esa-sceva/satcom-chunk-collection huggingface dataset:
# Ultrasonic Surface Rolling Process: Properties, Characterization, and Applications
## 3 Microstructural Development during USRP
### Microstructural Changes in Non-Ferrous Based Materials Subjected to USRP
#### 3.2.1 Ti Alloys
The microstructure shown in Figure 7b contains dislocation cells and fine crystals at a depth of 20 \(\upmu\)m. Figure 7c represents the dislocation walls at a depth of 100 \(\upmu\)m from the surface. A large number of dislocations are visible at this depth. Dislocation walls are formed due to the interaction of dislocations with grain boundaries and other defects, thus forming dislocation tangles and further dislocation walls. The microstructure at a depth of 200 \(\upmu\)m from the surface is shown in Figure 7d. The diffraction spots in the SAED pattern corresponds to circle B in the figure. It is evident from the figure that many deformation twins are present at this depth. Furthermore, the phases were inclined at an angle of 30-40 degrees to the surface. [PERSON] et al. [59] explored the effect of the number of processing stages on the microstructure of Ti6 Al4V alloy processed under lubrication conditions. They noted that increasing processing stages increased depth and extent of refinement. The sample with 12 processing stages showed a microstructure consisting of grains of 100 nm-400 nm. The sample with the lowest number of USRP stages had marginal microstructural refinement. Successive stages increased the severity of deformation with accelerated dislocation slip, pileup, dislocation tangles, twin deformation, and twin-slip interaction. The authors revealed the presence of randomly oriented subgrains of 200 nm-500 nm scale on the specimen surface through TEM analysis. Similarly, [PERSON] et al. [60] have reported grain refinement in selective laser melted Ti6 Al4V alloy processed using USRP. [PERSON] et al. [61] correlated USRP parameters to microstructure and then to surface integrity of TC17, which is an \(\alpha+\beta\) titanium alloy. The authors observed a gradient microstructure up to 50 \(\upmu\)m. The microstructure consisted of a combination of deformation twinning, dislocation accumulation, and dynamic recrystallization. At the top surface, the grains were indistinguishable due to their ultrafine nature. They attributed the gradient microstructure to strain distribution. In addition, the authors reported the presence of a white layer of several micrometers at the top surface. This is attributed to the combined effect of sliding and rolling contacts. [PERSON] et al. [32] explored  
Figure 7: TEM images of USRP’ed Ti6 Al4V at a depth of (**a**) 5 \(\upmu\)m, (**b**) 20 \(\upmu\)m, (**c**) 100 \(\upmu\)m, and (**d**) 200 \(\upmu\)m. Reprinted with permission from ref. [58]. Copyright 2021 Elsevier. the role of USRP on improving the fatigue behavior of TB8 alloy (Ti with 15 wt.% Mo, Nb 2.9 wt.%, Si 0.2 wt.%, Al 3.2 wt.%) using static stress ranging from 480 MPa to 630 MPa. They demonstrated grain refinement at the surface. They also reported defect annihilation to an extent due to the heating effect. The authors summarized the absence of texture during USRP.
# Lightweight Conversion from Arithmetic to Boolean Masking for Embedded IoT Processor
## 2 Background  
### Masking Method  
Masking methods use random numbers to break the relationship between the power consumption of a crypto-device and sensitive values in a crypto-algorithm. Numerous articles have been published on a variety of masking types, such as Boolean masking [23], arithmetic masking [8], polynomial masking [24], and inner product masking [25]. Among these, Boolean masking and arithmetic masking are the most widely known masking types.  
Boolean masking and arithmetic masking use the following respective formulae:  
* Boolean Masking: \(x^{\prime}=x\oplus r_{x}\)
* Arithmetic Masking: \(A=x-r_{x}\) mod \(2^{k}\)  
Given \(k\)-bit values; \(x\) is a sensitive value depending on the key; and \(x^{\prime}\), \(A\), and \(r_{x}\) are Boolean masked values, arithmetic masked values, and masking values, respectively.  
Boolean and arithmetic masking are used for algorithms that consist of Boolean and arithmetic operations, such as ARX block ciphers [9; 10; 11], cryptographic hash functions [13], and stream ciphers [14]. More specifically, additions are computed on the arithmetic masking, and SHIFT, XOR, and AND are computed on Boolean masking. In this case, a conversion algorithm between arithmetic and Boolean masking is necessary, and this algorithm must be secure against DPA attacks.  
The first masking conversion algorithm was introduced by [PERSON] [26], but vulnerabilities were discovered in it against DPA attacks. In CHES 2001, [PERSON] proposed an improved masking conversion algorithm that guaranteed security against DPA attacks [15]. That algorithm can convert from Boolean to arithmetic masking with only 5 XORs and 2 subtractions. This algorithm has recently been extended to a higher-order masking scheme [20; 27]. According to [20; 27], these countermeasures have time complexity \(O(n^{2}\cdot k)\) and \(O(2^{n})\) for \(n\) shares on the security against \(t\)-th order DPA attacks, respectively.  
In contrast, conversion algorithms from arithmetic to Boolean masking have been improved through various efforts.
# Analysis of Dual-Band Direction of Arrival Estimation in Multipath Scenarios
## 3 Localization System
### DoA Estimation Method
For this approach, called minVAR, the cost function is defined as:  
\[\mathrm{C}^{(i)}_{\mathrm{MV}}(\theta)=\mathrm{var}[\textbf{R}^{( i)}(\theta)]=\mathrm{var}[\textbf{S}^{(i)}-\textbf{G}^{(i)}(\theta)]=\] \[= \frac{1}{N}\sum_{n=1}^{N}\left(\left(S^{(i)}_{n}-G^{(i)}_{n}( \theta)\right)-\frac{1}{N}\sum_{n=1}^{N}\left(S^{(i)}_{n}-G^{(i)}_{n}(\theta) \right)\right)^{2} \tag{6}\]  
Same as the previous case, the formal estimation is found with an operator similar to (5), but applied to the cost function (6). Considering the dual-band operation, the most effective exploitation of the available data is to combine the RSSIs at the two frequencies as a single input vector for the minVAR algorithm. Therefore, the extend gain vector **G** becomes:  
\[\textbf{S}=\left[\textbf{S}^{1},\textbf{S}^{2}\right],\quad\textbf{G}=\left[ \textbf{G}^{1},\textbf{G}^{2}\right] \tag{7}\]  
Operating with the combined data, the actual DoA still emerges as the minimum variance angle, in the sense described in (7).
## 3 Results and Discussion
### Numerical Results of DFT Full Spectrum
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Rot. Speed**} & \multicolumn{8}{c}{**Amplitudes (m)**} \\ \cline{2-13}  & \(-3\times\) & \(-2\times\) & \(-1\times\) & \(1\times\) & \(2\times\) & \(3\times\) & \(-3\times\) & \(-2\times\) & \(-1\times\) & \(1\times\) & \(2\times\) & \(3\times\) \\ \hline
15 Hz & - & - & \(1.27\times 10^{-7}\) & \(2.04\times 10^{-7}\) & - & - & - & - & \(1.46\times 10^{-7}\) & \(2.36\times 10^{-7}\) & - & - \\
30 Hz & - & - & \(1.31\times 10^{-6}\) & \(2.44\times 10^{-6}\) & - & - & - & \(6.94\times 10^{-8}\) & \(1.39\times 10^{-6}\) & \(2.62\times 10^{-6}\) & \(7.41\times 10^{-8}\) & - \\
35 Hz & - & - & \(6.67\times 10^{-6}\) & \(1.27\times 10^{-5}\) & - & - & \(2.30\times 10^{-7}\) & \(1.11\times 10^{-6}\) & \(5.32\times 10^{-6}\) & \(1.11\times 10^{-5}\) & \(1.24\times 10^{-6}\) & \(1.78\times 10^{-7}\) \\
45 Hz & - & - & \(1.89\times 10^{-6}\) & \(3.97\times 10^{-6}\) & - & - & - & \(1.26\times 10^{-7}\) & \(1.85\times 10^{-6}\) & \(3.93\times 10^{-6}\) & \(1.26\times 10^{-7}\) & - \\
60 Hz & - & - & \(6.07\times 10^{-7}\) & \(1.39\times 10^{-6}\) & - & - & - & \(2.06\times 10^{-8}\) & \(7.19\times 10^{-7}\) & \(1.65\times 10^{-6}\) & \(2.30\times 10^{-8}\) & - \\ \hline \end{tabular}
\end{table} 
Table 1: Magnitudes of the most relevant peaks of Figure 9—elliptical bearing (preload 0.25). Figure 10: DFT full spectrum of simulated results in elliptical bearing 1 (preload 0.8): (**a**) linear, (**b**) nonlinear. For the three-lobed bearing 1, with preload 0.25, it is possible to see in Figure 11 and Table 3 that amplitudes \(1\times\) and \(-1\times\) are greater for the nonlinear case (mainly the forward component \(1\times\)). Higher vibration orbits are more prone to nonlinearities coming from the hydrodynamic bearings. So, it is possible to state that the three-lobed bearing is more susceptible to present nonlinear behavior, as can also be seen by the presence of additional harmonics in the signal. Comparing Tables 1 and 3, it is perceivable that these differences are higher for the three-lobed bearing. Regarding the other harmonic components for all rotations, they have similar behavior to those observed for the elliptical bearing cases. Finally, Figure 12 and Table 4 show the results for the three-lobed bearing 1 with preload 0.8. This case has somewhat different behavior. The only frequency components observed are \(1\times\) and \(-1\times\), indicating a purely elliptical shape of the orbit for both linear and nonlinear models, even though the orbits obtained with the two models are quite different, as shown in Figure 8. By observing the rotating speeds and comparing the models, it can be seen that the \(1\times\) amplitudes have very close values, with just slight differences. However, the nonlinear \(-1\times\) amplitudes have higher values when compared to the linear ones, indicating the deformations in the orbits (Figure 8), which means more anisotropy in the system, since in this case, the backward component is greater.
# Simply Applicable Method for Microplastics Determination in Environmental Samples
## 2 Results and Discussion
### Identification of MPs Coarse Fraction (1-5 mm)
#### 2.1.5 Polyethylene  
PE, used to develop the method, presented in this study, was high density PE (HDPE), which has a higher melting point as compared to low density PE [14]. In an inert atmosphere, PE crosslinking started at 202 \({}^{\circ}\)C, while decomposition started at 292 \({}^{\circ}\)C. However, the presence of oxygen was reported to strongly enhance decomposition. Therefore, the decomposition of PE in the presence of air is possible at lower temperatures [14]. For identification of PE using HS-SPME-GC-MS, a fragment ion of \(m/z\) 85 was proposed. XIC of \(m/z\) 85 consists of a series of higher alkanes with a carbon number from C\({}_{12}\) (t\({}_{\rm R}\) = 6.546 min) to C\({}_{21}\) (t\({}_{\rm R}\) = 17.960 min) (Figure 5, Table S7), and pentadecane (t\({}_{\rm R}\) = 10.664 min) was chosen for its specificity in the polymer blend containing PE. The reproducibility of the method was expressed as an RSD of five chromatographic peaks for pentadecane, which was 20%.  
Figure 4: HS-SPME-GC–MS extracted ion chromatogram (XIC, \(m/z\) 142) of PP (5 replicates) with mass spectra for 4,6-dimethyl 2-heptanone (7).  
The presence of pentadecane in PE samples was confirmed by the reference compound and a tight overlay of MPs PE and PE reference material (Figure S8). The selected fragment ion was very common and abundant among hydrocarbons, especially in the environmental samples. Therefore, the suitability of selected characteristic compounds for PE and PP identification was tested in realistic samples containing high hydrocarbon load (soil matrix with added mineral oil and PE and PP MPs, see Section 2.2.2).
## 2 Technical Design of the Device
### Analytical Model  
The device anatomy, in addition to the assembly of its components, can be modeled by a suitable system with few parametric quantities, showing the dynamic behavior of the device. In the analytical model of the ATMID, the variable inertance (b) links into the small allowable viscous damping coefficient (\(\mathrm{c_{d}}\)) due to the internal damping of the CVT and the elastic spring (\(\mathrm{k_{d}}\)) in parallel. Later on, they were serially connected to the attached mass (m). Moreover, in order to express the practical behavior of the ATMID, friction, caused by the interaction between components, is generated. It was taken into consideration in the viscous damping coefficient (\(\mathrm{c_{d}}\)). It should be pointed out that in this research, the _variable inertance_ could be attained using AID, and later an adaptive TMD with variable inertial force is presented. However, the damping of TMD was obtained from the very small internal damping of the CVT, not viscous oil damping. Nevertheless, a variable inertance integrated with a variable viscous oil damping was introduced through an adaptive viscous inertance damper (AVID) in our previous research [32].  
Figure 3: Core of the continuously variable transmission (CVT): (**a**) perspective (**b**) cross section photograph (**c**) diagrammatic view in overdrive mode.
# Study of the Process of Packet Arrival at a Multiservice Node
## 2 Mathematical Model of a Multiservice Node  
A multiservice node can be viewed as a black box [7], as shown in Figure 1. This black box is used as a model for a queuing system. Four more processes are defined for such a system in addition to the function \(A(t)\).  
The process \(B(t)\) is the CDF of the holding time of requests (packets) in a queuing system that is used as a model of a multiservice node. Using the process \(C(t)\), various control algorithms for a queuing system can be specified. The process \(D(t)\) is the CDF of the interval between the moments at which successfully proce  
Figure 1: Model of the multiservice node.  
the queuing system. Some requests cannot be processed for a number of reasons, and they are lost. The process \(E(t)\) allows this phenomenon to be described.  
Below, mainly the function \(A(t)\) is reviewed, with reference to its properties that make it possible to choose the distribution \(B(t)\), calculate the main parameters of the \(D(t)\) and \(E(t)\) processes, as well as to choose the ideal control algorithms represented as the process \(C(t)\).
## 7 Discussion
### Influence of Temperature Settings  
The impact of temperature settings on the models' performance is also noteworthy. Lower temperatures generally resulted in more consistent and accurate diagnoses, while higher temperatures introduced more variability. This highlights the importance of carefully selecting temperature settings to balance precision and creativity in diagnostic predictions, aligning with Hypothesis 2.  
Low temperatures consistently produced clearer, more precise, and well-justified diagnostics, making them reliable for actionable decision-making in healthcare settings, as per Hypothesis 2.1. The focus is primarily on well-supported, probable conditions, avoiding speculative diagnoses. Higher temperatures increase variability and creativity, which can be valuable for exploring rare or nuanced conditions but often comes at the cost of precision and clarity. High temperatures are better suited for brainstorming or exploratory tasks rather than definitive diagnostics, confirming Hypothesis 2.2.
# Grid Anchor Lane Detection Based on Attribute Correlation
## 3 Methods
### Model Inference  
The model produces multiple layer outputs, with \(A^{2}\) serving as the model's final output. \(A^{2}\) still represents the lane line within the grid anchor system, but during testing or practical use, it is necessary to output the sequence of coordinates for the lane line. Therefore, this section outlines the process by which the grid anchor data are converted into lane line coordinates. The classification data from the first \(n+3\) channels of the grid anchors can be used as the classification basis for the anchors. First, the values of the first \(n+3\) channels of the predicted results are thresholded at 0.5, with the following thresholding formula:  
\[\hat{A}^{2}_{c,i,j}=\begin{cases}1,\;if\;A^{2}_{c,i,j}\geq 0.5\\ 0,\;if\;A^{2}_{c,i,j}<0.5\end{cases} \tag{24}\]  
Based on the definition of the attributes, an anchor must meet three conditions to be classified as a lane line:  
1. The anchor must contain lane line features, with the existence attribute \(\hat{A}^{2}_{0,i,j}=1\);
2. The lane line within the anchor must be a part of a single lane line, with the sum of the instance attribute channel values equaling \(1\), \(\sum_{c=1}^{n}\hat{A}^{2}_{c,i,j}=1\);
3. The anchor classified as a lane line must contain directional features, with the sum of the direction attribute values greater than or equal to \(1\), \(\hat{A}^{2}_{n+1,j,j}+\hat{A}^{2}_{n+2,j,j}\geq 1\).  
Anchors that meet the above three conditions are classified as lane line anchors. Then, the coordinates of the lane line point \((\hat{x}_{i,j},\hat{y}_{i,j})\) are calculated using the center coordinate of the grid anchor and their offset attribute values.  
\[\hat{x}_{i,j}=\frac{(2j+1)W}{2W_{s}}+A^{2}_{n+1,i,j},\;\hat{y}_{i,j}=\frac{(2i +1)H}{2H_{s}}+A^{2}_{n+2,j,j} \tag{25}\]  
In the predicted lane line coordinates, points with the same instance attribute values \(\left(\hat{A}^{2}_{1,i,j},\dots,\hat{A}^{2}_{n,i,j}\right)\) are clustered as belonging to the same lane line. Within the same lane line, the coordinate points are rearranged in ascending order based on their vertical coordinate values, which constitutes the inference result.
## 2 Dynamic Health Intelligent Evaluation Model
### Principle Description and Evaluation Index
#### 2.1.2 Principle Description  
The principle of satellite dynamic health intelligent evaluation model is shown in Figure 1. First, the dynamic temperature \(T_{n}(t)\) of satellite component \(n\) under different thermal load conditions can be calculated by using the TNM. Considering the influence of temperature and its difference on the failure probability of satellite components, fuzzy reasoning is used to quantitatively analyze the failure probability \(\xi_{n}(t)\) of components. Then, the health degree \(H_{k}(t)\) and failure factor \(F_{k}(t)\) of subsystem \(k\) are obtained by using the thermal health evaluation I to cooperative solve continuous process simulation and discrete event simulation. Finally, the thermal health evaluation II is used to calculate the satellite's health degree \(H(t)\) and failure factor \(F(t)\) according to the subsystem's health degree \(H_{k}(t)\).  
Figure 1: The principle of satellite dynamic health intelligent evaluation model: (**a**) Model schematic; (**b**) Topological system (satellite-subsystems-components).  
The model divides the satellite into satellite-subsystems-components topological system according to its functional composition. In general, a satellite consists of a payload and the common subsystems supporting the payload. Typical public subsystems include structural subsystem, thermal control subsystem (TCS), energy/distribution subsystem, telemetry tracking and command subsystem (TT&C), attitude/orbit control subsystem and propulsion subsystem [17]. Each subsystem is composed of different functional components. The topological system takes into account the series, parallel, vote and reserve relations of various components of the satellite, so as to reflect the influence of component failure on subsystems or satellite health more accurately.  
Section 2.2 uses the thermal network model to calculate the dynamic temperature distribution of components. In Section 2.3, the relationship between the failure probability of the component and the temperature of the component is analyzed quantitatively by fuzzy inference. Section 2.4 introduces a dynamic evaluation algorithm that calculates the health degree and failure factor of subsystems and satellite by analyzing the failure of components.
# A Semantic Model for Enhancing Data-Driven Open Banking Services
## 4 Reconciliation and Inference
### Semantic Reconciliation
A clear sample result from this context is shown in Table 11, where the GA finds an invoice whose amount (1039.88 E) exactly matches the sum of the two corresponding bank statements (800 E and 239.88 E). Differently, Table 12 contains a sample matching in which a bank movement amount (21,913.10 E) is used to pay the corresponding two invoices (16,637.50 E and 5275.60 E). It is worth noting that, without the previous semantic reconciliation concerning items and statements descriptions, these numerical matchings would be highly complex because of the number of possible combinations in the search space.
# The Role of Correlation in the Performance of Massive MIMO Systems
## 3 Received Signal Processing and Linear Precoder
To this end, the RZF precoder is defined as  
\[\mathbf{V}=\left(\hat{\mathbf{H}}\hat{\mathbf{H}}^{\mathrm{H}}+M\phi\mathbf{I}_ {M}\right)^{-1}\hat{\mathbf{H}}, \tag{7}\]  
where \(\hat{\mathbf{H}}\) denotes the imperfect channel estimation of the downlink true channel \(\mathbf{H}=[\mathbf{h}_{1},\mathbf{h}_{2},\ldots,\mathbf{h}_{l}]^{\mathrm{T}} \in\mathbb{C}^{l\times M}\), which is used for data precoding at the base station. The RZF precoder uses the pseudo-inverse of the estimated channel matrix with a regularizing noise power. Parameter \(\phi\) is defined as the regularization coefficient. This parameter is considered to be the inverse of the transmit SNR. As mentioned earlier, this study uses a practical channel consideration. To this end, the elements of the user channel are not isotropically distributed with an independent Rayleigh fading but rather are considered to be relatively correlated. This implies that \(\mathbf{h}_{u}\) has dominant spatial directivity. The following section discusses the imperfect channel state information.
## 2 Related Works
### Dataset  
ASVspoof  
Automatic speaker verification (ASV) is the authentication of individuals by performing analysis of speech utterances [5; 6]. The ASVspoof challenge is a series of challenges that promote the development of countermeasures to protect ASV from the threat of spoofing [7]. The ASVspoof 2019 challenge provides a standard database [8] for anti-spoofing. For our experiment, the logical access (LA) part of ASVspoof 2019 was used. The LA of the provided dataset consists of synthetic speech generated with the very latest, state-of-the-art text-to-speech synthesis (TTS) and voice conversion (VC) technologies, including Tacotron2 [9] and WaveNet [10] as well as bona fide user speech. The spoofing data in the ASVspoof 2019 database have been proven to be similar to the target speaker [8]. Most datasets for deep voice detection consist of deep voices for a single person. However, our proposal is to enhance the prevention of voice phishing by verifying the correct sender after performing verification primarily through deep voice detection. To accomplish this, we need a deep voice dataset for each user. This dataset is suitable for our work as it contains a deep voice for each user. Therefore, we adopted this dataset in order to show the utility of DeepDetection.