esa-sceva/satcom-chunk-collection huggingface dataset:
# Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation
## 5 Results and Analysis
### Analysis of Grassland Above-Ground Biomass in the Study Area  
Based on the above analysis, the Multilayer Perceptron (MLP) model was selected to simulate and invert the above-ground biomass (AGB) of the entire study area (Xiwuzhu-muqin Banner). ArcMap was used to map and analyze the inversion results (Figure 4). From the spatial distribution map of above-ground biomass, it is evident that there are significant spatial variations in AGB across the region.  
In the northern part of the area, most of the AGB is relatively low, primarily concentrated below 100 g/m\({}^{2}\) and represented by dark green. This indicates sparse vegetation cover in this region, which is constrained by factors such as climatic conditions and soil fertility, leading to lower biomass. In the central and southern parts, AGB predominantly ranges from 100 to 200 g/m\({}^{2}\), shown in light green, indicating better vegetation coverage and relatively abundant biomass. Some areas in the south-central region exhibit AGB values between 200 and 300 g/m\({}^{2}\) (yellow) and even a few areas exceeding 300 g/m\({}^{2}\) (red), demonstrating vigorous vegetation growth. In parts of the eastern region, AGB is higher, especially in certain concentrated areas where biomass reaches 200-300 g/m\({}^{2}\) or even exceeds 300 g/m\({}^{2}\) (indicated by yellow and red). These areas have dense vegetation and abundant biomass, likely benefiting from favorable hydrothermal conditions.  
Overall, AGB in Xiwuzhumuqin Banner increases gradually from the northern region to the central and southeastern regions. The northern region has relatively low biomass, while some areas in the central and southeastern regions show higher biomass, mostly concentrated between 100 and 300 g/m\({}^{2}\). This reflects a trend of increasing biomass from north to south and toward the eastern regions.
# Transfer Learning: Video Prediction and Spatiotemporal Urban Traffic Forecasting +
## 5 Discussion
For example, the effective look-back interval is limited to several previous frames, which is appropriate for video streams, but could be insufficient for traffic data with longer temporal patterns. Third, our results could be sample-specific (arterial roads), as there is a collection of evidence in the literature that indicates thatthe GCNN model works well for traffic data in different spatial settings [43; 44; 45]. Finally, the reason could alternatively be related to the spectral convolution of the graph, while another approach, spatial convolution, could be more appropriate for traffic data. The GCNN methodology itself is relatively new; hence, all these hypotheses require additional research. Transferring ideas, models, and algorithms between applied areas of video prediction and traffic forecasting opens a wide range of future research directions. Methodological advances that are now implemented in these applied areas in parallel, should be merged and tested for data sets of different natures. In this study, we tested only a limited set of video processing ideas and models, while the modern modeling toolbox is rich and many other exiting models could be adopted. We also considered only one transfer direction, from video prediction to traffic forecasting, while the opposite transfer (application of popular traffic forecasting models to video prediction) is also available. Finally, we presented transferring at two deeper levels, utilizing the same ideas and the same model architectures, while upper-level transfer learning and the application of pretrained models in another application area has a huge research potential and practical utility.
# DCS-TransUperNet: Road Segmentation Network Based on CSwin Transformer with Dual Resolution
## 5 Discussion  
Our model has two main advantages in innovation. Firstly, the proposed encoder of DCS-TransUperNet uses dual subnetworks to learn features representation from different scale inputs, which avoids the limitation of the fixed scale patch. Coarse-grained features are more easily captured by large patches, while small scales have the faculty of obtaining fine-grained features. Secondly, to obtain enhanced features, we propose a specific FFM to fuse the coarse- and fine-grained feature representations from the two encoder subnetworks. FFM differs from other fusion modules in that we integrate a two-layer native Transformer to increase the interaction of information instead of just using concat and convolution operations.  
Through visual analysis, the road segmentation results of our proposed model have better continuity and integrity. Furthermore, as shown in the green box in the second image in Figure 4, our model can also detect the roads blocked by trees because of Transformer's powerful global context modeling ability.  
We give four evaluation metrics, which are precision, recall, F1-score and IoU. Among them, precision and recall are a pair of reverse indicators. In other words, too high recall will inevitably lead to low precision. In order to better evaluate the performance of the model, the F1-score and IOU are the comprehensive embodiment of precision and recall. Through experimental analysis, the DCS-TransUperNet achieves state-of-the-art results on the Massachusetts roads dataset (F1-score 80.39%, IoU 65.36%) and DeepGlobe dataset (F1-score 73.48%, IoU 56.74%).  
In the experiment, we found that D-resunet achieves higher precision than our model. The reason is that D-resunet has a weak recall. From Table 2, compared with D-resunet, the recall of our model is improved by 7.09%, and the precision is only reduced by 2.84%. On the whole, our F1-score and IOU are still the best.  
In terms of inference speed, the Transformer-based model has obvious disadvantages compared with the CNNs model. The reason is that the calculation of the attention mechanism consumes a lot of computational power. In the future, we will try to reduce the time complexity of the attention mechanism and improve the inference speed of our model.
## 1 Introduction
For example, in practical TC operation, the switching device is affected by the changing ambient temperature due to the movement of the EMU, while the ambient temperature remains constant in AFT [13]; in addition, the switching frequency of the switching device (i.e., The number of currents turn-ons and turn-offs in one second) and currents are also constantly changing as the speed of the EMU or the passenger load changes, however the conventional AFT only considers the constant switching frequency and the constant test current [14]. Figure 1: Internal structure of the switchgear. In this paper, it shows that Weibull distribution is more suitable to describe the cumulative failure rate of high-power switching device fatigue based on the data from conventional AFT. Then, a novel bidirectional aging approach is proposed to show the aging effect from FWD inside the switching device. In the process of AFT, the experiment time is often too long under low current. Therefore, a weighted LSM (least squares method) is proposed to complete the piecewise fitting of BAFT (bidirectional AFT) experimental data, which improves the accuracy of modeling. Through the aging model that is proposed in this paper, the service life of switching devices under actual working conditions can be predicted and the fault trend can be evaluated online when the load current of switching devices is known.
## 5 Implementation of Multimedia Independent Multipath Routing Algorithms  
When multiple source nodes send data to the sink node at the same time, it poses a greater challenge to the construction of independent multipath routing. During the transmission process,  
Figure 6: Calculating the correction factor.  
some source nodes may exit or join dynamically. In order to make better use of the path resources, the source nodes need to readjust the path. In this paper, the idea of software definition is applied to centralize programming and control the multi-path routing of multiple source nodes through sink nodes, so as to realize the multi-source and multi-path coordination algorithm [20; 21]. There are three source nodes and one sink node in the model. Each source node sends data to sink through four paths.  
Considering that sink nodes are located in the center of the sensor network and multiple source nodes send data at the same time, the following issues need to be considered.  
1. How do source nodes distribute their paths when the distribution of source nodes is not uniform around the sink nodes;
2. How to distribute paths when the weights of source nodes are different;  
How to dynamically adjust the existing multipath routing after the source node joins and exits dynamically.
## 3 System Model
The term \(\alpha\) denotes the large-scale path loss exponent, which is assumed to be the same in both macro tier and pico tier for convenient analysis. Hence, the SINR of a typical user \(l\) based on its user type can be depicted as:  
Figure 2: The user equipments (UEs) scheduling strategy for macro base station (MBS) and pico base stations (PBS) with the further-enhanced inter-cell interference coordination (FeICIC) scheme. \[\gamma_{l}=\left\{ \begin{array}{l}\dfrac{\rho P_{m}hr_{m}^{-\alpha}}{\rho l_{m}+I_ {p}+\sigma^{2}},\text{if }l=pm\\ \dfrac{P_{p}hr_{p}^{-\alpha}}{\rho l_{m}+I_{p}+\sigma^{2}},\text{if }l=pp\\ \dfrac{P_{m}hr_{m}^{-\alpha}}{I_{m}+I_{p}+\sigma^{2}},\text{if }l=um\\ \dfrac{P_{p}hr_{p}^{-\alpha}}{I_{m}+I_{p}+\sigma^{2}},\text{if }l=up\end{array} \right., \tag{1}\]  
where \(I_{m}\) and \(I_{p}\) denote the full power aggregate interference from the macro tier and pico tier to UE \(l\), respectively, \(\sigma^{2}\) represents the thermal noise, \(\rho\) is the power reduction factor of MBS transmit power in PSF. \(P_{m}\) and \(P_{p}\) are the full transmission power of MBSs and PBSs, respectively. \(r_{m}\) and \(r_{p}\) are the nearest distance from MBSs and PBSs to a typical UE \(l\), respectively. The notations used in this paper are shown in Table 1.
# Effect of Compression Tights on Skin Temperature in Women with Lipedema
## 4 Discussion
### Skin Temperature after Exercise  
Regarding physical exercise, it increases the energetic demand resulting in an increase in heat production [33; 34]. In this way, the human body needs some mechanisms to dissipate the heat with the aim of maintaining thermic homeostasis [33; 34]. The beginning of exercise triggers the thermoregulatory response; it specifically increases the rate of sweating, which allows the decrease in skin temperature in the first minutes of the activity [35; 36]. Women with lipedema decreased their mean and maximum skin temperature variation in all the ROIs except in the lower leg after walking. In this part of the leg (posterior leg and anterior leg), they increased the mean skin temperature during the first test, and they maintained it during the second test. This reduction in skin temperature may be produced by vasoconstriction in the skin [18]. The energetic demand of the musculature and the absence of heat strain are facts that prioritize the blood in the muscle and not in the skin [37]. In addition, skin impairments produced as a consequence of lipedema, such as fibrosis, can also alter skin blood flow [22]. On the other hand, skin temperature variation was calculated because it allows for detecting skin temperature differences between conditions that, in some cases, cannot be detected by the average and maximum skin temperature due to the variability between participants in their baseline value [38].  
After the test, the control group obtained lower values of mean skin temperature after exercise in the hamstrings (\(p=0.045\)) and in the popliteal fossa (\(p=0.013\)) than in the test before de treatment. The flat compression tights group reduced skin temperature in the posterior leg in the second test in comparison with the first test (\(p=0.019\)). In the case of the circular tights group, they experienced the same tendency, but no significant differences were observed. In this way, the differences were observed in groups that followed the treatment and in groups without compression tights. Therefore, the effect cannot be associated with wearing compression tights, and it may be produced by vasoconstriction [18] or sweating [37]. Furthermore, flat knitting compression tights produced a skin temperature reduction in the lower leg, the opposite of the hypothesis, which affirms that this knitting would increase skin blood flow and skin temperature.
# Multi-Feature Fusion Event Argument Entity Recognition Method for Industrial Robot Fault Diagnosis
## 4 Experiment
### Model Performance Comparison  
We compared the model MF-GCN with 5 models that perform well in public datasets. Five comparison models are presented: Lattice LSTM, WC- LSTM, Simplified Lattice LSTM,LR-CNN, and Bert-CRF. In comparison with Lattice LSTM and WC-LSTM, the information loss problem of the latter two is not present in MF-GCN. In comparison with Simplified Lattice LSTMs and LR-CNN, although they solve the problem of information loss, they are not capable of exploiting the entity-relationship information that MF-GCN does. The scores of the above models are shown in Table 1. MF-GCN has achieved the best recognition effect in the mechanical fault detection dataset, with an F1 score of 93.58%.  
Additionally, we conduct comparative experiments to demonstrate the effectiveness of the MF-GCN feature fusion layer on the Weibo dataset. This dataset is a Chinese social media (Weibo) named entity recognition dataset (Weibo- NER-2015), which contains 1890 pieces of information collected from Weibo between November 2013 and December 2014, with a total of 1890 samples. The training set is 1350, the validation set is 270, and the test set is 270. The four labels are PER, LOC, GPE, and ORG. When the Weibo dataset is used for entity recognition tasks, the fusion of multiple text features can improve the recognition effect since the corpus and semantics are sparse. As the Weibo dataset lacks labeled event argument entity relationships, MF-GCN must remove the BiGCN entity relation inference layer for experiments. The final results are shown in Table 2. MF-GCN still achieves a good recognition effect after removing the BiGCN entity relation inference layer.
## 3 Results  
In this study, several experiments were designed in order to show the effectiveness of the proposed approach.  
The Classification Results of TLSE Method and Transfer Learning Method Based On Different CNN Models  
The first experiment examined the impact of different CNN models on the TLSE method by comparing the classification results. Three different CNN architectures are chosen to be base models: VGG model [50], ResNet-18 [51], and Inception-ResNet [52] model. The architectures of these three pre-trained CNN models are shown in Figures 5-7, respectively. The aim of this experiment was to show the adaptability of the TLSE, whether it can be adopted in the training of different CNN models. The classification results of TLSE method based on these three CNN models are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **VGG** & **ResNet** & **Inception-ResNet** \\ \hline Transfer learning method & 62.24\% & 61.51\% & 64.50\% \\ TLSE method & 63.16\% & 62.69\% & 65.56\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The classification results of TLSE method and transfer learning method based on different models.  
Figure 5: The architecture of VGG model [50].  
Figure 6: The architecture of ResNet model [51].  
As shown in the Table 2, the result of Inception-ResNet model reaches the highest classification rate than other models. The VGG model and ResNet model get comparable results.  
Secondly, to demonstrate the ensemble efficiency of the TLSE, a comparison experiment between TLSE method and the transfer learning method was designed.  
The TLSE method utilizes transfer learning method and snapshot ensemble learning method together towards the Herlev dataset, while the transfer learning is adopted solely on the target dataset. The data augmentation techniques, model architecture refinements and the modification of last fully connected layers are the same for both TLSE based and transfer learning based methods. The data augmentation techniques taken in this experiment are crop and rotation. The VGG model, Inception-ResNet model and ResNet-18 model were adopted as the pre-trained models for both TLSE and transfer learning.  
The comparison results of TLSE method and transfer learning method are reported in Table 2. By comparing the results with the TLSE method. There is a significant difference between the two groups, that TLSE based method achieves higher accuracies than the transfer learning method, suggesting that snapshot ensemble is crucial to achieve better representation. Table 2 also shows that the increments of accuracy are gained from all three models, which illustrates the robustness of the TLSE. The most striking result to merge from the data is that the TLSE method based on the Inception-ResNet model yield an accuracy of 65.56% on the pap-smear Herlev dataset, outperforming the single transfer learning method using the same base model.
# Decision-Making Model for Dynamic Scenario Vehicles in Autonomous Driving Simulations
## 4 Decision-Making Model for Dynamic Scenario Vehicles
### Safety Distance  
Figure 2 depicts the rectangular safety field that exists around each \(veh_{sv}\). The safety field changes in real time with the speed and acceleration of \(veh_{sv}\). A potentially dangerous situation occurs when the safety fields of two vehicles intersect. Based on the responsibility-sensitive safety model, we calculated the size of the safety field as follows:  
\[L_{b}^{safe}=(0.5+\kappa_{1})l+\frac{\left(v_{long}+a_{acc}^{max}\right)^{2}} {2a_{link}} \tag{2}\]  
\[L_{a}^{safe}=v_{long}\rho+\frac{1}{2}a_{acc}^{max}\rho^{2}+L_{b}^{safe} \tag{3}\]  
\[W_{a}=\kappa_{2}w+v_{lat}\rho+\frac{1}{2}a_{lat}\rho^{2}+\frac{\left(v_{lat}+ a_{lat}\rho\right)^{2}}{2a_{lat}^{2}} \tag{4}\]  
\[W_{b}=2\kappa_{2}w-W_{a} \tag{5}\]  
where \(L_{a}^{safe}\) and \(L_{b}^{safe}\) denote the distances from the center of \(veh_{sv}\) to the front and rear edges of the safety field, respectively; \(W_{a}\) and \(W_{b}\) indicate the distances from the left and right edges of \(veh_{sv}\) respectively, to the corresponding edges of the safety field; \(l\) and \(w\) represent the length and width of \(veh_{sv}\), respectively; \(\kappa_{1}\) and \(\kappa_{2}\) denote the initial expansion coefficients of the length and width, respectively; \(\rho\) indicates the driver's reaction time; \(v_{long}\) represents the current longitudinal velocity; \(a_{acc}^{max}\) denotes the longitudinal maximum acceleration; \(a_{brk}\) indicates the current longitudinal deceleration; \(v_{lat}\) represents the current lateral velocity; \(a_{lat}\) denotes the current lateral acceleration; and \(a_{lat}^{max}\) indicates the maximum lateral acceleration.  
Figure 2: Safety field of a scenario vehicle. The cyan rectangle represents the scenario vehicle, the arrow inside denotes the vehicle’s heading, and the red rectangle indicates the safety field of the vehicle.
## 4 Methods
### Research Questions  
The current paper aims to answer the following research questions:  
Q1: What are the primary methods and algorithms in AI-based dental image analysis?  
The primary aim of this study is to assess the utility of digital imaging techniques utilized in the dental profession by both researchers and clinical practitioners in the development of their models, frameworks, or applications. AI algorithms are tasked with handling a diverse array of data, which can be challenging to oversee, comprehend, and extract meaningful insights from. The objective of this study is to examine how applications, frameworks, and models utilize DLand ML methodologies.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Actual Positive (I)** & **Actual Negative (0)** \\ \hline Predicted Positive (1) & True Positive (TP) & False Negative (FN) \\ \hline Predicted Negative (0) & False Negative (FN) & True Negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The confusion matrix for a model consists of two classes.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Performance Metric** & **Description** \\ \hline Accuracy & Accuracy shows the number of correct predictions of our model. \\ \hline Precision & Precision computes the ratio of the correct prediction of positives from all predicted positives. \\ \hline Recall & Recall computes the ratio of the correct prediction of positives from all actual positives. \\ \hline F-measure & F1 score is a scaled measure between precision and recall. \\ \hline The area under the curve (AUC) & The AUC evaluates the overall performance of a model through all decision thresholds. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic performance metrics from the confusion matrix.  
Q2: What are the AI algorithms that are applied in a variety of dental applications, and how can they enhance the precision of diagnosis and treatment results?  
This research question aims to explore and categorize the many AI algorithms used in various dentistry applications. It also seeks to comprehend how these algorithms affect and improve treatment results and diagnosis accuracy in the dental industry.  
Q3: Which performance evaluation methods are applied to assess the AI-based dental image application's effectiveness?  
Evaluating the effectiveness of DLand machine learning (ML) models, frameworks, and methodologies in the context of digital imaging (DI) is the goal of this research question. The types of photos and other relevant data formats used in DL models will be examined and reported on as part of this study.
# OFDM Emitter Identification Method Based on Data Augmentation and Contrastive Learning
## 3 Proposed Method
### Data Collection  
In this paper, data are collected from five different phones, each data sampling length 100 ms, as shown in Figure 3, 1 ms including 14 OFDM symbols and 1 time slot number, data points two time slot, 14 OFDM symbols in the order 3 effective signal, a reference signal, six effective signals, a reference signal, 3 effective signals. Each OFDM signal has 108 subcarriers, and each subcarrier is represented by two IQ channels. The length of data stored in each file: 100 \(\times\) (108 \(\times\) 2 \(\times\) 14 + 1) = 302,500 Bytes.
# Integrated Radar and Communications Waveform Design Based on Multi-Symbol OFDM
## 6 Discussion
### Radar Performance of IRC Waveform  
The radar SNR performances of the IRC waveform at different ICPBOs are shown in Figure 8. It can be observed that the radar SNR loss is smaller at lower ICPBO, and the radar SNR performance of the IRC waveform is better than that of the initial waveform. More importantly, compared with [31], at ICPBO = 5 dB, the proposed algorithm can obtain radar SNR gains of 2.3 dB at ICPBO = 6 dB and 0.8 dB at ICPBO = 8 dB.  
One of the focuses of this paper is the sidelobe suppression along the zero-Doppler axis. In the following, we analyze the PSLR performance of Doppler in the range of [\(-31.25\) KHz, \(31.25\) KHz]. Figure 9 presents the PSLR performance of the waveforms before and after optimization along the zero-Doppler axis and non-zero Doppler domain, respectively. It can be observed that the PSLR of the IRC waveform along the non-zero Doppler domain is very high, much higher than that of the initial waveform, and the difference from zero-Doppler is only within 3.5 dB, which indicates that the proposed algorithm can be well applied to the detection of low-speed moving targets. Sidelobe suppression along the high Doppler domain is complex, which is not discussed in this paper, but will be our future research direction.  
Figure 8: Radar SNR loss of multi-symbol OFDM-based IRC waveforms, where Algorithm 3 is from [31].