esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This study compared two landslide time-series network prediction models and four spatio-temporal prediction models that incorporated overall spatial correlations using data from six GNSS monitoring points on the Dawuchang landslide. The main conclusions were as follows:  
1. The GRU model outperformed the LSTM model in terms of prediction accuracy (RMSE and MAE) at five monitoring points, with a 34.4% decrease in modeling time. By simplifying the LSTM structure, GRU reduced the number of parameters, improving training efficiency and prediction accuracy.
2. The T-GCN model, which considered spatial correlations, exhibited limited improvement in prediction accuracy. The added complexity in computation and prediction time was not justified by the marginal accuracy gains. This may be because of inaccuracies in spatial distance calculations or the insufficiency of the Gaussian function to capture local deformation effects in landslides.
3. The inclusion of external factors (rainfall and reservoir water levels) significantly enhanced the T-GCN model prediction accuracy. The RMSE and MAE values decreased substantially at all monitoring points; however, the response to external factors varied among monitoring points. Future research should focus on refining the dynamic lag representation of external factors to improve the model's predictive power.  
Conceptualization, J.M. and Q.Y.; methodology, J.M. and L.D.; software, Y.C. and C.O.; validation, L.Y., L.D. and Q.Y.; formal analysis, J.M. and L.D.; investigation, J.M. and L.D.; resources, L.Y. and Q.Y.; data curation, Y.C. and C.O.; writing--original draft preparation, J.M. and Y.C.; writing--review and editing, J.M. and L.D.; visualization, Y.C. and C.O.; supervision, L.Y.; project administration, L.Y. and Q.Y.; funding acquisition, J.M. and L.D. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Beijing Natural Science Foundation [grant number 8244051], National Natural Science Foundation of China [grant number 52404224], the project funded by the China Postdoctoral Science Foundation [grant number 2022M721845], the Open Fund of Technology Innovation Centre for Geohazard Monitoring and Risk Early Warning, Ministry of Natural Resources [grant number TICGM-2023-03], and the Geological Survey Program of China [grant number DD20211364].  
Not applicable.  
Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Wuhan Infoearth Information Engineering Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Delay-Sensitive Multi-Sensor Routing Scheduling Method for Underground IoT in Mines
## 5 Experimental Analysis  
### Experimental Parameter Settings  
Four different scale topologies are randomly generated to compare the algorithm's advantages and disadvantages. Considering the real scene, the four topologies of different sizes are all non-fully connected graphs. The greedy algorithm, Dijkstra algorithm, genetic algorithm based on greedy algorithm, and genetic algorithm based on Dijkstra coding are compared and analyzed.  
(1) Delay Performance Analysis  
In order to compare different algorithms, the results obtained by different algorithms are compared in a 'fitness' manner. The higher the fitness, the better the solution and the smaller the delay. We compare the fitness of different algorithm scheduling results. After the same iteration, the greater the fitness of the task, the better the algorithm's solution. The fitness calculation formula in this paper is  
\[(\tau_{Grad}-\tau_{*})/\tau_{Grad} \tag{2}\]  
where \(\tau_{Grad}\) is greedy algorithm delay and \(\tau_{*}\) are the delays of the algorithms to be evaluated.  
(2) Convergence Speed Performance Analysis  
If complete arrival cannot be achieved, the same round of iterations is performed on different algorithms to compare the proportion of completion. Under the same number of iterations, if the proportion of completion is higher, the algorithm is better. The calculation formula for the completion rate in this paper is the number of packets arriving before the deadline divided by the total number of packets.  
Table 1 is the experimental parameter configuration of the analysis in this paper. Table 2 is the experimental parameter configuration of the convergence speed performance analysis in this paper.  
In this experiment, fitness is the objective function of the problem, as shown in Equation (1). The value of fitness ranges from [0, 1]. Iters represents the number of iterations executed by the algorithm.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Network Topological Parameter** & **Task Parameter** & **Genetic Algorithm Parameters Based on Greedy Algorithm** & **Genetic Algorithm Based on Dijkstra Encoding** \\ \hline Station: 10 & Package: 200 & Mutation: 0.8 & Mutation: 0.8 \\ Station: 10 & Deadline: 45 & Population: 100 & Population: 100 \\ \hline Station: 15 & Package: 100 & Mutation: 0.8 & Mutation: 0.8 \\ Cache: 30 & Deadline: 35 & Population: 100 & Population: 100 \\ \hline Station: 15 & Package: 150 & Mutation: 0.8 & Mutation: 0.8 \\ Cache: 30 & Deadline: 40 & Population: 100 & Population: 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Delay performance analysis parameter configuration.
## 3 Numerical Simulation of CSAMT
### Fundamental Equation  
The electric field generated by a horizontal electric dipole (time factor \(e^{-i\omega t}\), angular frequency \(\omega\)) in an isotropic conductive medium satisfies the double curl equation [26]:  
\[\
abla\times\
abla\times\overset{\rightarrow}{\mathbf{E}}-i\omega\mu_{0}( \sigma-i\omega\varepsilon_{0})\overset{\rightarrow}{\mathbf{E}}=i\omega\mu_ {0}\mathbf{J}_{s}, \tag{1}\]  
where \(\sigma\) is the resistivity, \(\mathbf{J}_{s}\) is the current density vector of the electric dipole, and the magnetic permeability \(\mu_{0}\) of both the air and the subsurface medium is taken as the vacuum magnetic permeability, while the dielectric constant \(\varepsilon_{0}\) is the vacuum dielectric constant.  
Figure 3: Three-dimensional geophysical model of the Jinding lead–zinc deposit.  
The electric dipole source exhibits singularity. The total electric field is decomposed into the sum of the background field \(\overset{\rightarrow}{\mathbf{E}}^{p}\) (primary field) and the induced field \(\overset{\rightarrow}{\mathbf{E}}^{s}\) (secondary field). The background field \(\overset{\rightarrow}{\mathbf{E}}^{p}\) is directly solved using a one-dimensional geoelectric model [27], while the induced field \(\overset{\rightarrow}{\mathbf{E}}^{s}\) is solved using the finite element method to avoid the calculation of source singularity.  
\[\overset{\rightarrow}{\mathbf{E}}=\overset{\rightarrow}{\mathbf{E}}^{p}+ \overset{\rightarrow}{\mathbf{E}}^{s} \tag{2}\]  
The background field \(\overset{\rightarrow}{\mathbf{E}}^{p}\) also satisfies the double curl equation of the electric field. Equations (1) and (2) can be used to derive the double curl equation based on the secondary field \(\overset{\rightarrow}{\mathbf{E}}^{s}\):  
\[\
abla\times\
abla\times\overset{\rightarrow}{\mathbf{E}}^{s}+k^{2}\overset{ \rightarrow}{\mathbf{E}}^{s}=i\omega\mu_{0}(\sigma-\sigma^{p})\overset{ \rightarrow}{\mathbf{E}}^{p}, \tag{3}\]  
where \(k^{2}=-i\omega\mu_{0}(\sigma-i\omega e_{0})\). We know that on an electrical interface, the normal component of the electric field is discontinuous, while nodal finite elements require the electromagnetic field to be continuous in the normal direction. Therefore, the obtained finite element solution is often inaccurate and needs to be corrected. In the source region, the electric field solution of nodal finite elements does not satisfy condition \(\
abla\cdot\left(\overset{\rightarrow}{\mathbf{E}}^{\rightarrow}\right)=- \
abla\cdot\left(\overset{\rightarrow}{\mathbf{J}}/i\omega\right)\); in the source-free region, it does not satisfy condition \(\
abla\cdot\left(\overset{\rightarrow}{\mathbf{\epsilon}}\overset{ \rightarrow}{\mathbf{E}}\right)=0\). A divergence correction condition needs to be added to Equation (3) [28].  
\[\
abla\times\
abla\times\overset{\rightarrow}{\mathbf{E}}^{s}+k^{2}\overset{ \rightarrow}{\mathbf{E}}^{s}+\
abla\cdot\overset{\rightarrow}{\mathbf{E}}^ {s}=i\omega\mu_{0}(\sigma-\sigma^{p})\overset{\rightarrow}{\mathbf{E}}^{p} \tag{4}\]
# Real-Time Performance Measurement Application via Bluetooth Signals for Signalized Intersections
###### Abstract  
Improving the performance at signalized intersections can be achieved through different management styles or sensor technologies. It is crucial that we measure the real-time impact of these variables on intersection performance. This study introduces a Bluetooth-based real-time performance measurement system applicable to all signalized intersections. Additionally, the developed method serves as a feedback tool for adaptive intersection management systems, providing valuable data input for performance optimization. The method developed in the study is applied at the Refik Cesur Intersection in the Polatth district of Ankara where delay values are calculated based on traffic flows and data from Bluetooth sensors positioned at strategic locations. Initially, the intersection operated under a fixed-time signaling system, followed by a fully adaptive signaling system the next day. The performance of these two systems is compared using the Bluetooth-based application. The results show that the average delay per vehicle per day is 58.1 seconds/vehicle for the fixed-time system and 45.3 seconds/vehicle for the adaptive system. To validate the Bluetooth-based performance measurement system, the intersection is modeled and simulated using Aimsun Simulation Software Next 20.0.4. The simulation results confirm the findings of the Bluetooth-based analysis, demonstrating the effectiveness of the adaptive signaling system in reducing delays.  
bluetooth; traffic analysis; performance analysis; signalized intersection; delay parameter; fully adaptive signaling system; simulation modeling +
Footnote †: journal: Applied  
0000-0001-9993-3300]Yalcinli  
1 Robotics Automation Control Laboratory (RAC-LAB), Department of Electrical and Electronics Engineering, Konya Technical University, 42250 Konya, Turkey; [EMAIL_ADDRESS] (B.A.); [EMAIL_ADDRESS] (A.D.)
## 1 Introduction
Metal materials that are electrical conductors or semi-conductors such as germanium and silicon have an electrical resistivity of 1.59 \(\times\) 10\({}^{-8}\)\(\sim\)2.5 \(\times\) 10\({}^{2}\)\(\Omega\).m. In addition, it is known that carbon materials have an electrical resistivity of 3.5 \(\times\) 10\({}^{-5}\)\(\Omega\).m [32; 33; 34]. Therefore, when FRCM is fabricated by adding carbon fiber with excellent conductivity, it can be converted into cement mortar with electrical conductivity (easy to flow electricity) by greatly lowering the electrical resistance of non-conductive cement mortar to the level of 10\({}^{1}\)\(\Omega\).m. Figure 1: Electrical resistivity according to various materials. On the other hand, research has been conducted continuously until now about carbon and steel fibers since they have conductivity which is very useful for improving the mechanical and electrical properties of FRCM [35]. In particular, carbon-based highly conductive materials include carbon fibers, carbon nanotubes, graphene, and graphite powder, while metal fibers include steel fibers and stainless fibers. When chopped carbon fibers with excellent properties such as high electrical conductivity, thermal conductivity, heat resistance, incombustibility, and corrosion resistance are used, the electrical resistance can be lowered to a certain level. That is why they are suitable for various applications such as self-sensing and self-monitoring [36; 37; 38; 39; 40], traffic monitoring [41; 42], de-icing of frozen road pavements [43; 44; 45], corrosion protection of rebar [46], electrical grounding [47], and electromagnetic shielding [48; 49], etc. In Korea, many studies on multifunctional FRCM with high conductivity have been conducted, while some foreign countries, such as Russia, North America, and Japan are improving their usability by actively developing advanced new materials for construction. However, studies on the mechanical performance improvement of FRCM incorporating various fibers into cement mortar have been continuously conducted, but until now, studies on the effect of incorporating carbon and steel fibers on electrical conductivity are somewhat insufficient. Adding conductive fibers such as carbon fiber and steel fiber to cement mortar can not only improve the mechanical properties but also effectively enhance the electrical conductivity performance of cement mortar. The ultimate purpose of the present study is to develop a manufacturing technology for multifunctional FRCM with high conductivity by mixing carbon and steel fibers which are conductive materials. In this study, therefore, attempts were made to review the effect of varied fiber volume fractions of conductive CFRCM and SFRCM containing carbon and steel fibers on the fresh and mechanical properties as well as electrical conductivity in comparison with PM. Furthermore, the surface shape and composition of the developed conductive FRCM fracture surface were analyzed by a scanning electron microscope (SEM) and energy-dispersive X-ray spectrometer (EDS).
## 2 Materials and Methods
### Phenolic Compounds Determination  
Total phenolic and flavonoid contents as well as free radical-scavenging assay were determined using UV-VIS spectrophotometer (Spectronic 1201, [PERSON], Houston, TX, USA). All experiments were performed using 1 cm optical path length quartz cuvette. Inorder to harmonize results, all spectrophotometric analysis results were calculated in rutin equivalents (mg of rutin standard per 1 g of dry raw material: RE mg/g). Procedures were as previously described by [PERSON] et al., 2010 [31].  
Total phenolic content was determined using the Folin-Ciocalteu colorimetric method. An aqueous stock solution of 4% sodium carbonate (Na\({}_{2}\)CO\({}_{3}\)) was prepared and stored in refrigerator (4 \({}^{\circ}\)C). 100 uL of sample extract was added to 3000 uL of stock solution, mixed, and 100 uL 2 N Folin-Ciocalteu reagent was added. After 30 min incubating at room temperature the sample absorbance was measured at 760 nm. Results were calculated in rutin equivalents (RE mg/g).  
The separation and identification of phenolic compounds and determination of their radical scavenging activity were carried out using modular HPLC system consisted of mobile phase binary pump (Hewlett Packard, Waldbronn, Germany), autoinjector (Perkin Elmer, Norwalk, CT, USA), chromatographic column (125 mm \(\times\) 4.60 mm LiChrospher 100 RP-18e, particle size 5 um (Merck, Darmstadt, Germany), UV detector (\(\lambda\) = 254 nm) \"Spectra 200\" (Spectra Physics, Norwalk, CT, USA), DPPH reagent pump (Phoenix 20 CU, Carlo Erba Instruments, Milan, Italy), DPPH reaction detector (\(\lambda\) = 517 nm) \"Linear UVIS 200\" (Linear Instruments, Reno, NV, USA) and data processing system. Mobile phases were as follows: A--bidistilled water, acidified with trifluoroacetic acetic acid (0.05% vol.), B--methanol. Following mobile phase gradient was applied: from 0 min. to 4 min. component B concentration is changed from 1% to 30%, on 20 min. B is set to 43%; on 30 min. B is set 50%, on 40 min.--B is set to 99% and hold for 5 min. Flow rate: 0.75 mL/min, injection volume 10 uL Compounds were determined according to the retention times using external standard method. Retention time of standard compounds was accordingly: _trans_-synagic acid--19.44 min., _trans_-p-coumaric acid--23.27 min., hesperetin--23.83 min., rutin--24.73 min., rosmarinic acid--25.84 min.  
The content of total flavonoids was determined by AlCl\({}_{3}\) colorimetric method. A stock solution containing 60 mL of methanol (100%), 3 mL of acetic acid (33.0%), 12 mL of hexamethylenetramine (5%), 9 mL of aluminum chloride (10%) and 60 mL of bidistilled water was prepared and stored in the refrigerator (4 \({}^{\circ}\)C). Total flavonoid content was determined mixing 80 uL of extract with 1920 uL of stock solution. After 30 min incubation at 4 \({}^{\circ}\)C the absorbance was measured at 407 nm. Results were calculated in rutin equivalents (RE mg/g).
# Ensemble Clustering in GPS Velocities: A Case Study of Turkey
## 3 Results and Discussion
### Comparison of Clustering Results with Ensemble Clustering
In Cyprus island, we have five GPS sites and four of them are clustered in the Eurasian cluster except in the spectral clustering algorithm. One can think that Cyprus is another block that has similar GPS-derived velocities to Eurasia [7]. However, spectral clustering assigned these four sites in Anatolia apart from the other algorithms. Perhaps, a solution with Euler vector clustering [13] could enlighten this situation. One can see the result of the MCLA ensemble method that all four GPS sites were assigned in the Eurasian tectonic plate cluster. The other GPS sites that are assigned in different clusters (Figure 10) are generally exchanged in neighbor clusters except for the ADAN site as explained before. Compared to the block borders with [5], in our study, there is no small block under the Aegean block seen in Figure 10. A small part of this block is in our Aegean coast block and the rest is in Anatolia. In this study, ensemble clustering methods are tested and the results are analyzed. In the case of Turkey's GPS horizontal velocity field, it is seen that the MCLA method is compatible with clustering methods and eliminates the subjectivity of clustering methods. In addition, when compared with the literature, one can see that the results obtained can be used in block modeling without a priori information about the data. One should keep in mind that these results are specialized in the case of Turkey and should be supported with different examples. Figure 9: Comparison of individual clustering results with ensemble clustering for western Turkey with: (**a**) BIRCH; (**b**) HAC; (**c**) \(k\)-means; (**d**) mini batch \(k\)-means; (**e**) spectral clustering; (**f**) MCLA results. Black solid lines are the block model at [5]. The green dashed line is our block border according to the ensemble clustering result. Gray thin lines show active faults [23].
###### Abstract  
It has been reported that when irradiated with laser light non-resonant with the main absorption peaks, porphyrin molecules (4-[10,15,20-tris(4-sulfophenyl)-21,24-dihydropypropyl-5-yl]benzenesulfonic acid, TPPS) in an aqueous solution become 10,000 to 100,000 times more efficient in light-induced molecular aggregation than expected from the ratio of gradient force potential to the thermal energy of molecules at room temperature. To determine the mechanism of this phenomenon, experiments on the light-induced aggregation of TPPS in alcohol solutions (methanol, ethanol, and butanol) were performed. In these alcohol solutions, the absorbance change was orders of magnitude smaller than in the aqueous solution. Furthermore, it was found that the absorbance change in the aqueous solution tended to be saturated with the increase of the irradiation intensity, but in the ethanol solution, the absorbance change increased linearly. These results can be qualitatively explained by the model in which intermolecular light-induced interactions between molecules within a close distance among randomly distributed molecules in the laser irradiation volume are highly relevant to the signal intensity. However, conventional dipole-dipole interactions, such as the Keesom interaction, are not quantitatively consistent with the results.  
Keywords:light-induced force; gradient force; J-aggregate; porphyrin; aqueous solution; alcohol solution; TPPS; Frenkel exciton; pump-probe spectroscopy; nonlinear absorption spectroscopy +
Footnote †: journal: Applied Physics Letters
## 6 Experimental Results and Analysis
### Influence of Tape Arrangement
#### 6.3.2 Friction Factor Results  
The influence of the CVRT arrangement on the friction coefficient (\(f\)) is analogous to its impact on heat transfer, depicted in Figure 14. In this figure, the friction factor (\(f\)) rises with the attack angle. The backward CVRT provides 1-5% higher friction than a forward one. The backward arrangement extends the flow path. A longitudinal vortex covers the core area, increasing the contact surface between the hot surface and fluid flow, which provides greater shear stress.
## 3 Results and Discussion
#### 3.1.1 Signal Amplitude  
For the purpose of analyzing the effects of F-T cycles on the compression fracture damage, the relationships between signal amplitude and load level versus time during compression were obtained for the SBS-modified asphalt mixtures with basalt fiber under various F-T cycles. The load level was determined by the ratio of applied load and failure load. As shown in Figure 6a, the entire loading process and the AE signal amplitude can be divided into three stages by combining the intensity of the AE signal amplitude and the load level. At the first stage, the lower AE signal amplitude values, ranging from 40 to 50 dB, were generated and recorded during compression loading, while the load level was below 0.1. The lower AE signal amplitude values in the first stage, were considered to be the signal due to the mechanical noise during loading. Then, several microcracks were preliminarily formed under compression loading during the first stage. After entering the second stage, a significant increase of the AE signal amplitude began to appear, ranging from 50 to 70 dB when the load level was between 0.1 and 0.9. The AE signal amplitude values increased with increasing load levels. The increasing AE signal amplitude implies that cracks were formed at this stage and the cracks also developed into stable crack growth. As the compression loading continued, the AE signal reached the third stage. During this stage, the highest value of the AE signal amplitude occurred, which means that the cracks in the asphalt mixture specimen developed rapidly. Meanwhile, the AE signal amplitude showed a trend of slow decline after reaching the maximum value as the compression loading continued. This is because macroscopic cracks occurred when the damage accumulated to a certain degree, and eventually the asphalt mixture specimen was destroyed.  
Generally, the AE signal amplitude and load level have similar changes under various F-T cycles. However, by comparing the AE signal amplitudes of various repeated F-T cycles, it is clearly seen that the density of the AE signal amplitude gradually became slightly smaller. At the same time, the loading time is shortened to a certain degree with increasing F-T cycles. The first stage was gradually shortened, and the third stage was gradually increased. The occurrence of the second stage gradually moved forward. This change means that the asphalt mixtures processed with more F-T cycles began to have preliminary damage at lower load levels. Thus, it is evident that the F-T cycle has an obvious adverse influence on the compression strength of SBS-modified asphalt mixture with basalt fiber, which makes asphalt mixtures more likely to cause early failure under a compression load.