esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Crop Classification Using Random Forest  
In this study, the validity of the virtual image generation was assessed in relation to crop classification using the generated Sentinel-2 image as one of the inputs. Of the various machine learning-based classifiers, RF was employed as a classifier because of its superior classification performance in remote sensing image classification [66; 67; 68]. It should be noted that the main objective of crop classification is to analyze the impact of the virtual image on the classification performance, not the selection of the best classifier. As an ensemble classifier, RF maximizes the diversity of decision trees through the bootstrapping of training samples and the random subsampling of input features to improve classification performance [69]. The optimal values of the two hyperparameters, including the number of trees to be grown in the forest and the number of variables for the node partitioning, were determined using a grid search.  
The training samples for the RF-based supervised classification were extracted from the CDL in 2019. In early crop mapping in foreign countries, it is often difficult to collect sufficient training samples through visual inspection. To mimic this early crop mapping case, a very small number of pixels were extracted as training samples. From the preliminary test, 1056 training samples (approximately 0.1% of the total pixels in the study area) were finally utilized as training samples for RF-based classification. The number of training samples for each class was determined by considering the relative proportions of classes in the study area (Table 5). As a result, the number of training samples for minor crops in the study area, including wheat and alfalfa, is very small. Furthermore, to mitigate a biased sampling problem of major classes in supervised classification [13], we set the number of training samples for the double cropping class equal to corn and soybean. All of the pixels except the training samples were considered test samples for the accuracy evaluation. As very small training samples were extracted, the relative proportion of training samples to test samples was still approximately 0.1%, which is suitable for thoroughly evaluating the classification results for early crop mapping.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Layers** & **Output Dimension (P,P,F)** \\ \hline \({}^{*}\)Conv, L & (512,512,9) \\ Conv, L & (256,256,32) \\ Conv, BN, L & (128,128,64) \\ Conv, BN, L, Z & (66,66,128) \\ Conv, BN, L, Z & (63,63,256) \\ Conv, S & (62,62,1) \\ \hline \hline \end{tabular}
\end{table}
Table 4: List of the structures and hyperparameters of the PatchGAN-based discriminator (P: patch size; F: number of features; Conv: convolution; BN: batch normalization; L: leaky; S: sigmoid; Z: zero padding; *: convolution operation in the representation stage).
## 4 Design of Adaptive PID Controller Based on ISSA  
### Traditional PID Controller  
In the field of control systems, the PID (Proportional-Integral-Derivative) control algorithm, as a classic feedback control method, is widely employed to regulate the dynamic performance of systems. The PID controller adjusts the control output based on the current error \(e(t)\) (difference between setpoint and actual value) and the rate of change of the error \(ec(t)\), aiming to achieve stability, rapid response, and precise tracking of targets. The PID controller consists of proportional, integral, and derivative terms, corresponding to the current error, accumulated past errors, and the rate of change of the error, respectively.
# SOUNET: Self-Organized Underwater Wireless Sensor Network
## 5 Simulation Results
### Analysis of Results
#### 5.3.4 Signaling Overhead  
_HELLO_ packet flooding is a common method used for initializing a network and SOUNET and S\({}_{13}\) apply this method. As the network size increases, the number of _HELLO_ packets transmitted by nodes also grows, thus increasing the necessary signaling overhead. In our simulation, 49 _HELLO_ packets are transmitted during initialization (in the case of SOUNET and S\({}_{13}\)). On the other hand,  
Figure 14: The average and SD of \(\rho_{i}\): (**a**) \(\mu_{\rho}\); (**b**) \(\sigma_{\rho}\).  
Figure 15: Energy consumption.  
the other comparing scheme, Seaweb, uses the breadth-first search algorithm instead of the _HELLO_ flooding. This leads to higher number of signaling overhead; for example, in total about 900 packets were obtained from our simulation. During the maintenance phase, SOUNET adapts to the varying environment with the help of the overhearing packets. Therefore, no additional signaling is required and no large overhead is incurred during maintenance. S\({}_{13}\) and Seaweb do not have maintenance phase.
## 2 Methodology
### Methodological Workflow  
To improve clarity and reproducibility, the methodological workflow of the proposed OOCA method is summarized in Figure 4. The process begins with post-stack seismic data as input, followed by local dip estimation using a gradient-based approach. A Gabor transform is then applied for multi-frequency decomposition to capture structural variations across spectral scales. Pseudo seismic traces are extended in multiple directions and enhanced via inverse distance weighting. These components are used to construct multi-frequency, multi-direction structure-oriented model trace pairs. Cross-correlation is performed to evaluate coherence in each direction, and the direction with the lowest similarity is selected as optimal. Finally, an energy-weighted fusion of multi-frequency coherence volumes is conducted to generate a structure-consistent, high-resolution coherence attribute that simultaneously highlights large-scale faults and fine-scale discontinuities.  
Figure 3: Comparison of the geosteering coherence method in [PERSON] (2018) [47] (**a**) and the proposed geosteering coherence method (**b**). The red circle denotes the center trace, and \(\oplus\) and \(\otimes\) denote weighted average and cross-correlation operations, respectively.  
The method was implemented in MATLAB 2024b and tested on a Windows \(11\) workstation equipped with an Intel i7-12700K CPU, supporting multi-frequency and multi-core parallel computation, ensuring practical feasibility and reproducibility for researchers working with similar seismic datasets.
## 5 LSTM-Based Scheduling
### Sliding Window  
The lookback window represents the duration of past data provided to the model, with the current time as the reference point. The model is trained and performs predictions using data within this period. For example, if the lookback window is 24 h, the model is trained and predicts based on data from the current time up to 24 h ago.  
The forecasting horizon refers to the time interval into the future that the model aims to predict, with the current time as the reference point. It determines how far into the future the model intends to make predictions. For instance, if the forecasting horizon is 6 h, the model performs predictions for the timeframe from the current time to 6 h ahead.  
Applying the sliding window technique to chaotic long-term time series data, such as the Google Cluster Trace Dataset, offers several advantages:  
* Non-linear Behavior Detection: Chaotic data often exhibits distinct non-linear dynamic patterns. Utilizing the sliding window allows the model to capture and learn these non-linear patterns.
* Temporal Dependencies: Chaotic time series data involves crucial temporal dependencies. Sliding window considers data within specific periods, leading to a more accurate understanding of the temporal dependencies.
* Adaptability and Model Generalization: Sliding window aids the model in adapting to the dynamic nature of the data. Chaotic data can be challenging for prediction, but through the sliding window, the model can learn and generalize patterns within given periods.  
We explored the optimal lookback window and forecasting horizon through experiments on the Google Cluster Trace Dataset. In conclusion, the best prediction performance was observed with a lookback window of 3 and a forecasting horizon of 1.
## 3 Results
### Boundary Conditions
#### 3.1.3 Outdoor Conditions  
Table 7 presents the values (median, first quartile (P25), and third quartile (P75)) for T, pressure, CO\({}_{2}\), and wind speed outdoors for C1 and C2. The two campaigns were conducted under different weather conditions: The median temperature during C2 was higher than during C1 by 3.5 \({}^{\circ}\)C. Atmospheric pressure during C1 was 1.4% higher than that during C2. The CO\({}_{2}\) concentration was 4.7% higher than in C2 compared to C1. The wind speed (V) was 22.2% higher during C2 compared to C1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Room** & **Vent Component Type** & \begin{tabular}{c} **QREG, BASE** \\ **(m\({}^{3}\cdot\)h\({}^{-1}\))** \\ \end{tabular} & **V (m\({}^{3}\))** & \begin{tabular}{c} **QHIGH** \\ **(m\({}^{3}\cdot\)h\({}^{-1}\))** \\ \end{tabular} & \
\begin{tabular}{c} **QBASE** \\ **(m\({}^{3}\cdot\)h\({}^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} **ACR** \\ **(h\({}^{-1}\))** \\ \end{tabular} \\ \hline BR1 & Supply & / & 32.3 & 21 & 18 & 0.56 \\ \hline BR2 & Supply & / & 32.9 & 14 & 12 & 0.37 \\ \hline BR3 & Supply & / & 31.4 & 14 & 11 & 0.36 \\ \hline BR4 & Supply & / & 35.5 & 13 & 11 & 0.31 \\ \hline Kitchen + Living room & 2 Supplies & / & 110 & 38 & 27 & 0.25 \\ \hline Kitchen + Living room & Exhaust & 45 & 110 & 51 & 30 & 0.28 \\ \hline Bathroom 1 & Exhaust & 30 & 8.1 & 22 & 15 & 1.91 \\ \hline Bathroom 2 & Exhaust & 30 & 13.5 & 21 & 13 & 1.00 \\ \hline WC 1 & Exhaust & 15 & 4.5 & 16 & 10 & 2.31 \\ \hline WC 2 & Exhaust & 15 & 6 & 15 & 12 & 2.03 \\ \hline Whole house & Exhaust & 135 & 337 * & 124 & 81 & 0.25 \\ \hline \hline \end{tabular}  
*: other spaces as corridors, halls, staircase, account for 62.8 m\({}^{3}\).  
\end{table}
Table 6: Exhaust airflows in the French building code for this size of house at the base speed (QREG, BASE) and measured airflows at the maximal speed (Q\({}_{\text{HIGH}}\)) and at the base speed (Q\({}_{\text{BASE}}\)) with corresponding calculated ACRs in each room.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **C1** & **C2** \\ \hline \multirow{2}{*}{T (\({}^{\circ}\)C)} & MEDIAN & 3.1 & 6.6 \\ \cline{2-4}  & P25, P75 & 0.8, 5.1 & 3.1, 11.0 \\ \hline \multirow{2}{*}{P (Pa)} & MEDIAN & 1020 & 1006 \\ \cline{2-4}  & P25, P75 & 1014, 1022 & 1005, 1009 \\ \hline \multirow{2}{*}{CO\({}_{2}\) (ppm)} & MEDIAN & 409 & 428 \\ \cline{2-4}  & P25, P75 & 397, 439 & 417, 449 \\ \hline \multirow{2}{*}{V (m/h)} & MEDIAN & 0.9 & 1.1 \\ \cline{2-4}  & P25, P75 & 0.5, 1.4 & 0.5, 1.7 \\ \hline \end{tabular}
\end{table}
Table 7: Median value, P25, and P75 for T, P, CO\({}_{2}\), and wind speed outdoors during C1 and C2.
# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## 5 Discussion
### Effect of NDVI Optical Filter
Therefore, when applying the NDVI filter using \(NDVI_{li}\leq 0.4\) in the evening acquisition mode, the percentage of the non-irrigated plots with no detected irrigation events increased by 36% (58-22%) compared to the percentage obtained when no NDVI filter was applied. On the other hand, the percentage of the irrigated plots with no detected irrigation events increased only by 6% (9-3%) compared to that obtained when the filter was not applied. The significant increase in the percentage of non-irrigated plots with no detected peaks, between the case where NDVI filter was not applied and NDVI filter was applied at \(NDVI_{li}\leq 0.4\), encouraged the use of the post-processing optical filter despite of the loss of 8% and 6% in the irrigated plots for morning and evening acquisitions, respectively.
## 4 Experimental Process and Analysis
### Analysis of the Spectral Feature  
Figures 16 and A1 show the results of the spectral characterization based on the SHAP algorithm.  
Figure 16 presents an analysis of spectral features conducted on 40 randomly selected samples from the test set. The average SHAP values for each feature were calculated by averaging the SHAP values across these 40 samples, with the feature displaying the largest absolute SHAP value identified as the most important. Figure A1 visually illustrates the importance of each variable through color-coded points, where the y-axis represents feature relevance and the x-axis represents SHAP values. Red points indicate a higher feature importance, while blue points signify a lower importance, reflecting the impact of various input features on the prediction outcomes. As shown in Figures 16 and A1, the predicted concentration of the reactive red X-3B dye was most influenced by spectral reflectance at 442.26 nm and 452.79 nm, with a positive correlation between reflectance at these wavelengths and the predicted concentration. Similarly, the predicted concentration  
Figure 16: Four bar graphs illustrate the significance of various features in predicting different dye concentrations and ultrasonic pretreatment powers. (**a**) shows the top 30 spectral features contributing the most to the prediction of the reactive red dye concentration, (**b**) presents the top 30 features for the reactive yellow dye concentration, (**c**) displays the top 30 features for the reactive blue dye concentration, and (**d**) illustrates the top 30 features impacting the prediction of the ultrasonic pretreatment power.  
of reactive yellow X-RG was primarily influenced by the reflectance at wavelengths of 452.79 nm, 450.16 nm, 431.74 nm, and 455.43 nm. An increase in reflectance at 452.79 nm, 450.16 nm, and 455.43 nm was associated with a decrease in the predicted concentration, whereas an increase in reflectance at 431.74 nm correlated positively with an increase in predicted concentration. For the reactive blue X-3G dye, the reflectance at 402.90 nm and 431.74 nm had the most significant impact on the predicted concentration, with a strong positive correlation between the reflectance at 402.90 nm and the predicted concentration and a negative impact from increasing reflectance at 431.74 nm. In predicting the ultrasonic pretreatment power, reflectance at 701.81 nm, 434.37 nm, and 431.74 nm played a dominant role, with a higher reflectance at 701.81 nm corresponding to a higher predicted power, while the reflectance at 434.37 nm and 431.74 nm negatively correlated with the predicted power. Additionally, \(\lambda_{b}\), \(SAD\), and \(d_{Froblet}\) were found to be associated with the predictions of both dye concentrations and ultrasonic power, aligning closely with the spectral data analysis presented in Section 4.4.1. It should be noted that these conclusions are specific to the reactive dyes used in this study. Different types of dyes may exhibit varying spectral and chemical behaviors, and the model may require retraining to ensure accurate predictions.
# RS-YOLOX: A High-Precision Detector for Object Detection in Satellite Remote Sensing Images
## 4 Experiments
### Experiment Results of Proposed Methods
#### 4.4.4 Effects of SAHI  
As our experiments were trained using cut remote sensing images, when actually applied to large-sized remote sensing images, we could still detect targets only after the images were cut. When we used our trained model, Figure 8 displays the detection results of the remote sensing images after the images were cut.  
Our model performed well on these small images in Figure 8, detecting all the objects in these images. However, if we directly use the model to detect a high-resolution image, the detection effect will be poor, as shown in Figure 9. This is the detection result of a high-resolution image (a size of 3826 \(\times\) 3473). A large number of objects are not detected and there are many false detections. In response to this problem, we introduced the SAHI [40] open-source framework and mounted our trained YOLOX model onto the SAHI framework. The detection result obtained is shown in Figure 10. It can be clearly seen that the use of SAHI greatly improves the detection performance of the model for large-size images.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**DOTA-v1.5**} & \multicolumn{2}{c}{**TGRS-HRSRD**} & \multicolumn{2}{c}{**RSOD**} \\ \cline{2-7}  & **mAP\({}_{\textbf{50-95}}\) (\%)** & **Latency (ms)** & **mAP\({}_{\textbf{50-95}}\) (\%)** & **Latency (ms)** & **mAP\({}_{\textbf{50-95}}\) (\%)** & **Latency (ms)** \\ \hline YOLOX–BCE & 73.57 & 13.66 & 66.07 & 11.34 & 77.18 & 14.86 \\ YOLOX–FL & 75.08 & 15.91 & 66.92 & 13.18 & 77.46 & 16.53 \\ YOLOX–VFL & **75.52** & 14.04 & **67.13** & 11.87 & **78.03** & 15.27 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test results of different confidence loss functions.  
Figure 8: Detection results of small images on DOTA-v1.5 after the images were cut. (**a**) Plane, (**b**) basketball court, (**c**) ship, (**d**) large vehicle, (**e**) swimming pool, (**f**) tennis court, (**g**) storage tank, and (**h**) harbor.
## 1 Introduction
[20] tested the vibration transmission in the platform of a high-speed railway station with an integrated station-bridge structure, demonstrating a certain range of vibration rebound zones with increasing distance. Currently, the \"Code for Design of Railway Passenger Stations\" (TB 10100-2018) in China states that the \"building-bridge integrated\" structure is not recommended for main-line passenger railways where train speeds exceed 160 km/h [21]. Therefore, in \"building-bridge integrated\" stations with high-speed trains passing through the mainline, structural joints are commonly implemented. However, these joints can cause issues such as water leakage and seepage, highlighting the need to explore whether it is feasible to design such stations without structural joints on the mainline. Existing literature on train-induced vibrations in \"building-bridge integrated\" railway bridge-type station structures primarily focus on vibration characteristics, safety, and comfort. It also summarizes the station structure's vibration response, as well as the propagation and attenuation of wheel-rail noise. However,comparative research on the effects of setting versus not setting structural joints on the dynamic response, safety, and comfort of \"building-bridge integrated\" railway bridge-type stations is limited. To address this gap, this study selects two six-line railway bridge-type stations for analysis, considering two structural types of \"building-bridge integrated\" design: mainline \"building-bridge integrated\" and arrival-departure line \"building-bridge integrated\". A comparative study is conducted to assess the impact of setting structural joints on the dynamic performance of \"building-bridge integrated\" railway bridge-type stations with mainline speeds exceeding 160 km/h. This study aims to provide valuable insights for bridge station design standards and future code revisions.
## 3 Results and Discussion  
### Protein Content  
Larvae reared on different substrates (1S, 2S, 3S, and 4 CS) showed variations in protein content. Specifically, larvae from group 3L, reared on substrate 3S, exhibited the highest protein content at 59.08 \(\pm\) 0.07% (\(p\) < 0.05), while larvae from group 2S had the lowest protein content at 16.10 \(\pm\) 0.1% (\(p\) < 0.05) (Figure 2). These results have suggested that the choice of substrate significantly affected the protein content of mealworm larvae. These data are important for optimizing mealworm larvae as a protein source for various applications, such as animal feed and human consumption.  
[PERSON] et al. using brewer's spent grain have determined only 29.4% of protein in larvae, while in our study, we found 48.59 \(\pm\) 0.03% of protein when supplementing with carrots and brewer's yeast [29]. [PERSON] et al. applied former food products to _T. molitor_ rearing. Their results showed that when rearing mealworms on brewery-spent grains, a protein content of 51.34% was determined, while in our study, a very similar percentage of 48.54 \(\pm\) 0.03% was found [42]. [PERSON] et al. have studied the influence of different diets on the nutritional value of mealworms and have determined the protein content of 47.9 \(\pm\) 0.10% using a diet with wheat bran (on a dry weight basis). Whereas in our study, 3L larvae grown on a similar substrate (additionally inserted brewer's yeast + carrot) showed a protein content of 59.18 \(\pm\) 0.07% [16].  
Figure 2: Protein content in substrate and mealworms. 1L—larvae (wheat bran + brewer’s yeast + green potatoes); 2L—larvae (wheat bran + brewer’s yeast + carrot); 3L—larvae (brewers’ spent grain + brewer’s yeast + carrot). 4 CL—larvae, control (wheat bran + brewer’s yeast + agar–agar gels); 1S—substrate (wheat bran + brewer’s yeast + sprouted potatoes); 2S—substrate (wheat bran + brewer’s yeast + carrot); 3S—substrate (brewers’ spent grain + brewer’s yeast + carrot); 4 CS—substrate, control (wheat bran + brewer’s yeast + agar–agar gels).  
[PERSON] and colleagues conducted a study determining the protein content in lyophilized _T. molitor_ larvae and have found an extremely high protein content, as high as 68.8 \(\pm\) 0.1%, while in our analogous 3L sample with wheat bran, a 9.62% lower protein content was detected, and it was similar to the amount found in fish (63%) or soy meal (55) [43; 44; 45].  
[PERSON] et al. have examined the influence of feed on the breeding of mealworms and, at the same time, reviewed the changes in nutritional value depending on the substrate used [46]. The results of the mentioned study showed that, using ware potatoes as a substrate, the amount of protein determined in the larvae reached 54.9 \(\pm\) 0.3 g/100 g (corresponding to 54.9%). In our study, using green and sprouted potatoes, 53.08 \(\pm\) 0.01% protein was found, so the results were similar to those of [PERSON] et al.'s study [46].  
Our findings have indicated that the protein content in mealworms was influenced by the protein content present in the chosen substrate during their growth (\(p<0.05\)).