esa-sceva/satcom-chunk-collection huggingface dataset:
# XTTS-Based Data Augmentation for Profanity Keyword Recognition in Low-Resource Speech Scenarios
###### Abstract  
As voice cloning technology rapidly advances, the risk of personal voices being misused by malicious actors for fraud or other illegal activities has significantly increased, making the collection of speech data increasingly challenging. To address this issue, this study proposes a data augmentation method based on XText-to-Speech (XTTS) synthesis to tackle the challenges of small-sample, multi-class speech recognition, using profanity as a case study to achieve high-accuracy keyword recognition. Two models were therefore evaluated: a CNN model (Proposed-I) and a CNN-Transformer hybrid model (Proposed-II). Proposed-I leverages local feature extraction, improving accuracy on a real human speech (RHS) test set from 55.35% without augmentation to 80.36% with XTTS-enhanced data. Proposed-II integrates CNN's local feature extraction with Transformer's long-range dependency modeling, further boosting test set accuracy to 88.90% while reducing the parameter count by approximately 41%, significantly enhancing computational efficiency. Compared to a previously proposed incremental architecture, the Proposed-II model achieves an 8.49% higher accuracy while reducing parameters by about 98.81% and MACs by about 98.97%, demonstrating exceptional resource efficiency. By utilizing XTTS and public corpora to generate a novel keyword speech dataset, this study enhances sample diversity and reduces reliance on large-scale original speech data. Experimental analysis reveals that an optimal synthetic-to-real speech ratio of 1:5 significantly improves the overall system accuracy, effectively addressing data scarcity. Additionally, the Proposed-I and Proposed-II models achieve accuracies of 97.54% and 98.66%, respectively, in distinguishing real from synthetic speech, demonstrating their strong potential for speech security and anti-spoofing applications.  
small-sample learning; data augmentation; insult speech recognition; speech generation; deep learning
# Maximizing Test Coverage for Security Threats Using Optimal Test Data Generation
## 2 Literature Review
The literature makes a strong case for the value of constraints specified in OCL at the design level. By outlining models that cannot be included, constraints specified in OCL help UML models be comprehensive, consistent, and accurate when used with them [11]. The unified modeling language (UML), which receives a lot of interest from scholars and professionals, has become a crucial standard for modeling software systems [27]. Extensive test data is produced when we conduct model-based testing [28]. Making test data that meet a specified adequacy criterion is a key challenge in software testing. In [29], the author has generated acceptance test cases for security threats using an unstructured misuse case description. By incorporating the misuse case description into the mal-activity diagram, a misuse scenario is created and inputs and triggers are determined. The flow of usage shown in the mal-activity diagram is used to design test cases. Test cases are created in [30] where misuse instances pose security issues. The findings show that the suggested misuse case creation method offers superior coverage for security issues. Their strategy, nevertheless, has to be better organized and provide a thorough explanation of the procedure. The techniques used to generate test cases from the use case were extensively available in the latest research. A use case model is initially created from the functional requirements. As a result of the use case model, test cases are created. The final stage involves using commercially available tools to execute the prepared test cases. In [31], the author proposed an approach to generate test cases from use cases, misuse cases, and 137 mitigations of use case descriptions. Early on in a product's development, this includes security features. In misuse scenarios, they recommend several improvements to make it easier to define security needs. It is evident from the above literature that there is a need to design consistent acceptance test cases for security threats (authentication and authorization) through structured misuse case descriptions for early-stage mitigation of security threats. This will help us to overcome the challenge of inconsistent acceptance test case design due to its reliance upon human judgment. For that, we have first identified misuse scenarios through structured misuse case descriptions against security threats such as authentication and authorization and their corresponding mitigation. We then model a combination of basic and alternate security threat mitigation scenarios in the mal-activity diagram. Besides two state-of-the-art approaches that maximize test coverage through optimal test data generation from source code [16; 32]. However, which among the modified condition decision coverage (MC/DC) and decision coverage (D/C) maximizes test coverage through optimal test data generation with minimum test conditions at the design level is yet to be explored. Besides, a combination of MC/DC with manual testing at the design level has not yet been experimented on.
## 1 Introduction
[12] was again demonstrated to provide reasonable estimate of \(q_{peak}\). In addition to deterministic analyses, probabilistic assessments were performed to evaluate the capacity of shallow and spudcan foundations. [PERSON] and [PERSON] [18] conducted analyses for probabilistic prediction of bearing capacity of shallow foundations on homogenous sand overlying spatially variable clay soil. [PERSON] et al. [19] performed random field FE analyses for probabilistic evaluation of bearing capacity of spudcan foundations embedded at various depths. The undrained strength of the soil was spatially varied with depth. Based on numerical outcomes, it was concluded that the non-stationary random field may generate lower values of mean probabilistic bearing capacity. [PERSON] et al. [20] adopted the probabilistic investigation approach to evaluate failure envelops of spudcan foundation under combined vertical, horizontal and moment loading space (VHM). It was concluded that presence of spatially variable soils reduces both the bearing capacity and failure envelopes. The literature review reveals that the previous studies focus on the penetration characteristics of sand overlying soft clay, because of the potential for a punch-through failure. However, there is a need to estimate the penetration resistance also in sand over stiff clay for operation of jack-up barges supported by spudcan foundations. [PERSON] and [PERSON] [21] performed CEL analyses to determine the penetration resistance in sand overlying stiff clay. However, the study focused on the resistance in the clay layer and an equation for \(q_{peak}\) was not presented. Based on a limited comparison with available centrifuge test results for \(s_{u}\) = 32.6 and 43.9 kPa, it was reported that the [PERSON] et al. [12] model provides favorable predictions of \(q_{peak}\). It should be noted that \(s_{u}\) values even for this validation do not cover the range encountered in the field. In this study, \(q_{peak}\) of spudcan foundations in sand overlying a soft to stiff clay layer was calculated using three-dimensional (3D) finite element analyses incorporating the CEL approach. A wide range of strength parameters were used, which include the effective friction angle of the sand layer (\(\phi^{\prime}\)) and undrained shear strength of the clay layer (\(s_{u}\)). The calculated bearing capacity results are compared with the analytical model. A new empirical correlation for one of the parameters of the conceptual model is proposed to improve the fit for not only soft clays, but also stiff clays. The enhancement in the prediction accuracy is thoroughly investigated through comparisons with the baseline simulations.
## 4 Control Strategies
### PMSG-Based Wind Turbine Control  
#### 4.2.1 Control for Ensuring MPPT  
The PMSG stator terminals are connected to a three-leg AC/DC voltage source converter. If the PMSG rated stator voltage is low enough, the AC/DC converter can directly connect to the microgrid DC bus, otherwise a transformer between the PMSG terminals and the AC/DC converter or a DC/DC converter between the AC/DC converter and the microgrid DC bus would be necessary.  
The main objective of the AC/DC converter is to optimize the power extracted from the turbine for any incoming wind speed. The wind microturbine has fixed-pitch blades at angle \(\beta\). Thus, according to Equations (5)-(7), the turbine efficiency factor \(C_{p}(\lambda,\,\beta)\) depends only on the tip speed ratio \(\lambda=(\omega_{t}R)/V\). For each wind speed \(V\), there is an angular speed of the turbine rotor \(\omega_{t}\) that maximizes \(C_{p}(\lambda,\beta)\), allowing the maximum available power to be extracted. This optimum angular speed is obtained from:  
\[\omega_{t}^{opt}=\frac{\lambda^{opt}V}{R} \tag{14}\]  
The PMSG reference rotor speed (in electrical radians per second) can then be obtained from \(\omega_{r}^{*}=(P/2)\omega_{t}^{opt}\). A vector control scheme is then applied to impose the reference rotor speed.  
The PMSG model is in a rotor-oriented reference frame, i.e., the d-axis is aligned with the permanent magnet flux. The reference d-axis stator current is then set to zero since a negative value would reduce the main flux and a positive value could cause magnetic saturation. Furthermore, if \(i_{sd}=0\), the electromagnetic torque becomes \(T_{e}=\frac{3}{2}\frac{P}{2}\Lambda i_{sq}\), so it can be controlled through \(i_{sq}\). The overall control scheme is depicted in Figure 6.  
Figure 5: PV output voltage references calculation for MPPT and for reduced generation to track load power demand.  
Figure 6: Control of the PMSG.
## 2 Model
### Single-Beam Metamaterial Structure
The design approach also closely aligned with the design and modeling approach of the metamaterial structure reported and verified experimentally by [PERSON] et al. [19]. Solving Equations (1)-(3) simultaneously by substituting Equation (2) into Equation (1), integrating through the length of the horizontal beam and applying orthogonality, the transmissibility of the single-beam metamaterial structure can be obtained. Further insights and discussion of the single-beam metamaterial structure are given in Section 3 of this article. Figure 2: Schematic representation of the unit cell and the local resonator as typically modeled. \(L_{c}\) represents the length of each unit cell.
## 2 Wind Speed Modeling
### Probability Distribution Model
#### 2.1.1 Parameter Distribution Model  
In the parameter distribution model, wind speed is presupposed to be effectively described by probability density functions based on historical wind data. The probability density function can be either a single function or a combination of two or more functions. Weibull distribution, Rayleigh distribution, and normal distribution are the widely used single distribution functions [22; 23; 24; 25]. More specifically, the wind speed modeling by using Weibull distribution mainly has the following steps [23; 24].  
**Step-1.** Assuming that the measured wind speed sequence (\(v_{1}\), \(v_{2}\),..., \(v_{n}\)) obeys the two-parameter Weibull distributions, i.e.,:  
\[F(V)=P(v\leq V)=1-\exp[-(V/c)^{k}] \tag{1}\]  
\[f(V)=(k/c)(V/c)^{k-1}\exp[-(V/c)^{k}] \tag{2}\]  
where \(c\) and \(k\) are the scale and shape parameter of the Weibull distribution, respectively, and \(c\) reflects the average wind speed of wind farm; \(V\) is the given wind speed m/s.  
**Step-2.** The maximum likelihood method is used to construct the logarithm likelihood function:  
\[L(k,c)=\sum_{i=1}^{n}\left[\ln k+(k-1)\ln V_{i}-k\ln c-(V_{i}/c)^{k}\right] \tag{3}\]  
Let the gradients \(F_{1}=\frac{\partial L(k,c)}{\partial k}=0\), and \(F_{1}=\frac{\partial L(k,c)}{\partial c}=0\), then:  
\[F_{1}=\sum_{i=1}^{n}\left[1/k+\ln V_{i}-\ln c-(V_{i}/c)^{k}\ln(V_{i}/c)\right]=0 \tag{4}\]  
\[F_{2}=\sum_{i=1}^{n}\left[-c/k+(k/c)(V_{i}/c)^{k}\right]=0 \tag{5}\]  
**Step-3.** The initial values of \(c\) and \(k\) are selected. After using the iterative algorithm to find the optimal values that satisfy the convergence criterion max \(\{\,\mid\Delta k,\,\Delta c\,\mid\,\}\)\(\leq\)\(\varepsilon\), where \(\varepsilon\) is a preset small positive value, the Weibull probability distribution model is obtained.  
The \(X^{2}\) test and the Kolmogorov-Smirnov test, which creates a relatively small error when a theoretical parameter distribution is rejected, are usually used to evaluate the precision of the parameter distribution model. The two tests reflect relative percentages, and can be added together. A larger unified metric value represents a worse goodness-of-fit. Other indexes, such as square error and mean-square-root error, are also available for precision evaluation [25].
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 2 Particularities and Constraints
### Unreliable Communication Channel  
On account of the nature of the transmission medium and physical properties of the environments, underwater acoustic channel is temporally and spatially variable.
## 3 A Proposed Approach
### Planning Space Representation
#### 3.2.3 Dynamic Obstacle Representation  
The representation of dynamic obstacles in the path planning space has been of great interest. In order to ensure the consistency of planning space, the FM method is also adopted to represent the dynamic obstacles in this research. However, constant speed functions are not suitable considering the fact that different directions around dynamic obstacles have different risk distributions. Therefore, a specific speed function has been devised as illustrated in Figure 3. \(\alpha\) is the angle relative to the course of a dynamic obstacle, and \(W(\mathbf{p})\) is the speed function of the FM method used for dynamic obstacles, which is defined as follows:  
\[\left.\begin{array}{c}W(\mathbf{p})=\rho\\ \frac{(cosa)^{2}}{a}+\frac{(sima)^{2}}{b}=\frac{1}{\rho^{2}}\end{array}\right\}, \tag{2}\]  
where the values of \(a\) and \(b\) depend on \(\alpha\). There are four different cases: Case 1, \(\alpha\in(0,0.5\pi]\), \(a\) and \(b\) are set to \(r_{1}\) and \(r_{2}\), respectively; Case 2, \(\alpha\in(0.5\pi,\pi]\), \(a\) is equal to \(r_{3}\) and \(b\) is equal to \(r_{2}\); Case 3, \(\alpha\in(\pi,1.5\pi]\), \(a\) and \(b\) are, respectively, \(r_{3}\) and \(r_{4}\); Case 4, \(\alpha\in(1.5\pi,2.0\pi]\), \(a\) equals to \(r_{1}\), and the corresponding \(b\) equals \(r_{4}\). In addition, the safety distance thresholds in the four directions of the bow, starboard, stern, and port are denoted with \(d_{1}\), \(d_{2}\), \(d_{3}\), and \(d_{4}\), respectively, and meet the following conditions:  
\[\frac{d_{1}}{r_{1}}=\frac{d_{2}}{r_{2}}=\frac{d_{3}}{r_{3}}=\frac{d_{4}}{r_{4}}. \tag{3}\]  
Based on the above model, the representations called \(M_{d}\) of dynamic obstacles are easy to be modeled. As shown in Figure 4, the representation of a dynamic obstacle is modeled with different configurations. Figure 4a shows the representation with a configuration that \(r_{1}\), \(r_{2}\), \(r_{3}\), and \(r_{4}\) are set to 4.0, 1.0, 1.0, and 1.0, respectively, and the corresponding safety distance thresholds \(d_{1}\), \(d_{2}\), \(d_{3}\), and \(d_{4}\) are 200 m, 50 m, 50 m, and 50 m. As for Figure 4b, the parameter \(r_{2}\) and distance threshold \(d_{2}\) are changed to 1.5 and 75 m. The choice of the above parameters is partially inspired by the work in [25].  
The representation of dynamic obstacles at time \(t\) is labeled with \(M_{d}(t)\). Different from static obstacles, the representation of dynamic obstacles varies over time, which means that \(M_{d}(t_{1})\) is usually not equal to \(M_{d}(t_{2})\) when \(t_{1}\
eq t_{2}\).  
Figure 2: The binary map and a representation of the static scenario (\(d=100\) m). (**a**) The binary map, (**b**) The static obstacle representation.
## 3 Results
### Water Constituents  
Uncertainty associated with the detection of water constituent concentration was affected by \(SNR\), bottom depth, and benthic cover (Figure 7). Higher \(SNR\) consistently reduced detection uncertainty. However, while benthic cover uncertainty decreased with \(D^{\prime}\), uncertainty in water constituent concentration increased in shallow water, due to the diminished water signal. Both \({a^{\prime}}_{g,450}\) and \({C^{\prime}}_{cal}\) were generally detectable with low uncertainty for \(SNR\geq 500\) and \(D_{o}>1\); \(\Delta a_{g,450}<\pm\) 0.05 m\({}^{-1}\) and \(\Delta{C^{\prime}}_{cal}\) \(<\pm\) 0.04 g m\({}^{-3}\) respectively. At \(D^{\prime}=1\) m (\(D_{o}=0.4\)), retrieval uncertainty for these parameters increased significantly, especially for \(SNR=100\). If the benthic cover was spectrally similar to the material suspended or dissolved within the overlying water column, uncertainty increased. For example, uncertainty in the detection of chlorophyll concentration was relatively large for scenarios where the reference bentnos was composed of healthy coral (in \(S1\) and \(S5a\)), although increasing \(SNR\) did result in a modest reduction in uncertainty. This is because absorption by phytoplankton is similar to absorption by zooxanthellae, the chlorophyll-containing coral symbiont (in \(S5a\)), and the combination of coral and benthic algae (in \(S1\)). For \(SNR=100\), the reference concentration was indistinguishable from the entire range of test concentration, \(0\leq C_{chl}\leq 1\) mg m\({}^{-3}\). However, uncertainty in \(C_{chl}\) detection improved when the benthic cover was composed of uncolonized sand (in \(S5b\)). In this case, increasing \(SNR\) from 100 to 500 resulted in a significant decrease in detection uncertainty. At the same time, uncertainty in detecting \(C_{cal}\) was smaller when the benthic cover was coral or algae and increased when the substrate was uncolonized sand.  
Figure 6: Detection uncertainty in the fractional cover of Porites (\(B^{\prime}_{c,p}\)) and benthic algae (\(B^{\prime}_{a}\)) relative to the reference condition (scenario \(S4\), \(B^{\prime}_{c}=1\)). The uncertainty in the fractional cover is expressed as the difference between the upper bound of the test condition and the reference condition (solid gray line). Note that uncertainty in this scenario can only be positive (overestimated), since in this scenario \(B^{\prime}_{c,p}\) and \(B^{\prime}_{a}\) are zero.
# Battery Management System Hardware Concepts: An Overview
## 4 Measurement
### Voltage Measurement
#### 4.2.1 Cell Voltage Measurement  
As mentioned in Sections 2.2 and 3, the acquisition of cell voltages is usually done by integrated BMS Frontend chips. The chips that are on the market at the time this is written allow connection of cell counts of three up to 16 (e.g., TI's BQ76 PL536A [9], BQ76 PL455A [27], Linear Technology's LT680x series [42], Intersil's (Milpitas, CA, USA) ISL78600 [43], and Maxim-IC's MAX17843 [44]). Secondary supervisory ICs such as LT6801 by Linear Technologies [45] or Maxim-IC's MAX17880 [46] can improve redundancy and therefore system reliability by a large amount.
# Spatio-Temporal Distribution of Deep Convection Observed along the Trans-Mexican Volcanic Belt
## 3 Material and Methods  
### MODIS Data  
MODIS is a passive imager mounted on both the Terra and Aqua sun-synchronous polar-orbiting satellites, with equator crossings for the descending and ascending nodes at approximately 10:30 A.M. and 1.30 P.M. local time, respectively. It is a 36-channel whiskkroom scanning radiometer measuring between 0.405 and 14.385 \(\upmu\)m. It has a viewing swath width of 2330 km and nadir spatial resolutions of 250 m, 500 m and 1000 m, depending on the channel. A large number of data products from MODIS observations are useful to describe features of the land, the oceans and the atmosphere. MODIS observations can be used, for example, in studies of geophysical processes and phenomena from local to global scales. MODIS atmosphere products are archived into two categories: pixel-level retrievals (Level-2) and global gridded statistics (Level-3).  
In this study, Collection 6 (C6) of the cloud optical and micro-physical data at a 1 km resolution as well as macro-physical products at a 5 km resolution from Level-2 MODIS data were collected [35]. The C6 algorithm contains several changes and improvements compared with C5, as described in [PERSON] et al. [36]. In the Level-2 products, the MODIS cloud mask was used to discriminate clear-sky pixels from clouds. In the next step, the cloud optical and micro-physical properties were retrieved for cloudy pixels. Additionally, cloud properties derived from thermal infrared bands were obtained [37]. MODIS data were stored in Hierarchical Data Format (HDF) and divided into MOD06_L2 (collected from the Terra platform) and MYD06_L2 (collected from the Aqua platform). Ten years (2008-2017) of different parameters, such as cloud top pressure (CTP), cloud optical thickness (COT) and cloud fraction (CF), were collected for the study area. The temporal coverage of MODIS observations from Aqua and Terra was from 16:00 GMT to 22:00 GMT (11:00 to 17:00 local time) in the study area.
# Impacts of MT-HVDC Systems on Enhancing the Power Transmission Capability
## 3 Power Transfer Capability Evaluation  
Power transfer capability means the maximum power transfer between different zones keeping the system within its security limits. Conventionally, static security assessment is employed to study  
\begin{table}
\begin{tabular}{c c c} \hline
**Bus No.** & **R (\%)** & **X (\%)** & **B (\%)** \\ \hline B1-B5 & 0.0000 & 1.667 & - \\ B2-B6 & 0.0000 & 1.667 & - \\ B3-B11 & 0.0000 & 1.667 & - \\ B4-B10 & 0.0000 & 1.667 & - \\ B5-B6 & 0.2500 & 2.500 & 4.375 \\ B6-B7 & 0.1000 & 1.000 & 1.750 \\ B7-B8 & 1.1000 & 11.00 & 19.25 \\ B8-B9 & 1.1000 & 11.00 & 19.25 \\ B9-B10 & 0.1000 & 1.000 & 1.750 \\ B10-B11 & 0.2500 & 2.500 & 4.375 \\ \hline \end{tabular}
\end{table}
Table 2: Line parameters of Kunder’s AC network.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Line-Data** & **Length** & **R (\(\Omega\)/km)** & **L (mH/km)** & **C (\(\mu\)F/km)** & **G (\(\mu\)S/km)** \\ \hline O.H.L (DC) & 900 km & 0.01140 & 0.93560 & 0.01230 & 0.0000 \\ O.H.L (AC) & 600 km & 0.02000 & 0.85320 & 0.01350 & 0.0000 \\ Cable (DC) & 900 km & 0.00950 & 2.11200 & 0.19060 & 0.0480 \\ Cable (AC) & 150 km & 0.01840 & 2.57290 & 0.23150 & 0.0590 \\ \hline \end{tabular}
\end{table}
Table 4: CIGRE B4 transmission lines data.  
\begin{table}
\begin{tabular}{c c c} \hline
**Parameter** & **G1-G2** & **G3-G4** \\ \hline X\({}_{\rm d}\) (pu) & 1.8000 & 1.8000 \\ X\({}_{\rm q}\) (pu) & 1.7000 & 1.7000 \\ X\({}_{\rm l}\) (pu) & 0.200  
transfer capability due to limitations in computational time, specifically in online environment. Thus, load flow analysis (LFA) is deployed to observe thermal and voltage limits. The linearized power flow technique [31] and continuation power flow algorithm [36] are used to evaluate the voltage and thermal limits considering the list of credible contingencies.  
However, the dynamic performance of the system must be considered while evaluating the transfer capability. Hence, precise limits can only be obtained if dynamic security assessment (DSA) is done, observing small-signal stability and transient voltages [37]. However, conflict of objectives may occur if two or more aspects are considered simultaneously for security evaluation [38]. As authors focused on inter-area power transfer capability in this paper, thus only small-signal stability is studied with static evaluation. In order to analyze power transmission capability, two aspects are focused and discussed in subsequent sections; (i) power transmission through a specific corridor and (ii) security region determination.