esa-sceva/satcom-chunk-collection huggingface dataset:
# Side-Channel Vulnerabilities of Unified Point Addition on Binary Huff Curve and Its Countermeasure
## 6 Comparisons  
We compared the proposed method with the previously presented unified point addition operations with respect to computational cost. Also, we compared the proposed method with the previously unified point addition formulae to which we applied the blinding operands of field multiplication. In this work, as the side-channel atomic algorithms, we considered (i) the proposed method, (ii) the unified point additions in [8; 9], and (iii) the application of the blinding operands of a field multiplication [10] on the unified point addition method in [8; 9]. We analyzed two aspects, that is, security against SCAs and computational cost. Table 3 shows the security against SCAs. The unified point additions described in [8; 9] using the blinding operands in [10] are secure against ROSETTA and HCCA.  
The computational costs of [8; 9] are the same. Also, the computational cost of the proposed unified point addition method is the same as that of the previous one. Thus, the computational costs of the algorithms are affected by the additional cost of \(w\)RPC and [10]. Let \(w=32\) and let \(n\) be the bit  
\begin{table}
\begin{tabular}{r c c c} \hline
**Algorithm** & **SPA** & **ROSETTA** & **HCCA** \\ \hline
[8] & insecure & insecure & insecure \\
[9] & secure & insecure & insecure \\
[8] using [10] & secure & secure & secure \\
[9] using [10] & secure & secure & secure \\ proposed method & secure & secure & secure \\ \hline \end{tabular}
\end{table}
Table 3: The security against side-channel attacks (SCAs) of algorithms.  
size of a finite field. Also, let \(t=\lceil n/32\rceil\). We consider that \(n\) has one of the bit sizes of the standard binary curve in FIPS 186-3 [20] (\(233,283,409\), and \(571\)). The computational cost of an iteration of the algorithms is shown in Table 4.  
In Table 4, \(M\) is the number of \(w\)-bit multiplications of a field multiplication. Namely, \(M=t^{2}\) in [8,9] and in the proposed method. Also, \(M=t^{2}+2t+1\) in [8,9] with [10]. The additional cost is the number of \(w\)-bit multiplications of \(w\)RPC in the proposed method. Namely, (additional cost) \(=2*(3*t)\) for the proposed method. The total cost is the number of \(w\)-bit multiplications of an iteration of the side-channel atomic algorithm using unified point additions. Namely, (total cost) \(=17*M+\) (additional cost). The ratio is the overhead of the algorithm when the original algorithm [8,9] is assumed as 1. This shows that the proposed algorithm is about 0.2\(\sim\)4.4% slower than [8,9]. However, the methods from [8,9] are not secure against ROSETTA and HCCA. The proposed method is about 8.5\(\sim\)17.5% faster than the previous methods from [8,9] using [10], which are secure against ROSETTA and HCCA. In addition, the previous methods ([8,9] using [10]) also require random number generation for \(r_{1}\) and \(r_{2}\) in each field multiplication.
# Enhancing Impact Localization from Fluid-Pipe Coupled Vibration under Noisy Environment
## 3 Results and Discussion
### Time Delay Estimation and Localization
Figure 11: Wavelet\(-\)based time delay estimation with high burst noises; (**a**) vibration acceleration signal measured at S1, (**b**) vibration acceleration signal measured at S2, (**c**) CWT at S1, (**d**) CWT at S2, (**e**) time delay estimation at S1, (**f**) time delay estimation at S2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Impact Event**} & \multicolumn{2}{c}{**Time Difference Estimation (s)**} & \multicolumn{2}{c}{**Localization Error (m)**} \\ \cline{2-5}  & **Conventional** & **Wavelet** & **Conventional** & **Wavelet** \\  & **Cross-Correlation** & **Based** & **Cross-Correlation** & **Based** \\ \hline
1 & 0.2793 & 0.2794 & 3.75 & 3.62 \\
2 & 0.2806 & 0.2803 & 2.37 & 2.72 \\
3 & 0.2788 & 0.2794 & 4.29 & 3.64 \\
4 & 0.2793 & 0.2791 & 3.81 & 3.93 \\
5 & 0.2800 & 0.2798 & 3.02 & 3.24 \\
6 & 0.2776 & 0.2795 & 5.50 & 3.6 \\
7 & 0.2779 & 0.2777 & 5.18 & 5.38 \\
8 & 0.2790 & 0.2781 & 4.13 & 5.06 \\
9 & 0.2784 & 0.2806 & 4.71 & 2.44 \\
10 & 0.3064 & 0.2785 & 24.2 & 4.55 \\
11 & 0.3094 & 0.2795 & 27.3 & 3.56 \\ \hline \multicolumn{5}{c}{Mean value} & \multicolumn{2}{c}{8.024} & 3.795 \\ \hline \multicolumn{5}{c}{Standard deviation} & \multicolumn{2}{c}{8.836} & 0.903 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Impact event localization results.
# Evaluation of Structural Stresses of Mountain-Embedded Railway Systems
## 2 Simulation Model for the Mountain-ERS Track
### Material Properties
#### 2.3.1 Concrete
the part that exceeds the strain is presented as an exponential function. The stress-strain model for the tensile behavior is shown in Figure 6.  
\[f_{t}=E_{c}\varepsilon_{t}\ (0\leq\varepsilon_{t}\leq\varepsilon_{cr}) \tag{4}\]  
\[f_{t}=f_{cr}\left(\frac{\varepsilon_{cr}}{\varepsilon_{t}}\right)^{0.4} \tag{5}\]  
\[f_{cr}=1.40*\left(\frac{f_{ck}}{10\text{MPa}}\right)^{(2/3)} \tag{6}\]  
where \(\varepsilon_{cr}\) is the tensile strain at which the tensile stress is equal to the peak stress (\(f_{cr}\)) value, \(f_{t}\) is the tensile stress, \(\varepsilon_{o}\) is the compressive strain (\(\varepsilon_{o}=2f_{c}^{\prime\prime}/E_{c}\)), \(f_{c}\) is the compressive stress at strain (\(\varepsilon_{c}\)), and \(\varepsilon_{u}\) is the ultimate compressive strain, which can be set to 0.0038 with 0.85 \(f_{c}^{\prime\prime}\). In this study, both linear and nonlinear material analyses were conducted. In the linear analysis, the elastic modulus of the concrete material corresponding to the compressive strength and Poisson's ratio was applied for structural analyses.
## 4 Conclusions
### Opening on Satellite Selection  
The satellite selection problem has been addressed through many different approaches, leading to computationally (sub-)optimized algorithms [51]. Nevertheless, in the context of a universal channel, the challenge becomes selecting the next best signal, not only the next best satellite. Not only the number of option increases, but also does the selection algorithm complexity, thus requiring a re-spin of the existing selection algorithms. Indeed, the satellites geometry may not be sufficient anymore: frequency diversity targeting ionosphere corrections, signal effective (Gabor) bandwidth for precise pseudo-ranging measurements, signal availability in case of jamming as well as measurement ambiguity are all contributing factors to be accounted for, _i.e._, a new research topic in itself that could address the traditional satellite navigation limitations at once.  
Some thoughts on such a signal selection strategy go as follows. On cold start, all GPS L1 C/A signals are searched for in parallel to reduce as much as possible the Time To First Fix (TTFF). Once ephemerides are downloaded, known visible satellite orbits allow channels to be progressively assigned to modernized signals (most of which have dual-components, as seen in Table 1) from the same constellation, eventually on different frequency bands if the RF front-end allows, with a reduced search grid (_i.e._, known Doppler and estimated chip alignment). Furthermore, this initial Position Velocity and Time (PVT) solution would help reducing the search span of signals on otherconstellations. A key factor in resolving this signal selection challenge consists in maintaining the ideal pool of next best signals based on an adapted version of the FRIG algorithm [51]. Hence, the proposed universal channel combined with a new signal selection algorithm and a navigation data fusion strategy could become a powerful tool in cognitive receivers.  
[PERSON] conceived, designed and performed the experiments and analyzed the data; [PERSON] contributed the laboratory and the initial GPS L1 CA only receiver platform. [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# Anisotropy and Directivity Effects on Uniaxial Compression of Carbonaceous Slate Form Jinman Mine
## 2 Methods  
### Sample Preparation  
Figure 1 shows the sampling location, partial slate samples, and the mineral contents of rock samples. The carbonaceous slate samples were obtained from the Jinman Mine in Lancang River Gorge in Lamping County, Yunnan Province. The sampling location was located in the secondary overturned anticline of Enqi Anticlinorium as shown in Figure 1a. Affected by geological tectonics, carbonaceous slates in this area have been impacted by floor heave and slabbing failure. From X-ray diffractometer analysis, gray slate is composed of quartz (71%), muscovite (15%), albite (11%), graphite (3%), while circenous slate is composed of quartz (55%), muscovite (19%), albite (15%), calcium (8%), and graphite (3%), as shown in Figure 1c,d. The average dry density and average longitudinal wave velocity of slate is 2.68 g/mm\({}^{3}\) and 6.49 km/s, respectively.  
Cuboid samples of size 50 mm \(\times\) 50 mm \(\times\) 100 mm were used for uniaxial compression tests per the Code for Rock Tests of Hydroelectric and Water Test Conservancy Engineering (GB/T50266-2013), as shown in Figure 1b. The relationship between bedding structure and loading direction was controlled to study anisotropic mechanical properties. The loading methods were such that the principal stress was perpendicular to the slate structure for vertical loading, and the principal stress was parallel to the slate structure for parallel loading. A total of 36 rock samples including 18 gray samples and 18 cinerous samples were used for this study.
## 5 Decoding the Intricacies of Blind Source Separation (BSS): A Deep Dive into Core Principles  
This technique uses a mathematical model interpreting EEG signals as linear combinations of independent source signals and artifacts [89; 90; 91; 92; 93]:  
\[\mathbf{X}=\mathbf{A}\mathbf{S}+\mathbf{N}, \tag{1}\]  
where \(\mathbf{X}\) is the \(m\times n\) matrix of EEG signals (\(m\), of electrodes, \(n\), number of samples). \(\mathbf{A}\) is the \(n\times p\) unknown mixing matrix (\(p\), number of source signals and artifacts), which represents how the source signals and artifacts combine. \(\mathbf{S}\) is the \(p\times n\) matrix of the source signals, including both the signals of brain interest and the artifacts. Finally, \(\mathbf{N}\), of size \(m\times n\), represents the measurement noise. The goal is to estimate \(\mathbf{A}\) and separate \(\mathbf{S}\) from \(\mathbf{X}\), eliminating artifacts from \(\mathbf{S}\) to reconstruct clean EEG signals.  
The use of a single BSS technique encounters limitations due to the variety of artifacts and the complexity of the EEG signals, making universal effectiveness difficult. Challenges include signal variability, assumptions that are not always valid, and sensitivity to set up parameters, which can affect the accuracy of artifact removal.  
Addressing artifacts in EEG signals requires an integrated approach that combines several BSS techniques and preprocessing methodologies, rather than the use of a single technique. This strategy takes advantage of the complementary advantages of various methods, improving the quality of the EEG signal for more accurate analysis and expanding the practical use of clean EEG data.
# Sentence Representation Method Based on Multi-Layer Semantic Network
## 5 Results and Discussion
### Ablation Analysis
#### 5.2.1 The Influence of Semantic Levels  
In this chapter, semantic layer ablation experiments are carried out on the SNLI dataset for different layers of multi-layer semantic networks. The experimental results are shown in Figure 7.  
Figure 7 shows the change of the representation network's accuracy rate with different semantic layers on the SNLI test set. The alignment of different examples in the figure corresponds to a different number of semantic layers (vectors). When the number of semantic layers is less than five, the SNLI data set's accuracy increases with the number of layers. When the number of semantic layers exceeds five, with the SNLI data set as shown in the curve with a vector equal to 7 or 9 in the figure, the accuracy decreases with the increase of the number of layers.  
Figure 6: Visualization results of multi-layer semantic representation network.  
To sum up, too many or too few layers of the multi-layer semantic network may result in the model's poor effect. They cannot improve the sentence representation ability. This situation is that different semantic levels represent the specific semantic information (word meaning, word order, phrase) of a sentence. Too few semantic levels will lead to the loss of important semantics in obtaining sentence semantics. On the contrary, too many semantic levels may lead to redundancy of semantic information of sentences, both of which will have a negative impact on sentence representation.
# Passive Intermodulation at Contacts of Rough Conductors
## 4 Mechanical Nonlinearities at Conductor Contacts  
Mechanical deformations of electric contacts and conductor joints in connectors, MEMS switches, micro and nano electronic circuits have been extensively studied in the literature, see, e.g., [49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 64; 71] and references therein. The performance and longevity of contacts in RF devices proved to be affected by conductor surface roughness and stiffness. The high-frequency applications of MEMS have inspired the detailed studies of the effects of micro deformations on the contact resistivity, losses and reliability of conductor joints. The first models taking into account roughness of the conductor surfaces in microcontacts had been developed in [60; 64] and later refined in [50; 51; 52; 53].  
The contact resistance \(R_{c}\) of asperities, defined in (1), is proportional to the electrical resistivity \(\rho_{c}\) and depends on the size \(a\) of a contact spot. In a broad range of the contact pressure variation, \(\rho_{c}\) of good conductors remains practically constant whereas the linear and nonlinear deformations of the contact spots become the dominant factors. It has also been observed in [64] that these deformations are weakly affected by thickness \(t\) and permittivity \(K\) of an insulating layer, as illustrated by Figure 7, if its energy level remains above the Fermi levels of the conductive surfaces. The asperity deformations and contact area expansion are also closely connected to the electrical and thermal effects discussed earlier and demonstrated by Figure 1.  
Figure 7: Dimensionless contact area \(A^{\star}\) vs. dimensionless electrical contact resistance \(R^{\star}\) for contacting rough surfaces with different mechanical properties and fractal parameters (\(K=6\), \(t=10\) Å, \(\varphi_{0}=2\) eV, and \(I=5\) mA). Reproduced from [64] with the permission of AIP Publishing.
# Text Alignment in the Service of Text Reuse Detection
## 3 Methodology  
Ref. [5] presents an advanced Hebrew/Aramaic alignment method in their research. They employed the Needleman-Wunsch (NW) global alignment algorithm [6] and established a distance metric for word comparison.  
Several modifications are necessary to adapt this method for the text reuse detection framework. First, it is assumed that only a limited portion of one text has been reused in the other. Consequently, the NW global alignment algorithm replaces the Smith-Waterman (SW) local alignment algorithm.  
It should be noted that the expectation is not verbatim reuse; instead, the alignment process should accommodate the substitution of words with similar ones, the replacement of words with their abbreviations, and the compression of multiple words into acronyms. Additionally, it should account for typographical errors between the two texts. Finally, while the word order may vary, the algorithm should be able to detect and align word transpositions.  
In the subsequent sections, we outline the modifications proposed to address the text reuse criteria established in [5] work. Section 3.1 elaborates on the design of the distance function, which quantifies word distances for all necessary alterations. Section 3.2 explains the cost function, which generates our unique score matrix. Section 3.3 details the adjustments made to the SW traceback function, specifically tailored to accommodate word transpositions and multi-token alignments.
## 1 Introduction
Regarding the accuracy and robustness of the estimation of the signal, the residual signal levels are different in dissimilar classes. Finally, we select the SVM technique for classification. The contributions of this paper can be summarized as follows:  
* Indirect signal modeling by extracting the state-space nonlinear function using the combination of machine learning-based regression and ARXU technique. * Indirect signal estimation by using the combination of the PI observer, extended integral term, VS-Lyapunov technique, and self-tuning network-fuzzy system. * Combination of AMRXU, AVSPMI, and SVM-based approaches for bearing fault detection and classification. The remainder of this paper is organized as the follows: The proposed Scheme is briefly described, and the block diagram of the proposed scheme has been shown in Section 2. The data acquisition from the Case Western Reverse University (CWRU) dataset is described in Section 3. Next, the preprocessing is explained in the Section 4. In Section 5, first, a smart autoregressive signal modeling is proposed over normal signal modeling. Next, a self-tuning hybrid-based observer is designed for signal estimation in normal conditions. The signal condition is classified by machine learning-based classifier in Section 6. The experimental results are presented in Section 7, and the conclusion is presented in Section 8.
# Estimating Thermal Material Properties Using Solar Loading Lock-in Thermography
###### Abstract  
This work investigates the application of lock-in thermography approach for solar loading thermography applications. In conventional lock-in thermography, a specimen is subjected to a periodically changing heat flux. This heat flux usually enters the specimen in one of three ways: by a point source, a line source or an extended source (area source). Calculations based on area sources are particularly well suited to adapt to solar loading thermography, because most natural heat sources and heat sinks can be approximated to be homogenously extended over a certain region of interest. This is of particular interest because natural heat phenomena cover a large area, which makes this method suitable for measuring large-scale samples. This work investigates how the extended source approximation formulas for determining thermally thick and thermally thin material properties can be used in a naturally excited setup, shows possible error sources, and gives quantitative results for estimating thermal effusivity of a retaining wall structure. It shows that this method can be used on large-scale structures that are subject to natural outside heating phenomena.  
infrared thermography; solar loading thermography; lock-in thermography; passive thermography; thermal thickness; thermal effusivity; infrastructure; NDT +
Footnote †: journal:  
active thermography setups would be able to excite properly. Measuring very large structures at once is possible. This is particularly interesting for monitoring buildings and large infrastructure alike. This work focuses on large vertical, thermally thick (d \(>\) \(\Lambda\)) structures. Multiple assumptions and approximations were made to use the theoretical lock-in thermography formulas on solar loading thermography data. These assumptions result in systematic error. Later a calibration method will be shown, that eliminates any linear errors that were made, by means of a calibration against known material property values.  
Related work shows that this method can be successfully used for detecting defects and characterize their depths [3]. Other related work focusses on the time when the sun is obstructed, and a shadow is cast, triggering the thermal camera to begin a pulse-phase-thermography (PPT) measurement [4]. For this work these exact requirements of shadow cast are not needed. Instead, this work specifically choses the 24 h period to suppress transient effects (like cloud shadows or short-term weather effects like gusts of wind) and generate results dependent only of the much slower 24 h excitation intensity. Furthermore, only qualitative results have been shown (i.e., cracks or delaminations) and not quantitative measurements of material properties. This has the benefit of not only being able to detect defects, but also characterize the material under test.
# Research on Reliability Assessment of Mechanical Equipment Based on the Performance-Feature Model
## 5 Conclusions  
This paper presented the reliability assessment method of mechanical equipment based on the performance degradation path, which was developed for reliability through exploring the relationship between signal characteristics and performance degradation. The signal characteristic correlated with the degradation of performance was obtained, then the estimation method based on the feature-performance model was more practical. Performance degradation path modeling can help to find performance degradation distribution fitting, and model parameter estimation shows the difference between the samples; then the Pseudo Failure Characteristics (PFCs) can be calculated based on the threshold gained from experience. Verification of PFC distribution indicated the validity of failure features, and the function describing the failure characteristic distribution was chosen to calculate the reliability curve. In this paper, a case study of tool wear was used to verify the method. The vibration signal characteristic \(Gnt\) was proved to be highly correlated with tool wear. The wear-feature model that fits tool wear distribution was used to estimate model parameters of the different tools, then the PFCs were calculated based on the tool wear threshold that was set at 0.16 mm in this paper. Verification of failure characteristics indicated that Exponential distribution could be used to describe the characteristic distribution. Then, reliability curve and failure characteristic prediction could be obtained. In further work, the method can be used to calculate real-time estimate tool life and improve processing quality. The paper contributes to our understanding of performance degradation and provides a basis for reliability evaluation of mechanical equipment by the degradation path.  
[PERSON] conceived and designed the methods; [PERSON] devised and validated the model; [PERSON] and [PERSON] analyzed the data; and [PERSON] contributed to optimizing the programming.  
This work was supported by National Science and Technology Major Project, China (No. 2017 ZX04008001), and National Defense Fundamental Research Foundation, China (No. JCKY2016601C006).  
The authors declare no conflict of interest.