esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodologies  
### Manifold Learning for Low-Dimensional Feature Extraction  
As introduced in Section 1, ML, aiming to explore the intrinsic geometry of dataset, has proven its effectiveness in nonlinear reduction dimensionality. Recently, various ML methods have been developed, e.g., isometric mapping (Isomap) [21], locally linear embedding (LLE) [33], Laplacian eigenmaps (LE) [34], Hessian eigenmaps (HE) [35], and local tangent space alignment (LTSA) [36]. Compared with these approaches, the computation of the Isomap is the simplest, with only one parameter to be determined. Hence, the Isomap was applied for the manufacturing multi-parameter analysis in this paper. Below the steps of the algorithm are described as follows [21]:  
Let \(\mathbf{X}=[\mathbf{x}_{1},\,\mathbf{x}_{2},\,\ldots,\,\mathbf{x}_{n}]\) denotes the manufacturing multi-parameter set with dimension \(n\), and \(\mathbf{Y}=[\mathbf{y}_{1},\,\mathbf{y}_{2},\,\ldots,\,\mathbf{y}_{m}]\) denotes the low-dimensional space with dimension \(m\).  
Step 1. Construct the neighborhood relationships. When the Euclidean distance \(d(\mathbf{x}_{i},\,\mathbf{x}_{j})\) between \(\mathbf{x}_{i}\) and \(\mathbf{x}_{j}\) is closer than the radius \(\varepsilon\), or \(\mathbf{x}_{i}\) is one of the \(K\)-nearest neighbors of \(\mathbf{x}_{j}\), \(\mathbf{x}_{i}\) and \(\mathbf{x}_{j}\) can be regarded as neighbors.  
Step 2. Compute the shortest distances. Define the graph, \(G\), over all data points by connecting \(d(\mathbf{x}_{i},\,\mathbf{x}_{j})\). If \(\mathbf{x}_{i}\) and \(\mathbf{x}_{j}\) are neighbors, \(d_{G}(\mathbf{x}_{i},\,\mathbf{x}_{j})=d(\mathbf{x}_{i},\,\mathbf{x}_{j})\), otherwise, \(d_{G}(\mathbf{x}_{i},\,\mathbf{x}_{j})=\infty\). Evaluate the geodesic distances, \(\mathbf{D}_{G}\), between all pairs of data points by computing their shortest path distances, \(d_{G}(\mathbf{x}_{i},\,\mathbf{x}_{j})\), using [PERSON]'s algorithm [37].  
Step 3. Construct \(m\)-dimensional embedding. This step is realized through the classical multidimensional scaling method, as follows: Let \(\lambda_{p}\) be the \(p\)-th eigenvalue (in decreasing order) of the matrix \(\tau(\mathbf{D}_{G})=\) -**HSH**/2 (**S** is the matrix of squared distances \(\mathbf{S}_{\bar{\eta}j}=[d_{G}(\mathbf{x}_{i},\,\mathbf{x}_{j})]^{2}\), and \(\mathbf{H}=\) **I**-(1/N)**EE**T is the centering matrix, where **I** means unit matrix with \(N\) dimension (length of \(\mathbf{x}_{i}\) or \(\mathbf{x}_{j}\)), and **E** denotes the vector of all ones), and \(\mathbf{v}_{p}{}^{i}\) is the \(i\)-th component of the \(p\)-th eigenvector. Then, set the \(p\)-th component of the \(m\)-dimensional coordinate vector \(\mathbf{y}_{i}=(\lambda_{p}\,\,\mathbf{v}_{p}{}^{i})^{0.5}\).  
Based on the description above, the radius, \(\varepsilon\), or the number of neighbors, \(K\), is the only parameter in the Isomap algorithm. In this paper, the \(K\)-Isomap was applied for dimension reduction. To our knowledge, there is no general theory for determining the \(K\) (a common-used approach is empirical method). In addition, the instinct low dimension, \(m\), was specified by the maximum likelihood estimator (MLE) [38].
# Quantum Key Distribution in the Presence of the Intercept-Resend with Faked States Attack
###### Abstract  
Despite the unconditionally secure theory of the Quantum Key Distribution (\(QKD\)), several attacks have been successfully implemented against commercial \(QKD\) systems. Those systems have exhibited some flaws, as the secret key rate of corresponding protocols remains unaltered, while the eavesdropper obtains the entire secret key. We propose the negative acknowledgment state quantum key distribution protocol as a novel protocol capable of detecting the eavesdropping activity of the Intercept Resend with Faked Sates (\(IRFS\)) attack without requiring additional optical components different from the \(B\)B84 protocol because the system can be implemented as a high software module. In this approach, the transmitter interleaves pairs of quantum states, referred to here as parallel and orthogonal states, while the receiver uses active basis selection.  
b 2016; 23 December 2016
###### Abstract  
Fingerprint analysis has long been a cornerstone in criminal investigations for suspect identification. Beyond this conventional role, recent efforts have aimed to extract additional demographic information from fingerprints, such as gender, age, and nationality. While studies have demonstrated promising accuracy in gender classification based on fingerprints, practical implementation faces challenges, including the often low quality of crime scene fingerprints. This study presents a pioneering comparison of gender classification across diverse datasets, considering variations in fingerprint image quality. We examine the results from four databases, encompassing both public and private sources, employing state-of-the-art Data-Centric AI (DCAI) approaches for enhanced classification. Our findings reveal that a conservative Convolutional Neural Network (CNN)--specifically VGG--proves effective, achieving an accuracy ranging from 70% to 95% based on fingerprint quality. DCAI methods contribute a noteworthy 1-4% improvement. Notably, for partial or low-quality fingerprints, the periphery emerges as a critical determinant of gender classification. This study contributes insights into practical gender classification from fingerprints, emphasizing the significance of the fingerprint periphery. Furthermore, we provide the source code for future research and accessibility in real-world applications.  
Keywords:fingerprint analysis; gender classification; Data-Centric AI (DCAI); forensic science; partial or low-quality fingerprint images +
Footnote †: journal: _Applied Sciences_
## 2 Methodology
### Numerical Simulation  
#### 2.3.1 CFD Model  
CFD has been widely recognized as an independent research method due to its low cost compared with experimental measurements [40; 41]. In this study, a CFD model based on the commercial program FLUENT was used to perform the simulation, which was validated by experimental measurements. The built model was based on the environment chamber used in the experiments, as shown in Figure 4. The room layout and geometric dimensions of the CFD model were identical to those of the chamber.  
Figure 4: Computational model (unit: mm).  
Appropriate boundary conditions are essential to ensure the accuracy of the CFD numerical simulation results. The supply air inlets and exhaust outlets were set as the velocity inlet boundary and outflow boundary conditions, respectively. The indoor heat sources, including thermal manikins and ceiling lamps, were set as constant heat flux boundary conditions. The envelope was defined as a constant wall temperature. The expression of the governing equations and the boundary condition settings for the numerical simulations are presented in Tables 3 and 4, respectively.  
The standard \(k\)-\(\varepsilon\), RNG \(k\)-\(\varepsilon\), and SST \(k\)-\(\omega\) models perform well in predicting the distributions of air velocity and temperature, according to previous studies [42]. The standard \(k\)-\(\varepsilon\) and RNG \(k\)-\(\varepsilon\) models used the standard wall function. The SST \(k\)-\(\omega\) model solved throughout the near-wall flows [43]. In this study, the prediction accuracies of the three turbulence models were compared, and the model with the highest accuracy was used for the next simulation calculation. The discrete ordinate (DO) model was used to calculate the heat transfer between different wall surfaces (walls, windows, human body surfaces, and lamps). The Boussinesq model was used to consider the buoyancy effect [44]. A second-order upwind scheme was used to discretize the momentum, energy, and turbulence equations. SIMPLE was used as the pressure-velocity coupling method. The energy-scaled residuals and radiation intensity residuals were set to \(1\times 10^{-6}\), and the other scale residuals were set to \(1\times 10^{-4}\).
# On the Safe Deployment of Matrix Multiplication in Massively Parallel Safety-Related Systems
###### Abstract
However, safety standards require not only guaranteeing that outputs are correct but also that internal components are periodically diagnosed to detect random hardware errors and reduce the probability of latent errors. Hence, existing built-in hardware safety mechanisms shall be complemented with software-based diagnosis mechanisms whenever the required diagnostic coverage is not achieved. This paper paves the way towards the safe implementation of CNN-based safety solutions on massively parallel GPU-based platforms through the application of error-detection mechanisms using execution signatures (ESs), which also support an indirect functional diagnosis of used GPU components (without the need of component design and implementation details knowledge). To this end, we focus on the most computationally expensive operation of the CNNs, the matrix-matrix multiplication (MMM), that is the central part of machine learning libraries such as YOLOv3, accounting for 67% of YOLO's execution time [12]. In this way, and focusing on the CUTLASS library [13], we make the following contributions:  
* We define and implement a catalog of diagnostic mechanisms to compute an array of ESs in a matrix-matrix multiplication defined in CUTLASS for GPUs. Additionally, we define two commonly used architectural safety patterns where this catalog can be implemented to support fault-tolerance;
* We analyze the performance impact and the diagnostic coverage (DC) of this catalog of mechanisms for multiple matrix dimensions in a NVIDIA Jetson Xavier NX GPU and  
Figure 1: Example of a single-bit error impact in an object detection application based on CNNs. (**a**) Error-free or single-bit error (bit 705) inference; (**b**) Single-bit error (bit 734) inference. compare our results against sequential and advanced vector extensions (AVX)-based implementations [14];
* We perform an in-depth analysis of the tradeoff between DC and performance for a specific matrix dimension evaluating the required achievable DC by the IEC 61508 standard for different safety integrity levels (SILs) and architectural patterns. The rest of the paper is structured as follows: Section 2 presents the required background. Section 3 describes the error-detection adaptations for the matrix-matrix multiplication. Section 4 describes and analyzes the results and makes a comprehensive analysis. Section 5 discusses related work. Finally, Section 6 concludes the paper and presents directions for future research.
## 3 Materials and Methods
### Data Preprocessing  
In the context of our study, data normalization is employed to scale features to a common range between 0 and 1 or \(-1\) and 1, which make it simpler to compare and evaluate the data.  
Different normalization techniques can be applied, including min-max scaling, z-score normalization, and log transformation [28].  
Z-score normalization is employed to transform each instance of the original data xi into \(x_{i}^{\prime}\) using the following equation:  
\[x_{i}^{\prime}=\frac{x_{i}-\mu}{\sigma} \tag{2}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature Name** & **Feature Description** \\ \hline CTRLPackets & AODV routing control packets: a black hole node may advertise a fake and optimized route to the destination. Thus, it is essential to keep track of the routing control packets and detect any changes in their number advertised by a node. \\ \hline CountRREQ & Number of Route Request messages that are used by the nodes to discover new routes to other nodes in the network. In the case of a black hole attack, the malicious node may not generate any RREQ messages because it is not interested in receiving any packets, and instead, it drops all the packets that it receives. \\ \hline CountRREP & Number of Route Reply messages that are sent by nodes in response to RREQ messages to establish a route to the destination node. In the case of a black hole attack, the black hole sends fake RREP; if a large number of RREP messages are received from a single node, it could be an indication that the node is a potential black hole. \\ \hline CountRERR & Number of Route Error (RERR) Messages: a black hole node may generate an abnormal amount of RERR messages, indicating that the node is not following the protocol’s correct path discovery mechanism. \\ \hline Throughput & Throughput measures the amount of data that can be transferred in a given time. A sudden decrease in throughput can be an indication of a black hole attack. \\ \hline SentPckts & The number of sent packets. The attacker node captures and drops all routing packets, leading to a significant decrease in the number of successfully sent packets. \\ \hline ReceivedPckts & The number of received packets. The rogue node selectively drops all the data packets received from other nodes, which may cause a decrease in the number of bytes that are received. \\ \hline DroppingRatio & The dropping ratio measures the percentage of packets dropped by a node, and this feature can be used to detect if a node is dropping a higher number of packets than normal. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Relevant features for black hole attack detection.  
where \(\mu\) and \(\sigma\) denote the mean and standard deviation, respectively.
# Quality Assessment of DSMs Produced from UAV Flights Georeferenced with On-Board RTK Positioning
## 4 Discussion
### DSM Repeatability and Accuracy
However, as the depth map was strongly filtered, some smoothness of the DSMs could be expected. In addition, the meadows at the end of a dry summer did not yet recover from a recent cut, so the grass was not well developed. The assessment of the DSM accuracy on roads, car parks, and meadows (Figure 7) shows that the average RMSE is very similar when using a traditional ground control or AAT with at least one GCP: 5.2 cm in the former case, and 5.6 cm in the latter. In other words, the two control configurations have a very similar accuracy potential under the experiment settings (about two GSD). Overall, the DSM RMSE with the **RTK** configuration is 8.5 cm. These results compare well with those obtained by [PERSON] et al. (2016) with an eBee RTK on 180 checkpoints. From a flight in the **RTK** configuration, they found a 5 cm-resolution DSM in non-vegetated areas, with a RMSE in elevation of 8.9 cm, against 3.0 cm with traditional georeferencing with GCPs. They therefore concluded that AAT is not appropriate to projects requiring high vertical accuracy. Indeed, if self-calibration is employed with camera stations only, relatively large height errors can be expected with high probability, due to bias in the estimation of the camera principal distance. However, the results found with the **RTK+IGCP** configuration mitigate such conclusions to some extent.
## 6 Strong Wind Analysis in the Best Parameter Configuration  
The issue of high wind is vast, and there are many interpretations of this definition. According to the currently applicable Enhanced Fujita scale [36; 37; 38; 39], we can define the wind category depending on the wind speed. Using the same scale, you can also predict the damage that may occur. The author of the scale divides the wind into groups from EF0 to EF5, assigning each a range of speeds that the wind must reach. Depending on the interpretation, cases from EF1 or EF2 can be considered heavy winds. Three speeds were adopted for further analysis:  
1. 38 m/s (138 km/h) above which the wind can be classified as EF1--case C21;
2. 50 m/s (178 km/h) above which the wind can be classified as EF2--case C22;
3. 60 m/s (218 km/h) above which the wind can be classified as EF3--case C23.  
Based on the previously obtained values presented in Tables 3 and 4, case C16 was selected as credible. The model used in the calculations of the three high wind cases is identical to case C16, therefore the stress values from all of them will be compared with it in Table 5.  
The extreme values (max--positive and min--negative) characterizing the wind pressure are presented in detail in the graphs in Figure 12.  
Figure 12: The extreme values characterizing the wind pressure [Pa] graphically presented using charts: (a) max—positive, (b) min—negative.  
\begin{table}
\begin{tabular}{c c|c c c c c c c} \hline \hline
**Case’Point** & **Velocity [m/s]** & **A** & **B** & **C** & **D** & **E** & **F** & **G** & **H** \\ \hline C16 & 22 & \(-54.4\) & \(-34.9\) & \(-108.9\) & \(-81.7\) & \(-11.5\) & \(-256.0\) & \(-276.3\) & \(-173.6\) \\ \hline C21 & 38 & \(-137.9\) & \(-49.7\) & \(-235.4\) & \(-234.8\) & \(-52.1\) & \(-851.7\) & \(-898.4\) & \(-577.8\) \\ \hline C22 & 50 & \(-200.5\) & \(-46.3\) & \(-583.7\) & \(-450.1\) & \(-96.1\) & \(-1516.0\) & \(-1536.2\) & \(-1073.4\) \\ \hline C23 & 60 & \(-361.5\) & \(-280.4\) & \(-698.5\) & \(-614.9\) & \(-155.9\) & \(-2245.1\) & \(-2258.5\) & \(-1603.6\) \\ \hline Case,Point & 1 & 1 & K & L & M & N & O & Max & Min \\ \hline C16 & \(-154.7\) & \(-112.6\) & \(-125.5\) & \(-133.2\) & \(-131.3\) & \(-111.9\) & \(-92.9\) & \(320.9\) & \(-583.7\) \\ \hline C21 & \(-504.0\) & \(-421.0\) & \(-344.8\) & \(-392.9\) & \(-378.1\) & \(-340.1\) & \(-334.5\) & \(973.6\) & \(-1947.5\) \\ \hline C22 & \(-908.5\) & \(-830.0\) & \(-572.7\) & \(-575.4\) & \(-604.4\) & \(-651.6\) & \(-704.3\) & \(1701.8\) & \(-3492.1\) \\ \hline C23 & \(-1303.0\) & \(-1340.2\) & \(-726.7\) & \(-767.5\) & \(-918.7\) & \(-890.8\) & \(-932.7\) & \(2445.9\) & \(-5177.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Stress results depending on wind speed. Comparison of models C21, C22, C23 with the standard case C16.
# Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs
## 4 Linear Programming-Based Mathematical Formulation
### Throughput Maximization Using Linear Programming  
The objective function is formulated to maximize the network throughput considering the minimization of energy consumption. In our proposed schemes (2 hop-AHH-VBF and QF-AHH-VBF), data packets are relayed using multi-hoping mechanism. The network throughput is the total number of data packets received successfully at the sink. Considering link quality for successful transmission, \(\delta\) is defined as the threshold value for the neighbor number of a sender node. The link quality check is to ensure successful packet delivery. Moreover, the energy required for transmitting a packet should be less than the residual energy of a node participating in the forwarding process as stated in C1. The distance between a pair of sender and receiver nodes must be greater than \(D_{ij}^{min}\) and less than \(D_{ij}^{max}\) as in C4. All of these linear constraints are taken into consideration while formulating an objective function represented in Equation (33),  
\[Min\sum_{i=1}^{N}T_{p}(i);\ \ \ \ \forall\ i\ \in\ N \tag{33}\]  
\[Min\sum_{r=1}^{r_{max}}T_{p}\times P;\ \ \ \ \forall\ i\ \in\ N, \tag{34}\]  
such that:  
Figure 6: Feasible region for end-to-end delay minimization.  
C1:\(E_{TX,RX}\leq E_{r}\)  
C2:\(P_{link}\geq\delta\)  
C3:\(E_{TX,RX}\geq E_{th}\),  
where \(E_{th}\) is the residual energy required for transmission and reception:  
C4:\(0<D_{ij}\leq D_{ij}^{max}\). Feasible solution lies in the shaded region as can be seen in Figure 7.
## 4 Experiment Setup and Results
### Constant Force Control with ADRC  
This experiment is performed to validate the performance of the proposed system. The system response performance experiment is conducted under two different polishing forces. Figure 9 depicts the ADRC controller's force response at the desired force of 1 N without disturbance. The force control system attains the intended force of 1 N after 450 ms of adjustment time and oscillates around 1N. The average force is 1.01 N, with the highest inaccuracy of 0.1 N in force control. Figure 10 depicts the force response at the intended force of 2 N during the dynamic process with the disturbance caused by robot positioning inaccuracy at 14.3% magnetorheological moisture. The force rises in 910 ms from 0.5 N to 2 N without overshooting, has a peak error of less than 0.3 N, and has an average force of 1.98 N. Figure 11 depicts the force response at the desired force of 2N during the dynamic process with the disturbance caused by robot positioning error at 15.3% magnetorheological moisture. The time of force to increase from 0.5 N to 2 N is 735 ms with a 0.1 N overshoot. The force control peak error is less than 0.3 N, and the average force is 1.96 N. As shown in the results, the system provides a quick response to disturbance and stabilizes it.
# Chemical Compositional Changes in Over-Oxidized Fish Oils
## Discussion
### Comparison of Oxidation in Different Fish Oils  
While hoki liver oil is a good source of EPA and DHA, on a global scale, it is used in relatively small volumes compared to other fish oils. Therefore, the susceptibility to oxidation and associated chemical changes of the hoki liver oil were contrasted with the far more commonly used refined anchovy oil. In Condition B, the induction time dropped more gradually over time in hoki liver oil compared to the anchovy oil, which showed drastic decreases in the second half of the incubation. Nevertheless, the induction times attained in the end of the incubation were nearly identical, as were PV, p-AV, and TOTOX, indicating that over the longer period of time, both oils had lost antioxidant protection to the sustained condition of thermal oxidation in the dark (Condition B). The final TOTOX number of 117 meq O\({}_{2}\)/kg (Condition B) is four times larger than the voluntary industry limit of 26, again illustrating the marked rancidity achieved [18]. This result indicates that while at earlier time points the well-stabilized anchovy oil resisted thermal oxidation better than the antioxidant-free hoki liver oil, at longer periods of time, the anchovy oil also quickly lost antioxidant protection.  
In contrast, a marked difference in sensitivity to photo-oxidation at high oxygen tension (Condition A) was found between hoki liver and anchovy oil. The anchovy oil proved to be remarkably resistant throughout the 30-day period to this form of oxidative challenge, while the antioxidant-free hoki liver oil became rancid rapidly. While the total polyunsaturated fat content of both oils was comparable, the octoperol contents were vastly different. The high level of mixed octoperols present in anchovy oil likely support the delayed occurrence of free radical propagated lipid peroxidation as reflected in reduced primary and ensuing secondary oxidation, as seen in the profile of PV, p-AV, TOTOX, volatiles, and induction time, as compared to hoki liver oil where the antioxidant protection conferred by the naturally present \(\alpha\)-tocopherol is considerably lower. The oligomer level in anchovy oil remained below the 1.5% limit set by the European Pharmacopoeia [65], indicating that the oil remained relatively intact even after 30 days. When, after 30 days, the anchovy oil also started to succumb to the strong oxidative environment, octoperol levels had decreased by only \(\approx\)20%. The correct use of mixed octoperols is an example of how a refined fish oil, which in theory is highly susceptible to oxidation, can be stabilized appreciably through the appropriate use of antioxidants [17]. The level of transition metals was not determined but would provide further information on the sensitivity of the tested oils toward facilitating lipid peroxidation.