esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Step 1: Community Data Collection  
Collecting and gathering suitable visual data was the first step in a successful implementation of the technique. Similar to many innovative structural assessment methods based on computer-vision-based technology, images provided useful information containing many viewpoints, at frequent time intervals, and for many locations. However, this application was different compared with typical damage-detection algorithms, as graffiti is an obvious and visible object in an image. Hence, while individual images and their qualities are considered to be more critical in damage-detection algorithms, overall image dataset variety and volume were more important for this application, including collection frequency and inclusiveness of the key structures.  
Once the data were collected, our approach was to use the structure-from-motion (SfM) technique to establish a geometric relationship between images through orthophotos of facades of the TOC. The technical details of SfM are provided in the next subsection. In SfM, sufficient image overlap between images from various distances and orientations was required, although the specific amount of overlap needed varied depending on the image quality (e.g., resolution, noise, occlusions). Consequently, the collection of a large number of images was essential to this technique. Of course, such a large number of images having high temporal and spatial resolutions were necessary for monitoring the TOC.  
Involving citizens can provide a major breakthrough in this type of data requirement. Crowdsourcing to obtain a sufficient quantity of visual data would satisfy this condition. A variety of ordinary individuals such as residents, workers, tourists, and visitors present in a community were encouraged to capture the necessary images. Smartphones are broadly used around the world for capturing such data, and the data can be uploaded to any readily available cloud service (e.g., Google Drive, Dropbox, Box). Social-media technologies (e.g., Instagram, Facebook, and YouTube) further accelerated this mechanism for broad data capture by making many resources of visual data available online [40]. Specific crowdsourcing strategies for visual assessment were discussed in our previous work, and are not repeated here [41; 42]. For best results, the requirement in this step was the collection of a large number of images to cover the relevant surfaces of the TOC that may have been  
Figure 3: Faster R-CNN is a unified network of region proposals and key feature maps. This algorithm was used in this work to construct a robust graffiti detector.  
exposed to graffiti. A set of images was required to be collected within each assessment period to update the most recent scene of the TOC for detecting graffiti markings.
## 3 Simulation Studies and Results
### Comparison of Prediction Accuracy  
In this study, three classical prediction models were used to compare the prediction accuracy of the LSTM model. They are: random walk (RW) [57], stacked autoencoder (SAE) [18], and support vector machine (SVM) [58]. The RW model is the simplest, using the current state to predict the state of the next time, the formula is expressed as \(\widetilde{X}t+1=X(t)\). SAE is a deep neural network model that has performed well in predicting time series in recent years. For SAE, we used a five-layer network, which includes three hidden layers. The optimizer is RMSprop, and the batch size is 256 for the optimal performance of the model. SVM is also one of the more typical algorithms in machine learning and has been widely used in classification and prediction problems. It solves the problem that the original data is linearly inseparable in the original space by selecting a kernel function to map the data to high-dimensional space. We chose the radial basis function (RBF) as the kernel function in this study, and set the penalty parameter \(C=1000\) and the kernel coefficient parameter gamma = 0.01. The autoregressive integrated moving average (ARIMA) model was also considered, but it was dismissed due to the long running time when processing large amounts of data.  
Similarly, we used RMSE and MAE to evaluate the prediction accuracy of each model. For all models, the same data sets were used, and the model performance is shown in Figure 8.  
Figure 7: Comparison of observed and predicted one-day noise value on different data sets: (**a–h**) dataset 01_a, 02_a, 03_a, 04_a, 01_b, 02_b, 03_b, 04_b.  
It is vividly obvious from Figure 8 that, in the four prediction models for environmental noise time series, the performance of the LSTM network model proposed in this study is the best, and both the RMSE and MAE are the lowest.
# Feasibility, Design, and Deployment Requirements of TCR for Bistatic SAR radiometric Calibration
## 3 Bistatic RCS Characteristics
### FEKO Electromagnetic Simulation
The results of previous studies about TCR for bistatic SAR calibration may be obtained in this case [11]. However, we found the elevation angle of the peak RCS for different bistatic angles are different, as shown in Figure 8a. The main lobe is gradually disappearing and the energy is moving to the side lobe when the bistatic angle is greater than \(20^{\circ}\), so we set \(20^{\circ}\) as the upper limit for the range of feasible bistatic angle. Then, we got the bistatic RCS azimuth patterns of the TCR as the elevation angle corresponding to its individual peak RCS, as shown in Figure 8b. It can be seen that the bistatic RCS patterns are still wide and flat, even when the bistatic angle increases beyond \(6^{\circ}\). These azimuth patterns in Figure 8b are different from those of Figure 7, and the peak RCSs are larger than those of Figure 7 at the same bistatic angle. So, we concluded that the TCR can be used as the bistatic calibration target as the bistatic angle increases below \(20^{\circ}\). In this condition, we should align the TCR boresight  
Figure 7: The bistatic RCS azimuth pattern of the TCR based on FEKO electromagnetic simulations. according to the bistatic angle during the deployment, instead of just fixing the elevation angle at 54.74\({}^{\circ}\). However, the lower peak RCS will affect the SCR as the bistatic angle become larger. Combined with the electromagnetic simulation results and the above analysis, the results can be divided into two aspects. On the one hand, if we don't consider the change of TCR boresight with the bistatic angle, the TCR is not suitable for bistatic SAR calibration under large bistatic angles. Furthermore, the TCR meets the requirements of the calibration target for bistatic radiation calibration under the condition of small bistatic angles (less than 6\({}^{\circ}\)) when the bistatic SAR is working in tandem mode. On the other hand, we have also proposed that the bistatic angle can be extended to 20\({}^{\circ}\) by taking the change of the TCR boresight into account. In summary, if the TCR is aligned with the optimum boresight according to the bistatic angle during the deployment, it can also work at relatively large bistatic angles at the cost of sacrificing the SCR.
## 1 Introduction  
In nature, every circuit has constraints. In transportation circuits or circular transportation systems such as buses, railways, subways, trans, cable cars, etc., their main constraints are the stations. In the case of a railway system like the subway, the station is the main constraint on the circuit's speed, which directly impacts the system's transport capacity [1]. To maintain a high circuit speed, it is necessary to minimize the train's dwell time at the station, which involves maximizing the transfer of passengers between the platform and the train in the shortest time possible while maintaining high service levels.  
The physical and operational designs of a railway system are matters of engineering, but until today, the design of stations has been the responsibility of architectural offices, which are more concerned with the form than the function of the station. Thus, in Latin America, we have managed to build beautiful but useless subway stations. The efficiency of a station is determined by the transfer capacity of the platform [2]. This is the critical area of the station and the subject of the present research.  
In this research, the roots of the station design logic are analyzed, and each component is studied from both a physical and operational perspective (see example in Figure 1). The aim is to analyze, model, simulate, and evaluate the efficiency and capacity of each component individually, as well as in an integrated systemic manner. The behavior of passengers is studied from an operational perspective in corridors, stairs, elevators, platforms, and train access points. The passenger behavior within the station is modeled by establishing an algorithm that describes the interaction between the passenger and different infrastructures, and various scenarios will be simulated. With this, the efficiency and capacity of different platform configurations are evaluated by analyzing their characteristics, and the research concluded by describing which station configuration is ideal for specific environmental and operational conditions.  
The paper is structured as follows. In Section 2 the literature review is presented, followed by the methodology. In Section 4 the results are analyzed based on simulations. Finally, the conclusions are presented in Section 5.
# 3 GPP Evolution from 5G to 6G: A 10-Year Retrospective
## 6 3 GPP Release 19: 5G-Advanced Growth
### Toward 6G  
Release 19 lays the groundwork for 6G by addressing critical areas that will serve as stepping stones toward 6G, including channel modeling for ISAC and the 7-24 GHz spectrum. These channel models will be critical for the design and evaluation of 6G systems.  
ISAC is a key usage scenario of 6G networks, aiming to merge wireless communication with sensing capabilities [90]. ISAC allows the same infrastructure to support both data transfer and spatial sensing, which can detect and track objects. Release 19 initiates a comprehensive study on ISAC channel modeling, identifying deployment scenarios and refining channel models to support sensing. Key aspects of the ISAC channel modeling work include sensing target characterization, environment and mobility modeling, and spatial consistency.  
The 7-24 GHz spectrum has emerged as a key frequency range for 6G development, offering a balance between wide-area coverage and high data rates [91]. Release 19 focuses on validating and enhancing existing 3 GPP channel models for this spectrum. Existing models are verified and refined using new measurement and ray tracing data to ensure their accuracy for this intermediate spectrum range. The larger antenna arrays expected in this frequency range require additional modeling considerations, such as near-field propagation effects and spatial non-stationarity [92]. These factors influence the accuracy of beamforming and MIMO operations, which are crucial for high-performance 6G networks.
## 3 Result and Discussion
### Degradation and Oxidation of Bacon Proteins during LTS
#### 3.4.2 Protein Oxidation  
Protein oxidation influences flavor development. The content of sulfhydryl groups, which form inter- and intramolecular disulfide links upon protein oxidation, decreases as the oxidation process progresses; this decrease serves as an indicator of the extent of protein oxidation in meat products [39]. Changes in the content of protein sulfhydryl groups in bacon during LTS are shown in Figure 3A. As expected, the total number of sulfhydryl groups significantly decreased from 6.34 nmol/g to 2.07 nmol/g (\(p\) < 0.05), indicating the occurrence of protein oxidation. The data clearly show that the duration of LTS had a substantial impact on protein oxidation in bacon. Moreover, these results align with those reported by [PERSON] et al. [40], who showed that protein oxidation led to a decrease in the sulfhydryl content in smoked bacon.  
Additionally, the carbonyl content can be used to characterize the degree of protein oxidation. The decreasing trend in protein thiol groups was consistent with the formation of carbonyl compounds (Figure 3B), whose content increased from 1.76 nmol/mg to 6.18 nmol/mg (\(p\) < 0.05) by the end of LTS, indicating that protein oxidization continued to occur at this stage. This may be due to the continuous changes in processing temperature and catalysis by oxidases. In a study of Cantonese sausages, the same trend was observed regarding the carbonyl content during processing [41]. [PERSON] et al. [42] reported a significant increase in the total carbonyl content of bacon during the smoking process and noted that the temperature during bacon processing was the main factor affecting the carbonyl content. It has also been suggested that proteolysis promotes protein oxidation in meat [43].
# Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data
## 3 Results
### Validation of Estimated NPP  
Time-inconsistencies were found between Bigfoot NPP and our estimation. The Bigfoot NPP data were acquired from 2000 to 2004, but our estimation results are from 2004, 2008 and 2012. To preliminarily validate the model, we calculated the average NPP in 2004, 2008 and 2012 at each Bigfoot site and compared this to the average BigFoot NPP for 2000-2004. In general, a good linear relationship exists between estimated NPP and BigFoot NPP (Figure 12). The R\({}^{2}\) between estimated NPP and BigFoot NPP was as high as 0.84, and RMSE was 160.16 g C\(\cdot\)m\({}^{-2}\).yr\({}^{-1}\). R\({}^{2}\) between MODIS NPP and BigFoot NPP was 0.55, and RMSE was 219.26 g C\(\cdot\)m\({}^{-2}\).yr\({}^{-1}\). A higher R\({}^{2}\) and lower RMSE demonstrate that the estimated NPP achieved a better precision than the MODIS NPP product and indicate the applicability and reliability of the MuSyQ-NPP algorithm in estimating global NPP.  
Some studies demonstrate that MODIS products tend towards overestimation of NPP at low productivity sites (such as SEVI) and underestimation at high productivity sites (such as TAPA) [46; 47]. Generally, the overestimation appears to be primarily a problem with high MODIS FPAR, and the underestimations are primarily a function of low values for the maximum light use efficiency [46; 47]. Scatter plots of estimated NPP in our study were located closer to the 1:1 line (Figure 5), especially at high productivity sites (such as TAPA, KONZ). Overestimates of FPAR at SEVI, METL and CHEQ in midesaon contributed to the high MODIS NPP estimates at those sites [46]. Problems with cloud effects on FPAR and LAI estimates also lead to some artificial variation in the MODIS products [46], but the data assimilation framework to generate the GLASS FPAR and LAI products can estimate temporally complete land-surface parameter profiles from time-series reflectance data even if some of the reflectance data are contaminated by residual clouds or missing data [13]. Therefore, NPP estimates at TAPA are more accurate than MODIS NPP.
## 3 Medium Access Control Layer
### Frequency-Division Multiple Access  
FDMA is a contention-free medium access scheme that divides the available bandwidth into different frequency bands, allowing different nodes to transmit and receive at the same time while avoiding collisions. However, these frequency bands have to be carefully assigned and used, because collisions may occur inside the same band, as well. For example, if each node is assigned a receiving frequency, when two or more nodes try to reach the same destination at the same time, a collision will occur.  
Figure 5: Classification of medium access control (MAC) protocols.  
This scheme was used in the early phases of the Seaweb project [63]. Three clusters were deployed, and within each cluster, TDMA was used; FDMA was chosen for inter-cluster communications. However, an inefficient use of bandwidth was reported, and the protocol was also vulnerable to fading and multipath. Since then, FDMA has been considered to be unsuitable for underwater acoustic networks [4; 89].
## 1 Introduction
A too large decomposition level will increase calculation complexity without significant improvement in the denoising effect; a too small decomposition level cannot achieve desirable denoising results. In previous studies, it was determined based on prior knowledge about signal energy distribution in the wavelet domain [6] or through trial and error [9; 10; 11], which is expert-dependent and subjective. Automatic approaches are introduced in references [17; 19] aimed at guaranteeing that most energy of the signal is decomposed into detail sub-bands. There is no widely accepted method for decomposition level determination of the above-mentioned approaches. Threshold estimation, together with threshold function, is widely studied and plenty of achievements are acquired. The threshold estimation methods include sqtwolog threshold, rigrsure threshold, heursure threshold, and minimaxi threshold [20; 21; 22], of which the modified scale dependent threshold based on SURE estimator [12; 14; 18] is widely used in PD signal denoising and adopted in this paper. The threshold rules involve soft threshold, hard threshold, and other threshold functions. As above, plenty of studies have been developed about the three major problems of wavelet threshold denoising. However, in most studies, the three problems are considered in isolation, while in fact, they are closely related with each other. To improve PD signal denoising performance, this study presented a novel wavelet shrinkage scheme. First, a new optimum wavelet method is introduced. At each scale, the optimum wavelet is selected as the one maximizing the energy ratio of coefficients beside and inside the range formed by the threshold, which represent the signal to be reserved and noise to be removed, respectively. The method is aimed at increasing the coefficients' energy corresponding to signal and decreasing the coefficients energy corresponding to noise, which means that the wavelet is most appropriate for extracting the PD pulses immersed in noise. Besides, a novel idea for decomposition level determination is proposed accordingly, i.e., after each decomposition, the energy composition of the approximation coefficientsis analyzed to judge whether to perform the next scale decomposition or not. The innovations of the proposed scheme are as follows: (1) the optimum mother wavelet is selected with the purpose to improve the SNR of coefficients combined with the threshold at each scale, (2) the decomposition level is determined considering the influence of the optimum mother wavelets at all scales instead of the frequency analysis before the wavelet decomposition, (3) the proposed scheme is applied to the simulated PD signals and measured PD signals of large generators, obtaining outstanding results.
## 3 Results and Discussion
### Influence of Rib Height on SIF of Crack Tip
The change of rib height has a significant effect on the change of SIF at the crack tip of the integral panel. In order to ensure the fracture resistance of the integral panel, the height of the rib should be within a reasonable range, usually more than 30 mm.
# Multi-Objective Motion Control Optimization for the Bridge Crane System
## 4 Numerical Simulation
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Parameter** & \multicolumn{3}{c}{**Solution 1**} & \multicolumn{3}{c}{**Solution 2**} & \multicolumn{3}{c}{**Solution 3**} \\ \hline \multirow{3}{*}{\(Q\)} & 470.19 & 0 & 0 & 0 \\  & 0 & 219.74 & 0 & 0 \\  & 0 & 0 & 465.33 & 0 \\  & 0 & 0 & 0 & 2.01 \\ \hline \(R\) & \multicolumn{3}{c}{2.21} & \multicolumn{3}{c}{2.05} & \multicolumn{3}{c}{2.42} \\ \hline \([k_{1},k_{2},k_{3},k_{4}]\) & \([14.60,13.13,-14.66,-0.93]\) & \([15.89,13.93,-15.89,-0.95]\) & \([13.99,12.75,-13.75,-0.90]\) \\ \hline \(K_{I}\) & \multicolumn{3}{c}{0.02} & \multicolumn{3}{c}{0.02} & \multicolumn{3}{c}{0.04} \\ \hline \(K_{D}\) & \multicolumn{3}{c}{166.82} & \multicolumn{3}{c}{169.34} & \multicolumn{3}{c}{175.86} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of three solutions. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Performance** & **Solution 1** & **Solution 2** & **Solution 3** \\ \hline Settling time (s) & 4.21 & 2.68 & 1.86 \\ Maximum payload swing (deg) & 1.00 & 2.00 & 4.00 \\ ITAE of swing & 2469.21 & 2993.82 & 3999.16 \\ ITAE of positon & 910.92 & 328.69 & 154.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of three solutions. ITAE: integration of the time-weighted absolute value of the errors. In order to verify the anti-disturbance performance, an acceleration excitation is given to the crane, thereby leading to a certain swing angle for the payload. Here, the acceleration is set as 0.8 m/s\({}^{2}\), and its duration as 0.2 s. The responses of the TP, LQR, and proposed algorithm are compared in Figure 9. It can be observed that the maximum payload swing of TP is 7.3\({}^{\circ}\) when the external disturbance occurs; obviously, it cannot meet the control demands. In addition, the LQR algorithm can suppress the swing, and the maximum swing is 2.03\({}^{\circ}\). However, the whole duration is still 1.66 s. The performance of the proposed algorithm is superior to that of other control methods, and the settling time is 1.44 s. Thus, it proves that the algorithm can achieve fast, stable ability and realize immune control of external disturbances. Next, experimental validation is performed to further justify the feasibility of the proposed algorithm. Figure 8: Performance of three controllers. Figure 7: Control response of trajectory planning (TP), linear quadratic regulator (LQR), and the proposed method. Figure 9: Response of different controllers with external disturbances.
## 4 Results
### Reference SOC Trajectory  
The NLP-based ECMS calculated the reference SOC trajectory by using NLP. Accuracy and computational time were important factors. Accuracy indicates how close the results of NLP were to the global optimal solution.  
The results of NLP are compared with DP models in the comparison group. Table 3 compares the NLP and DP results. The calculation time is the time spent calculating the reference SOC trajectory. The sum of error is the difference between the global optimal solution and the reference SOC trajectory obtained by DP and NLP. As a result, NLP was the quickest and the most accurate to calculate the reference SOC trajectory.  
Case 0, Case 2, Case 5, and Case 6 are results for the reference framework obtained by the DP model. Case 0 takes the longest time because it has the largest number of input/state grids and the smallest step size. Because the most accurate DP model is used, Case 0 is used as a reference because it is the closest result to a global optimal solution.  
Figure 10: Comparison of DP results (2): engine torque.  
Figure 9: Comparison of DP results (1): state of charge.  
In order to be applied to DP based ECMS, the DP model of Case 2, Case 5, and Case 6 has been simplified. Fewer input/state girds and larger step sizes were used, which can reduce the computation time. Because the model is simplified, however, the results were moved away from the global optimal solution. The cost function of the DP is the fuel consumption of the HEV as shown in Equation (1). The cost function used in the DP of each case and the system model (the equations, parameters, maps, etc. shown in Section 2) are also identical.  
The NLP case is a result obtained by NLP. The cost function and system models of NLP and DP are different. The cost function and system model of NLP are shown in Section 3.2. However, because the target vehicle is same, same vehicle parameters are used as shown in Section 2.  
The tendency of the reference SOC trajectory is important because it determines the charging or discharging of the EM. As shown in Figure 11, the reference SOC trajectory obtained by NLP was similar to the global optimal solution. As shown in Figure 12, the states of ICE (\(x1\)) and EM torque (\(x2\)) were found by the NLP input to be first-order delay functions. Additionally, the states of ICE and EM torque were limited by constraints on the maximum/minimum ICE and EM torque.