esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Parsing Log Data  
Logs are textual data in semi-structured form. Each log event conforms to a strict syntax. For example, Blue Gene/L (BGL) logs [36] are composed of a header and a descriptive message. We distinguish between the following types of header parameters in the BGL dataset: Label, Timestamp, Date, Node, Time, NodeRepeat, Type, Component, Level. They store information about the log and take strictly defined values, e.g., timestamp in UNIX format or log level as an element from the set of all log levels (FATAL, FAILURE, SEVERE, ERROR, WARNING, INFO). The header is followed by a message that describes precisely the information contained in a given log entry and does not have a strictly defined syntax. Listing 1 provides an example log event. The log line starts with a header, consisting of various parameters, such as ID number, date, etc., which is followed by a descriptive message.  
Before the parsing process, the header part of a log event is discarded. Variable parameter values in the header could adversely affect the parsing process, resulting in the generation of too many templates. Detecting anomalies in log parameter values is a task for a different classification model. After discarding the header, the parser in the  
Figure 1: Model architecture for detecting anomalies in log files.  
parsing process replaces the parameters within the message with the \(<*>\) token. Listing 2 represents the result of the parsing of an example log event presented in Listing 1.  
**Listing 2.** Example log template.  
CE <*>, at <*>, mask <*>  
In our experiments, we used IBM's Python 3 implementation of the Drain parser [37], which makes use of a fixed depth tree to parse logs in a streaming and timely manner. In the parsing process, Drain creates lists of template groups that are located in the leaves of the parsing tree. Each group contains a log template and a list of log entry identifiers that have been assigned to it. The other nodes of the tree, including the parent node, contain well-defined rules according to which the process of finding the appropriate group for a given log entry is performed. One such rule is to search by the number of tokens in a given log. Another rule is based on the assumption that initial tokens in log entries are more often fixed character strings, rather than parameters.  
After assigning the appropriate path based on the initial tokens, the log goes to the leaf, where the parser selects the appropriate group for the log. This selection is made based on a metric designed to determine which template contains the most tokens in common with the log being parsed. Finally, the log is assigned to the group containing the template for which the metric score was the largest and the metric score exceeded the threshold value specified in the parser configuration. A new template is created if none of the groups contain a template for which the metric value has exceeded the similarity threshold.
## 3 Results and Discussion  
### g-C\({}_{3}\)N\({}_{4}\) Characterisation  
Synthesized g-C\({}_{3}\)N\({}_{4}\) powder and melamine precursor were characterized in terms of structure and morphology (Supplementary Materials, Figures S2-S7). A UV-Vis spectrum was acquired to characterize the melamine precursor before synthesis to g-C\({}_{3}\)N\({}_{4}\), and the spectrum shows significant absorbance only in the UV region (\(<\)275 nm), as seen in Figure S2. After the melamine synthesis, a powder-like morphology with agglomerated particles under 100 nm in size was observed in the SEM analysis, and the presence of only C and N elements was confirmed from corresponding EDXS spectra (Figure S3). In the FTIR (Figure S4), the peaks that appear in the 1200-1700 cm\({}^{-1}\) region are related to the stretching modes of C=N and C-N hetero-cycles, and the peaks at 875 and 804 cm\({}^{-1}\) indicate the presence of the stretching mode of the triazine ring [51]. Micro-Raman analysis proves the presence of g-C\({}_{3}\)N\({}_{4}\) from the peaks at 211, 359, 471, 588, 706, 768, 984, 1117, 1151, 1232, and 1312 cm\({}^{-1}\), respectively (Figure S5 and Table S1). Also, the peaks 1360 cm\({}^{-1}\) (D band) and 1580 cm\({}^{-1}\) (G band) confirm the formation of C-N [52; 53]. XRD analysis further confirmed the presence of the graphitic-like phase of C\({}_{3}\)N\({}_{4}\) lattice system given by the (002) peak (Figure S6). An increase in UV absorbance was observed in the 400-500 nm range with a maximum at 400 nm, and the Tauc plot demonstrates that the direct bandgap of the synthesized g-C\({}_{3}\)N\({}_{4}\) powder is 2.92 eV (Figure S7).
## 3 Results
### General Characteristics of Studies
#### 3.2.1 Correlations Between Gaze Behavior and Shooting Performance
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gaze Behavior** & **Elite vs. Near-Elite [95\% CI], \(p\)-Value** & **Data Analysis Results** & **Systematic Review Results** \\ \hline QE Duration & 2.55 [0.15, 1.01], \(p\) = 0.01 & \begin{tabular}{c} Elite athletes exhibited \\ significantly longer QE \\ durations, strongly linked \\ to higher SA (\(p\) = 0.0143). \\ \end{tabular} & \begin{tabular}{c} Younger players (\textless{}18 years) had \\ greater variability in QE \\ durations. Adult: The same as \\ the data analysis results. \\ \end{tabular} \\ \hline FFD & 1.56 [\(-\)0.10, 0.97], \(p\) = 0.12 & \begin{tabular}{c} Positive SA correlation, but \\ no significant difference \\ between competition \\ levels. \\ \end{tabular} & \begin{tabular}{c} Younger players (\textless{}18 years) \\ showed greater variability in \\ FFD. Adult: The same as the \\ data analysis results. \\ \end{tabular} \\ \hline FD & 1.35 [\(-\)0.44, 2.41], \(p\) = 0.18 & \
\begin{tabular}{c} No significant relationship \\ with SA or \\ competition-level \\ differences. \\ \end{tabular} &
\begin{tabular}{c} More experienced players \\ exhibited longer FD, reflecting \\ more advanced SA. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Summary of gaze behavior correlations at different competition levels. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Metric** & **Number** & **Effect** & **z-Value** & \(p\)**-Value** & **95\% CI** & **I\({}^{2}\) (\%)** & **Q-Value** & **\(p\)-Value for** \\  & **of Studies** & **Size** & **z-Value** & \(p\)**-Value** & **95\% CI** & **I\({}^{2}\) (\%)** & **Q-Value** & **Heterogeneity** \\ \hline QE Duration & 5 & 0.58 & 2.55 & 0.01 * & 0.15, 1.01 & 0.0\% & 0.00 & 0.4471 \\ FFD & 3 & 0.43 & 1.56 & 0.12 & \(-\)0.10, 0.97 & 0.0\% & 0.00 & 0.7860 \\ FD & 4 & 0.98 & 1.35 & 0.18 & \(-\)0.44, 2.41 & 93.0\% & 1.9605 & \textless{}0.0001 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Meta-analysis results comparing elite and near-elite athletes in terms of QE duration, FFD, and FD. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Estimate** & **Std. Error** & **z-Value** & \(p\)**-Value** & **95\% CI** \\ \hline Intercept & \(-\)2.0446 & 0.9083 & \(-\)2.251 & 0.0244 * & \(-\)3.8249, \(-\)0.2644 \\ QE duration on SA & 0.0034 & 0.0014 & 2.4507 & 0.0143 * & 0.0007, 0.0060 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Meta-regression results data of QE duration on SA at different competition levels.
# Proposed Fuzzy-NN Algorithm with LoRa Communication Protocol for Clustered Irrigation Systems
## 4 Experimentation and Evaluation Results  
In order to test and validate the FITRA irrigation algorithm, the authors constructed two similar independent OWS systems, one using the MVA algorithm, and another using the FITRA algorithm. Both systems utilize 8 ground moisture sensors that have been scattered into two neighboring fields of 100 m\({}^{2}\) accordingly. The position of the sensors relevant to the actuator was almost identical for both systems. The high-level architecture of the two OWS systems was similar to that of Figure 2. The only distinction of the two irrigation systems was the decision algorithm that controls the actuator. In the first system the decision algorithm was based on the majority algorithm (MVA), while in the second system used the proposed by the authors' fuzzy state and then NN state of the FITRA algorithm. The thresholds (_Athress_) for the soil moisture sensors were set to 700, since the sensor produced values between 130 and 1023. The 1023 value corresponds to total dry ground while the 130 value corresponds to muddy ground. The threshold 700 (\(\approx\)2/3 \(\times\) 1023), was chosen and the plantations used was that of tomatoes using the same tomato variety (Belladona). The authors used 100 tomato  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Frame Count** & **Av. RTT** \\ \hline GPRS & 4850 & 420 ms \\ LoRa & 26,580 & 580 ms \\ \hline \hline \end{tabular}
\end{table}
Table 3: FITRA Communication Protocol Performance results over GPRS and LoRa OWS system.  
plants per field, using sparse planting of 60 cm horizontal plant distance 120 cm vertical row distance (statistically 1 plant per square meter). Values from the ground moisture sensors, actuator states and water consumption has been monitored and stored to a MySQL database at the Application Server. The experiment lasted 150 days (March-July 2017). The first four months, March-June, used the fuzzy FITRA algorithm, while the other month (July) used the neural network FITRA algorithm implementation, with the March month dataset as the training dataset.
# Improved Cryptanalysis of Some RSA Variants
## 1 Introduction
### Our Contribution  
In the attack proposed by [PERSON] et al. [13], the key strategy for breaking the schemes with the equation  
\[ed\equiv 1\pmod{(p^{2}-1)(q^{2}-1)},\]  
involves transforming the key equation into a modular polynomial equation of the form  
\[x_{1}(x_{2}^{2}+ax_{2}+b)\equiv-1\pmod{e}.\]  
By solving this equation, the extracted solution satisfies  
\[(x_{1},x_{2})=(k,p+q-\bar{p}-\bar{q}),\]  
where \(\bar{p}\) and \(\bar{q}\) are approximations of the primes \(p\) and \(q\), respectively. This enabled them to break the schemes in some scenarios.  
In this paper, we extend the work of [PERSON] et al. (1998) by transforming the extended key equation  
\[eu\equiv z\pmod{(p^{2}-1)(q^{2}-1)},\]  
into the generalized modular equation  
\[x_{1}(x_{2}^{2}+ax_{2}+b)\equiv x_{3}\pmod{e}.\]  
We demonstrate how the solution of this modular equation can be applied to attack the RSA variants characterized by the key equation \(ed\equiv 1\pmod{(p^{2}-1)(q^{2}-1)}\), whenever two parameters \(u\) and \(z\) exist such that \(eu\equiv z\pmod{(p^{2}-1)(q^{2}-1)}\), and are sufficiently small, in addition to a known approximation of one of the RSA prime factors.  
Notice that the equation \(eu\equiv z\pmod{(p^{2}-1)(q^{2}-1)}\) is a generalization of the standard key equation \(ed\equiv 1\pmod{(p^{2}-1)(q^{2}-1)}\), and the private exponent \(d\) and \(u\) are related by the formula  
\[u\equiv dz\pmod{(p^{2}-1)(q^{2}-1)}.\]  
As a byproduct, we design a polynomial-time algorithm for attacking the mentioned schemes for a significantly broader range of public exponents compared to existing methods.
# The Gradient and the Hessian of the Distance between Point and Triangle in 3D
## 1 Introduction  
The algorithm developed by [PERSON] (1965) evaluates the distance function between a point and a triangle in 3D. The input parameters are coordinates of four points, which are involved in the point-triangle setup. Three points are vertices of the triangle, with the remaining point being the one to which the distance is computed. The motivation for this work is the need to obtain the gradient and the Hessian of that distance with respect to the input variables. Each of the four input points possesses three coordinates, giving 12 independent variables for the distance function. The distance function, accordingly, has 12 first derivatives and 12 \(\times\) 12 second derivatives, the components of the symmetric Hessian matrix.  
Our approach is to differentiate the distance function provided by [PERSON] (1965) and extend that algorithm with evaluation of first and second derivatives. The characteristics of the algorithm remain the same, i.e., the time and space complexity of the algorithm is O(1). A linear array of first derivatives and a 2D array of second derivatives are added to the output. The main contribution of the authors is the derivation of the expressions for the gradient and the Hessian of the distance. A potential application of this algorithm is in finding penetration penalty forces and their differentials for colliding polygonal objects in finite element (FE) simulations.  
Numerical simulations of mechanical systems often involve contact interactions, and the penalty method is a common way of addressing this problem [PERSON] (1965). Implicit FE approaches rely on calculating derivatives of the forces generated in collisions, which in turn require first and second derivatives of the distance function. The FE simulation ARCSim Arcsim (1992) relies on penalty contact resolution for modeling deformation and fracture. ARCSim includes an approximation technique for obtaining the gradient and the Hessian of the distance function based on the surface normal of the interacting object. The approximation is not always accurate, which results in reduction of time steps, which sometimes halts the simulation.  
In another method described by [PERSON] and [PERSON] [PERSON] and [PERSON] (1965), the distance from the interior point to the surface of the object is precomputed on the nodes of interior polygonal mesh, representing a distancefield. This discretized field allows one to estimate the spacial gradient of the distance. The accuracy is limited by the resolution of the mesh. In general, such approximation techniques are inaccurate, and there is a need for developing and utilizing the exact formula.
## 4 Test Scenario: AC Signal Analysis of the Sum of 4 Sines with Arbitrary Frequencies
### Discussion  
The choice of the platform and tool comes first. The hardware-accelerated implementation not only speeds up but also enables the application of sophisticated methods such as sine fitting in both linear and non-linear methods. In general, vectorizing the results results in a faster execution but more memory consumption.  
A comparison of the methods on an empirical scale is depicted in Figure 8. In this graph, the experimental data from the previous section from CMSIS-STM32 hardware acceleration are collected and are scaled with the help of \(log(1+x)\). In this graph, smaller bars are better. The most accurate solution is sine-fit in both amplitude and phase accuracy. However, they suffer from long runtime. In this study, the linear least-squares sine-fit was deemed sufficient and enough to provide good results in a reasonable runtime. Yet, both sine-fit methods require expansive memory allocation that may not be available in all the microcontrollers. Therefore, FFT or Goerzel, both with correction are all-rounder solutions, could be used as an alternative, as they are both fast and memory-saving and have a good amplitude and phase accuracy. Alternatively, when phase accuracy is the most desired, DTFT could be used instead.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\boldsymbol{\varphi}\) & **0.0000** & **0.7854** & **0.5236** & **1.5708** \\ \hline DFT & 0.1900 & 1.5867 & \(-0.7761\) & 1.0036 \\ DFT (w. correction) & 0.0099 & 0.8009 & 0.7393 & 1.6785 \\ DTFT & 0.0118 & 0.7568 & 0.4602 & 1.5628 \\ X-corr & \(-0.0000\) & \(-2.3876\) & \(-2.6389\) & 1.5959 \\ Sine-fit & 0.0000 & 0.7854 & 0.5236 & 1.5708 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Phase results returned by AC signal analysis algorithms for the considered test scenario.  
Figure 7: Amplitude spectrum using DFT on the test scenario.
_Applied_  
_sciences_  
_Article_  
_Solving Backward Heat Conduction Problems_  
_Using a Novel Space-Time Radial Polynomial_  
_Basis Function Collocation Method_  
**[PERSON] 1,2,3**(r)**, **[PERSON] 3,**(r)**, **[PERSON] 3**(r)** and **[PERSON] 2**  
1 Center of Excellence for Ocean Engineering, National Taiwan Ocean University, Keelung City 20224, Taiwan; chkst266 mail.ntrou.edu.tw  
2 Center for Occupational Safety and Health, National Taiwan Ocean University, Keelung City 20224, Taiwan; [EMAIL_ADDRESS]  
3 Department of Harbor and River Engineering, National Taiwan Ocean University, Keelung City 20224, Taiwan; [EMAIL_ADDRESS]  
4 Correspondence: [EMAIL_ADDRESS]  
Received: 30 March 2020; Accepted: 1 May 2020; Published: 5 May 2020  
**Featured Application: The novel space-time radial polynomial basis function collocation method is first proposed for solving the backward heat conduction problems in this study. The proposed method can be applied to inverse problems with remarkably high accuracy; even severely ill-posed inverse problems under large noises are considered.**  
**Abstract:** In this article, a novel meshless method using space-time radial polynomial basis function (SRBPBF) for solving backward heat conduction problems is proposed. The SRBPF is constructed by incorporating time-dependent exponential function into the radial polynomial basis function. Different from the conventional radial basis function (RBF) collocation method that applies the RBF at each center point coinciding with the inner point, an innovative source collocation scheme using the sources instead of the centers is first developed for the proposed method. The randomly unstructured source, boundary, and inner points are collocated in the space-time domain, where both boundary as well as initial data may be regarded as space-time boundary conditions. The backward heat conduction problem is converted into an inverse boundary value problem such that the conventional time-marching scheme is not required. Because the SRBPF is infinitely differentiable and the corresponding derivative is a nonsingular and smooth function, solutions can be approximated by applying the SRBPF without the shape parameter. Numerical examples including the direct and backward heat conduction problems are conducted. Results show that more accurate numerical solutions than those of the conventional methods are obtained. Additionally, it is found that the error does not propagate with time such that absent temperature on the inaccessible boundaries can be recovered with high accuracy.  
**Keywords:** collocation method; space-time; radial polynomial; basis function; heat conduction
## 1 Introduction
As a consequence, the present dataset of Swarm LP \(T_{\rm e}\) measurements is that large to cover different latitudes, local times, seasons, and also solar activity levels. Contrary to the electron density, \(T_{\rm e}\) data have not been extensively exploited so far; only a few and rather narrowly focused studies have been published, such as [17; 24]. In this context, we aimed to investigate the main climatological trends of \(T_{\rm e}\) in the topside ionosphere by taking advantage of seven full years (from the beginning of 2014 to the end of 2020) of Swarm \(T_{\rm e}\) observations by comparing the corresponding spatial, diurnal, and seasonal trends with those modeled by IRI. Recently, to improve the accuracy of Swarm \(T_{\rm e}\) data, [PERSON] et al. [24] proposed a correction to observations, based on the comparison with three ISRs located at different latitudes. In this study, we applied the Lomidze correction to Swarm data to verify its performance when compared against IRI-modeled \(T_{\rm e}\) data and \(T_{\rm e}\) data observed by Jicamarca (12.0\({}^{\circ}\)S, 76.8\({}^{\circ}\)W), Arecibo (18.2\({}^{\circ}\)N, 66.4\({}^{\circ}\)W), and Millstone Hill (42.6\({}^{\circ}\)N, 71.5\({}^{\circ}\)W) ISRs over the last decades. The paper is organized as follows. In Section 2, the Swarm and ISRs \(T_{\rm e}\) datasets, along with the IRI \(T_{\rm e}\) model and the Lomidze correction, are discussed. In Section 3, an overall comparison between Swarm-measured (original and after applying the Lomidze correction) and IRI-modeled \(T_{\rm e}\) values is first given based on the entire dataset composed by the three Swarm satellites; in addition, Swarm B and IRI \(T_{\rm e}\) data are climatologically compared with the corresponding values recorded by the Jicamarca, Arecibo, and Millstone Hill ISRs. In the same section, we also describe and discuss the geographic, diurnal, and seasonal trends shown by both Swarm B and IRI data. Section 4 is devoted to the final discussion of our results and is supported by the conditioned probability density function analysis of Swarm-measured and IRI-modeled data. The conclusions and the future developments are the subject of Section 5.
# Adaptive Allocation Algorithm for Multi-Radio Multi-Channel Wireless Mesh Networks
## 1 Introduction
This is the motivation for our development of an adaptive channel allocation (ACA) in this paper. Our objective in this paper is to propose a new adaptive channel allocation algorithm for a multi-radio, multi-channel wireless mesh network with a practical constraint. The practical constraint is on the number of network interface cards (NICs) that can be installed per AP. In this algorithm, we characterize a decision function for the channel re-allocation. Our algorithm is separated into two phases: a static assignment phase and an adaptive assignment phase. In the static (initial) phase, an appropriate, fixed number of network interface cards (NICs) with appropriate channels are assigned to APs, utilizing the connection with the most extreme number of hosts related to each AP, such that the performance is maximized while the maximum number of hosts per AP is minimized. In the adaptive phase, the decision function chooses whether the channel re-allocation must be performed or not, guided by the traffic balance between the links to the GW. The decision function is also intended to minimize the number of channel reassignments to reduce the turn off time of the network tasks during channel re-allocation methods. The effectiveness of our proposed algorithm was verified through Network Simulator 2 (NS-2) simulations. We used our modified 802.11n module on the NS-2 version 2.34 presented in [13]. This paper is organized as follows, In Section 2, we present some of the related work for dynamic channel allocation techniques. Section 3 contains an overview of a mathematical formulation of the adaptive channel allocation problem. In Section 4, we introduce the proposed ACA algorithm. In Section 5, we discuss the simulations and results. Finally, the conclusion is presented in Section 6.