esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data and Methodology
### Urban LULC Modeling
#### 3.4.4 Model Application  
Once all predicted LULC maps were assessed for acceptable accuracy, the model was used to predict the future LULC in 2014 and 2035. Although the demographic and economic data were introduced as inputs of the model already, much literature [50; 51; 52; 53] also suggests that it will improve the good performance of the model to incorporate the population and economic growth rate for future predictions. This study considers that reason to integrate both important data to transition rate calculation through the statistical extrapolation and SD.  
Statistical extrapolation is a method to estimate the land demand (transition probability) based on the future population. The probabilities of land changes were predicted from an existing LULC and population change. The predicted values were generated by simple linear regression [31]. In this study, the change in population throughout the specific time period was compared with the change in each LULC category to develop a ratio of the future change using ordinary forecast and trend functions.  
SD is a non-spatial simulation technique developed in the late 1950s by [PERSON] of the Massachusetts Institute of Technology. SD was originally applied in the engineering field called \"Industrial Dynamics\" and also applied in the urban planning field, namely \"Urban Dynamics\". It has attracted adequate attention as a tool for system characteristics investigation rather than predictions based on numerical simulations [54]. To calculate the transition probability of land demand using SD, this study applied the existing and projected population proposed by DOPA and NESDB in 2014 and 2035, respectively, to compute annual growth rates. The economic growth rates, calculated from the GPP, were also used in the model. The process in SD for land demand calculation can be divided into two parts: the first is driving factors module and the second is a land use module. The driving factors module handled the impact of population and economic growth while the land use module paid attention to the interaction and conversions of land use classes that were driven by population and economic data. For example, the demand for the urban class would be increased if the population and GPP growth rates increased. On the other hand, the agriculture and forest classes would decrease because of those factors. This study calculated the land demands using STELLA software from isee system. It was used to design the stocks (land use classes) and flows (driving forces) and to automatically generate relevant equations according to the feedback loop of the land demand in the SD environment. Once all were completed, the model estimated the land demands for each LULC class of the interested year, which were then converted into the transition probability matric.
## 2 Materials and Methods
### Implementation and Evaluation
To simulate such practical conditions without introducing architectural confounders, we applied bilinear upscaling to the input parcels while keeping both the patch size and model architecture fixed. This allowed us to isolate the effects of patch coverage on performance. To systematically examine these effects, we applied bilinear interpolation to upscale parcel images by different scaling factors (1\(\times\), 2\(\times\)). When each parcel image is up-scaled by a fixed factor and patches of the original resolution are then extracted, two opposing effects arise: (i) context reduction--every patch covers a smaller ground area, so the information available to the network per patch decreases; and (ii) patch-count increase--a larger parcel yields more patches, giving the model a greater number of predictions to aggregate. Because these two effects work in opposite directions, upscaling presents a clear trade-off between the loss of contextual detail and the benefit of improved aggregation from an increased number of patches. Patch predictions are combined by multiplying their softmax probabilities class-wise so that consistent classes are reinforced while noisy predictions are attenuated; this aggregation provides a compensatory effect at the parcel level. Our experiment measured the drop in patch-level performance caused by context reduction and the extent to which parcel-level aggregation recovers that loss. We also compared the robustness of RPE on its own, CBS on its own, and the combined RPE + CBS scheme under reduced-context conditions. Finally, we verified whether the additional patches generated by up-scaling translated into higher parcel-level accuracy, thereby strengthening the case for tiling-based inference. As illustrated in Figure 9, the same farmland parcel is processed at 1\(\times\) (no upscaling) and 2\(\times\) (bilinearly upscaled) by our inference pipeline. At the 2\(\times\) scale, patch coverage decreases to approximately 1/4 compared to the 1\(\times\) scale, reducing context information per patch. However, the total number of extracted patches increases, allowing the model to aggregate more patch-level predictions. This visual comparison underscores the trade-off between reduced contextual information per patch and an increase in the number of patches contributing to parcel-level aggregation.
# New Landslide Disaster Monitoring System: Case Study of Pingding Village
## 3 Results
### Monitoring Data Analysis
#### 3.2.1 Rainfall Data  
Influenced by Typhoon Sinlaku, the total cumulative rainfall from 11 September to 16 September at the observation stations was 349 mm, with the greatest rainfall intensity being 40.5 mm/h. A hyetograph of the cumulative rainfall during the time when the emergency response team was active, showing the rainfall intensity and the cumulative rainfall conditions, is provided in Figure 12. The yellow line in the figure represents the vigilance values, while the red line refers to the action values. The hourly rainfall reached 12 mm, and the daily cumulative rainfall was 86.5 mm at 6 a.m. on 14 September, which reached the threshold of the yellow level of vigilance. The hourly rainfall reached 20.5 mm, and the daily cumulative rainfall was 140.5 mm at 2 p.m. on 14 September, which reached the threshold of the red level of vigilance. No abnormal phenomena were observed in the comparison between the monitoring results of the GPS and retractable meters (Figure 13). The local village heads, the Yunlin County Government, and the related soil and water conservation departments were informed through the disaster report process of this system, as shown in Figure 14. It is assumed that the topographic features of the Pingding area, which increase the possibility of torrential rainfall within short periods of time, lead to higher rainfall intensity and heavier cumulative rainfall. Therefore, more data concerning local typhoons and torrential rainfalls will be collected in the future to determine whether the vigilance and action values of the rainfall in this area should be raised.
# Machine-Learning-Based Path Loss Prediction
## 1 Introduction
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Ref.** & **Freq. (MHz)** & **Environment** & **Input Parameters** & **Output Parameter** & **Test Model** & **RMSE (dB)** & **Study Area** \\ \hline
[26] & 700-2600 & Kent (Melbourne, USA) &
\begin{tabular}{l} Tx Gain, Position Height, \\ Distance \\ \end{tabular} & Path Loss & ANN & 0.636 & Other \\ \hline
[27] & 14000, 18000, 22000 & \begin{tabular}{l} Indoor Environment (South \\ Africa) \\ \end{tabular} & \
\begin{tabular}{l} Indoor Environment (South \\ Incidence, Transmitter \\ Antenna Height \\ \end{tabular} & Path Loss & SVR, DT, RE, ANN, KNN &
\begin{tabular}{l} SVR = 0.93, ANN = 0.006, \\ KNN = 0.91, DT = 1.16, \\ RF = 0.91 \\ \end{tabular} & Other \\ \hline
[28] & 811, 2630 & \
\begin{tabular}{l} University Campus \\ (Denmark) \\ \end{tabular} &
\begin{tabular}{l} Local coordinates (lat. and \\ keng), Tx indicator, Distance, \\ 3D Distance, Satellite Image \\ \end{tabular} & Path Loss & DNN & 4.1 & Other \\ \hline
[29] & 2500 & \
\begin{tabular}{l} Urban/Suburban \\ Rural (China) \\ \end{tabular} &
\begin{tabular}{l} Distance, Tx height, Rx \\ height, Obstruction loss (DL), \\ Calculated by Dropout \\ method, Frequency, Crossed \\ distance, di \\ \end{tabular} & Path Loss & ANN & 4.1-6.8 & Other \\ \hline
[30] & 854,71 & Urban (Brazil) &
\begin{tabular}{l} Distance, Terrain elevation, \\ Horizontal angle, Vertical \\ angle, Littleitude, Longitude, \\ Antenna horizontal \\ vertical attenuation \\ \end{tabular} & Path Loss & SVR & 1.76-4.55 & Other \\ \hline
[31] & 10 (VVLC) & \
\begin{tabular}{l} Campus Road and Indoor \\ Parking Garage \\ \end{tabular} &
\begin{tabular}{l} Cyclied place, Tc Light Type, \\ Rx decribeter type, Tx height, Rx gradient, \\ Environmental conditions \\ \end{tabular} & Path Loss & RF & 3.95 & V2V \\ \hline
[32] & 600 & Urban (Ecuador) & \
\begin{tabular}{l} Distance, Base station (BS) \\ height, Mobile station (MS) \\ height, Carrier frequency, Rx \\ coordinates (X, Y), L&LG or \\ NLG status \\ \end{tabular} &
\begin{tabular}{l} Distance, Base station (BS) \\ height, Mobile station (MS) \\ height, Carrier frequency, Rx \\ coordinates (X, Y), L&LG or \\ \end{tabular} & Path Loss & SVR, RF, kNN, ANN & 5.9-6.4 & Other \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont.
## 3 Results and Discussion
### Catalytic Tests
During the N\({}_{2}\)O pre-treatments, the catalysts were first reduced and re-oxidised afterwards. As observed in Figure 6, rhodium was progressively reduced and only Rh (0) was identified in both catalysts after the 15 min pretreatment, while after 60 min both Rh (III) and Rh (0) were observed again. The behavior of the cerium oxidation state (Figure 6) was qualitatively similar to that of rhodium, Ce (IV) being first slightly reduced to Ce (III) and reoxidised afterwards. It has to be mentioned that only the trend of the cerium oxidation state must be considered, but not the absolute values, since the reducing environment of the XPS measurements (high vacuum and an electron beam) could affect such absolute oxidation state values. These XPS results (Figure 6), together with the N\({}_{2}\)O conversion profiles included in Figure 5, evidence that the RhOx/CeO\({}_{2}\) catalysts suffer an activation process at 225 \({}^{\circ}\)C. The reduction of Rh (III) to Rh (0) after 15 min suggests that the interaction between RhOx with ceria is poor on fresh catalysts, otherwise it would be expected that rhodium remains partially oxidized due to the rhodium-ceria interaction [29]. Only after some time the real steady state is achieved, and both rhodium and cerium  
Figure 6: **(a)** Rh (0) and **(b)** Ce (III) percentages determined by XPS after _in situ_ thermal treatments with 1000 ppm N\({}_{2}\)O at 225 \({}^{\circ}\)C for different times. appear partially reoxidised evidencing that some surface transformations have occurred. The reoxidation of the catalysts (both of rhodium and cerium) is stronger for the most active catalyst (Rh250 Ce25; see Figure 6 for time =60 min), suggesting a deeper transformation during the activation period. The easier reduction of Rhodium on the ramp calcination catalyst could be related to a worst stabilizing effect of the ceria support.
## 3 Materials and Methods
### Analysis of the Forecasting Methods
#### 3.1.3 Simple Moving Average Method  
The Simple Moving Average Method, having in mind that the last observation in a time series has a greater influence on the future than the first observation, takes an average of solely the last observations for predicting the future. The Simple Moving Average Method takes the average of the last 12 months and forecasts this average value for the following months (Figure 4). The result is better than the Simple Average Method or Naive Forecasting Method. The seasonal effect cannot be captured by this method, but there is a trend line in the forecast that increases linearly. The evaluation criterion RMSE for this model is equal to 31,157.
## 2 An Overview of the Literature on Solar Forecasting
Research HighlightsThis current study differs from previous studies in that it compares genetic algorithm (GA), recurrent neural networks (RNN), and the K-nearest neighbours (KNN) models in short-term forecasting of high-frequency global horizontal irradiance using South African data. To the best of our knowledge, this is the first paper to carry out such a comparative study of the models using South African data. South Africa receives sunlight throughout the year, so the natural advantage of South Africa is that it is included in the world's highest in renewable energy. The global average annual 24-h solar radiation for South Africa has been about 220 W/m\({}^{2}\), 150 W/m\({}^{2}\) for parts of the US, and about 100 W/m\({}^{2}\) towards Europe and the United Kingdom [37]. The following is a summary of the highlights and findings of this study. 1. Based on the RMSE and rRMSE, GA was found to be the best model. However, based on MAE and rMAE, RNN was the best model. 2. From the Diebold-Mariano tests, the null hypothesis that the forecast accuracy between a pair of forecasts from the two methods is the same was rejected for all the three pairs. 3. Based on the Murphy diagrams, the GA dominated both RNN and KNN, meaning that it provides the greatest predictive ability. 4. Based on the Giacomini-White tests, GA was found to have the best conditional predictive ability compared to the other two models. 5. The results from this paper yield improved results from the previous papers. 6. To the best of our knowledge, this is the first paper to compare the genetic algorithm, the K-nearest neighbour method, and recurrent neural networks in short-term forecasting of global horizontal irradiance data from South Africa. The rest of the following paper is as follows: Section 3 is the materials and methods used in this paper. The empirical results and discussion are in Section 4. The conclusion is provided in Section 5.
## 2 System Model
### MAP Turbo Decoding
\[\widetilde{\alpha}_{k}(s)=\sum_{s^{\prime}}\widetilde{\gamma}_{k}\big{(}s^{ \prime},s\big{)}\widetilde{\alpha}_{k-1}\big{(}s^{\prime}\big{)} \tag{5}\]0 Block diagram of (**a**) turbo encoder, (**b**) decoder, and the (**c**) state transition diagram of turbo codes. The computation of \(\alpha,\beta\), and \(LLR\) values constitute the major computational part of the algorithm, which has a high impact on power consumption and silicon area.
# Retrofitting of a Multi-Span Simply Supported Bridge into a Semi-Integral Bridge
## 5 Finite Element Analysis
### Response Spectrum Analysis  
According to the guidelines for the seismic design of highway bridges [23], a response spectrum seismic analysis of the bridge was carried out, and the longitudinal and transverse earthquake actions of the bridge were considered, respectively. The seismic resistant category is class C in the class II site, the characteristic period is 0.4 s, the seismic fortification intensity is 7 degrees (acceleration 0.15 g), and the damping ratio is 0.05. The design response spectrum functions of E1 (earthquake action of the engineering site with a short recurrence period corresponding to the first level of fortification) and E2 (earthquake action of the engineering site with a long recurrence period corresponding to the second level of fortification) can be obtained, as shown in Figure 13. The applied loads included the dead load and earthquake action [41].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Bridge System**} & **Abutment** & **Midspan** & **Interior Support** \\ \cline{2-5}  & **Shear Force** & **Moment** & **Shear Force** & **Moment** \\  & **(kN)** & **(kN\(\cdot\)m)** & **(kN)** & **(kN\(\cdot\)m)** \\ \hline Simply supported bridge & 505.9 & 1003.4 & 505.9 & 0 \\ \hline Semi-integral bridge & 482.9 & 891.9 & 538.6 & \(-\)423.8 \\ \hline Scale down (\%) & 4.5 & 11.1 & \(-\)6.4 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of beam internal forces in different bridge systems.
## 3 Results and Discussion
### Feature Wavelengths Based on SPA and VCPA  
The selected optimal wavelength variables using the SPA and VCPA algorithms are shown in Figures 6 and 7, respectively. For SPA, the 12 optimal wavelengths (1025 nm, 1075 nm, 1106 nm, 1230 nm, 1282 nm, 1296 nm, 1388 nm, 1450 nm, 1464 nm, 1555 nm, 1643 nm, 1737 nm) were selected because they had a higher probability of being selected than other wavelengths (Figure 5). At the same time, the 13 optimal wavelengths (1057 nm, 1075 nm, 1123 nm, 1214 nm, 1279 nm, 1365 nm, 1416 nm, 1468 nm, 1523 nm, 1590 nm, 1643 nm, 1686 nm, 1744 nm) were selected by VCPA and they had the highest probability of being selected (Figure 6). The above two sets of spectral characteristic variables did not have completely consistent wavelengths, but they were all distributed near the main absorption peaks, which was similar to the optimization results of the NRS algorithm. Therefore, it  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\beta\) & \(\delta\) & **Numbers** & **Optimal Wavelengths/nm** \\ \hline \multirow{8}{*}{0.75} & \multirow{8}{*}{0.05} & \multirow{8}{*}{16} & 1043, 1057, 1106, 1204, 1206 \\  & & & 1298, 1343, 1354, 1450, 1454, 1592 \\  & & & 1651, 1654, 1676, 1690, 1771 \\ \cline{2-4}  & \multirow{8}{*}{0.10} & \multirow{8}{*}{13} & 1057, 1140, 1204, 1206 \\  & & & 1298, 1332, 1354, 1450, 1555 \\  & & & 1620, 1650, 1752, 1762 \\ \cline{2-4}  & \multirow{8}{*}{0.15} & \multirow{8}{*}{12} & 1057, 1123, 1204 \\  & & & 1245, 1298, 1306, 1354, 1450 \\  & & & 1458, 1476, 1651, 1762 \\ \cline{2-4}  & \multirow{8}{*}{0.20} & \multirow{8}{*}{10} & 1057, 1116, 1204 \\  & & & 1210, 1282, 1354, 1450 \\  & & & 1454, 1651, 1762 \\ \hline \multirow{8}{*}{0.85} & \multirow{8}{*}{0.05} & \multirow{8}{*}{12} & 1086, 1122, 1148, 1227, 1282 \\  & & & 1400, 1530, 1608, 1689, 1719, 1739, 1762 \\ \cline{2-4}  & \multirow{8}{*}{0.10} & \multirow{8}{*}{10} & 1055, 1148, 1234, 1276, 1400 \\  & & & 1567, 1616, 1682, 1754, 1786 \\ \cline{1-1} \cline{2-4}  & \multirow{8}{*}{0.15} & \multirow{8}{*}{8} & 1062, 1148, 1234, 1354 \\  & & & 1576, 1698, 1754, 1778 \\ \cline{1-1} \cline{2-4}  & \multirow{8}{*}{0.20} & \multirow{8}{*}{6} & 1086, 1234, 1562 \\  & & & 1650, 1712, 1721 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selection results of optimal wavelengths by NRS-a and NRS-b.  
Figure 5: Selection of optimal wavelengths by NRS-b.  
was necessary to further compare and verify the representativeness of the selected bands by different algorithms through the ability of the established classification model.
## 3 Materials and Methods
### Determination of Strawberry Volatiles using HS-SPME-GC/MS  
The sample preparation method was adopted from our previous study with modifications [44]. Frozen strawberries (300 g) were ground using a blender (BL682 Auto-IQ, Nutri Ninja, Boston, MA, USA) for 1 min. Then, 5 g of the ground frozen strawberries was transferred to a glass headspace vial (Gerstel, Baltimore, MD, USA), and 5 mL of saturated sodium chloride solution was added. An internal standard mixture (octanal-d16, ethyl butanoate-d3, and n-hexyl-d13 alcohol) was added to the glass headspace vial with a final concentration of 10 \(\mu\)g/kg and was sealed immediately using a magnetic crimp cap (Gerstel).  
After 10 min of equilibration, a 2 cm solid phase microextraction (SPME) fiber coated with divinylbenzene/carboxen/ polydimethylsiloxane (DVB/CAR/PDMS) was used to extract the volatiles. The volatiles were absorbed for 30 min at 40 \({}^{\circ}\)C and desorbed for 30 s at 250 \({}^{\circ}\)C. Volatiles in strawberry were determined using gas chromatography (7890B, Agilent technologies, Santa Clara, CA, USA) coupled with a mass selective detector (5977B, Agilent technologies) and separated on a DB-WAX capillary column (30 m \(\times\) 0.25 mmi.d., 0.25 \(\upmu\)m film thickness; Agilent technologies). The oven temperature was set to 40 \({}^{\circ}\)C for 2 min; the temperature was increased by 4 \({}^{\circ}\)C/min to 120 \({}^{\circ}\)C and held at 120 \({}^{\circ}\)C for 5 min. Then, the temperature was increased to 250 \({}^{\circ}\)C at a rate of 20 \({}^{\circ}\)C/min and held at 250 \({}^{\circ}\)C for 5 min. The solvent delay was set to 3 min. Helium (99,999%) was used as a carrier gas at a flow rate of 1 mL/min. The inlet temperature was 250 \({}^{\circ}\)C, and an inlet liner (0.75 mm i.d., Agilent technologies) was used for the splitless mode. The temperatures of the transfer line, ion source, and quadrupole were 240 \({}^{\circ}\)C, 230 \({}^{\circ}\)C, and 150 \({}^{\circ}\)C, respectively. The electron energy was 70 eV. Mass spectra were obtained in the range of \(m/\)\(\sim\) 30-350 at a rate of 3.06 scans/s.
# Vehicular Visible Light Communication for Intersection Management +
## 4 V2X Cooperative System Evaluation
### Queuing System: Dynamic Traffic Signal Phasing
It is possible for the IM to execute the following operations based on the information in the request: modifies the durations of the cycle; enters the fixed times; changes the durations of states with the transportable from any state at a dynamic time; and modifies the intersection coordination offset, depending on the request information. Figure 10: Simulated scenario: Four-legged intersection and environment with the optical infrastructure (\(X_{ij}\)), the generated footprints (1–9) and the CV. The PoC assumes that all the leaders approach the intersection with similar velocities at different times (Figure 11). Vehicle \(a_{1}\) was the first to request to cross the intersection and informed IM about its position and also that four others follow it at their positions with their speeds. Phase 1, sequence 3, therefore, begins at \(t^{\prime}a_{1}\). Vehicle \(b_{1}\) requests access later and includes the mappings of its two followers in its request. As the order to cross conflicts  
Figure 11: Requested phasing of traffic flows. Figure 12: Matrix of states at a four-legged intersection. with \(a_{i}\) movement, he and his followers will pile up on the stop line increasing the total waiting time of the \(b_{i}\) cars. The fourth sequence is an adaptive sequence (Figure 12). Due to the presence of a medium E-W traffic scenario, the IM extends the green time in order to accommodate the passage of all the \(a_{i}\) followers as well as the simultaneous passage of the arriving \(c_{i}\) (Figure 8). From a capacity point of view, it is more efficient if vehicle \(c_{1}\) is given access (Phase 2) before vehicle \(b_{i}\), and vehicle \(c_{2}\) is given access before vehicle \(e\), forming a west left turn of set of vehicles (platoon) before giving way to the fourth phase with north and south conflicting flows, as stated in Figure 5. Meanwhile, the speed of vehicle \(e\) was reduced, increasing the total accumulated time in the S-N arm. Adaptive sequences 8 and 9 kick off Phase 3 (Figure 11) and the sequence times will be adjusted according to the variation of \(r_{t}\) for the left turn of the \(b_{i}\) cars. A new phase, Phase 4, begins and includes two adaptive sequences, sequence 12 and 13. Their time intervals will be as short as possible, which will free up capacity in the cycle for the E-W flows that are heavily loaded. Taking into account the accumulated total waiting time in each arm, an 85 s cycle is recommended for this type of flow. The times associated with each sequence can be visualized in Figure 11.  
So, the real-time detection of the spatiotemporal data based on urban road network traffic status can provide rich and high-quality basic data and fine-grained assessment of control effects for traffic control.