esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### System Design Methodology
#### 3.2.1 Mandatory Access Control  
In a relational DBMS, unauthorized access to the database represents a failure to maintain confidentiality. Similarly, unauthorized modifications to the data indicate a breach of data integrity. Furthermore, a lack of access to database system services reflects a failure to ensure availability. The designed aspects of MLS/DBMS are explored further in the following subsection.  
In the MLS/DBMS model, discretionary access control is combined with MAC to classify users and data into specific security levels, as defined by the Bell-LaPadula model. This popular framework for MLS assigns every subject and object a security classification, such as Top Secret (\(TS\)), Secret (\(S\)), Confidential (\(C\)), or Unclassified (\(U\)), following the hierarchy \(TS\ \geq\ S\ \geq\ C\ \geq\ U\). Two key restrictions are imposed on data access to ensure security. Addressing these restrictions, the model identifies and classifies each access authentication within a relational DBMS during access control.  
As designed in this novel model, Figure 3 illustrates the allowable data transfers within the MLS framework. It provides a detailed analysis of permitted data interactions between a subject operating at the (\(S\)) level and various objects with different security levels. These interactions as previously discussed are governed by the Bell-LaPadula model's two key strategies: \"no read-up\" and \"no write-down\".
## 4 Conclusions  
The simulated two-stage differential input pair CTLE can successfully compensate for 16 dB of channel loss at a 3 GHz Nyquist frequency and can open the closed-eye 6 Gb/s NRZ signal with an energy efficiency of 8 pJ/bit and a BER of 0.16 \(\times\) 10\({}^{-12}\) from a 2\({}^{31}-1\) pseudorandom binary sequence input. The TIA topology can achieve higher bandwidth, ameliorating the trade-off between bandwidth and DC gain. The AFE compensated for 12 dB of channel loss at a 15 GHz Nyquist frequency and opened closed eyes for a 30 Gb/s PAM4 signal from a pseudorandom binary sequence input; it consumed 27 mW of power from a 1.8 V supply.  
[PERSON] contributed to the schematic design, simulation in software, and simulation results of the proposed circuits, as well as writing the paper. [PERSON] contributed to advice on the design process, simulation, and editing of this paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to acknowledge the Chip Implementation Center (CIC), Taiwan, for its EDA tools, facility, and technology support.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **[1]** & **[4]** & **[15]** & **[23]** & **This Work** \\ \hline Technology CMOS & 28 nm & 28 nm & 40 nm & 40 nm & 180 nm \\ Power supply & 1.5 V & 0.9 V & 1 V & 1.2 V & 1.8 V \\ Equalization & 14-tap CTLE & CTLE+DFE & CTLE & CTLE & CTLE \\ Data rate range (Gbps) & 1.25–28.5 & 32 & 8–10 & 20 & 5–30 \\ Power (mW) & 560 & 240 & 10 & 12.8 & 27 \\ Channel loss (@GHz) & 34 dB@14 & 37 dB@16 & 17 dB@4 & 18 dB@10 & 28 dB@15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison.  
Figure 19: (**a**) Histogram of the eye diagram reveals eye widths for levels 0, 1, 2, and 3. (**b**) Threshold histogram of the eye diagram reveals the bit period of levels 0/1, 1/2, and 2/3.
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 5 Future Research Directions
### Standardization and Interoperability  
Ensuring standardization and interoperability among different localization technologies will be essential for widespread adoption. Research should focus on developing unified frameworks and protocols that allow seamless communication and data exchange between different RTLS systems, regardless of the underlying technology [36].
# A Self-Recovery Fragile Image Watermarking with Variable Watermark Capacity
## The Proposed Scheme
### Watermark Generation and Embedding
#### 3.1.1 Authentication Watermark  
From the analysis in Section 2, we know that the singular values contain the most energy information of a matrix. Therefore, in this paper, the singular values are adopted as the authentication watermark. For each block after preprocessing, the SVD transform is performed. The trace of singular value matrix is calculated and coded into binary from \(\mathbf{B}=(b_{1},b_{2},\cdots,b_{8},b_{9})\). Then two exclusive-or-operations are applied to \(\mathbf{B}\), and 2-bit authentication watermark \(\mathbf{P}=(p_{1},p_{2})\) is generated for each block, which can be expressed as:  
\[p_{1}=b_{1}\oplus b_{2}\oplus\cdots\oplus b_{8}\oplus b_{9},\,p_{2}=b_{2}\oplus b _{4}\oplus b_{6}\oplus b_{8}. \tag{3}\]  
To enhance the security of authentication watermark, a binary pseudo-random sequence \(\mathbf{S}_{1}=(s_{1},s_{2})\) with a secret key \(key1\) is produced, where \(s_{1}\) and \(s_{2}\) are the elements in \(\mathbf{S}_{1}\). Then the encrypted authentication watermark \(\mathbf{P}_{\rm e}=(p_{\rm e1},p_{\rm e2})\) is generated by:  
\[p_{\rm e1}=p_{1}\oplus s_{1},\,p_{\rm e2}=p_{2}\oplus s_{2}. \tag{4}\]
## 3 Materials and Methods
### Statistical Evaluation  
To rigorously compare classifier performance, we applied the paired \(t\)-test, a statistical method used to evaluate whether the mean difference between two paired sets of observations is significantly different from zero.  
Formally, given two sets of paired measurements \(\mathbf{X}=\{\mathbf{x}_{1},\mathbf{x}_{2},...,\mathbf{x}_{n}\}\) (e.g., ANN accuracies) and \(\mathbf{Y}=\{\mathbf{y}_{1},\mathbf{y}_{2},...,\mathbf{y}_{n}\}\) (e.g., Random Forest accuracies), we define the differences \(\mathbf{D}=\{\mathbf{d}_{i}=\mathbf{x}_{i}-\mathbf{y}_{i}\}\).  
We calculate the following:  
The mean difference is calculated as in Equation (5):  
\[\overline{\mathbf{d}}=\frac{1}{n}\mathbf{\sum}_{i=1}^{n}\mathbf{d}_{i} \tag{5}\]  
The standard deviation of the differences as highlighted in Equation (6):  
\[\mathbf{s}_{\mathbf{d}}=\sqrt{\frac{1}{n-1}\mathbf{\sum}_{i=1}^{n}(\mathbf{d}_{i}-\overline{ \mathbf{d}})^{2}} \tag{6}\]  
The \(t\)-statistic is given by Equation (7):  
\[\mathbf{t}=\frac{\overline{\mathbf{d}}}{\mathbf{s}_{\mathbf{d}}/\sqrt{n}} \tag{7}\]  
The \(t\)-statistic is compared to the \(t\)-distribution with \(\mathbf{n}-1\) degrees of freedom to obtain the \(\mathbf{p}\)**-value**. If \(\mathbf{p}<0.05\), the difference is considered statistically significant. This test is well suited to model comparisons because we evaluate performance on the same data splits, making the pairs naturally dependent.
## 2 Methodology
### Data Preprocessing  
Data preprocessing forms the foundation of the entire method, with its quality directly affecting the accuracy of subsequent analyses. The data preprocessing in this study includes three main steps: redundant data removal, anomalous data removal, and missing data imputation.
## 3 Experimental Outcomes
### Adsorption Kinetics  
The solute separation from solution adhering to adsorbent is known as adsorption which is dynamic in its nature. Kinetic models provide an insight on the performance of adsorption of CV dye on NIFGS with time as an independent variable. The studies will have a great impact on scaling for commercial applications. To provide the variation in adsorption rate, a concentration of 50, 100 and 200 g mL\({}^{-1}\) of CV dye was used to carry out kinetic studies at 303, 313 and 323 K. Results are presented in Table 2. Kinetic data of adsorption of CV on NIFGS was analysed using the pseudo-first order model Equation (7), Table 1[32] and pseudo-second order model Equation (8), Table 1[33]. For the CV-NIFGS system the latter fits more appropriately than the other.
# Inverse Modeling of Seepage Parameters Based on an Improved Gray Wolf Optimizer
## 3 Inverse Model of Seepage Parameters
### Procedure of the Inversion Model  
The procedure of the inversion model could be summarized by specific steps. The steps are as follows.  
Step 1: Set initial parameters of IGWO. The number of search agents \(S\), the maximum number of iterations \(T\), and the bounds of hydraulic conductivity values, \(\mathbf{K}_{min}\) and \(\mathbf{K}_{max}\), are determined initially.  
Step 2: Initialize the population. Equation (1) is applied for initialization, ensuring that the initial populations are uniformly distributed in the solution space.  
Step 3: Calculate the fitness of individual gray wolves. The three gray wolves with the top fitness values are selected as \(\alpha\), \(\beta\), and \(\gamma\).  
Step 4: Update the position. Determine the distance of the gray wolf from the three dominant wolves, respectively. Orient the location and calculate the weighting factors of the three wolves. The position of the gray wolf at the fourth level is updated according to Equation (12).  
Step 5: Iterative Judgment. Determines whether the maximum number of iterations has been achieved. If not, skip to the third step. Otherwise, end the iterative procedure. Output the objective hydraulic conductivity and the corresponding fitness value.  
Step 6: Positive verification. The combination of the target hydraulic conductivity is substituted into the finite element model for positive verification. Compare the calculated and monitored values of hydraulic head and leakage volume and evaluate the reasonableness of the simulation.  
Figure 3 presents the flow chart of the model.
# Dynamic Thermal Rating and Development of Renewable Energy Zones in Sicily
###### Abstract  
Sicily, an Italian region in the south of Italy, is a candidate for becoming an energy \"hub\" in the Mediterranean in the coming years. Its geographical location, between the African continent and Europe, makes it a key hub for the distribution of electricity flows. The region has great potential for the development of renewable energy sources. Inspired by the Australian \"Renewable Energy Zones\" model, the identification of existing Renewable Energy Zones (REZs) and the proper design of future REZs on the territory will simplify and improve the operation of the network. A very effective tool in managing REZs is Dynamic Thermal Rating (DTR) technology. This technology allows for dynamic rating of the high voltage transmission system, ensuring that the maximum allowable current is transported while preventing the annealing of the electrical conductor and complying with all necessary safety parameters. The objective of the work carried out in collaboration with Terna S.P.A. (Italian Transmission System Operator) was to evaluate the application of this technology on the Sicilian electricity network in order to reduce potential network congestion and system inefficiencies. The results obtained demonstrate the significant potential of Dynamic Thermal Rating in Sicily. These devices would allow a significant increase in the current flow of electrical lines without violating safety limits.  
dynamic thermal rating; Sicily; electrical transmission system; wind power curtailment; critical span; grid congestion Article
# A Comprehensive Review of Computer Vision in Sports:
## 7 Applications in Sports Vision
### Virtual Umpires  
In cricket and tennis, Video Assistant Referees (VAR) and Decision Review Systems (DRS) have used Hawk-eye, slow-motion replays, and some other technologies. However, the catch is that these involve a request from players or team for review when an umpire's or referee's decision has some uncertainty and then involves other parties to assist the main umpire. The whole process is time-consuming and takes away the momentum and excitement of the game.  
The latest camera technology supporting AI software creates a situation where an umpire's role is limited to on-field behavior management of players rather than making critical decisions. For example, in the case of tennis, computer vision is used for detecting placement and speed of the ball; therefore, the need for a line umpire is eliminated. The future scope can be an umpire's earpiece and glasses assisting the decision instantly and eliminating the necessity of reviews.
One of the major hurdles is the statistical issue with highly non-IID and imbalanced data. Because of the complications of object-detection tasks and the huge weights of CNN models, the FedAvg algorithm is incapable of performing object-detection tasks. The primary aim of this work is to construct a productive model for real-world object recognition in FL using the proposed FP-SFOA-DQN-FL. The entities involved in this designed model are nodes and servers. Here, local training is performed based on local data at every node, and the data are updated on the server. After that, model aggregation is carried out on the server, and the global model is downloaded at every node. Thereafter, updated training takes place based on the downloaded global model and local model at every epoch. In the training model, indoor images are taken into account as inputs, and they are subjected to a pre-processing stage, where the mechanism is carried out utilizing a bilateral filter to make the image desirable for further processing. Once the pre-processing is commenced, object detection is conducted by employing SegNet, and it is trained using PSFOA. The derived PSFOA is the combination of PO and SFOA. Following this process, features like ResNet, Shape Local Binary Texture (SLBT), gray level co-occurrence matrix (GLCM), speeded-up robust feature (SURF), oriented fast and rotated brief (ORB), and the Hierarchical Skeleton features are extracted. Finally, object recognition is performed utilizing DQN, in which parameters of the network are tuned optimally using designed FP-SFOA. The local updation and aggregation at the server are modified based on the CAViaR model. Contributions of this research:  
* FP-SFOA-DQN-FL for real-world object identification in FL: an efficacious model is developed for real-world object recognition in FL using FP-SFOA-DQN-FL. * The object detection is conducted based on SegNet, and this classifier is optimally biased utilizing PSFOA. * The object identification is accomplished utilizing DQN, and this network is optimally tuned based on modeled FP-SFOA. * The FP-SFOA is derived by the consolidation of the FC concept with PO and SFOA. The organization of this article is as follows: the literature review of former approaches associated with real-world object recognition in FL is explained in Section 2, along with its benefits and constraints that provoke the investigators to construct an effective framework. The designed model and its whole process associated with real world object recognition are enumerated in Sections 3 and 4 discusses the outcomes of developed FP-SFOA-DQN-FL. Section 5 provides the satisfactory conclusion of this research along with its future scope.
###### Abstract  
For countries with limited access to conventional hydrocarbon gases, methane hydrates have emerged as a potential energy source. In view of the European Union's requirements to reduce the energy intensity of technological processes and increase energy security, it appears promising to accumulate natural gas and biomethane in the form of hydrate structures and release them if necessary. Storing gas in this form in an energy-efficient manner creates interest in developing and innovating technologies in this area. Hydrates that form in gas pipelines are generated by a more or less random process and are an undesirable phenomenon in gas transportation. In our case, the process implemented in the proposed experimental device is a controlled process, which can generate hydrates in orders of magnitude shorter times compared to the classical methods of generating natural gas hydrates in autoclaves by saturating water only. The recirculation of gas-saturated water has been shown to be the most significant factor in reducing the energy consumption of natural gas hydrate generation. Not only is the energy intensity of generation reduced, but also its generation time. In this paper, a circuit diagram for an experimental device for natural gas hydrate generation is shown with complete description, principle of operation, and measurement methodology. The natural gas hydrate formation process is analyzed using a mathematical model that correlates well with the measured hydrate formation times. Hydrates may become a current challenge in the future and, once verified, may find applications in various fields of technology or industry.  
methods: machine; hydrate; modelling; energy storage; experimental device +
Footnote †: journal:  
_Article_  
Controlled Production of Natural Gas Hydrates in an Experimental Device with an Internal Circulation Circuit
# Antenna on Chip (AoC) Design Using Metasurface and SIW Technologies for THz Wireless Applications
## 1 Introduction
The results confirm this has notable improvement in the radiation characteristics of AoC.
# Towards Digitalization of Water Supply Systems for Sustainable Smart City Development--Water 4.0
###### Abstract  
Urban water supply systems are complex and dynamic in nature, and as a result, can be considered complex to manage owing to enhanced urbanization levels, climate change, growing and varying consumer demands, and limited water resources. The operation of such a system must be managed effectively for sustainable water supply to satisfy the growing consumer demand. This creates a need for intelligent systems for the purposes of operational management. In recent years, computing technologies have been applied to water systems to assist water utilities in addressing some of these problems. Unfortunately, previous ICT solutions have not been able to provide the necessary support for applications to deal with the dynamics of water supply system environments. With the increasing growth in technology, the water sector is moving to the full phase of digitalization to enhance the sustainability of systems. Thus, a new industrial revolution in the water context (Water 4.0) is being researched. This is referred to as Water 4.0, which offers better possibilities to enhance the sustainability of water supply system operations. This paper presents an overview of Water 4.0 and its applications in enhancing water supply system operations. Key features of Water 4.0 are discussed. Furthermore, challenges and future opportunities offered by technology for sustainable operation of municipal water services are discussed.  
AI; cyber physical system; ICT; Industry 4.0; Internet of Things; Water 4.0; water loss; water supply system +
Footnote †: journal: Applied Sciences
# CPU-GPU-Memory DVFS for Power-Efficient MPSoC in Mobile Cyber Physical Systems
## 5 Proposed Methodology: CGM-DVFS  
### Overview of CGM-DVFS  
Figure 3 presents a block diagram of our proposed CGM-DVFS methodology. CGM-DVFS is not just an approach, but also an automated agent that sets the appropriateDVFS on CPU, GPU and memory to achieve the desired performance of the executing application while consuming the least amount of power. For each \(App_{i}\) in \(S_{App}\), the profiling of \(App_{i}\) (this step is denoted as _Profiling_) is performed such that for different combinations of \(DVFS_{b_{i}}\), \(DVFS_{L_{i}}\), \(DVFS_{S_{i}}\) and \(DVFS_{m_{i}}\), the corresponding value of \(Prf_{i}\), the corresponding peak temperature instance (\(T_{i}\)) and the corresponding power consumption (\(P_{i}\)) of the device are recorded and stored on the disk storage memory. More on Profiling is provided in Section 5.2. From the set containing the profiled values of \(S_{Prf}=\{Prf_{1},Prf_{2},\ldots Prf_{i}\}\), the desired performance \(Prf_{desired}\) is searched based on the equation: \(Prf_{desired}\in S_{Prf}\); where \(Prf_{i}\geq Prf_{desired}\). Now, for all the possible values of \(Prf_{i}\) that are equal or greater than \(Prf_{desired}\) from \(S_{Prf}\), the agent searches for the value with the least power consumption such that \(P_{least}=min(S_{p})\); where \(S_{p}=\{P_{1},P_{2},\ldots P_{i}\}\) (\(P_{1},P_{2},\ldots P_{i}\) are the corresponding power consumption of \(Prf_{1},Prf_{2},\ldots Prf_{i}\)). The agent then fetches the associated \(DVFS_{b_{i}}\), \(DVFS_{L_{i}}\), \(DVFS_{S_{i}}\) and \(DVFS_{m_{i}}\) configuration (this step is denoted as _Fetch desired config_), and then the desired DVFS values of big CPUs (\(DVFS_{b_{desired}}\)), LITTLE CPUs (\(DVFS_{L_{desired}}\)), GPUs (\(DVFS_{b_{desired}}\)) and memory (\(DVFS_{m_{desired}}\)) are set to this configuration (this step is denoted as _Set desired DVFS_).  
_Note:_ During _Profiling_, if the executing application (\(App_{i}\)) is not executed for a long enough time to observe all the DVFS combinations of CPU, GPU and memory to profile the performance and power consumption, then the profiled values will be a subset of \(S_{Prf}\), denoted by \(S^{\prime}_{Prf}\), where \(S^{\prime}_{Prf}=\{Prf_{1},Prf_{2},\ldots Prf_{j}\}\) and \(S^{\prime}_{Prf}\subset S_{Prf}\). In such a case, the agent searches \(Prf_{desired}\) from \(S^{\prime}_{Prf}\) based on the value with the least power consumption.
## Materials and Methods
### Nano-Release Study-General Experimental Design  
A damaged nanocomposite's surface might exhibit NMs that are not fully covered with polymer anymore and thus partially protrude the polymer. In turn, this scenario might facilitate the release of NMs when the nanocomposite gets in contact with food. The objective of the stress test was to simulate foreseeable real-life stress conditions on plastics and rubber materials and to see whether or not a stress-induced release of NMs is possible. The general experimental design of the so-called nano-release experiment is schematically displayed in Figure 1. The experiment can be subdivided into three parts:  
* Pre-stress: relevant mechanical, thermal, and chemical stress conditions that are realistic in practice
* Abrasion test: basic stress test to see whether NMs can be abraded from the nanocomposite's surface using a dry simulant with an abrasive character simulating intensive use and handling of the nanocomposite.
* Analytical detection of released NMs: depending on the nature of NM, particle- and/or element-specific detection techniques are used to demonstrate whether or not NMs were released from the nanocomposite  
During the abrasion test, the nanocomposite is placed in an abrasion cell, loaded with a dry substance, and shaken on a laboratory shaker. Parameters are set to allow the dry substance to homogeneously scrub over a defined area of the nanocomposite's surface. The type and amount of the dry substance (e.g., salt or sand as an alternative food simulant) as well as the duration and intensity (frequency) of the shaking can be adjusted to achieve an intense but homogeneous abrasion of the surface. The abraded material and the dry food simulant are subsequently collected and analyzed for the presence of potential NMs through Asymmetrical Flow Field-Flow Fractionation (AF4), with Multi-Angle Laser Light Scattering (MALLS) detection for particulate substances (\"particle specific\"), and/or, if applicable, Inductively Coupled Plasma Mass Spectrometry (ICP-MS) for the solubilized particle-specific elements (\"element specific\"). In this study, with the case of CB as NM, element specific measurement was not applied due to the inapplicability of ICP-MS to the determination of carbon.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**CB type** & **Polymer Matrix, Thickness** & **CB Loading(\%)** \\ \hline Printerx® 80 “PX 80” & LDPE plaque, 3 mm & 2.5 \\ Printerx® 85 “PX 85” & LDPE plaque, 3 mm & 2.5 \\ N550 & EPDM plaque, 2 mm & 37.2 \\ N772 & EPDM plaque, 2 mm & 45.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Low density polyethylene (LDPE) and ethylene propylene diene monomer (EPDM) based carbon black (CB)-nanocomposites used as test materials.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Property** & **PX 80** & **PX 85** & **N550** & **N772** \\ \hline OAN a (mL/100g) & 105 & 54 & 122.8 & 65.9 \\ BET surface area (m\({}^{2}\)/g) & 220 & 200 & 39.0 & 30.0 \\ Mean primary particle size (nm) & 16 & 16 & 55 & 75 \\ \hline \hline \end{tabular}  
* Oil Absorption Number.  
\end{table}
Table 2: Typical properties of the CB grades.