esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Modeling and Theoretical Foundations  
This part briefly introduces the Chen chaotic system used for chaotic masking of speech signals in this paper and present the theoretical preparation for constructing secure transmission of speech signals. In addition, the convolutional model for multipath transmission of speech signals is constructed, and the BSS process under this model is illustrated to make the theoretical basis for the independent vector analysis algorithm.
## 3 Results and Discussion
### Structure and Morphology Characterization  
The structure and morphology of the formed thin films were tested and confirmed by FESEM analysis. Figure 3 shows the morphology of the photoelectrode prepared at different annealing temperatures, namely, (a) pre-annealing, (b) 450 \({}^{\circ}\)C, and (c) 550 \({}^{\circ}\)C. The pre-annealed photoelectrode surface formed a uniformly grown flower-like structure. The high magnification FESEM images in Figure 3a revealed the formation of microscale flowers, which are composed of micro-sized petals with an average thickness of \(\sim\)6 \(\upmu\)m. According to [PERSON] et al., urchin-like spheres are composed of Co\({}_{3}\)O\({}_{4}\). When the annealing temperature increased to 450 \({}^{\circ}\)C, the color of the photoelectrode changed from purple to black. FESEM observation revealed that the Co\({}_{3}\)O\({}_{4}\) produced a microflower that started to decompose (Figure 3b). With increasing annealing temperature, the Co\({}_{3}\)O\({}_{4}\) changed to mixed phase Co\({}_{3}\)O\({}_{4}\)CoO, and the morphology of the sample changed to microflowers with less thick nanowire petals (Figure 3c) [33].  
Figure 2: XPS analysis of the pre-annealed, 450 \({}^{\circ}\)C and 550 \({}^{\circ}\)C, thin films samples. (a) Full spectra (b) Co 2p spectra (c) O spectra.  
Details of the microstructures of Co\({}_{3}\)O\({}_{4}\) and mixed Co\({}_{3}\)O\({}_{4}\)CoO were examined using HRTEM, and specifics were examined with selected area electron diffraction (SAED) (Figure 4). Lattice fringes of Co\({}_{3}\)O\({}_{4}\) annealed at 450 \({}^{\circ}\)C are shown in Figure 4(a1-a3). The SAED of Co\({}_{3}\)O\({}_{4}\) shown in Figure 4(a3) reveals the ring and spot pattern in a well-defined manner, demonstrating that the Co\({}_{3}\)O\({}_{4}\) sample was highly crystalline. However, for the mixed Co\({}_{3}\)O\({}_{4}\)CoO annealed at 550 \({}^{\circ}\)C, the SAED showed a small spot that made rings, which were designated as polycrystalline. According to [PERSON] et al., (1971) each spot arising from brag was reflected from individual crystalline [34]. As shown in Figure 4(b3), a SAED ring demonstrated the presence of a mixed phase that was well-blended at this phase. The first ring indicated Co\({}_{3}\)O\({}_{4}\), whereas the second ring indicated CoO.
# Barrel Shifter Physical Unclonable Function Based Encryption
## 4 Barrel Shifter PUF Design  
We evaluate a barrel shifter as a potential invertible and commutative PUF. The block diagram of a barrel shifter is shown in Figure 7. For simplicity, only two shift levels are shown. In a BS-PUF, text (plaintext/ciphertext) input are the bits to be shifted; the key input determines the shift path for the text input.  
Output logic is added to capture path delay \(D(i,i^{\prime})\). An event counter is initialized to 0. The \(RST\) signal simultaneously starts the event counter and releases the input message. The delay is captured by reading the event counter when the output logic detects a voltage transition. Finally, an entanglement block in the output logic entangles delay with the message bit.  
Each shift stage is logically similar to an arbiter PUF stage. Barrel shifter PUF is designed to implement rotation functions. Key bits determine the shift amount \(s=\sum_{i=0}^{k}(key_{i}es2^{i})\). Thus, \(key_{i}\) is applied from LSB to MSB, from left to right. Figure 7 provides an example; \(key_{0}=\{key_{0}=0,key_{1}=1\}\) encodes for right shift by 2 in the second stage. Consequently, the same text bit traverses a different path providing a different delay value for different keys.  
Figure 7: Block diagram of the delay test circuit with two propagation examples. When \(key_{0}=1\) and \(key_{1}=0\), \(i_{0}\) passes through the dark grey path. There is one bit shift at the first level and no shift at second level, \(i_{0}\rightarrow\)\(o_{1}\). When \(key_{0}=0\) and \(key_{1}=1\), \(i_{0}\) passes through the light grey path. There is no shift at the first level and there is a two-bit shift at the second level, \(i_{0}\rightarrow\)\(o_{2}\).  
The delay variation is generated by transistor-level mismatch and doping variability. Variation accumulates over several stages. Delay is then large enough to be detected by the output logic.
# Development and Evaluation of Nano-Silica Sustainable Concrete
## 3 Results
### Rapid Chloride Permeability (RCP)  
The average passing charges of three specimens and the penetrability class as specified by ASTM C 1202 of each mixture are shown in Table 5. Partially replacing the Portland cement with 1% and 2% dosages of nano-silica improved the resistance of the 70% GGBS concrete mixtures (M2 and M3) to chloride penetration by 34.8% and 52.7%, respectively, when compared to the control mixture (M1). Therefore, the chloride ingress of the 70% GGBS concrete mixtures decreased as the nano-silica dosage increased. On the other hand, the partial replacement of cement with 1% dosage of nano-silica reduced the passing charges of the 30%  
GGBS concrete mixture (M5) by 57.8% with respect to the control mixture (M4), changing the penetrability class from low to very low. Similarly, the 2% nano-silica addition reduced the chloride ingress of the 30% GGBS concrete mixture (M6) by 33.7% and changed the penetrability class from low to very low when compared to the control mixture (M4). However, the addition of a 2% dosage of nano-silica was not as effective compared to the 1% dosage of nano-silica in improving the resistance of the 30% GGBS concrete mixtures against chemical attacks. In general, adding small dosages of nano-silica had a noticeable effect on decreasing the conductivity of the concrete and refining the pore structure of all the mixtures due to the pozzolanic reaction and the nano-filler effect of nano-silica, which made the microstructure of the concrete mixtures more homogeneous and less porous. Consequently, this improved the resistance of the concrete mixtures against the physical penetration of chloride ions. Thus, nano-silica addition improves the durability of the concrete making it more sustainable.
# Semantic-Enhanced Foundation Model for Coastal Land Use Recognition from Optical Satellite Images
## 2 Workflow of Methodology  
Figure 2 presents the architecture of the proposed methodology, which includes four parts. The first part determines the land cover category system of coastal land uses or the land cover categories relevant to coastal land use. The second part extracts water areas from optical satellite images with a foundation model called the Segment Anything Model (SAM). The third part classifies non-water remaining regions in the optical satellite images into multiple land covers with an image gridding strategy and another foundation model called ViTAE-RVSA. The fourth part converts the results of land covers into semantically enriched coastal land uses through the vector space model and semantic similarity analysis.  
Figure 1: Illustration on challenges of coast land use recognition.
## 4 Blockchain Application in SG 2.0
### Transaction Workflow  
The workflow is divided into three sections. Any communications or negotiations between the buyer and seller are referred to as \"energy deals\" [124]. One example of interaction before a trade is the posting of a user's offer/request over the network. Various mechanisms can be used to protect data and confidentiality (See Figure 10). The seller's bids are recognized in the bids between the buyer and seller to determine the user's right to be treated with respect [125].  
* Entities: engage in energy trading and are categorized into three types. These companies will be able to use smart meters and blockchain technology.
* The central utilities manager consists of the community, electricity providers and network owners who own the physical and technological resources for energy sharing and transmission. It is responsible for formalizing global order and regulation.
* Energy generators: those that have large reserves of energy supply the grid with energy that uses conventional and renewable energy sources. Examples of commercial energy suppliers include national grid monitors, small grid owners and turbine owners.
* Energy to consumers/prosumers: Consumers have the added benefit of producing and distributing excess electricity to other network consumers. Private homes, hybrid vehicles and large structures can be consumers/prosumers.  
Acceptance of payment methods is one way to maximize consumer value. As a result, the consumer will be more excited because they will receive instant rewards and investment opportunities. Second, there may be a way to encourage repeat customers. For example, if the consumer sells to the government, the buyer can be paid with power, cryptocurrency or changes in their bill.  
Figure 10: Blockchain-based energy trading Taxonomy.  
In peer-to-peer energy trading, real-time monitoring and supervision is critical. The idea behind demand response is to shift the energy burden from low-demand users to high-demand customers. For example, a household requires less energy in the morning than an office building does. Likewise, the situation reverses at night. As a result, power can be distributed as needed.
# Unveiling the Role of Minerals and Trace Elements of Thermal Waters in Skin Health
## 2 Minerals, Trace Elements, and Other Compounds of TSW in Skin Health
### Cations
#### 2.2.3 Magnesium  
Magnesium salts are known for their anti-inflammatory properties and wound-healing properties. A topical application of a cream containing 2% magnesium and calendula was able to reduce the recovering time in diaper dermatitis [80]. All of the magnesium salts, except magnesium dihydrogen phosphate, accelerated barrier repair, but research performed by [PERSON] et al. [81] demonstrated that magnesium chloride aqueous solutions contained calcium chloride at different molar ratios. When the calcium-to-magnesium ratio was lower than 1, the mixture accelerated barrier repair. The application of an aqueous solution of 10 mM magnesium chloride and 10 mM calcium chloride was found to hasten the barrier recovery more effectively than a solution of 10 mM magnesium chloride. Magnesium exerts a pivotal role in the growth and differentiation of keratinocytes, and it is responsible for helping the skin preserve a lack of water, thus acting as a natural moisturizing agent. These effects are very useful in treating and managing pathologies such as psoriasis and atopic dermatitis [64; 82].  
Several studies have been performed with magnesium-rich TSW, mainly in the Dead Sea, where the content of magnesium salts is very high. Studies have shown that magnesium-rich Dead Sea salt solutions are able to improve skin barrier function, increase skin hydration, and reduce inflammation in atopic dermatitis [44]. When TSWs rich in calcium and magnesium are added to cosmetics formulations, the penetration of both cations is enhanced, and they can reach viable skin layers in a formulation-dependent manner [61].
## 2 Methodology
### Vehicle Path Feature Extraction
#### 2.4.1 OD Point Addition Strategy  
To accurately describe vehicle entry and exit positions and directions within the road network, virtual OD (Origin-Destination) points need to be added at network edges. This study designs a systematic OD point addition strategy:  
According to the naming convention established in this paper, OD point numbering uses three-character notation, with the first two digits representing the corresponding intersection number and the third digit indicating direction (a/e for upward, b/f for downward, c/g for leftward, d/h for rightward). This naming scheme not only facilitates understanding and management but also supports batch processing with regular expressions.  
In practical application, flow anomalies were discovered in the road segments between intersections 15-16 and 21-22, suggesting the possible existence of unmarked branch roads or community entrances. Therefore, in addition to adding original OD points at network edges, supplementary OD points were added for these anomalous segments (Figure 5). This flexible OD point addition strategy improves path identification accuracy.
## 2 Materials and Methods
### LCOE Observations  
The LCOE BEIS model, as stated by the UK government that LCOE is not a strike price. In contrast, providing the nominal expected pre-tax cost of generation required for a project to break even. The generated figure, however, is particularly vulnerable to the costs of data accumulated in step one. This can be mitigated by using a sensitivity analysis of the parameters. However, the likelihood of the scenario occurring requires additional analysis [16].  
Additional weaknesses of the metric are listed below:  
* Standard levels of grid integration and strengthening requirements for technologies
* Variations in applied hurdle rates
* Market Conditions
* Developments within industry  
\begin{table}
\begin{tabular}{c|c|c}
**CAPEX** & **OPEX** & **Expected Generation** \\ \hline \multirow{8}{*}{Pre-developed costs Construction costs Infrastructure} & Fixed * OPEX & \multirow{8}{*}{Capacity of plant Expected availability Expected deficiency} \\  & Variable * OPEX & \\  & Insurance & \\  & Connection costs & \\ Pre-developed costs Construction & Carbon transport and storage costs & \\  & Decommissioning fund costs & \\  & Heat revenues & \\  & Fuel prices & \\  & Carbon costs expected & \\ \hline \end{tabular}  
* Fixed OPEX costs are incurred regardless of the energy produced whereas variable OPEX costs represent the combined annualized capital and Operation and Maintenance costs of each process.  
\end{table}
Table 1: BEIS’s LCOE Parameters.  
* Non-site-specific generalization
* Hidden costs/assumptions (land lease, component degradation, etc.)  
Despite all listed weaknesses above, project sensitivity is used in the developed calculator models for this thesis to negate some of the weakness associated with the parameter data. Henceforth the LCOE theoretical model has a justifiable merit to commercial enterprises.  
Basic inputs are similar in design to BEIS with the model calculation returning the minimum price at which energy must be sold for a project to break even [8]. Estimated LCOE figures for wind energy are published from projected representative utility-scale projects for both floating and fixed turbine design, as published in the 2015 Cost of Wind Energy Review. Breakdown of the LCOE model equation with energy and financial assumptions illustrated can be seen in [16]. Considering the breakdown of assumptions, it can be seen the calculations are applicable to site specific conditions rather than the gross standardized approximations associated with the BEIS model. It should be noted that under specific conditions, expected valuations will be equal to the BEIS metric.
# XTTS-Based Data Augmentation for Profanity Keyword Recognition in Low-Resource Speech Scenarios
## 1 Introduction
This approach reduces reliance on extensive manual data collection while enhancing keyword classification performance, making the system more suitable for real-world applications like insult speech monitoring. This study's contributions are summarized as follows:* We propose a generative corpus method based on XTTS [6], combining preprocessing with minimal raw data. This approach reduces reliance on large raw corpora and leverages diverse voices from public datasets, enhancing sample variety and significantly boosting keyword recognition performance;
* Through experimental data analysis, we investigate the impact of training, validation, and test set ratios on model performance, further optimizing the proportion of synthetic versus real speech to determine the best configuration;
* The proposed novel data augmentation technique expands the dataset multiple fold, improving system accuracy by 33.55% and demonstrating its effectiveness in addressing data scarcity through speech synthesis;
* By integrating real and synthetic corpora, we not only alleviate data shortages but also enable discrimination between synthetic and real speech within the same model architecture, achieving high-accuracy true-false speech identification. The remainder of this paper is structured as follows: Section 2 elaborates on the proposed methodology, encompassing the XTTS-based data augmentation technique, the speech signal preprocessing pipeline, the design of sliding window and confidence score statistics mechanisms, the architectural details and implementation of the CNN-based Proposed-I model and CNN-Transformer hybrid Proposed-II model, and ethical considerations addressing the potential misuse and implications of the proposed techniques. Section 3 presents the experimental results, comparing the impact of different dataset ratios, data augmentation strategies, and sliding window techniques on model performance, alongside an analysis of real versus synthetic speech identification. Section 4 summarizes the key findings of this study, discusses the practical implications of the proposed approach, and outlines directions for future research.
###### Abstract
* Analyze and compare existing studies relating to the detection of the abuse of CPLS as C&C servers. \begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**CPLS Category**} & \multicolumn{1}{c}{**CPLS Platforms**} \\ \hline Online Cloud Storage Sites & Dropbox, Google Drive, OneDrive, pCloud, \\  & Yandex Disk, Mega, Alibaba Cloud, CloudMe \\ \hline Social Media Platforms & Twitter, Facebook, Instagram \\ \hline Business Communication Platform & Slack, Teams \\ \hline Online Developers repository & Pastebin, Github, Microsoft TechNet \\ \hline Content Sharing Platforms & YouTube, Imgur, ImgBB \\ \hline Email Service & Outlook, Gmail, Exchange Web Services (EWSs) \\ \hline Instant Messaging Platforms & Facebook Instant Messenger, Telegram \\ \hline Miscellaneous & Google Scripts, File.io, Discord, Quora, Google Sites, Google Cloud Messaging (GCM) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Abused CPLS platforms as C&C communication channels. * Provide insights into the types of CPLS that are commonly targeted for abuse as C&C servers along with the employed attack techniques. * Identify new challenges and propose directions for future research. The rest of this paper is structured as follows: The background is presented in Section 2 followed by discussions of related surveys in Section 3. Section 2.3 provides a description of the threat model. The systematic literature review's methodology adopted for this research is explicated in Section 4. Section 5 focuses on the research findings and discussion, while Section 7 delves into works related to abuse detection. Section 8 highlights the directions for future research, and finally, Section 9 concludes the paper.
# Affordable Broadband with Software Defined IPv6 Network for Developing Rural Communities
## 3 Affordable Broadband for Rural Communities
### Unlicensed Spectrum  
The government of Nepal has already announced that all cable television companies must transform their cable service network from analogue to digital television broadcasting. In this context, not all channels of the ultra-high frequency TV band are occupied at each location. The portion of the spectrum left unused by broadcasting, known as TV white spaces (TVWS) that have traditionally been used for analogue TV broadcasting and its opening for cognitive access, has greater opportunity to avoid spectrum scarcity issues [35; 36]. Radio signals with TVWS are suitable for travelling long distances over hills, mountains, and around/through buildings. This is more applicable in the context of rural Nepal, where in most places it is not possible to connect via wired network. Most of the countries worldwide are exploring the ways to use TVWS for wireless broadband communications [17; 35; 36]. Hence, low power telecommunications transmissions leading to affordable broadband for rural communities, can also be achieved with the use of TVWS.
# A Least Squares Ensemble Model Based on Regularization and Augmentation Strategy
## Appendix A
### Support Vector Regression (SVR)  
SVR approximates a linear function \(f(x)\) in the following form:  
\[f(x)=w^{\mathrm{T}}x+b \tag{10}\]  
where the coefficients \(w\) and \(b\) are the weight vector and bias term, respectively. This linear function can be constrained to the following optimization problem [10; 11; 52]. Using \(\varepsilon\) as the insensitive loss function, the corresponding SVR, which is called \(\varepsilon\)-SVR, can be represented as follows:  
\[\left|y-(w^{\mathrm{T}}x+b)\right|=\begin{cases}0,&\left|(w\cdot x_{i}+b)-y_{ i}\right|<\varepsilon\\ \left|(w\cdot x_{i}+b)-y_{i}\right|-\varepsilon,&\left|(w\cdot x_{i}+b)-y_{ i}\right|\geq\varepsilon\end{cases} \tag{11}\]  
where \(\varepsilon\) is a positive constant. The characteristic of this function is that the fitting errors, which are below \(\varepsilon\) can be ignored; thus, it has strong anti-noise properties. To measure the degree of deviation from the \(\varepsilon\) insensitive band of training samples, two relaxation factors are introduced; thus, the objective function of the SVR optimization is:  
\[\min_{w\in\mathbb{R}^{N},b\in R}\frac{1}{2}\|w\|^{2}+C\sum_{i=1}^{n}\left( \varepsilon_{i}+\xi_{i}^{*}\right) \tag{12}\]  
Further, the constraint conditions are:  
\[\begin{array}{l}\mathrm{w}x_{i}+b-y_{i}\leq\varepsilon+\xi_{i},\;\;i=1,2, \cdots,n\\ y_{i}-(\mathrm{w}x_{i}+b)\leq\varepsilon+\xi_{i}^{*},\;\;i=1,2,\cdots,n\\ \xi_{i},\xi_{i}^{*}\geq 0\end{array} \tag{13}\]  
By introducing a Lagrange function for the optimization problem, the mathematical expression of SVR can be obtained by solving the dual formula:  
\[f(x)=\sum_{i=1}^{m}\left(a_{i}-a_{i}^{*}\right)K(x_{i},x)+b \tag{14}\]  
where \(m\) is the number of support vectors, \(K(x_{i},x)\) is the kernel function, and \(a_{i}\) and \(a_{i}^{*}\) are the Lagrange multipliers; \(b\) is obtained by:  
\[\begin{array}{l}b=y_{j}-\sum_{i=1}^{n}\left(a_{i}-a_{i}^{*}\right)K(x_{i},x_ {j})+\varepsilon,a_{j}\in(0,C)\\ b=y_{k}-\sum_{i=1}^{n}\left(a_{i}-a_{i}^{*}\right)K(x_{i},x_{j})-\varepsilon,a _{k}^{*}\in(0,C)\end{array} \tag{15}\]  
In this paper, the Gaussian kernel was used; it is shown as:  
\[k(x_{i},x)=\exp(-\frac{\|x-x_{i}\|^{2}}{\sigma^{2}})=\exp(-\gamma\|x-x_{i}\|^ {2}),\gamma>0 \tag{16}\]