esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
We argue that it is important to investigate the attitudes of people towards new mobile security services and explore whether factors that contribute to the usability of BBCA affect users' attitude towards these services [27]. Specifically, the following research question has guided this study: What factors affect the intention of smartphone users to use BBCA technologies? This research intends to examine the factors associated to BBCA adoption, revealing the fundamental elements that motivate people's decisions and behavior towards this technology. To achieve this, we have built and validated a conceptual model using key elements of the Protection Motivation Theory (PMT) along with constructs that derive from the current literature related to biometrics authentication and have conducted an online survey. The survey questionnaire focuses on the identification of elements that motivate users to adopt BBCA. This work endeavors to provide a comprehensive overview of users' perspectives towards the booming BBCA applications. The structure of this paper is as follows. In Section 2, we analyze Behavioral Biometrics Continuous Authentication technologies, mainly from the users' point of view, while Section 3 focuses on related work. In Section 4, we present the theoretical background and the research model of this work. Subsequently, in Section 5 we present the survey results, which are further discussed in Section 6. In Section 7, we consider the implications and the limitations of our research, as well future research directions, and finally conclusions are given in Section 8.
# Adaptive Metrics for Adaptive Samples
## 3 Results
### Interleaving  
A filtration is a nested family of sets. In this paper, we consider filtrations \(F\) parameterized by a real number \(\alpha\geq 0\) so that \(F(\alpha)\subset\mathbb{R}^{d}\), and whenever \(\alpha<\beta\), we have \(F(\alpha)\subseteq F(\beta)\). Often, our filtrations are sublevel filtrations of a real valued function \(f:\mathbb{R}^{d}\to\mathbb{R}\). The sublevel filtration \(F\) corresponding to the function \(f\) is defined as:  
\[F(\alpha):=\{x\in\mathbb{R}^{d}\mid f(x)\leq\alpha\}.\]  
**Definition 5**.: _A pair of filtrations \((F,G)\) is \((h_{1},h_{2})\)-interleaved in an interval \((s,t)\) if \(F(r)\subseteq G(h_{1}(r))\) whenever \(r,h_{1}(r)\in(s,t)\) and \(G(r)\subseteq F(h_{2}(r))\) whenever \(r,h_{2}(r)\in(s,t)\). We require that the functions \(h_{1},h_{2}\) to be nondecreasing in \((s,t)\)._  
The following lemma gives us an easy way to combine interleavings.  
**Lemma 11**.: _If \((F,G)\) is \((h_{1},h_{2})\)-interleaved in \((s_{1},t_{1})\) and \((G,H)\) is \((h_{3},h_{4})\)-interleaved in \((s_{2},t_{2})\), then \((F,H)\) is \((h_{3}\circ h_{1},h_{2}\circ h_{4})\)-interleaved in \((s_{3},t_{3})\), where \(s_{3}=\max\{s_{1},s_{2}\}\) and \(t_{3}=\min\{t_{1},t_{2}\}\)._  
Proof.: If \(r,h_{3}(h_{1}(r))\in(s_{3},t_{3})\), then we have \(F(r)\subseteq G(h_{1}(r))\subseteq H(h_{3}(h_{1}(r)))\). Similarly, if \(r,h_{2}(h_{4}(r))\in(s_{3},t_{3})\), then \(H(r)\subseteq G(h_{4}(r))\subseteq F(h_{2}(h_{4}(r)))\).
## 3 Some Statistical Properties
### Moments and Related Measures  
The \(r\)th moment about the origin of the PDD is defined as  
\[E(X_{PDD}^{r})=\frac{r\left(\frac{\theta}{\alpha}\right)^{-\frac{r}{\beta}} \Gamma\Big{(}\frac{r}{\beta}\Big{)}}{2\beta^{3}(2\alpha^{2}+\theta^{2})}\Big{[} 4\alpha^{2}\beta^{2}+\theta^{2}(\beta+r)(2\beta+r)\Big{]},\;r=1,2,3,\ldots \tag{10}\]  
For \(r=1\), the mean of the PDD is  
\[E(X)=\frac{\Gamma\Big{(}\frac{1}{\beta}\Big{)}\left(\frac{\theta}{\alpha} \right)^{-1/\beta}}{2\beta^{3}(2\alpha^{2}+\theta^{2})}\Big{[}4\alpha^{2} \beta^{2}+(\beta+1)(2\beta+1)\theta^{2}\Big{]}\]  
The inverse moment is defined as  
\[E\bigg{(}\frac{1}{X_{PDD}^{r}}\bigg{)}=\frac{\alpha^{2}\Gamma\Big{(}3-\frac{r} {\beta}\Big{)}\left(\frac{\theta}{\alpha}\right)^{r/\beta}\left(\frac{\theta ^{2}}{\alpha^{2}}+\frac{4\beta^{2}}{2\beta^{2}+r^{2}-3\beta r}\right)}{2(2 \alpha^{2}+\theta^{2})},r=1,2,3,\ldots \tag{11}\]  
In Table 1: the mean \((\mu_{X})\), coefficient of variation \((v_{X})\), standard deviation \((\sigma_{X})\), and coefficient of skewness \((\delta_{X})\) of the IDD for selected values of parameters.  
Figure 5: The hazard function plots of the PDD for some values of parameters.  
Figure 6: The RHF plots of PDD for different values of parameters.  
Based on Table 1 it can be seen that the values of the population standard deviation and mean are decreasing as \(\theta\) values are increasing. However, the \(v_{X}\) and \(\kappa_{X}\) values are increasing.
## 3 Improvement of Envelope Analysis
### Methods for Overcoming Impulsive Noises
Figure 11b shows the difference between the peak magnitude at the rotational frequency of 18 Hz and the baseline magnitude of the lower frequencies for the STFT in the presence of an impulsive noise shown in Figure 7a. In Figure 11, every normal case appears very close to zero or negative values. On the other hand, the defect has a much larger value, and even if much impulsive noises occur, the difference from the normal and the defect is still large. In the conventional method of detecting defects through envelope analysis, if the magnitude at the rotation frequency is larger than the threshold, it is judged as a defect. The method proposed in this study judges a defect if the difference between the magnitude at the rotational frequency and the baseline magnitude of the lower frequencies in the envelope spectrum is larger than the threshold. Figure 10: Flowchart for proposed method.
## 3 Noise in PLC Networks  
### Traditional Noise Classification  
Traditionally, the noise in PLC channels has been classified into two main categories [18; 19]:  
* Background noise, which is always present and changes slowly in time (from minutes to even hours);
* Impulsive noise, which consists of very powerful noise burst normally taking between microseconds and milliseconds.  
The background noise can in turn be considered as the sum of the contribution of two types of noise, namely [22]:  
* Colored background noise, which is in turn caused by the summation of numerous low-power sources of noise. This type of noise has a relatively low PSD (Power Spectral Density) and is named \"colored\" because, on the contrary to the white noise, its frequency response is not flat, but varies. The pink noise, for instance, whose PSD is inversely proportional to the frequency, is a well-known example of this type of noise.
* Narrowband noise, which consists mainly of amplitude-modulated impulses. This type of noise may be caused either by broadcast stations or by switching power supplies on the network (being also referred to as periodic impulsive noise asynchronous to the mains frequency in the latter case).  
Figure 3: Overview of PRIME (PoweRline Intelligent Metering Evolution) MAC (Medium Access Control) frames.  
There are also two types of noise that can be considered to form the impulsive noise, namely [22]:
* Periodic impulsive noise synchronous to the mains frequency, which is caused by power supplies and consists mainly of impulses that last some microseconds with a repetition rate of 50 or 100 Hz.
* Asynchronous impulsive noise, which is caused by switching transients to the network and consists of random impulses that last from microseconds up to milliseconds, being much stronger than background noise.
# Encryption of 3D or Higher-Dimensional Images: A Review
## 3 Methodology and Research Analysis
### Search Methodology  
Figure 6 shows the fluxogram of the search methodology, in which the string adopted in the Web of Science platform was encryption AND image AND (3D OR 3-D OR three-dimensional OR \"three dimensional\" OR \"light field\" OR \"point cloud\" OR plenoptic OR Loot OR RedanBlack OR 8 iVFB OR 8 iVSLF).  
We found 763 articles on the Web of Science platform. Afterwards, every single article was analyzed based on the exclusion criteria as follows:  
1. It was not written in English;
2. It did not have the full text;
3. The title and abstract were not within the purpose of the review;
4. The full text showed that the paper was not within the aimed scope.  
Furthermore, only articles published between 2019 and 2024 were taken into consideration.  
The last step brings to the study a total of 16 relevant articles (7 from ScienceDirect, 4 from Optica, 3 from Springer, 1 from IEEE, and 1 from ACM), and the analysis was completed on 17 September 2024.
## 4 Proposed Solution
### Main Characteristics of the Multi-Agent Gradient Descent Algorithm (MAGD)
To ensure a fair comparison between single-agent and multi-agent approaches, Adam's maximum iterations were extended to 750 and 1000 in separate experiments. Table 26 displays the execution times for MAGD (with 500 iterations) and Adam across these three configurations. Additionally, Table 27 provides the mean number of convergence epochs (\(\pm\)standard devi \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Comparison** & \(p\)**-Value** & **Significant Difference** & **MAGD Performance** \\ \hline MAGD vs. FMA & 0.31 & No & - \\ MAGD vs. SBGD & 0.33 & No & - \\ MAGD vs. Adam & 0.6 & No & - \\ MAGD vs. GD & 0.6 & No & - \\ \hline \hline \end{tabular}
\end{table} 
Table 24: \(p\)-values comparing MAGD execution time with other algorithms for neural network training before removing the outlier dataset. Symbols (\(\uparrow\)/\(\downarrow\)) denote whether the difference signifies outperforming (\(\uparrow\)) or underperforming (\(\downarrow\)) of MAGD, while the absence of a symbol indicates no significant difference. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Comparison** & \(p\)**-Value** & **Significant Difference** & **MAGD Performance** \\ \hline MAGD vs. FMA & 0.007 & Yes & \(\uparrow\) \\ MAGD vs. SBGD & 0.002 & Yes & \(\downarrow\) \\ MAGD vs. Adam & 0.141 & No & - \\ MAGD vs. GD & 0.172 & No & - \\ \hline \hline \end{tabular}
\end{table} 
Table 25: \(p\)-values comparing MAGD execution time with other algorithms for neural network training after removing the outlier dataset. Symbols (\(\uparrow\)/\(\downarrow\)) denote whether the difference signifies outperforming (\(\uparrow\)) or underperforming (\(\downarrow\)) of MAGD, while the absence of a symbol indicates no significant difference. ation) and the mean execution time (\(\pm\)standard deviation) for MAGD and Adam under the respective experimental setups. From Table 27, we can see that Adam's execution time is approximately the same as MAGD's when Adam is run for 750 epochs. Table 28 presents the accuracy scores of MAGD and Adam for 500, 750, and 1000 iterations, offering further insight into their relative performance. Additionally, Table 29 shows the \(R^{2}\) scores for MAGD and Adam across multiple datasets with varying maximum iterations. To evaluate the performance of MAGD compared to Adam with extended iterations, we examine their absolute superiority ratio \(P_{super}\) that is computed according to Equation (29) in terms of accuracy score across classification datasets and \(R^{2}\) score across regression datasets, in addition to introducing a second ratio called the average time penalty, \(ATP\), which quantifies the average of additional computational time incurred by one algorithm relative to another. It is calculated as:

\[ATP(A_{1},A_{2})=\frac{1}{n}\cdot\frac{T_{A_{1}}-T_{A_{2}}}{T_{A_{2}}}, \tag{30}\]

where \(T_{A_{1}}\) and \(T_{A_{2}}\) are the execution times of \(A_{1}\) and \(A_{2}\), respectively, and \(n\) is the number
# Smart Grid Fault Mitigation and Cybersecurity with Wide-Area Measurement Systems: A Review
## 2 Background
### WAMS and Smart Grid Architecture Model
By transforming raw data into meaningful insights, WAMS empowers grid operators to make informed decisions in real time. 4. **Functional Layer: Strengthening Grid Resilience** WAMS aligns closely with the SGAM functional layer by supporting advanced grid control and operational strategies. Its functionalities include voltage regulation, frequency control, and islanding detection, all of which are critical for the integration of renewable energy. WAMS enables dynamic load shedding and fast fault-clearing mechanisms, minimizing system disruptions and reducing fault-clearing times. Furthermore, its real-time fault detection capabilities enhance the grid's self-healing properties, ensuring that power systems remain operational even under adverse conditions. By supporting these functions, WAMS contributes to the development of a more resilient and adaptive grid. 5. **Business Layer: Enabling Cost-Efficiency and Compliance** At the business layer, WAMS provides significant economic and regulatory benefits. By enabling accurate and reliable grid operations, WAMS supports demand response strategies and enhances energy trading in markets that include renewable energy sources. It also ensures compliance with regulatory standards for grid reliability and renewable energy integration. These capabilities enable utilities to optimize their operations while achieving long-term sustainability goals. Moreover, WAMS facilitates the planning and operation of power systems in ways that align with market dynamics and future energy needs. The integration of a WAMS into the SGAM framework represents a new frontier in modern power system operations. With its real-time monitoring and fault detection capabilities, WAMS addresses critical challenges in fault management, grid stability, and the integration of renewable energy sources. Through its alignment with the five SGAM layers, WAMS not only enhances grid reliability and resilience but also positions power systems for a sustainable and economically viable future. As the energy landscape continues to evolve, WAMS will remain a cornerstone technology in enabling the transition to smarter, more adaptive grids.
# Application of Artificial Neural Network (ANN) in Predicting Box Compression Strength (BCS)
## 2 Method
### Development of ANN Model Architecture  
An ANN model is typically composed of three types of layers: an input layer, one or more hidden layers, and an output layer. Since one or two hidden layers are generally  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Min** & **Max** \\ \hline ECT (lbs/inch) & 26.13 & 107.4 \\ \hline Length (inch) & 8 & 25.00 \\ \hline Width (inch) & 5.25 & 19.00 \\ \hline Depth (inch) & 5.00 & 26.50 \\ \hline BCS (lbs.) & 347.32 & 2172.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Minimum and maximum values of data incorporated into real dataset with 395 data points.  
Figure 2: Histograms of input features of extracted dataset with 395 data points.  
sufficient for solving nonlinear problems, this study evaluated models with up to two hidden layers. Figure 3 presents a conceptual diagram of the ANN structure developed in this study.  
During the ANN model training process, the dataset was randomly split into two subsets: 70% of the data was used for training the model, and the remaining 30% was reserved for testing the model's accuracy.  
In order to address nonlinear problems and improve computational efficiency, activation functions are commonly employed in ANN models. Typically, different activation functions are used in the hidden and output layers. In this study, the Rectified Linear Unit (ReLU) activation function was applied in the hidden layer [18], while the Sigmoid activation function was used in the output layer [19]. ReLU was chosen for the hidden layer because it helps mitigate the vanishing gradient problem, which often occurs in deep networks with multiple hidden layers, and it enhances computational efficiency by selectively activating a subset of neurons [20]. The Sigmoid function was selected for the output layer because it efficiently produces an output \(\text{p}\in[0,1]\), which can be interpreted as a probability--an essential feature for the intended prediction task [19]. Plots of the ReLU and Sigmoid functions are shown in Figure 4.  
Figure 4: ReLU function and Sigmoid function used.  
Figure 3: A conceptual diagram of the ANN structure used in this study.
## 3 System Design
### Client  
On the client side, the system provides a graphic user interface with geovisual analytical tools to customize analysis and view resulting maps and plots. Geovisual analytical tools normally contain multiple interactive tools, dynamic graphs and live-linked views of data representation [36]. All the functions on the client are implemented using HTML5 [37] and JavaScript.  
Figure 5 shows how the geovisual analytical tool performs for exploring climate data. When the user connects to the interface through a web browser, the client is automatically connected to the database. Information about data and function is initialized based on the metadata stored in the database and shown on the interface. Users can issue a request for visualization or analysis according to the available information. Based on the types of requests, different visual results such as images are returned and displayed as maps or statistical plots. Users can expose the underlying patterns of climate data through dynamic controls of visual results.  
Figure 5: Mechanism of geovisual analysis on the client side.
## 5 Experimental Verification
### Experimental Results  
In order to verify the effectiveness of the proposed control strategy, after determining the parameters of the control system, the sinusoidal signal is used as the desired displacement command to conduct a tracking control experiment. In the sinusoidal signal tracking experiment, four controllers, PID control, traditional sliding mode control (SMC), adaptive sliding mode control (ASMC), and adaptive sliding mode control with PD-type iterative learning control (ILC-ASMC), were selected for comparison. The tracking experiment uses the reference command of a 40 Hz sinusoidal trajectory with a maximum peak value of 20 \(\upmu\)m to test the tracking performance under three control schemes.  
In this experiment, the iteration time interval is set to [0, 0.5] s, and the number of iterations is set to a fixed value of 10 times.  
Figure 5: Open-loop frequency responses.
## 1 Introduction
### Previous Works
The change in the value of the objective function is:  
\[\Delta f=f(x^{\prime})-f(x)=\sum_{i\in S}\big{(}x^{\prime}{}_{i}-x_{i}\big{)}E (x_{i})+\sum_{i,j\in S,i<j}\big{(}x^{\prime}{}_{i}-x_{i}\big{)}\big{(}x^{ \prime}{}_{j}-x_{j}\big{)}q_{i,j} \tag{7}\]  
Furthermore, after changing \(x\) to \(x^{\prime}\), the update for \(E\big{(}x_{j}\big{)},j=1,\ldots,n\), can be calculated as follows:\[\forall j\in N\backslash S,E(x_{j})\gets E(x_{j})+\sum_{i\in S}(x_{i}^{ \prime}-x_{i})q_{i,j} \tag{8}\] \[\forall j\in S,E(x_{j})\gets E(x_{j})+\sum_{i\in S\backslash\{j \}}\big{(}x_{i}^{\prime}-x_{i}\big{)}q_{i,j}\]  
As explained in [11], the evaluation of change in the objective function of Equation (7) can be done in \(O\big{(}r^{2}\big{)}\), i.e., evaluating \(f(x^{\prime})\) from \(f(x)\). The update in Equation (8) requires \(r\) calculations for each \(j\) in \(N\backslash S\), and \(r-1\) calculations for each \(j\) in \(S\). Thus, overall, the update for all \(n\) variables can be performed in \(O(nr)\). Note that for any two elements \(ij\) = 1, \(\ldots\), \(n\), and \(i<j\), we can define:  
\[E^{\prime}(x_{i})=E(x_{i})-q_{i}-q_{ij}x_{j}, \tag{9}\] \[E^{\prime}(x_{j})=E(x_{j})-q_{j}-q_{i,j}x_{i}.\]  
Using Equation (9), a useful way to express Equation (7) is Equation (10). \[\Delta f=\sum_{i\in S}\left[(1-2x_{i})E^{\prime}(x_{i})+\sum_{j\in S_{j}\leq i }(1-x_{i}-x_{j})q_{i,j}\right] \tag{10}\]  
A simple exhaustive \(r\)-flip search is provided in Algorithm 2. The complexity of the problem indicates that the use of a larger value of \(r\) in the \(r\)-flip local search can make the implementation of the search process more time consuming. Meanwhile, the larger value of \(r\) can provide an opportunity to search a more diverse area of search space and thus possibly reach better solutions. To overcome such conflicts, researchers often use \(r\) = 1 (and occasionally \(r\) = 2) as the basic components of their more complex algorithms, such as F&F, VNS, and MENS. Below, in Theorem 1 and Proposition 1, we prove that after reaching the locally optimal solution with respect to a 1-flip search, the implementation of an \(r\)-flip search can significantly be reduced. Further, related results are also provided to allow for the efficient implementation of an \(r\)-flip search within an algorithm. ```
Initialize: \(n\), \(x\), evaluate the vector \(E(x)\), value of \(r\) Flag = 1  Do while (Flag = 1)  Flag = 0  For each combination \(S\subset N\) and \(|S|\leq r\), evaluate \(\Delta f\), Equation (7): If \(\Delta f>0\): \(x_{i}=1-x_{1}\), for \(i\in S\), update E(x) using Equation (8), Flag = 1  End while
```  
**Algorithm 2**Exhaustive \(r\)-flip local search.
# Satellite-Based Sea Ice Navigation for Prydz Bay, East Antarctica
## 1 Introduction
Now there are four Chinese stations in Antarctica including Great Wall Station (built in February 1985), Zhongshan Station (built in February 1989), Kunlun Station (built in January 2009), and Taishan Station (built in February 2013) (Figure 1). The first two stations operate all year, and the latter two stations currently only operate in austral summer because of severe weather conditions. RV Xuelong has carried out the logistic support and personnel exchange annually for the four stations since 1994. En route in Prydz Bay, the vessel mostly encounters first-year sea ice (FYI) with typical thickness of 0.5-1.0 m [11; 12]. Closer to the coast, namely within the last 30 km of the journey the vessel enters a land-fast ice regime. Most of this ice is thermodynamically-grown FYI with thickness between 1.5 m and 2.0 m [13; 14]. Many icebergs calving from nearby ice shelfs can be grounded in the Bay, locally modifying the sea ice conditions. This complex setting provides challenges to ice navigation. In this study we developed a satellite-based sea ice navigation system (SatSINS) for RV _Xuelong_ navigation in the Prydz Bay. The system utilizes ice information obtained from visual analysis of space-borne remote sensing data, satellite-driven SIC charts, estimated SIT from a thermodynamic model, and relevant meteorological data. This combination reveals key information about ice conditions and subsequently assists in developing a route for the ship. The later task implies a hypothesis that the availability of the aforementioned scientific products should facilitate the planning of the route. This hypothesis has been tested during three voyages of RV _Xuelong_ to Zhongshan Station in the spring (November) of 2011-2013. The test involves calculation of the deviation of the planned route (output from SatSINS) from the actual route (adjusted by the ship masters), which was based on immediate observations of the ice field in the vicinity of the ship. SatSINS would be an additional asset to prepare for the growing forecast of human activities and their impacts on the Antarctic region, which are primarily motivated by the Antarctic explorations by nations and commercial tourism.