNW arrays are generally fabricated on Si wafer substrates. However, tests on conventional SiNW-based solar cells cannot determine whether the photo-current is derived from SiNWs or from the Si wafer. Herein, SiNW arrays were fabricated on a silicon-on-insulator substrate with a 10-\(\upmu\)m-thick silicon layer for measuring the photocurrent of the SiNW only. The 9 \(\upmu\)m-long p-type SiNW arrays were applied to a solar cell structure fabricated using an n-type H-doped amorphous Si layer, thereby confirming the photovoltaic effect. However, the device exhibited a conversion efficiency of 0.0017% because of a low short-circuit current (\(J_{\text{sc}}\)) and a low open-circuit voltage (\(V_{\text{oc}}\)). The low \(J_{\text{sc}}\) resulted from a high series resistance and high absorption loss from the amorphous Si layer, whereas the low \(V_{\text{oc}}\) resulted from the high surface recombination velocity of the SiNW array structure. Therefore, reducing the surface recombination of SiNW-based solar cells can improve their conversion efficiency.  
Keywords:silicon nanowire; solar cell; metal-assisted chemical etching; optical confinement; silicon-on-insulator substrate +
Footnote †: journal: Applied Physics Letters
## 3 Results
### Heavy Metal Removal
#### 3.2.5 Lead Removal Rate  
Figure 8 shows the lead removal rates of kaolin and kaolin/nanomaterial samples over 90 days. Initially, the lead concentration in the leachate was 0.68 mg/L. Kaolin samples with nanomaterial added for 30-90 days had a higher lead removal rate than kaolin. The lead removal rates increased with increasing ratio of all nanomaterials in kaolin. Similar to other heavy metal removal results, the highest lead removal rates in the 1% and 5% nanomaterial additives were achieved by adding Oltu clay, aluminum oxide, and iron oxide to kaolin, respectively. On the other hand, slightly different from the other heavy metal removal results, the lead removal rate of 5% aluminum-added kaolin was found to be higher than that of 1% Oltu-clay-added kaolin. However, the kaolin + 5% Oltu clay sample showed the best performance in terms of lead removal at all times examined. The highest lead removal rate was 48.52% for kaolin + 5% Oltu clay at day 60.  
Figure 7: Copper removal rate of kaolin and kaolin/nanomaterial samples.
## 6 Training Algorithm with Multi-Dimensional Aligned Q-Fusion
### Multi-Dimensional Q-Network  
D3 ynQN and other DQN-style algorithms typically configure a Q-network to output a uni-dimensional vector with each unit for the value of a specific action. Regarding DRBPS, unfortunately, there will be hundreds of such actions (see Section 4). Each of them means a possible permutation of the remaining green times for the two concurrent phases in the dual ring. It is very difficult for the agent to directly learn this many individual actions [47].  
To address this challenge, we propose the \"multi-dimensional Q-network\". It improves D3 ynQN via the technique of dimensionality expansion explored in [47], aimed at reducing the magnitude of the number of network outputs to be considered. We begin by treating each element of action \(A_{t,i}\), referred to as a \"sub-action\" \(a_{t,i}^{k}\), where \(k\in\{1,2\}\). We modify the network from directly outputting action values \(Q_{\theta}(S_{t},A_{t,i})\) to a branch structure that outputs multi-dimensional sub-action values (see Figure 5):  
\[Q_{\theta}^{d}\Big{(}S_{t},a_{t,i}^{d}\Big{)}, \tag{13}\]  
where \(d\in\{1,2,\dots,D\}\) denotes the sub-action dimension. Each dimension has \(E\) sub-action choices.  
In our case, with DRBPS, the number of dimensions, \(D=2\), corresponds to the two rings. The number of sub-actions per dimension, \(E=26\), corresponds to the remaining green time options for the current phase in each ring. By turning into multi-dimensional outputs, the network now only needs to estimate \(26\cdot 2=52\) values of sub-actions in two dimensions, instead of the original \(26^{2}=676\) values of combinatorial actions. We have the agent separately select the sub-action with the maximum value among each dimension:\[\begin{cases}a_{t,i}^{1}=\arg\max_{a_{t,i}^{1,e}}\Big{[}Q_{\theta}^{1}\Big{(}S_{t },a_{t,i}^{1,e}\Big{)}\Big{]},\\ a_{t,i}^{2}=\arg\max_{a_{t,i}^{2,e}}\Big{[}Q_{\theta}^{2}\Big{(}S_{t},a_{t,i}^{ 2,e}\Big{)}\Big{]}.\end{cases} \tag{14}\]  
Then, the selected sub-actions in both dimensions can compose the action \(A_{t,i}=\langle a_{t,i}^{1},a_{t,i}^{2}\rangle\).
# Search Space Reduction for Localization and Tracking of an Acoustic Source
## 4 Conclusions and Future Work  
An approach to reduce the search space for the 3D localization and tracking of an acoustic source was proposed and tested on experimental data, with encouraging results. Critical to the success of this approach was the availability of a 2D initial guess, which was explored to determine a particular interval of source bearings; from this, it followed that the guess significantly reduced the search space of parameter combinations required to obtain the position of the source in a three-dimensional environment. Certainly, this guess cannot be expected to be available in all types of scenarios; this is an issue that will require further analysis considering additional sets of experimental data, particularly those in which out-of-plane propagation is known to be relevant. The broadband processing of acoustic receptions also needs to be considered, as well as replica calculations for a three-dimensional field of sound speed. Finally, the lack of navigational data does not allow us to determine the error of source localization and tracking; this issue is expected to be reviewed considering observations for which the geographical coordinates of the source  
Figure 8: Slice of \(\hat{B}(r,z,\phi)\) at \(r_{\odot}=5435\) m for the stationary source at 331 Hz, file 1, with the start marking the source position at \(z_{\odot}=65\) m and \(\phi_{\odot}=93^{\circ}\).  
Figure 9: Cartesian coordinates for source localization and tracking.  
are known. An association of the suggested approach with parallel processing and/or neural network architectures can be expected to speed up significantly the processing of acoustic data, which is required to locate an underwater source.  
Conceptualization, methodology and validation: O.C.R., L.Z. and X.C.; software: O.C.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors are deeply grateful to the CMRE (former SACLANTCEN) for sharing online the experimental data, together with supporting MATLAB code for the calculation of the covariance matrix and relevant documentation of the 1993 Mediterranean Experiment.  
Contact first author.  
The authors declare no conflict of interest.
## 4 Energy Efficiency Optimization
### Transmit Power Optimization Given the Density of Picocell BSs  
In this section, we analyze the optimal transmit power of picocell BSs. The optimization problem can be expressed as  
\[\begin{array}{ll}\underset{P_{T2}}{\max}&\eta_{EE}\\ &s.t.\ P_{T2}\geq P_{\min},P_{T2}\leq P_{\max}\end{array} \tag{16}\]  
In Equation (14), \(\eta_{EE}\) is the objective function of the optimization problem, and transmit power is the optimal variable. Here, \(P_{\min}>0\) and \(P_{\max}>0\) are the minimum and maximum transmit power of the BSs, respectively. We assume, without loss of generality, that \(P_{\min}\to 0\) and \(P_{\max}\to P_{T1}\). Through symbolic computation and simulation, we find that the objective function is also a convex function and has the global optimum. Similar to the solution to the optimization problem given in Equation (14), the golden section method is adopted to find the optimal solution to the optimization problem, and the solution is given by  
\[\widetilde{P_{T2}^{opt}}=\max\{P_{\min},\min\{P_{T2}^{*},P_{\max}\}\} \tag{17}\]  
where \(P_{T2}^{*}\) is the only stationary point of the objective function.
# Flexible Simulation Platform for Generating Realistic Waveforms with Voltage Notches
## 5 Comparison of Simulations with Field Measurements
### Selection of Field Data  
Validation of the simulation platform is based on measurements of three-phase current and voltage waveforms with notches from a single industrial LV network installation in Germany. It is acknowledged that utilizing data from only one installation may limit the generalizability of the validation. However, due to data availability constraints, this installation provided the most comprehensive set of measurements over an extended period, capturing various operating conditions of a six-pulse controlled rectifier.  
A set of 164 records from the industrial site are selected for analysis. The sampling frequency of voltage and current waveforms is 20.48 kHz. No additional data beyond the rated phase-to-phase voltage (\(U_{s}=400\) V), the rated frequency (\(f_{s}=50\) Hz), and the voltage and current waveforms are available; for instance, short-circuit power and network topology are unknown. Therefore, the first challenge consists of identifying a plausible network topology of the industrial site based on knowledge acquired from simulations.  
Analysis of the field measurements indicates that the operating point of the rectifier does not vary significantly over time. This is likely due to the rectifier serving a specific, consistent load in the industrial process. Consequently, the variability in the measured voltage and current waveforms is limited, which presents a challenge for validating the simulation platform across a wide range of operating conditions.
# Amorphous Oxide Thin Film Transistors with Nitrogen-Doped Hetero-Structure Channel Layers
###### Abstract  
The nitrogen-doped amorphous oxide semiconductor (AOS) thinfilm transistors (TFTs) with double-stacked channel layers (DSCL) were prepared and characterized. The DSCL structure was composed of nitrogen-doped amorphous InGaZnO and InZnO films (a-IGZO:N/a-IZO:N or a-IZO:N/a-IGZO:N) and gave the corresponding TFT devices large field-effect mobility due to the presence of double conduction channels. The a-IZO:N/a-IGZO:N TFTs, in particular, showed even better electrical performance (\(\mu_{\text{FE}}\) = 15.0 cm\({}^{2}\cdot\)V\({}^{-1}\cdot\)s\({}^{-1}\), SS = 0.5 V/dec, \(V_{\text{TH}}\) = 1.5 V, \(I_{\text{ON}}/I_{\text{OFF}}\) = 1.1 \(\times\) 10\({}^{8}\)) and stability (\(V_{\text{TH}}\) shift of 1.5, \(-\)0.5 and \(-\)2.5 V for positive bias-stress, negative bias-stress, and thermal stress tests, respectively) than the a-IGZO:N/a-IZO:N TFTs. Based on the X-ray photoemission spectroscopy measurements and energy band analysis, we assumed that the optimized interface trap states, the less ambient gas adsorption, and the better suppression of oxygen vacancies in the a-IZO:N/a-IGZO:N hetero-structures might explain the better behavior of the corresponding TFTs.  
amorphous oxide semiconductor (AOS); thin film transistor (TFT); nitrogen-doped amorphous InGaZnO (a-IGZO:N); nitrogen-doped amorphous InZnO (a-IGZO:N); hetero-structure +
Footnote †: journal: Applied Physics
## 3 Theoretical Framework and Hypotheses Development
### Hypotheses Related to Moderating Factors
#### 3.2.4 Hypothesis Related to Previous Experience  
Previous experience was used in the research [6] as part of the behavior demonstrated by the student towards using YouTube as a learning tool. Researchers categorized previous experience as weak, good, or excellent. Hence, the following hypothesis is developed based on [6].  
**Hypothesis 12 (H12).**_Previous experience has a significant moderating effect on information adoption (IA) of YouTube as learning tool._
## 4 Conclusions  
The study aimed to determine the best method for measuring wine turbidity using machine learning techniques. According to the Mann-Whitney U test (\(p\)-value \(<\) 0.05), the 'yausabara' fining agent is the most efficient in clarifying Burgundy variety grape wine, as there was a statistical difference between fining agents. Although almost all models trained with MATLAB's Neural Net Fitting program had a good performance (R\({}^{2}\) from 0.773 to 0.988, MSE from 0.002 to 0.057, NRSME from 4.9 to 24 and AIC from \(-\)387.2 to \(-\)814.4), it was found that the Artificial Neural Network (ANN) with 25 neurons and Levenberg-Marquardt algorithm presented not only a good training performance but a good validation performance too (R\({}^{2}\) of 0.985, MSE of 0.004, NRSME of 6.01 and AIC of \(-\)160.12). The SVM model was the only visible method that did not fit the system's data from those trained with MATLAB's Regression Learner app. However, among all the other models, the exponential generalized regression prediction (GPR) also yielded good training performance metrics, especially in NRSME and AIC, which have values under 5.5 and \(-\)900, respectively.  
Furthermore, during the validation of the models, it was discovered that the ANN trained using MATLAB's Neural Net Fitting technique provided the most accurate representation of the study system, having better validation performance metrics (three of four). This is because the Levenberg-Marquardt algorithm is particularly effective in dealing with smaller networks and is suitable for scenarios where complex relationships must be modelled and learned. The system studied in this research required such complex modelling. Hence, the algorithm was chosen; however, further research is necessary to expand the sampling spectrum and validate the results so the model can be applied to a broader range of wines and the results can be validated more confidently. The results of this research provide a valuable tool for winemakers and researchers and can be used to improve wine quality and consistency.  
Conceptualization, J.C.D.-Q.; formal analysis, E.M.D.L.C.R.; investigation, E.M.D.L.C.R.; methodology, E.M.D.L.C.R., M.L.-F. and J.C.D.-Q.; supervision, J.N.-P., J.-M.P.-C. and J.C.D.-Q.; visualization, M.L.-F. and R.E.-V.; writing--original draft, E.M.D.L.C.R. and J.C.D.-Q.; writing--review and editing, J.N.-P., J.-M.P.-C. and R.E.-V. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data are available at [[https://www.kaggle.com/datasets/juancarlosdelavega/](https://www.kaggle.com/datasets/juancarlosdelavega/)]([https://www.kaggle.com/datasets/juancarlosdelavega/](https://www.kaggle.com/datasets/juancarlosdelavega/)) wine-turbidity-vs-clarifying-and-dosage, accessed on 7 March 2024. The code in this study is available on request from the corresponding author. Conflicts of Interest: The authors declare no conflicts of interest.
# Numerical and Experimental Investigations of a Micromixer with Chicane Mixing Geometry
## 1 Introduction
The results showed a sharp increase of mixing index by Re > 10 due to observed recirculation in micromixers cells. [PERSON] et al. [27] showed an effectiveness of utilizing a chicane channel using a Villermaux-Dushman reaction. The maximal conversion was stated between Q = 0.5 mL/min and Q = 0.75 mL/min in case of flows diluted in water with the same volume flow rate. Although this cannot indicate the optimal volume flow rate, it proves great mixing efficiency when using higher flow rates. [PERSON] et al. [28] have investigated a micromixer with circular mixing chambers, which are similar to the meander elements in the investigated micromixer. According to the results, the mixing index increases rapidly with the Reynolds number and the recirculation zones play a crucial role especially in case of Reynolds numbers Re > 5. Moreover, the square-law dependency of the pressure drop from the Reynolds numbers was found. From the above references, it is obvious that both meander design and circular zones with recirculation do enhance mixing, but the main condition for the effect are high values of Reynolds numbers. This study is aiming to research the acceleration of the mixing process in detail and its connection with flow regimes in a wide range of Reynolds numbers (from 0.05 to 259). The aim of most studies was an investigation of a micromixer with only a zigzag, meander, or circular chamber structure, while the micromixer in the present study consists of meander elements as well as recirculation chambers. In this work, different flow regimes, their ranges, influence on mixing intensity and pressure drop in the passive micromixer with chicane mixing geometry (\"S-form\") are investigated. Information about the flow regimes and their effects in the micromixers can open new opportunities in mass-transport process acceleration, such as when using vortex flow or engulfment flow regimes. The study was carried out in two stages. Firstly, the numerical simulation was made with varying Reynolds number. Secondly, in order to verify the simulation results, some experiments using a microphotography analysis unit were also carried out.