esa-sceva/satcom-chunk-collection huggingface dataset:
# Acceleration of Service Life Testing by Using Weibull Distribution on Fiber Optical Connectors
## 2 Theoretical Background
### Weibull Stress Random Analysis  
By using the n value from Equation (3) in the median rank approach function stated by Equation (11) [25], the corresponding cumulated failure percentile \(F(t_{i})\) is determined as  
\[F(t_{i})=\frac{i-0.3}{n+0.4} \tag{11}\]  
Now, by using the \(F(t_{i})\) elements in the linearized form of the reliability function given in Equation (7), determine the corresponding \(Y_{i}\) elements as in Equation (12), and then compute its corresponding arithmetic mean value as in Equation (13) [24],  
\[Y_{i}=\ LN(-LN(1-((i-0.3)/(n+0.4)))) \tag{12}\]  
\[\mu_{y}=\sum_{i=1}^{n}\frac{Y_{i}}{n} \tag{13}\]  
Additionally, the reliability index that corresponds to the \(Y_{i}\) value [24] can be determined as  
\[R(t)=exp\{-exp\{Y_{i}\}\} \tag{14}\]  
By using the \(\beta\) and \(\eta\) parameters, the \(\sigma_{\rm 1i}\) values can be calculated using the \(t_{0i}\) value that corresponds to each \(Y_{i}\) element as  
\[t_{0i}=\exp\{Y_{i}/\beta\} \tag{15}\]  
Next, the \(\sigma_{\rm 2i}\) value is determined as  
\[\sigma_{2i}=\eta\times t_{0i} \tag{16}\]  
And the \(\sigma_{\rm 1i}\) value is determined as  
\[\sigma_{1i}=\eta/t_{0i} \tag{17}\]  
Since the Weibull ALT presented in this investigation is based on the stress induced by the mechanical vibration in the fiber optic connectors. Now let us review the vibration analysis.
# Analytical Solution for the Problem of Point Location in Arbitrary Planar Domains
## 2 Related Work
### Ray-Crossing Approaches  
For a long time, one of the most popular algorithms was the one based on the idea of defining an infinite line starting at the point being analyzed: the number of intersections with the polygon boundary gives indications for the solution, as described, for example, by [PERSON] and [PERSON] [5]. Briefly stated, a point belongs to a polygon if an infinite straight line starting on it intersects the polygon an odd number of times in one direction (Figure 2). This algorithm is also known as the ray-crossing [13], the crossing number [14] algorithm, and the even-odd rule algorithm [15].  
One traditional issue with the ray-crossing algorithm was that it may require further tests to account for the cases where the intersections of the line coincide with one or more vertices or even with an entire edge of the polygon itself. Implementations of this algorithm can be found in many places, and the earlier most well-known implementations occur in the works of [PERSON] [6], [PERSON] [7], and [PERSON] [16].  
The ray-crossing algorithm is simple and of limited computational cost, \(\{\mathcal{O}(n)\}\), but, for more functionality, it may require additional steps, hence reducing its elegance. Additionally, applying its technique to generally shaped areas other than polygons, although possible in theory, could pose huge computational and practical difficulties since systems of simultaneous equations might need to be managed and solved. Still, and although it surely has not been intended for that, its applicability for more general self-intersecting (non-simple) domains may fail, as illustrated in Figure 3, where the point \(e\) would be evaluated as being in the \"outside\", which contradicts both the mathematical concept based on the winding number [17], and the human perception of \"insideness\" on a folded (and/or twisted) polygon.  
Figure 3: The classic ray-crossing algorithm is not meant to deal with non-simple or self-intersecting polygons. Point \(e\) would be detected as “outside”, but it is “inside”.  
Figure 2: The traditional ray-crossing algorithm is based on the parity of intersection points. An odd number of intersections of a horizontal line starting on the point being tested along one of its two sides indicates that the point is inside the polygon, and an even number of intersections (also 0) indicates that the point is outside.
# Mechanisms of Noise Transmission in Velocity Broad-Band Seismometers: Modeling and Analysis
## 4 Simulation
### Verification of the Amplitude-Frequency Characteristics of Noise Transfer Functions  
#### 4.3.1 Theoretical Noise Transfer Functions  
Using the parameters listed in Table 1, we computed the theoretical amplitude-frequency curves for the noise transfer functions. These curves, representing the expected behavior of the noise transfer functions across different frequency ranges, are presented in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Suspension** & **Forward Path** & **Feedback Path** & **Differential Amplifier** \\
**Pendulum** & **Forward Path** & **Feedback Path** & **Amplifier** \\ \hline \(\zeta=0.035\) & \(K_{d}^{c}=57,296\) V/m & \(\tau=46.2\) & \(A_{3}=2.0179\) \\ \(\omega_{0}=\frac{2\pi}{13}\) rad/s & \(A_{1}=4.3589\) & \(R_{1}=664\) k\(\Omega\) & \(\omega_{3}=32.4675\) rad/s \\ M = 0.216 kg & A\({}_{2}=1.464\) & \(R_{2}=200\) k\(\Omega\) & \(\omega_{4}=30.303\) rad/s \\ \(\mathrm{K_{m}}=1\) & \(\omega_{1}=30.303\) rad/s & \(C_{1}=10\) μF & \\ \(\mathrm{K_{f}}=1\) & \(\omega_{2}=0.0915\) rad/s & \(\mathrm{E}=108\) (m/s\({}^{2}\))/A & \\  & & \(R_{\mathrm{coll}}=58\)\(\Omega\) & \\ \hline \hline \end{tabular} \({}^{\dagger}\) The parameter values were sourced from a publicly available paper.  
\end{table}
Table 1: Parameter values of the CS60 seismometer \({}^{1}\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Noise Sources** & **White Noises** & **1/f Noises** \\ \hline \(\mathrm{S_{n_{m}}(f)}\) & \(\mathrm{S_{n_{n}}(f)=2.53\times 10^{-20}\left(m/s^{2}\right)^{2}/Hz}\) & \\ \(\mathrm{S_{n_{ev}}(f)}\) & \(\mathrm{S_{n_{ev}}^{w}(f)=1.4\times 10^{-15}\) V\({}^{2}/Hz}\) & \(\mathrm{S_{n_{ev}}^{f}(f)=\frac{1.4\times 10^{-15}}{I}\) V\({}^{2}/Hz}\) \\ \(\mathrm{S_{n_{int}}(f)}\) & \(\mathrm{S_{n_{int}}^{w}(f)=1\times 10^{-13}\) V\({}^{2}/Hz}\) & \(\mathrm{S_{n_{int}}^{f}(f)=\frac{1\times 10^{-15}}{I}\) V\({}^{2}/Hz}\) \\ \(\mathrm{S_{n_{f}}(f)}\) & \(\mathrm{S_{n_{f}}^{w}(f)=9.38\times 10^{-19}\) V\({}^{2}/Hz}\) & \(\mathrm{S_{n_{f}}^{f}(f)=0}\) \\ \(\mathrm{S_{n_{da}}(f)}\) & \(\mathrm{S_{n_{da}}^{w}(f)=2\times 10^{-15}\) V\({}^{2}/Hz}\) & \(\mathrm{S_{n_{da}}^{f}(f)=\frac{2\times 10^{-15}}{I}\) V\({}^{2}/Hz}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The internal noise source parameter values of the CS60 seismometer.
## 4 Experiments, Results, and Discussion
### Performance Metric  
The following detection metrics were considered during model evaluation using test data:  
* True Positive (TP): It refers to the number of attack samples out of the total number of samples that were accurately detected as attacks.
* False Positive (FP): It refers to the number of normal samples that were incorrectly identified as attacks.
* True Negative (TN): It refers to the number of benign samples that were accurately recognized as normal.
* False Negative (FN): It refers to the number of attack samples that were wrongly classified as normal.
* **Accuracy**: It represents the ratio of the number of correct classifications to the total number of inputs and is calculated as follows: \[\text{Acc}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}}\] (6)
* **Precision**: It gives the ratio of successfully classifying attacks to the total number of expected attack outcomes, which can be calculated as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Total** & **Train** & **Test** \\ \hline Normal & 24,301 & 19,368 & 4933 \\ DDoS\_UDP Attack & 14,498 & 11,574 & 2924 \\ DDoS\_ICMP Attack & 14,090 & 11,179 & 2911 \\ Ransomware Attack & 10,925 & 8634 & 2291 \\ DDoS\_HTTP Attack & 10,561 & 8381 & 2180 \\ SQL\_injection Attack & 10,311 & 8343 & 1968 \\ Uploading Attack & 10,269 & 8162 & 2107 \\ DDoS\_TCP Attack & 10,247 & 8045 & 2202 \\ Backdoor Attack & 10,195 & 7967 & 2228 \\ Vulnerability\_scanner Attack & 10,076 & 8089 & 1987 \\ Port\_Scanning Attack & 10,071 & 8011 & 2060 \\ XSS Attack & 10,052 & 8017 & 2035 \\ Password Attack & 9989 & 7956 & 2033 \\ MITM Attack & 1214 & 976 & 238 \\ Fingerprinting Attack & 1001 & 798 & 203 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data Distribution for Training and Testing.  
\[\text{Pre}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{7}\]
* **Recall**: It offers the ratio of accurately categorized attacks to all anticipated attack outcomes, which is determined as follows: \[\text{Rec}=\frac{\text{TP}}{\text{FP}+\text{FN}}\] (8)
* **F\({}_{\text{1}}\)-Score**: It gives the ratio of successfully classifying attacks to the total number of expected attack outcomes, which can be calculated as follows: \[\text{F}_{\text{1}}=2\cdot\frac{\text{Precision}\cdot\text{Recall}}{\text{ Precision}+\text{Recall}}\] (9)
* **True Positive Rate (TPR)**: A true positive state occurs when an activity is detected by the IDS as an attack and the activity truly represents an intrusion. TPR is sometimes also regarded as the detection rate and can be represented by the following expression: \[\text{TPR}=\frac{\text{TP}}{\text{TP}+\text{FN}}\] (10)
* **False Positive Rate (FPR)**: When the IDS detect an activity as an attack when it is actually benign or normal, the state is known as a false positive rate. A false positive could be regarded as a false alarm rate too, which can be calculated as follows: \[\text{FPR}=\frac{\text{FP}}{\text{FP}+\text{TN}}\] (11)
## 2 Security Considerations for Marine Network Protocols
### Authentication  
A further requirement for the firmware is identity verification (i.e., confirm that a receiver or a sender is who they claim to be). While there have been proposals for  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Security Consideration** & **Primary Risk** \\ \hline Confidentiality & Access to private data \\ Integrity & Modification of data \\ Availability & Inability to access data or resource \\ Authentication & Inability to confirm identity \\ Authorization & Improper access to resource or data \\ Non-repudiation & Inability to confirm an action made by an identity \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of security considerations.  
authentication mechanisms in the past, very few directly target marine network protocols. However, some of the protocols that ships use support optional authentication mechanisms. These include the Global Navigation Satellite System (GNSS) [13] and systems adopted from vehicular networks [14]. Hence, in theory, these protocols can be adapted to support authentication for marine networks in future revisions.
# NMR-MPar: A Fault-Tolerance Approach for Multi-Core and Many-Core Processors
## Appendix A Implementation Details of the Code
### Matrix Multiplication Details  
The application is based on an assembler optimized version of a cooperative \(256\times 256\) matrix multiplication. The matrix multiplication is performed 8192 times, and the result \(C\) is the summation of these computations as stated in (A1). The iteration of the matrix operation is done to guarantee that each cluster computes enough time so that all the clusters work in parallel during a considerable time slice.  
\[C=\sum_{n=1}^{256}A\times B \tag{1}\]  
A, B and C are single precision floating-point matrices. The size of the matrix was chosen so that data remain in the local SMEM memory.  
Each slave computes \(\sfrac{1}{4}\) of the result. The main process of each CC creates 16 POSIX threads, one for each PE. Therefore, each PE calculates \(\sfrac{1}{64}\) of the result by executing the same assembler optimized version. Since there is no hardware memory coherency in the compute cluster, each PE ensures memory coherency by software means, by updating shared data before (read coherency) and after the computation (write coherency). In addition, the RM calls the memory coherency functions when using the shared data.  
The MPPA connections created for each instance of this application are summarized as follows. For the master in the IO cluster and the slaves in the CC cluster: (1) two syncs for synchronization, (2) one portal to receive the results and (3) four portals to broadcast the results.
# S-PDR: SBAUPT-Based Pedestrian Dead Reckoning Algorithm for Free-Moving Handheld Devices
## 3 SBAUPT-Based Pedestrian Dead Reckoning (S-PDR)
### Azimuth Step-Based Attitude Update (SBAUPT)
#### 3.2.1 Step-Based Update (SBUPT)  
Tilt angles--roll and pitch--are computed based on gravity components. A common constraint to apply tilt angle corrections is to use static periods where the accelerations are only corresponding to gravity components. When using smartphones for navigation, there are usually no static periods and the accelerometer is measuring the net force of gravity and linear acceleration of the device.  
SBUPT [98] exploits periods of motion with nearly steady walking speed, where the accelerations average in a step will tend to gravity and can be used to estimate the tilt angles. To estimate the roll and pitch angles from the step-window accelerations, they need to be in the same reference frame. Usual methods use transformation to LLF to project all accelerations into the same reference frame, but using this approach introduces historically accumulated errors into the projection process. SBUPT overcomes this problem by defining a step frame (SF)--which is the final BF in the step-window. SF is an arbitrary relative frame with unknown attitudes but has known relative attitude to other BF instances within the step, and uses the in-step gyroscope measurements to transform all in-step accelerations to the same reference frame. A least squares estimator is developed and used to refine the gravity estimates and reduce the effect of leftover linear acceleration components. This process eliminates accumulation of errors as the process resets with each detected step and does not rely on absolute attitude values estimated. This correction process can be applied frequently on a step basis if the step average passes the threshold condition indicating a near-constant motion speed. Tracking the full attitude states during the step is obtained by the attitude mechanization process.
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 3 Carbon Satellite Remote Sensing Inversion Techniques
### Summary  
This section provides an overview of the various inversion algorithms and their accuracies applied to satellite data for deriving CO\({}_{2}\) column concentrations. These inversion algorithms are generally classified into two main categories: empirical statistical methods and physical methods.Empirical statistical methods can bypass the complex calculations required for radiative transfer. However, they often suffer from significant inversion errors due to factors such as sample set selection, model simplifications, and inadequate error estimation. Consequently, these methods typically cannot produce reliable global CO\({}_{2}\) satellite products and are usually employed to supply the a priori information needed by physical algorithms [113].  
Despite advancements in remote sensing inversion algorithms both domestically and internationally, there is still a need for improvement. Enhanced algorithms and refined orthorectified radiative transfer models are required to achieve high-quality observations of CO\({}_{2}\) and CH\({}_{4}\) with improved accuracy, coverage, reliability, and computational efficiency.
# FDBP-InSAR: An Efficient Algorithm for InSAR Imaging via Frequency Domain Back Projection
## 2 InSAR Imaging Model
### Problem Formation  
In the conventional frequency-domain-based algorithms, the procedure of image formation mainly decomposes the two-dimensional focusing into two cascaded one-dimensional processing, furthermore, FFT is used to achieve imaging focusing in the frequency domain. Hence, its computational complexity is \(\left(N^{2}\log N\right)\) for an \(N\ \times\ N\) image. Although these algorithms are very fast, they are limited by several assumed conditions. E.g., if there are high-order phase compensation errors, these methods may be very difficult to accurately compensate for the error and suffer from serious defocusing.  
As mentioned above, unlike frequency-domain-based algorithms, TDBPA adopts point-by-point accumulation in the spatial domain, so the accuracy of the image focusing and phase preservation can be significantly improved. According to the procedure of point-by-point accumulation, it leads to an increase in the computational complexity of TDBPA to \(\left(N^{3}\right)\). Obviously, for a large scene with \(10000\times 10000\) cells, TDBPA is 2500 times slower than the frequency-domain algorithm. It seriously affects the real-time processing capability of the radar data. Although there are some fast BP imaging methods based on the improvement of methods and optimization of operation flow, e.g., FFBP method and GPU parallelization, the improvement of methods will lose precision and phase, and GPU parallelization is limited by hardware performance. Therefore, how to achieve high-efficiency, high-fidelity InSAR imaging with high precision is still a huge problem.  
At present, a frequency-domain-based back-projection algorithm can not only ensure the imaging accuracy but also improve the imaging efficiency, which provides ideas for solving the problem of InSAR imaging.
# Self-Assembled MoS2/ssDNA Nanostructures for the Capacitive Aptasensing of Acetamiprid Insecticide
###### Abstract  
The aim of this work is to detect acetaminophen using electrochemical capacitance spectroscopy, which is widely used as a pesticide in agriculture and is harmful to humans. We have designed aptasensing platform based on the adsorption of a DNA aptamer on lipoic acid-modified MoS2 nano-sheets. The biosensor takes advantage of the high affinity of single-stranded DNA sequences to MoS2 nano-sheets. The stability of DNA on MoS2 nano-sheets is assured by covalent attachment to lipoic acid that forms self-assembled layer on MoS2 surface. The biosensor exhibits excellent capacitance performances owing to its large effective surface area making it interesting material for capacitive transduction system. The impedance-derived capacitance varies with the increasing concentrations of acetamiprid that can be attributed to the aptamer desorption from the MoS2 nanosheets facilitating ion diffusion into MoS2 interlayers. The developed device showed high analytical performances for acetamiprid detection on electrochemical impedance spectroscopy EIS-derived capacitance variation and high selectivity toward the target in presence of other pesticides. Real sample analysis of food stuff such as tomatoes is demonstrated which open the way to their use for monitoring of food contaminants by tailoring the aptamer.  
aptasensor; MoS2; pesticide; neonicotinoid; capacitance +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Untargeted Analysis of Volatile Profiles of Raw and Cooked Rice Samples  
To explore the proximity among volatile profile datasets of raw and cooked rice samples of varieties studied, untargeted metabolomics analyses were performed. The results of principal component analysis (PCA) of the volatile profile dataset of raw and cooked rice samples are shown in Figure 2. PCA is the initial step in multivariate data analysis, which is routinely performed to understand the diversity pattern among the dataset [22]. Taking into consideration the principal components PC1 and PC2, only 8.1% of phenotypic variance is accounted for volatile profiles in the raw rice samples (Figure 2a). However, cooked rice samples of some varieties studied showed distinct grouping according to their aromatic and non-aromatic nature (Figure 2b). However, the observed total percentage of variance explained by PC1 and PC2 was only 11.7%, which indicates the high number of independent sources of variability in the matrix. Cooked rice samples of IR-64 formed a well-separated cluster from other varieties in the PCA scores plot. The mild-aromatic traditional varieties, Kala Namak-1 and Kala Nuniya, were clustered together and considerably discriminated from non-aromatic IR-64 and aromatic varieties. In this PCA plot, among aromatic varieties, Pusa Basmati-1 and Pusa Basmati-1509 had a separate cluster, which was detached from the other traditional aromatic varieties. Interestingly, traditional aromatic landraces were grouped together, which demonstrates close similarity among their volatile profiles. Altogether, these results indicated that the volatile profiles of aromatic and non-aromatic rice varieties were more distinguishable in cooked rice than their raw samples.
## 2 Materials and Methods
### Heat Transfer in Coaxial WBHEs
#### 2.3.1 Coaxial WBHE: Coefficient of Heat Exchange between Outer-Pipe Fluid and the Wellbore Exterior  
In an analysis of the energy balance equation for the fluid in the outer pipe (injection pipe) of a coaxial WBHE, a proper estimate of the parameter \(k_{w}\) is fundamental for a correct evaluation of the heat exchange between the outer-pipe fluid and the drilled geological formations.  
For a coaxial WBHE, the heat exchange coefficient for the injection pipe can be expressed as the sum of heat transfer components, expressed in terms of thermal resistance values (\(R_{w}\)) [24]:  
\[R_{w}=R_{s}+R_{a}+R_{c} \tag{5}\]  
where \(R_{s}\) is a function of time that represents thermal resistance due to conductive heat transfer in the rock, \(R_{a}\) is the thermal resistance due to convective heat transfer in the pipe and \(R_{c}\) is the thermal resistance due to conductive heat transfer through the casings of the well.  
In the evaluation of total thermal resistance, the conductive term prevails; consequently, thermal exchange is directly proportional to the convective transfer coefficient. Conductive thermal resistance (\(R_{s}\)) can be expressed as follows:  
\[R_{s}=\frac{1}{2\lambda_{s}}In\frac{2\sqrt{\alpha_{s}t}}{r_{w}} \tag{6}\]  
where \(\lambda_{s}\) (W/mK) is the thermal conductivity of the rock, and \(\alpha_{s}\) (m/s) is the thermal diffusivity of the rock. In Equation (6), the numerator of the argument of the natural logarithm represents the time-dependent radius of the thermal influence of the well (\(r_{s}\)). This parameter considers the change, over time, of the heat flux into WBHE surroundings geological formations [24].  
Convective thermal resistance (\(R_{a}\)) can be determined by the following equation:  
\[R_{a}=\frac{1}{2r_{c}h_{f}} \tag{7}\]  
where \(r_{c}\) is the external radius of the external casing, and \(h_{f}\) is the convective heat transfer coefficient, which was calculated using the Nusselt number (Nu) and a form of the Dittus-Boelter equation that assumes turbulent flow inside the tubes (Reynolds number \(\geq\) 104) [36]:  
\[h_{f}=\frac{Nu\lambda_{f}}{2r_{c}} \tag{8}\]  
\[Nu=0.023 Re^{0.8}Pr^{0.4} \tag{9}\]  
with \(Pr=\frac{\rho c_{f}H}{\lambda_{f}}\) and \(Re=\frac{\rho v_{f}2r_{c}}{\mu}\).  
Finally, thermal resistance to heat conduction through the casings of the well can be determined as follows:  
\[R_{c}=\sum_{i=1}^{n}R_{\lambda i}=\frac{1}{2}\sum_{i=1}^{n}\frac{1}{\lambda_{ i}}In\frac{r_{c,i+1}}{r_{c,i}} \tag{10}\]where \(\lambda_{i}\) is the thermal conductivity of the rock in correspondence with the different casings of the well. Generally, due to the high thermal conductivity of the steel piping, the total thermal resistance of the casing is negligible compared to the rock's thermal resistance.  
As a result, the heat exchange coefficient \(k_{w}\) can be correctly determined as follows [22]:  
\[\frac{1}{k_{w}}=\frac{2r_{c}}{2\lambda_{s}}In\frac{4\sqrt{a_{s}t}}{2r_{w}}+ \frac{1}{h_{f}} \tag{11}\]  
where \(r_{c}=r_{w}\) as the thickness of the external tube is negligible.