esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Performance Assessment Based on Eddy Covariance

Table 4: Multiple comparison test of the four ET products using the mean absolute error of all sites and various vegetation types. Figure 3: Boxplots of mean absolute error (MAE) for all four ET products and EC ET in 8-day (**a–c,g,h**) and monthly (**d–f,i–l**) scales. It is obvious that the performances at 8-day scales are highly consistent with performances at monthly scales in each land cover type for all the four products (Figure 3 and Table 5). This is also consistent with their performance when using all the land cover sites. ET performance comparison of the four products in different land cover types indicates that no product can perform best for all the land cover types (Table 5 and Figure 3). For evergreen broadleaf forests, PML-V2 performs best because it has both the lowest 8-day (6.61 mm) and monthly (22.01 mm) RMSE among the four products. For deciduous broadleaf forests, BESS performs best with its 8-day and monthly RMSE of 6.49 and 21.45 mm, respectively. BESS also performs best for croplands as it has the lowest monthly RMSE of 22.35 mm among the four products. For shrubs, grasslands and mixed types, GLASS has consistently the lowest RMSE among the four products. Although GLASS, BESS and PML-V2 have their corresponding best performance with some land cover types, no significant MAE difference is found among the three products in all the land cover types except cropland, in which a significant MAE difference is found both between BESS and GLASS and between BESS and PML-V2 (Table 4). It is worth noting that MOD16 does not have the best performance in any land cover type as it records the highest RMSE in almost all land cover types, and significant MAE differences are mostly found between MOD16 and the other three products (Table 4).
## 4 Modeling Process and Results
### Construction of the GD-GPR-SI Model  
For the traditional bivariate statistical models, each factor class has the same weight, causing all values in the same class for continuous factors to be weighted equally, which is contrary to [PERSON]'s First Law of Geography. To solve this problem, the GPR algorithm was used to optimize the weights obtained by the SI model.  
First, for continuous factors, the following eight factors were included: distance from roads, distance from main rivers, altitude, distance from hydropower stations, aspect, slope, degree of relief, and TWI. The weight of each factor class obtained by the SI model was used as the weight of the central value of the class. Then, the central value of the class was used as the independent variable, its weight value was used as the dependent variable, and GPR was used to perform regression learning, giving the weight of all factor values (as shown in Figure 7). For discrete factors, including soil erosion intensity, stratigraphy, land cover, and lithology, the weights of the SI model were used as final weight values.  
Figure 6: The results of recursive feature elimination based on GeoDetector.  
MATLAB R2020b software was used to implement GPR. The results of the regression are presented in Figure 8, which shows that the trends of factor weights change with varying factor values. The RMSE values of the models for each factor are listed in Table 3. Finally, the weights of all factors were accumulated to obtain the LSI of each evaluation unit. This hybrid model was named the GD-GPR-SI model.
## 8 Conclusions  
This study provides a comprehensive analysis of the application of digital twin technology in intelligent manufacturing production lines for automotive hubs. The primary objective was to design and implement an intelligent production line based on the principles of digital twins. The specific contributions of this research are outlined as follows:  
A comprehensive architecture for an intelligent production line system utilizing digital twin technology is proposed. This architecture employs a hierarchical design approach that encompasses four layers: the physical production line layer, the data layer, the digital twin production line layer, and the application service layer. By integrating industrial Internet of Things (IoT) technology, the framework facilitates real-time mapping and seamless integration between the physical production line and the digital environment.  
Utilizing OPC UA technology, a data perception platform framework has been developed for the intelligent production line system of automotive wheels, based on the concept of digital twins. This framework effectively addresses the challenges related to data acquisition and transmission from the underlying equipment. It facilitates data interaction and fusion between the physical production line and the digital twin, thereby enhancing the visualization of the production process.  
The operational application of the system, combined with collaborative debugging and simulation results, demonstrates its capability to achieve real-time data acquisition and visualization of the intelligent production line's operations. The system fulfills the requirements for three-dimensional real-time monitoring of the production line, thereby confirming its feasibility.  
The current research has successfully established capabilities for virtual simulation and virtual-real data acquisition and storage, as well as the three-dimensional real-time monitoring of an intelligent production line for automotive wheel hubs. Future investigations will further explore the integration of advanced technologies, such as big data analytics and artificial intelligence algorithms, to enhance quantitative analysis, diagnosis, evaluation, optimization, prediction, and decision-making processes, for the intelligent production line of automotive wheel hubs, with the aim of improving its performance and intelligence.  
Figure 14: Synchronous running state diagram of physical production line and digital twin production line.
## 3 Results and Discussion
### Laboratory Experiment
#### 3.2.1 Time Analysis  
The results of the comparison of assembly times for the assembly of products P1 and P2 on traditional and smart manual workstations are shown in Table 1. At the smart manual workstation, all workers completed the assembly faster than at the traditional workplace. Product P1 is assembled at the traditional manual workstation in an average of 216.8 s, which is 24% longer than at the smart workstation (175.3 s). For product P2, workers spend an average of 187.4 s on the traditional workstation, which is 14% longer than the results on the smart workstation (159.7 s). The time analysis also shows that P1 is more complex, and this complexity means that more time is spent on the assembly process. It should be emphasized here that the reduced time is only the result of the implementation of the new assembly system and was not the primary goal of the research, so we do not want workers to have more stringent time norms. This result is an indication that the proposed assembly system makes the workplace with the worker more efficient. After analyzing the videos of the experiment and the workers' user experience, we found two significant time wastages on the traditional manual assembly workstation. First, when workers search for parts in the grab containers, and second when workers do not follow the instructions or get lost reading the assembly sequence from the paper instructions.  
Disadvantages of a smart assembly workstation that result from reviewing videos and interviews with the workers are:  
* The existing LPM software has a small delay between the display of the current step and the next step, which in the assembly process is shown as the worker waiting for further instructions. This leads to time wastage and lowers added value and productivity.
* When the worker assembles the product for the first time, he or she receives a lot of information (lights on, pick-by-light technology, LPM, rotation of the assembly nest, movement of grab containers), which causes initial confusion. However, this is only a transitional state, which on average lasts just for two assembly steps.
* The LPM software displays the current and the next assembly operations in parallel, which has confused some workers to follow the next instructions without clicking the \"Next\" button after completing the current step.
* The laser beam emitted by the pick by light technology should be more intense, as it is not sufficiently visible on the blue bottom of the grab containers.  
We have considered all constructive criticisms and limitations related to the current smart assembly workstation, which provide a good basis for improvements and the definition of development guidelines in future work.
# Sensitivity of Optical Satellites to Estimate Windthrow Tree-Mortality in a Central Amazon Forest
## 4 Discussion
### Relating Satellite Data and Field Data
In our study, pixels outside/inside observational subplots may have been incorrectly accounted for. Our plots were georeferenced with a navigation GPS (Garmin 64 CSx), which has a nominal spatial resolution usually \(>\)5 m. This effect was likely more relevant for WorldView 2. Geolocation errors for Landsat 8 are at the order of 30 m within the United States, and 50 m globally [112]. For Sentinel 2 and WorldView 2, global errors are at the order of 12.5 m [113; 114] and 3.5 m [115], respectively. However, previous studies using the same plot size indicated a high correspondence between ground truth and the NPV signal obtained with Landsat [1; 2]. As previously demonstrated, \(\Delta\)NPV is highly correlated with windthrow tree mortality [1; 2; 23]. As supported by our results (Figure S5, Table S5), using NPV\({}_{\mathrm{norm}}\) for quantifying windthrow tree-mortality also produced robust and unbiased estimates. Nonetheless, the nature of such a relationship can be affected by variations in forest deciduousness and disturbance history (e.g., fragmented forests with larger proportion of edges or those submitted to multiple disturbances). Ideally, imagery covering the target forest should be available for pre- and post-disturbance conditions, and with the shortest possible time-interval between scenes.
## 4 Discussion
### Limitations  
There are several limitations and uncertainties in this study. Firstly, the evaluation of five types of ES was limited in terms of reflecting the overall ESS level due to data availability and quality constraints [89]. In addition, the equal-weight superposition calculation method used to calculate total ESS may have overlooked significance of various ES types in Shanxi Province. Future studies should strive to include a more comprehensive range of ES indicators and consider their relative importance. Secondly, the selection of indicators to represent ESD focused on land development intensity, population density, and GDP per area, assuming that all types of ESs have the same demand. This oversimplification may not accurately reflect spatiotemporal changes in ESD. Further research is needed to refine the measurement of ESD and capture its dynamics. Thirdly, this study compared the changes in ESS and ESD between 2000 and 2020, overlooking the temporal volatility of ES. Incorporating temporal dynamics would enhance our understanding of the coordination relationship between ES supply and demand.  
The use of county-level administrative boundaries was advantageous [90], providing official statistical data; however, this limits our understanding of causality and spatial heterogeneity in coordination relationships [91]. Shanxi Province has diverse topography, vegetation and natural geographical features, and there is a significant spatial mismatch between ESS and demand. Thus, it is difficult to reveal the variation of ESS at a local scale in the spatial units of county. In addition, spatial heterogeneity and scale effects impact the relationship between ESS and ESD [92]. Focusing only on a single scale tends to miss information about the correlation between scales, and the influence mechanism is inevitably one-sided. Future research should use a multi-scale analysis to capture the correlation between scales and comprehensively investigate influence mechanisms.  
Additionally, our study assumed that ESD in a county is solely provided by the local ecosystem without considering the flow of ecosystem services across county boundaries. For example, water resources can originate from upstream regions, and food shortages in a city can be mitigated via food trade [93]. Future studies should account for the cross-boundary flow of ES, as services from neighboring ecosystems can also contribute to ESD. Furthermore, while GDM helped identify the strength of influence of socio-ecological factors on the spatial pattern of CCD, it was unable to capture whether this effect was positive or negative. Exploring the positive or negative nature of these effects would provide a more comprehensive understanding of the coordination relationship between ESS and ESD.
# Residual Transformer YOLO for Detecting Multi-Scale Crowded Pedestrian
## 4 Experiments and Results
### Evaluation Metrics and Experimental Details  
In this paper, the experimental evaluation metrics include FPS (frame/s, representing the detection speed of the algorithm), Precision, Recall, mAP (mean average precision). F1 score is the harmonic mean of precision and recall, used to assess a model performance in classifying both positive and negative samples. GFLOPS (Giga Floating-point Operations Per Second) measures the computational complexity of a neural network model. It is typically considered in conjunction with hardware computational performance to evaluate the computational requirements and speed, and IOU (Intersection over Union) represents the overlap between the ground truth box and the prediction box. All formulas as follows:  
\[Precision=\frac{TP}{(TP+FP)} \tag{8}\]  
\[Recall=\frac{TP}{(TP+FN)} \tag{9}\]  
\[F1=2.\frac{Precision\text{-}Recall}{Precision+Recall} \tag{10}\]  
\[mAP=\frac{\sum_{i=1}^{k}AP_{i}}{k} \tag{11}\]  
where \(TP\) represents the positive samples that are correctly classified. \(FP\) represents the positive samples that are incorrectly classified. \(FN\) represents the negative samples that are incorrectly classified. \(k\) represents the number of categories. \(AP_{i}\) represents the average precision of the current category.  
In all experiments in this section, the initial learning rate lr = 0.01, lr is adjusted by cosine annealing during training, the model weight optimizer is SGD. mAP is divided into \(mAP_{50}\) (mean Average Precision at 0.5 IOU) and \(mAP_{50:95}\) (mean Average Precision from 0.5 to 0.95 IOU). Usually, \(mAP_{50}\) is a common IOU threshold used to determine the effectiveness of a detection. \(mAP_{50:95}\) provides a more comprehensive performance assessment, as it considers multiple IOU thresholds.
## 1 Introduction
### Main Contributions  
In order to improve the accuracy and robustness of the SOC estimation algorithm, based on ECMs in the whole SOC area, this paper aims to make three contributions:  
1. By comparing and analyzing nine models and five commonly used parameter identification algorithms, the most suitable ECM and parameter identification algorithm are decided.  
The whole SOC area is divided into the high SOC area and low SOC area. Different ECMs and parameter identification algorithms are adopted considering SOC distribution. Based on this, a multi-model and multi-algorithm method is developed to fit the battery model. Experimental results show that the proposed composite model has higher model accuracy compared with a single model.
3 According to the error characteristics of EKF and AH, a fuzzy fusion SOC estimation algorithm, combining AH and EKF in the whole SOC area, is proposed, and the accuracy and robustness of the proposed algorithm are verified by six cases.
## 5 Conclusions  
We propose an Adaptive Mask-Based Interpretable Convolutional Neural Network (AMI-CNN) that enhances model interpretability by selectively emphasizing important features and suppressing less relevant ones through the application of a masking mechanism on multi-channel spatial features during the training process. Additionally, a depthwise convolution is introduced before the fully connected layers to transform local spatial features into global ones, thereby optimizing the decision-making capability of the classifier.  
AMI-CNN is compared with other commonly used models on two datasets: PSD and constellation phase histogram. The experimental results demonstrate that AMI-CNN exhibits superior interpretability by effectively explaining its decision-making process through analysis of the model's attention features.  
In the PSD dataset, AMI-CNN primarily focuses on features within the main lobe region, consistent with the theoretical basis of power spectral LIME. In contrast, attention features of LIME and SHAP are more dispersed. Grad-CAM performs well on low-order  
Figure 11: Comparison of PG-Acc for different modulation formats on the PSD dataset.  
Figure 10: Comparison of PG-Acc for different modulation formats on the constellation phase histogram dataset.  
modulation formats such as QPSK and 8 PSK but lacks robustness in identifying key features in more complex 16 QAM modulation formats. Due to the unique characteristics of the PSD dataset, we cannot use MSE as an evaluation metric to assess the interpretability of the results. Therefore, we only discuss the performance based on the PG-Acc metric. AMI-CNN achieves 100% PG-Acc across all modulation formats, while Grad-CAM, LIME, and SHAP achieve 66.67%, 14.44%, and 17.22%, respectively. These results further confirm the significant interpretability advantage of the AMI-CNN model.  
In the constellation phase histogram dataset, AMI-CNN demonstrates superior interpretability from both qualitative and quantitative perspectives. In qualitative analysis, AMI-CNN accurately identifies features consistent with theoretical expectations and reflects small-scale data variation trends precisely. In quantitative analysis, evaluated using Mean Squared Error (MSE) and the localization-based metric pointing game accuracy (PG-Acc), AMI-CNN achieved the lowest MSE values (0.0114, 0.0197, and 0.0274 for QPSK, 8 PSK, and 16 QAM, respectively) among all methods. Moreover, AMI-CNN achieved 100% PG-Acc across all modulation formats in the constellation phase dataset, compared with 99%, 98%, and 97% for Grad-CAM, LIME, and SHAP, respectively. In the PSD dataset, AMI-CNN also reached 100% PG-Acc, significantly outperforming other interpretability techniques. These results further highlight the superior interpretability of AMI-CNN.  
In conclusion, AMI-CNN demonstrates significant advantages in model interpretability. The alignment of its focused feature regions with optical theory enhances the trustworthiness of its model decisions, which is particularly valuable for non-experts or individuals lacking experience in this field.
# Robust Symbol Timing Synchronization for Initial Access under LEO Satellite Channel
## 5 Conclusions  
The robust fine STR scheme using known symbols is proposed for short burst packets such as LB and CB bursts for initial access. It can utilize _FFT_ computation because signal power is not influenced by CFO in the frequency domain. The PER performance of the proposed scheme has been compared with the representative scheme among the existing schemes. The simulation results confirm the feasibility of the accurate timing estimation under the LEO satellite channel. We anticipate that the proposed scheme can be effective in short packets when UTs corresponding to VSAT try to access the network, initially in the presence of a large Doppler offset and timing uncertainty when proper GNSS operation is not available. In our future work, we will analyze the performance based on the proposed STR by adding the burst detector in the frequency domain. It can provide insights into packet arrival in random access, along with packet error induced by channel distortion. From a system perspective, this study will shed light on latency time that arises during the initial access procedure in the LEO satellite channel.  
The contributions of [PERSON] were to create the main ideas, perform simulation, and mathematical framework and theoretical analysis, while [PERSON] advised the ideas about the system model and crystallized mathematical framework and theoretical analysis. All authors have read and agreed to the published version of the manuscript.  
This research was supported financially by the Ministry of Trade, Industry and Energy (MOTIE) and the Korea Institute for Advancement of Technology (KIAT), Korea through the International Cooperative R&D program. (project no. P0016154, Development of high efficiency GaN MMIC power amplifier and 5G/6G Modem technology).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
ACI  
Adjacent Channel Interference  
AVN  
Absolute Value Nonlinearity  
BCT  
Broadcasting Composite Table  
CPM  
Continuous Phase Modulation  
CB  
Control Burst  
CFO  
Carrier Frequency Offset  
COTS  
Commercial On-The-Shell  
CR  
Carrier Recovery  
DA  
Data-Aided  
DD  
Decision Directed  
DVB  
Digital Video Broadcasting  
FDD  
Frequency Division Duplexing  
FFT
Fast Fourier Transform  
FLS  
Forward Link Signaling  
FPGA  
Field Programmable Gate Array  
GEO  
Geostationary Earth Orbit  
GNSS  
Global Navigation Satellite System  
GPP  
Generation Partnership Project  
GW  
Gateway
## 3 Methods
### Step 3: Survey and Statistical Analysis  
To derive an evaluation component of auditory experience that takes into account the presence of the metaverse environment, a survey was conducted using a five-point Likert scale on the auditory factors that enhance the presence of the metaverse environment. The participants were recruited using the following criteria: they had used a VR device at least once to understand their familiarity with the virtual world, played games and metaverse platforms at least once or twice a week, and listened to music or media content at least two or three times a week to be able to discriminate auditory stimuli. The total number of participants was 232 (139 males and 93 females), with 67 participants aged 10-19, 60 participants aged 20-29, 60 participants aged 30-39, 25 participants aged 40-49, and 20 participants aged 50-59, with 80.6% of participants aged 10-39. The survey was also subjected to PCA to reduce the data with as little loss of information as possible. The PCA was performed in IBM SPSS(r) Statistics, with a varimax factor rotation, and an RA was performed on the derived values to confirm the appropriateness of the newly derived evaluation components of auditory experience.