esa-sceva/satcom-chunk-collection huggingface dataset:
# Analyzing the Factors Influencing Time Delays in Korean Railroad Accidents
## 3 Data Descriptions
### Details of Human- and Technical-Involved Events  
As mentioned above, because there is sufficient amount of data on human and technical factors since 2008, this study limited these two factors for the time delay estimation. Figure 5 presents the distribution of accident delay times by human and technical factors. The percentage of accident delays recorded as zeros was 41.4% for human factors and 9.1% for technical factors, showing a very large difference. This is the reason why the average delay time due to human factors is lower than that due to technical factors, as shown in Table 2 above. In other words, this means that in the case of an accident caused by human factors, it is much more likely that a delay time of zero will be recorded than an accident caused by technical factors. Then, records with excessively large delay times were defined as outliers and excluded from the analysis. The cutoff for outliers was defined as twice the interval between the 1 st and 3 rd quartiles, added to the 2 nd quartile. As a result, the baseline for outliers for human factors was calculated to be 93 min, and for technical factors to be 96 min. However, in this study, the baseline for all outliers was set at 100 min. Outliers exceeding 100 min were 83 cases for the human factors and 104 cases for the technical factors, respectively. After excluding these outliers, the final data to be used for analysis was 3161 cases for the human factors and 3246 cases for the technical factors. The mean and variance of cases where time delay occurred (excluding cases without time delay) were 30.6 and 347.4 for human factors and 31.9 and 347.9 for technical factors, respectively. Table 3 identifies the explanatory variables used in modeling and presents the descriptive statistics for the human and technical factors. The statistics reveal that approximately 75% of accidents can be attributed to factors associated with human involvement, within the category of human factors, while roughly 96.5% of incidents are linked to delays in operations, categorized under technical factors. This observation shows the higher likelihood of events arising from human factors evolving into accidents, whereas those originating from technical factors are more inclined to manifest as  
Figure 4: Causes of accidents of UIC member in 32 different countries (see page 28 of the UIC Safety Report 2023). Figure 5: Distribution of delay time due to human and technical factors. incidents. In terms of seasonal distribution, the number of occurrences related to human factors does not exhibit clear patterns. From the technical factors, however, there is a noticeable decrease in occurrences during the spring and fall seasons compared to other periods. This phenomenon can be attributed to the climatic conditions in Korea, with hot and humid summers featuring numerous rainy days, and cold, dry winters. Consequently, technical factors are more likely to result in accidents and incidents during these weather seasons.
## 2 Materials and Methods
### Polarization-Sensitive OCT Imaging  
A detailed description of the PSOCT system used in this study can be found in a previously published paper [10]. In brief, it consists of a 1310 nm swept laser source with 110 nm spectral bandwidth and 50 kHz sweep rate, a Michelson interferometer with a single circularly polarized illumination of the sample, and a polarization-diverse balanced detection. PSOCT imaging was done utilizing custom acquisition software and took about 30 s per volume. The underlying concept of polarization and image formation for PSOCT based on the Jones and Stokes formalism has been widely described, e.g., by [PERSON] et al. [26]. The DOP was calculated from spatially averaged and noise-corrected Stokes components [13], and covers values in a range from 0 (complete depolarization, yellow) to 1 (no depolarization, blue). For the analysis of DOP values, a region of interest (ROI) of approximately 2 mm \(\times\) 2 mm at the center of the slabs was selected and the mean DOP to a depth of 300 \(\upmu\)m (thinnest enamel layer thickness of the slabs, assuming a refractive index at 1300 nm of 1.631 [27]) was calculated. Cross-sectional images (B-scans) of the color-encoded DOP with intensity overlay of 45 dB dynamic range above the determined noise level [11; 13] and depth projections (en-face) of the mean DOP to a depth of 300 \(\upmu\)m were used for representation. In order to ensure constant measurement conditions and to prevent hydration-related variations of the scattering in porous enamel [28], the PSOCT measurements were performed with a water drop on top of the slabs, covered by a cover slip.
## 4 Open-Source Libraries
### Lapack  
* _Summary_: LAPACK [38] is a standard library aimed at solving problems of numerical linear algebra, such as systems of linear equations and eigenvalue problems.
* _Eigenvalue Methods_: QR and QZ algorithms.
* _Matrix Formats_: cannot handle general sparse matrices, but is functional with dense matrices. In fact, LAPACK is the standard dense matrix data interface used by all other eigenvalue libraries.
* _Returned Eigenvectors_: LAPACK algorithms are 2-sided, i.e., return both right and left eigenvectors.
* _Dependencies_: a large part of the computations required by the routines of LAPACK are performed by calling the BLAS (Basic Linear Algebra Subprograms) [56]. In general, BLAS functionality is classified in three levels. Level 1 defines routines that carry out simple vector operations; Level 2 defines routines that carry out matrix-vector operations; and, Level 3 defines routines that carry out general matrix-matrix operations. Modern optimized BLAS libraries, such as ATLAS (Automatically Tuned Linear Algebra Software) [57] and Intel MKL (Math Kernel Library), typically support all three levels for both real and complex data types.
* _GPU-based version_: MAGMA [39] provides hybrid CPU/GPU implementations of LAPACK routines. It depends on NVidia CUDA. For general non-symmetric matrices, MAGMA includes the QR algorithm for the solution of the LEP, but does not support the solution of the GEP.
* _Parallel version:_ ScaLAPACK (Scalable LAPACK) [58] provides implementations of LAPACK routines for parallel distributed memory computers. Similarly to the dependence of LAPACK on BLAS, ScaLAPACK depends on PBLAS (Parallel BLAS), which, in turn, depends on BLAS for local computations and BLACS (Basic Linear Algebra Communication Subprograms) for communication between nodes.
* _Development_: as a eigensolver, LAPACK is the successor of EISPACK [59]. The first version of LAPACK was released in 1992. When compared to EISPACK, LAPACK was restructured to include the block forms of QR and QZ algorithms, which allows for exploiting Level 3 BLAS and leads to improved efficiency [60]. The latest version of LAPACK is 3.7 and it was released in 2016.
## 5 Validation of a Decentralized Monitoring of PDSs via ESEMs
### Accuracy Analysis  
As mentioned in Section 4.2, a load flow calculation gives the reference values for the state variables (\(x_{lf}\)) and measurements (\(z_{lf}\)). Vector (\(x_{lf}\)) is used to analyze the accuracy of the state variables estimated by ESEMs using the Relative Mean Error (\(EMR_{30}\)) and the Standard Deviation (\(\sigma_{EMR}\)) as given in Equations (6) and (7).  
Figure 4: ThingsBoard Dashboard and electronic devices  
Figure 5: Network Topology of the PDS used to carry out the tests\[EMR_{30}=\frac{1}{n_{p}}\cdot\sum_{K=1}^{n_{p}}\left|\frac{\hat{x}_{k}-x_{k}^{lf}}{ x_{k}^{lf}}\right|\cdot 100 \tag{6}\]  
\[\sigma_{EMR}=\sqrt{\frac{1}{n_{p}}\cdot\sum_{K=1}^{n_{p}}\left(\left|\frac{\hat{ x}_{k}-x_{k}^{lf}}{x_{k}^{lf}}\right|\cdot 100-EMR_{p}\right)^{2}} \tag{7}\]  
where, (\(K\)) denotes the number of buses of the PDSs, (\(\hat{x}_{k}\)) the result of the estimation process of 30 samples, and (\(x_{k}\)) the result of the previously stored estimation process for reference.  
To perform the accuracy analysis considering \(EMR_{30}\) and \(\sigma_{EMR}\), based on \(z_{lf}\) 30 measurement samples that had been generated (from Monte Carlo simulations as presented in Section 4.2).  
Table 1 presents the comparison between the \(EMR_{30}\) and \(\sigma_{EMR}\) calculated by a Centralized Architecture and by the implemented Decentralized Architecture. In the centralized architecture, the WLS state estimation process is executed just once considering all the MV and LV network buses. On the other hand, in the decentralized architecture, 5 ESEMs perform a WLS state estimation process on the corresponding LV networks. After that, a WLS state estimation process is performed only on the MV buses, considering the net active and reactive powers estimated by the ESEMs in the distribution transformers.  
The mean processing time, for the 30 runs of the WLS state estimation process using the NVIDIA Jetson Nano as CPU, was stored for each LV network present in the test system and is presented in Table 2. It is noteworthy that the decentralized architecture enables parallel execution of the WLS state estimation in each ESEM. Mean processing time for the centralized approach is also presented in Table 2, where all system buses are processed on a single machine. For the centralized architecture, a computer was used with the following technical specifications: Intel Core i3 processor 1.80 GHz RAM 4 GB.
## 4 Results and Discussion
### Comparisons of Potential Sliding Surface with and without Subsurface Drainage  
Figure 16 demonstrates the effectiveness of the subsurface drainage on improving the slope stability of the landslide. The potential sliding area (Figure 16a-1) without the drainage remediation spreads out to a large area in the downslope and the corresponding potential sliding surface (PSS) along \(A\)-\(A^{\prime}\) profile (Figure 4) is also deeply seated near the bottom of drainage wells (Figure 16a-2). The study area is situated in an instability condition during typhoon season with a lower factor of safety of (\(FS\))\({}_{t=0\text{-day}}\) =1.383. A factor of safety \(FS\)\(<\)1.0 means that the slope is at failure and it was agreed that a \(FS\)=1.5 was required to meet long term stability of remedial works [28]. In Taiwan, three \(FS\) values were adopted as technical criteria for slope engineering design: (1) for ordinary time \(FS\)\(\geq\)1.50, (2) for earthquake \(FS\)\(\geq\)1.2, (3) for torrential rainfall \(FS\)\(\geq\)1.10. Moreover, [PERSON] [29] proposed a three-stage continuous spectrum of \(FS\) to define the stability state of slopes: \(FS\)\(>\)1.3 (stable), 1.0\(<\)\(FS\)\(<\)1.3 (marginally stable), and \(FS\)\(<\)1.0 (actively unstable). On the contrary, after the function of subsurface drainage and groundwater drawdown, the potential sliding area (Figure 16b-1) is confined at a limited area at the downslope and the PSS merely mobilizes at a comparatively shallow depth (Figure 16b-2). Eventually, the slope is maintained in a more stable situation with a higher factor of safety of (\(FS\))\({}_{t=20\text{-day}}\)=1.521.  
Figure 15: Sliding mechanism of study area without subsurface drainage in dry season (**a**) total incremental displacement field (**b**) sliding direction based on surface surveys (**c**) potential sliding area and factor of safety (**d**) potential sliding surface along \(B\)-\(B^{\prime}\) profile.
# Unlocking the Detoxification of Phenanthrene from Water Using Alkali-Activated Slag Mortar
## 3 Results and Discussions
### Phenanthrene Sorption Performance
#### 3.2.3 Effect of pH  
At various pH values, both GGBFS and AAS exhibited slightly fluctuating removal efficiencies and distribution coefficients for PHE, as depicted in Figure 5a. This stability suggested that PHE, being chemically stable due to its aromatic ring structure containing C=C bonds, did not undergo significant changes in hydrophobicity or transform into negatively charged species as pH levels increased [39]. The sorption of PHE onto AAS remained relatively consistent across a broad pH range, indicating that the extent of PHE sorption by AAS was similar under different environmental conditions. This observation underscores the robustness and reliability of AAS as a sorbent for PHE, offering consistent performance regardless of varying pH levels. These findings underscore the environmental suitability of AAS for water treatment, particularly in scenarios with pH fluctuations. The enhanced sorption properties of PHE make it a practical option for the removal of persistent organic pollutants from water.  
Figure 5: Effect of **(a)** pH and **(b)** competitive hydrophobic organic carbons (HOCs) on removal efficiency (R%) and distribution coefficient (\(K_{d}\), mL/g) of PHE using BFS and AAS. **(c)** Removal efficiency of PHE in several samples of real water. C\({}_{o}\) = 0.05 mg/L, solid/liquid = 1.0 g/L, pH = 5.5, contact time = 240 h, T = 23 \(\pm\) 1 \({}^{\circ}\)C. The properties of real water are shown in Table 6. Hydrophobic organic carbons: PYR, pyrene; PHE, phenanthrene; NAP, naphthalene; DiBP, diisobutyl phthalate; and DBP, di-n-butyl phthalate.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**adsorbent** &
\begin{tabular}{c} \(Q_{m}\) \\ **(mg/g)** \\ \end{tabular} & **Ref.** \\ \hline _Alkali-activated slag_ & 44.0 & _This study_ \\ \hline Smectite & 55.0 & [33] \\ \hline Magnesite–talc mixture & \textasci{-}60 & [34] \\ \hline Loess & 0.81 & [35] \\ \hline Waste brick & 0.03 & [36] \\ \hline Steel slag & 41 & [17] \\ \hline Magnetic graphene & 43.00 & [37] \\ \hline Alkali-activated biochar & 38.82 & [38] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparisons of the PHE sorption capacities of various sorbents.
# Weather-Aware Long-Range Traffic Forecast Using Multi-Module Deep Neural Network
## 4 Experimental Results and Discussion
### Summary and Discussion  
The overall MAE and MAPE of the prediction models are summarized in Table 2. The bold numbers show the best prediction errors for each case. TW-DNN has the lowest error in all three cases.  
How accurately the models forecast the congestion is summarized in Table 3. _Congested_ and _congestion-free_ are distinguished from the speed of 70 km/h. TW-DNN delivers the best result. Although heatmaps in Figures 9, 11 and 13 show the close match of prediction between TW-DNN and actual data, the prediction accuracy does not seem good enough, i.e., TW-DNN predicts lighter and shorter congestion than actual traffic data.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Mean Absolute Error**} & \multicolumn{3}{c}{**Mean Absolute Percentage Error**} \\ \hline  & **TW-DNN** & **CNN-Only** & \(k\)**-nn** & **ARIMA** & **TW-DNN** & **CNN-Only** & \(k\)**-nn** & **ARIMA** \\ \hline Case I & & & & & & & & \\ (Regular day) & **4.75** & 6.24 & 6.07 & 5.82 & **4.87** & 6.40 & 6.40 & 5.96 \\ \hline Case II & & & & & & & & \\ (Holiday) & **7.24** & 10.05 & 8.38 & 9.14 & **7.68** & 10.58 & 9.37 & 9.59 \\ \hline Case III & & & & & & & & \\ (Severe weather day) & **6.01** & 8.48 & 7.40 & 6.86 & **6.48** & 9.23 & 8.07 & 7.46 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average mean absolute error and mean absolute percentage error of the test cases.  
Figure 12: MAPE error of prediction models for Case III.  
The novel features of our prediction model are listed to summarize the experimental results.
* **Date-, time-, and weather-aware traffic prediction**: The real speed data show that time and weather do indeed affect road traffic. Upon addition of the time and weather modules, TW-DNN could predict congestion in unusual situations, while the others could not predict the congestion at all. They were not able to account for the weather and temporal condition.
* **Spatial correlation of traffic flow**: To reflect the spatial correlation of the traffic flow, TW-DNN predicts the speeds of the links better, using the traffic information of the surrounding links. \(k\)-nn and ARIMA cannot outperform TW-DNN because it does not have the traffic information of adjacent links.
* **Type of a link**: TW-DNN uses different predictors depending on the connection structures of the links. Meanwhile, CNN-only, \(k\)-nn, and ARIMA do not reflect the structural differences of the highway links. TW-DNN effectively predicted the speed values of interchange links having ramps, while the others did not.
###### Abstract  
Recently, we have reported a series of isatin hydrazone, two of them, namely, 3-((2,6-dichlororbenzylidene)hydrazono)indolim-2-one (**1**) and 3-((2-chloro-6-fluorobenzylidene)hydrazono)indolim-2-one (**2**) having potent cytotoxicity, showing cyclin-dependent kinases (CDK2) inhibitory activity and bearing recommended drug likeness properties. Since both compounds (**1** and **2**) showed inhibitory activity against CDK2, we assumed it would also have multiple receptor tyrosine kinases (RTKs) inhibitory activity. Considering those points, here, above-mentioned two isatin hydrazone **1** and 2 were synthesized using previously reported method for further investigation of their potency on RTKs (EGFR, VEGFR-2 and FLT-3) inhibitory activity. As expected, Compound **1** exhibited excellent inhibitory activity against epidermal growth factor receptor (EGFR, IC\({}_{50}\) = 0.269 uM), vascular epidermal growth factor receptor 2 (VEGFR-2, IC\({}_{50}\) = 0.232 uM) and FMS-like tyrosine kinase-3 (FLT-3, IC\({}_{50}\) = 1.535 uM) tyrosine kinases. On the other hand, Compound **2** also exhibited excellent inhibitory activity against EGFR (IC\({}_{50}\) = 0.369 uM), VEGFR-2 (IC\({}_{50}\) = 0.266 uM) and FLT-3 (IC\({}_{50}\) = 0.546 uM) tyrosine kinases. A molecular docking study with EGFR, VEGFR-2 and FLT-3 kinase suggested that both compounds act as type I ATP competitive inhibitors against EGFR and VEGFR-2, and type II ATP non-competitive inhibitors against FLT-3.  
Keywords:isatin-hydrazones; EGFR inhibitor; VEGFR-2 inhibitor; FLT-3 inhibitor +
Footnote †: journal: Applied Sci.
# Projected Augmented Reality Intelligent Model of a City Area with Path Optimization
## 2 Model Proposed
### Use of GA and A* for Solving the Travel Salesman Problem
#### 2.5.3 Application of the GA and A* to the TSP  
To apply the GA to solve the TSP problem, each solution is encoded as a chromosome. For example, if the user wants to visit locations {A, B, C, D}, each location is a gene and the different combinations of the letters are possible chromosomes.  
Figure 7 depicts an example of a minimalist population, created for a TSP where the goal is to visit five different places. As the picture shows, the population contains four individuals. Each individual consists of five genes, one gene for each place to be visited. The order of the genes in the chromosome is the order by which the places are expected to be visited.  
The fitness \(f\) of each chromosome \(j\) is calculated as the sum of the distances \(d\) calculated by A* between each two consecutive points \(i\), including the distance between the last and the first point, \(d(n,1)\). For \(n\) points, it is given by Equation (2):  
\[f_{j}=\frac{1}{d(n,1)+\sum_{i=1}^{n-1}d(i,i+1)} \tag{2}\]  
Solutions with a large distance to be traveled will receive a very low fitness and are eventually eliminated from the population of the genetic algorithm.  
For the example population of Figure 7, the fitness of chromosome C1 is the sum of the distances traveled to visit places 1-5-4-2-3-1, through the paths determined by A*. The fitness for C2 is the sum of the distances necessary to travel in a different order, starting with going from place 1 to place 2, then 3, then 4, then 5 and then getting back to 1.
# Diverse Roles of Previous Years' Water Conditions in Gross Primary Productivity in China
###### Abstract  
Gross primary productivity is one of the most important indicators of ecosystem function, which is related to water conditions and shown high interannual variation. Due to the time-lag effect, not only the current water condition but also the previous water conditions (e.g., one year before) impact the gross primary productivity (GPP). Revealing the impacts of current and previous years' water status is currently a hot topic. In this study, we designed a series of water deficit scenarios based on the meteorological dataset of the Climatic Research Unit (CRU) and then analysed the responses of the remote sensing-based moderate resolution imaging spectroradiometer (MODIS) gross primary productivity (GPP) in China, from which the role of water deficit in time periods was evaluated. The results indicate that the impact of climate factors (i.e., water, temperature and radiation) on GPP has a high spatial heterogeneity and that water-limited regions that are primarily distributed in North and Northwestern China show a stronger water-GPP relationship than water-unlimited regions. The water deficit that occurred in different periods had a variable impact on GPP. Specifically, GPP was primarily controlled by the current year's water conditions in the water-limited regions, with the importance value of 52.8% (the percentage of Increased Mean Square Error, %IncMSE) and 3.8 (the mean decrease in node impurity, IncNodePurity), but at the same time, it was conditionally affected by the water status in the previous year, with the importance value of 20.4% (%IncMSE) and 0.6 (IncNodePurity). The role of water in previous years is multifarious, which depends on the water conditions of the current year. The results revealed by the scenarios indicate that the influence of water conditions in the previous year was not statistically significant when the water conditions of the current year were in a drought. In contrast, when the current year's water conditions were normal or better, the water conditions in the previous year (i.e., one-year time lag) were also important and the increase of GPP significantly depended on the water condition (\(p<0.05\)). The diverse roles of water conditions in previous years on GPP and its non-ignorable time-lag effect revealed in this study imply that not only the current year's water condition but also its dynamic changes in previous years should be considered when predicting changes in GPP caused by climate change.  
gross primary productivity; vegetation response; water deficit; time-lag effect; random forest; China +
Footnote †: journal: remote sensing