esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
The predicted parameters of the \(\Delta\)LogR method and the neural network method are summarized in Table 3.  
Calculated results of the \(\Delta\)logR model and neural network model were compared (Table 3). R\({}^{2}\) of the neural network model is closer to 1 and RMSE is closer to 0 compared to those of the \(\Delta\)logR model, indicating that calculated results of the neural network are closer to measured TOC. Moreover, the neural network shows the better TOC calculation efficiency in the level of mathematical indexes. According to practical calculation effects of TOC (Figure 11), the neural network model is more applicable to accurate calculation of TOC in the background of quick changing lithology. On the contrary, the \(\Delta\)logR model can only reflect the overall variation trend of TOC, but cannot depict detailed changes of TOC.  
Figure 10: TOC prediction of LT1 well based on the neural network method: (**a**) Ma 56-10 and (**b**) Ma 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **R\({}^{2}\)** & **MAE** & **MRE** & **RMSE** \\ \hline \(\Delta\)LogR method & 0.33 & 0.0671 & 33.02\% & 8.41\% \\ Neural network method & 0.83 & 0.0322 & 15.88\% & 4.30\% \\ \hline \end{tabular}
\end{table}
Table 3: Summary table of evaluation parameters.  
The \(\Delta\)logR method used only two kinds of logging information during identification of source rocks. However, GR logging and DEN logging also can reflect information of source rock, which are not used in the \(\Delta\)logR method. This might be one of the reasons for the poor application effects of the \(\Delta\)logR method in the study area. According to previous analysis of correlation between the measured TOC and logging curves, it also found that the correlation between TOC of source rocks and resistivity in the study area is extremely small with almost no correlations. This might be another reason of poor application effects of the \(\Delta\)logR method in the study area. The \(\Delta\)logR method usually has good application effects in regions with stable lithology. In the study area, there's complicated and quick changing lithology, which also leads to the poor application effect of the \(\Delta\)logR method in this study area.  
According to research results in this study, neural network is significantly superior to the traditional \(\Delta\)logR method in terms of TOC calculation effect in regions with complicated and changing lithology. The predicted TOC distribution of neural network is closer to  
Figure 11: TOC calculation results of different models in well LT1.  
practical geological conditions. The neural network method has important significance to further petroleum exploration and development in the future.
## 3 Methodology
### Fire Diurnal Cycle and Daily FRE Generation  
It has been found from previous studies ([PERSON] et al., 2017; [PERSON] et al., 2017) that the diurnal variation of fire has very obvious Gaussian characteristics in a day (0:00-23:00, local time).At present, most of the studies ([PERSON] et al., 2017; [PERSON] et al., 2017; [PERSON] et al., 2017) on pollutant emission estimation are based on MODIS FRP fire diurnal cycle fitting. [PERSON] et al. (2017) found that the parameters related to the fire diurnal cycle have a high correlation with the ratio of FRP Terra to FRP Aqua (T/A). However, for some highly dynamic fires, the low time resolution of MODIS obviously has a high error uncertainty, and the correlation between T/A and h (the peak hour of the fire diurnal cycle) is only 0.2. Although [PERSON] suggested that h has little influence on the final estimated DMB, it is obvious that h is also a source of error.  
Here, we represent the fire diurnal cycle as a Gaussian function, parameterized using the Himawari-8 FRP diurnal cycle superimposed on a fixed baseline. [PERSON] et al. (2018) found that the active fire detection ability of Himawari-8 under different FRP is relatively consistent with that of VIIRS. Based on the method of [PERSON] et al. (2018), the fire diurnal information obtained by Himawari-8 was mixed with VIIRS data to obtain the parameters related to the fire diurnal cycle. For a given grid cell j at instantaneous time t, the mixed FRP is calculated by:  
\[\rm{FRP_{j,t}}=\rm{FRP_{j,VIRS-night}}+\left(\rm{FRP_{j,peak-Him}-FRP_{j,VIRS- light}}\right)e^{-\frac{(\rm{j-j_{peak}})^{2}}{2\sigma_{j}^{2}}} \tag{2}\]  
where \(\rm{FRP_{j,t}}\) is the instantaneous mixed FRP (MW) for grid cell j at time t, \(\rm{FRP_{j,VIIRS-night}}\) is the night-time (-01:00 LST) VIIRS-IM FRP value for grid cell j, \(\rm{FRP_{j,peak-Him}}\) is the peak value of the fire diurnal cycle fitted based on the hourly data of Himawari-8 FRP for grid cell j, t\({}_{\rm{j}}\) is the local solar time at time step t, t\({}_{\rm{j,peak}}\) is the local time at which the Himawari-8 FRP diurnal cycle peaks for grid cell j and \(\sigma\) is the standard deviation (dependent on fire duration) of the main peak of the Himawari-8 FRP diurnal cycle calculated by fitting a Gaussian function (using non-linear least squares) to the Himawari-8 FRP diurnal cycles.  
For each grid cell j, daily FRE was calculated by:  
\[\rm{FRE_{j}}=\int_{0}^{23}\rm{FRP_{j}}dt \tag{3}\]
# Experimental Analysis of Ultrasonic Multiple Scattering Attenuation through the Air with Fine Dust
## 3 Signal Processing  
In this study, the signal processing method is suggested for calculating the scattering attenuation of coherent waves through air with fine dust. Figure 1 presents an overview of the signal processing method used to compute the scattering attenuation from coherent wave responses. First, ensemble averaging is performed over \(M\) realizations.  
\[\langle\text{s}(x_{i},t)\rangle=\frac{1}{N}\sum_{m=1}^{M}\text{s}_{m}(x_{i},t), \tag{5}\]  
where \(\langle\text{s}(x_{i},t)\rangle\) and \(\text{s}_{m}(x_{i},t)\) are the ensemble-averaged coherent wave response at the \(i^{th}\) receiver position and \(m^{th}\) single-realization response at the receiver position, respectively. Then, the ensemble-averaged response \(\langle\text{S}(x_{i},t)\rangle\) is converted from the time domain to the frequency domain using Fourier transform, which is expressed as:  
\[\langle\text{S}(x_{i},f)\rangle=\int_{-\infty}^{\infty}\langle\text{s}(x_{i},t )\rangle e^{-j2\pi ft}dt, \tag{6}\]  
where \(\langle\text{S}(x_{i},f)\rangle\) is the ensemble-averaged response in the frequency domain. We assume that the magnitude spectra in the frequency domain \(A(x_{i},f)\) can be expressed in the following form:  
\[A(x_{i},f)=\frac{1}{x_{i}}A_{0}(f)e^{-\alpha(f)x_{i}}, \tag{7}\]  
where \(A_{0}(f)\) and \(\alpha(f)\) represent the ensemble-averaged wave magnitude at the source position and frequency-dependent scattering attenuation, respectively. Furthermore, \(A(x_{i},f)\) is expressed as:  
\[A(x_{i},f)=\sqrt{\left(\text{Re}[\langle\text{S}(x_{i},f)\rangle]\right)^{2}+ \left(\text{Im}[\langle\text{S}(x_{i},f)\rangle]\right)^{2}}, \tag{8}\]  
where Re[.] and Im[.] represent the real and imaginary parts of values in the bracket, respectively. Then, the scattering attenuation \(\alpha(f)\) is computed by considering the slope parameter of the least-squares fit of \(\ln[x_{i}A(x_{i},\ f)]\) given by:  
\[\ln[x_{i}A(x_{i},\ f)]=\ln[A_{0}(f)]-\alpha(f)x_{i}. \tag{9}\]Note that the assumed model in Equation (7) considers the geometric spreading with the \(1/x_{i}\) term. Therefore, the amplitude decay triggered by the geometric spreading in the three-dimensional space is compensated for in the computed scattering attenuation.
# Dimensional Reduction-Based Moment Model for Probabilistic Slope Stability Analysis
## 3 Dimensional Reduction Method and Moment
### Multiplicative Dimensional Reduction Method  
For MDRM, the response function \(y=\mathrm{g}(X)\) is evaluated with respect to a reference fixed input point, known as the cut point, with coordinates \(c\).  
\[c=(c_{1},c_{2},\ldots,c_{n}) \tag{7}\]  
where \(c_{1}\), \(c_{2}\),..., \(c_{n}\) corresponds to the mean value of each random variables \(x_{1}\), \(x_{2}\),..., \(x_{n}\). Thus, an \(i\)th cut function is obtained by fixing all the point random variables, except \(x_{i}\), at their respective cut point coordinates, which are generally chosen as the mean values (\(c_{1}\), \(c_{2}\),..., \(c_{n}\)) such that  
\[g_{i}(x_{i})=g(c_{1},\,c_{2},\,\ldots,\,c_{i-1},\,x_{i},\,c_{i+1},\,\ldots,\,c _{n}) \tag{8}\]  
According to the MDRM method [41], the response function is approximated in a multiplicative form as  
\[y=g(X)\approx g_{0}^{(1-n)}\times\prod_{i=1}^{n}g_{i}(x_{i}) \tag{9}\]  
where \(g_{i}(\cdot)\) is called the cut component function. For the detailed explanations, algorithm, and procedure of MDRM, some literature [39; 40; 41] can be referenced.
## 3 Results
### Splash Erosion as the Function of Total Kinetic Energy  
During the measuring period, 99 splash erosion records of the three soils were obtained from the investigated sites. After the data evaluation, a total of 80 records that had complete rainfall and splash erosion measurements were selected for further data analysis.  
For measured \(KE_{sum}\), the mean splash erosion rates ranged between 4 and 2503 g m\({}^{-2}\) for ZW, 5 to 1972 g m\({}^{-2}\) for MI, and 12 to 2508 g m\({}^{-2}\) for BK soils (Figure 4). The variabilities between the splash erosion replicates were larger for the measurements of \(KE_{sum}\) above 780 J m\({}^{-2}\).  
Significant (\(p<0.05\)) linear regression was obtained for the three soils, as the splash erosion for each soil was positively correlated to \(KE_{sum}\) with \(R^{2}\) of 0.52, 0.50, and 0.45 for the BK, ZW, and MI soils, respectively (Table 3). According to the linear model, BK soil yielded the highest splash erosion, followed by ZW and MI soils. RMSE indicated a high deviation of measured values and values predicted by linear relationships.  
\begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{3}{c}{**Study Sites**} \\ \cline{2-4}  & **Petzenkirchen** & **Mistelbach** & **Prague** \\ \hline Number of months measured & 12 & 11 & 9 \\ Precipitation (mm) & 68.7 & 94.0 & 46.5 \\ Kinetic energy (J m\({}^{-2}\)) & 1026.0 & 1876.9 & 661.7 \\ Rainfall erosivity (MJ mm ha\({}^{-1}\) h\({}^{-1}\)) & 131.6 & 378.9 & 241.0 \\ Erosivity density (MJ ha\({}^{-1}\) h\({}^{-1}\)) & 1.7 & 3.8 & 3.6 \\ Raindrop diameter (mm) \({}^{*}\) & 0.9 & 1.2 & 0.6 \\ \hline \end{tabular} \({}^{*}\) According to [PERSON] et al. [40].  
\end{table}
Table 2: Average monthly rainfall parameters measured at the three study sites.  
Figure 4: Mean splash erosion for Zwerbach (ZW), Mistelbach (MI), and Bilkovice (BK) soil versus rainfall event cumulative kinetic energy. The dashed–dotted line represents the fitted linear regression line for BK soil, while the dashed line represents ZW, and the solid line represents MI.
## 3 Problem Formulation and Objectives
### IIoT Model and Slice Types  
This section uses QoS class identifiers (QCIs) to determine the characteristics and requirements of different setting types that ensure the network controller can be handled by dividing prioritized data flows based on three slice types. QCIs from the 3 GPP TS 23.203 V12.2.0 [39] specification can be employed to represent pertinent example services. Table 1 outlines the QCI index, resource type, priority level, packet delay budget (PDB), and packet error loss rate (PELR) for various smart industry scenarios. Regarding resource types, guaranteed bit rate (GBR) ensures that end users receive a minimum bandwidth, even in times of network congestion. This is critical for applications requiring consistent performance. Conversely, non-GBR offers optimal service under normal conditions; however, it does not guarantee that the requested bandwidth will be available during high network usage periods. This type of service is suitable for applications where occasional delays are acceptable, such as video streaming or data uploads from environmental sensors. PDB and PELR serve as upper-bound thresholds, defining the maximum tolerable delays and packet loss rates between IIoT and the policy charging enforcement function. These parameters are crucial for maintaining the QoS in smart industrial applications, ensuring efficient energy utilization and resource demand capability in flexible and dynamic environments. Here, we dive through to describe three slice types of IIoT application use cases as follows:  
* QCI 3 ensures that the communication infrastructure supports the reliability and timely exchange of data critical for the automation of industrial processes and real-time monitoring applications. Hence, data is critical for automation in controlling the network environment's charge policy.
* QCI 70 ensures that mission-critical data in IIoT environments receives the highest level of service quality, characterized by ultra-reliability, low latency, high priority, enhanced security, and dedicated bandwidth.
* QCI 82 provides the resource capabilities for defining discrete automation, which involves controlling and monitoring manufacturing processes that handle individual parts or units, generally in environments such as assembly lines or robotics, where precision and real-time performance are crucial.
## 4 Real-Time Elbow and T-Junction Detection and Estimation (RT-ETDE) Framework
### T-Junction Estimation
#### 4.4.2 T-Junction Branch Radius Estimation Based on Circumcircle Estimation  
We propose a simple geometric solution for branch radius estimation based on estimating the circumcircle through 3 points. It reuses the \(C\)-band used for branch direction estimation as illustrated in Figure 12(a). We select the farthest points on \(Y_{S}Z_{S}\)-plane of each \(C\)-band as the feature since these points are located inside the branch. These points are projected to the \(X_{T}Z_{T}\)-plane and show the circle shape of the branch, as shown in Figure 12(b). To estimate the circle, we select three points from three \(C\)-bands. The first point is in the  
Figure 11: Estimating the start of subsequent straight pipe based on \(X\)-bands and centroids.  
Figure 12: T-junction branch direction estimation based on \(C\)-bands, average intensity, and the highest intensity point.  
\(C\)-band having the highest average intensity. The remaining two points are in the \(C\)-bands farthest away from the first \(C\)-band. This selection ensures maximum separation of points and improves estimation accuracy. The branch radius is the radius of the circumcircle of the triangle created by the selected three points. Let \(d_{a}\), \(d_{b}\), and \(d_{c}\) be the edge length of the triangle. The radius of circumcircle according to [18,19] is as follows:  
\[r_{B}=\frac{d_{a}d_{b}d_{c}}{4\sqrt{s(s-d_{a})(s-d_{b})(s-d_{c})}} \tag{7}\]  
where \(s\) is the semiperimeter of the triangle and is computed as \(s=(d_{a}+d_{b}+d_{c})/2\).
## Cases Studies
### Necessity of Harmonic and Negative Uncertainty Analyses  
To illustrate the necessity of uncertainty analysis, Scheme 2 is further analyzed by contrasting the _THD-ul_ and _THD-me_ indexes and the _Cune-ul_ and _Cune-me_ indexes which are minutely listed in  
\begin{table}
\begin{tabular}{c c c} \hline  & **Scheme 1** & **Scheme 2** \\ \hline
**Total load removal/kw** & 9830.21 & 10,429.43 \\ \hline Normal loads & All cut & All cut \\ \hline Sub-important loads & Cut 39, 72, 83 & Cut 19, 44, 49, 57, 72, 86 \\ \hline Important loads & All recovered & All recovered \\ \hline Number of switch actions & 2 times, 8–9 open, 10–87 close & 0 \\ \hline Frequency/Hz & 49.5273 & 50.02 \\ \hline Reactive compensation/kvar & Node 11 & 190 & 240 \\ \cline{2-3}  & Node 59 & 0 & 125 \\ \hline _Vone-ul_/\% & DG4 & 1.47 & 1.44 \\ \hline _Cune-ul_/\% & DG5 & 9.80 & 10.56 \\ \cline{2-3}  & min & 3.34 & 4.41 \\ \cline{2-3}  & max & 3.42 & 4.54 \\ \hline \end{tabular}
\end{table}
Table 1: Result of Scheme 1 and Scheme 2 for benchmark scenario.  
Tables 2 and 3, respectively. _THD-me_ and _Cune-me_ are the midpoints of the distribution ranges of THD index and negative sequence current, respectively.  
Through the above two tables, it can be illustrated that the values of _THD-me_ and _Cu-me_ are more conservative. Thus, recovery failure may be caused, as the scheme with actually exceeded power-quality indexes is treated as a feasible scheme. Reasons are as follows.  
(1) As all the nodes in _THD-me_ are smaller than 4%, Scheme 2 is a feasible scheme in the view of _THD-me_. However, meanwhile, the values of _THD-ul_ index of all the nodes are within (4.41%, 4.54%) which are larger than 4%.  
(2) _Cune-me_ index of DG5 is within 10%, so Scheme 2 can be treated as a feasible one from the perspective of _Cune-me_. However, the _Cune-ul_ index of DG5 is clearly exceeded.  
Thus, in accordance with operation practices of power grids, it is necessary to adopt the uncertainty analysis method to deal with the uncertainty of harmonic and negative sequence components.
# Synchronizing Many Filesystems in Near Linear Time
## 2 Definitions
### Command Types, Execution Order  
The input and output _values_ of \(\sigma=\langle n,x,y\rangle\) are \(x\) and \(y\), respectively, while the input and output _types_ are \(\operatorname{tp}(x)\) and \(\operatorname{tp}(y)\). Commands are classified by their input and output types using _patterns_. The command \(\langle n,x,y\rangle\) matches the pattern \(\langle n,\mathcal{P}_{x},\mathcal{P}_{y}\rangle\) if \(\operatorname{tp}(x)\) is listed in \(\mathcal{P}_{x}\), and \(\operatorname{tp}(y)\) is listed in \(\mathcal{P}_{y}\). In a pattern, the symbol \(\bullet\) matches any value. As an example, every command matches \(\langle\bullet,\mathbb{O}\mathbb{F},\mathbb{D}\mathbb{F}\mathbb{O}\rangle\).  
Commands with identical input and output values are _null commands_. Null commands do not change the filesystem (but can break it if the precondition does not hold). _Structural commands_ change the type of stored data. Structural commands are further split into _constructors_ and _destructors_. A constructor increases the type of the stored value, while a destructor decreases it. Thus, a constructor matches either \(\langle\bullet,\mathbb{O},\mathbb{F}\mathbb{D}\rangle\) or \(\langle\bullet,\mathbb{F},\mathbb{D}\rangle\), and a destructor matches either \(\langle\bullet,\mathbb{D}\mathbb{F},\mathbb{O}\rangle\) or \(\langle\bullet,\mathbb{D},\mathbb{F}\rangle\). Observe that \(\sigma\) is a constructor if and only if \(\sigma^{-1}\) is a destructor. Finally, non-null commands matching \(\langle\bullet,\mathbb{F},\mathbb{F}\rangle\) are _edits_.  
The binary relation \(\sigma\ll\tau\) between commands on parent-child nodes captures the notion that \(\sigma\) must precede \(\tau\) in the execution order.  
**Definition 2** (\(\ll\) relation, \(\ll\)-chain).: _The relation \(\sigma\ll\tau\) holds if the pair matches either \(\langle n,\mathbb{D}\mathbb{F},\mathbb{O}\rangle\ll(\tau n,\mathbb{D}, \mathbb{F}\mathbb{O})\), or matches \(\langle\tau n,\mathbb{O}\mathbb{F},\mathbb{D}\rangle\ll\langle n,\mathbb{O}, \mathbb{F}\mathbb{D}\rangle\). An \(\ll\)-chain is a sequence of \(\ll\)-related commands connecting its first and last element._  
The first case in Definition 2 corresponds to the requirement that before deleting a directory, its descendants should be deleted. The second case says that a file or directory can only be created under an existing directory. Observe that \(\sigma\ll\tau\) if and only if \(\tau^{-1}\ll\sigma^{-1}\), and in this case, either \(\sigma\) and \(\tau\) are both constructors, or both are destructors.
## 5 Computational Experiments and Numerical Results
### Numerical Results
#### 5.2.2 Bursty Traffic Load  
A burst pattern in which a substantial number of tasks arrived in a short period is emulated. This scenario examines how the burst task arrivals influence the objective values. The results are shown in Figure 9, revealing that the difference between the strategies is considerable. _SP_, _OI_, and _UP_ perform better than _FF_ because they have a sufficient buffer time to reassign tasks or conduct migrations to maximize the values of tasks selected to serve within a limited server capacity. _FF_ performs worse than other methods because it has no mechanism to switch servers off and no buffer time to reassign tasks. The results obtained by observing Figure 9 reveal that _CP_ exhibits the lowest performance. In this case, the tasks are not assigned to appropriate servers. It causes a higher number of blocked tasks and correspondingly generates penalties that reduce the total revenue and lower the objective value. _SP_ provides the most optimal solutions compared with other strategies. The gap is 0% for 100 to 500 tasks, thus indicating that optimal solutions are determined. In other cases, the values of the gap are 3.33% to 12.19%.  
Figure 8: Objective value versus different number of tasks in uniform traffic load.
# Secure Transmission for Buffer-Aided Relay Networks in the Internet of Things
## 4 Performance Analysis
### Secrecy Outage Analysis
#### 4.1.3 SCJ-OPA Scheme  
Since the difference between the SCJ and the SCJ-OPA scheme is that the latter operates the optimal power allocation at the source sensor, making a substitution of the parameters \(P_{S_{2}}\to P_{S_{3}}\), \(P_{R_{2}}\to P_{R_{3}}\) and \(P_{I_{2}}\to P_{I_{3}}\), we can obtain the secrecy outage probability for the NCE and CE scenarios under the SCJ-OPA scheme easily.  
However, recalling the closed-form expression and the optimization problem mentioned above, we find that an explicit expression for \(\alpha\) is intractable. Instead, considering that the value space of \(\alpha\) is limited, thus the optimal result can be obtained by numerical calculations, i.e., the grid-search solution or the straightforward search solution, and the computer complexity is also acceptable.
## 2 Materials and Methods
### Data Sources and Processing
#### 2.2.5 Human Activity Data  
Population count data were collected from the gridded product of global population (GPWv411) ([[https://sedac.ciesin.columbia.edu/data/collection/gpw-v4](https://sedac.ciesin.columbia.edu/data/collection/gpw-v4)]([https://sedac.ciesin.columbia.edu/data/collection/gpw-v4](https://sedac.ciesin.columbia.edu/data/collection/gpw-v4)), accessed on 11 October 2021) provided by Socioeconomic Data and Applications Center (SEDAC), with a spatial resolution of 30 arc seconds and covering the years 2000, 2005, 2010, 2015, and 2020. The function to obtain the data is ee.ImageCollection (\"CIESIN/GPWv411/GPW_Population_Count\").  
Human modification data were derived from the global human modification dataset (gHM) ([[https://sedac.ciesin.columbia.edu/data/set/lulc-human-modification-terrestrial-systems](https://sedac.ciesin.columbia.edu/data/set/lulc-human-modification-terrestrial-systems)]([https://sedac.ciesin.columbia.edu/data/set/lulc-human-modification-terrestrial-systems](https://sedac.ciesin.columbia.edu/data/set/lulc-human-modification-terrestrial-systems)), accessed on 11 October 2021) provided by SEDAC with a spatial resolution of 1000 m. The gHM provides a cumulative measure of global human modification of terrestrial land with values ranging from 0.0 to 1.0. The gHM is calculated from the proportion of a given location that has been modified and the intensity of modification associated with a specific type of human modification. Five major anthropogenic stressors were mapped around 2016 using 13 individual datasets: human settlement, agriculture, transportation, mining and energy production, and electrical infrastructure. The function to obtain the data in GEE is ee.ImageCollection(\"CSP/HM/GlobalHumanModification\").  
Since the storage formats, spatial resolutions, and projections of the above datasets are not compatible (Table 1), this study adopted the JavaScript code editor based on GEE to implement the calling, reprojecting, resampling, clipping, computing, and related pre-processing to reduce labor and hardware resource usage.