esa-sceva/satcom-chunk-collection huggingface dataset:
# A Stay Cable Icing Identification Method Based on the Fusion of U-Net and ResNet50
## 4 Experiment
### Experimental Results and Analysis
#### 4.2.1 Comparison of Segmentation Models
It preserves the context information of different scales by gradually upsampling and skipping the feature map so that the network can better capture the details and context information of the target. By calculating the MIOU changes, U-Net converged to 82.10%, SegNet converged to 72.89%, PSPNet converged to 73.85%, and DeepLab converged to 75.13%. U-Net was 6.97% higher than the DeepLab model with poor performance and 9.21% higher than the SegNet model with relatively poor performance. Therefore, it was well-suited to the icing segmentation task of stay cables regarding all-around performance. Based on the fact that only surface ice pixels and icicles pixels are used to judge the ic level of the cable, Figure 13 shows the F1 value and intersection ratio of each model on the surface ice category and the icicles category. For the surface ice category, the U-Net used in this paper was at least 1.21% and 2.27% higher than the DeepLab, PSPNet, and SegNet models in the F1 value and IOU, and the segmentation accuracy of the comparison model on the icicles category was not high. However, the U-Net was at least 12.74% and 15.08% higher than the DeepLab, PSPNet, and SegNet models in the F1 value and IOU. F1 score is a metric that comprehensively considers both precision and recall. A higher F1 score indicates better model performance and predictive accuracy. IoU (Intersection over Union), on the other hand, is a metric used to measure the degree of overlap between predicted bounding boxes or segmentation results and ground truth bounding boxes or segmentation results in object detection or image segmentation tasks. In this study, since joint operation was required, the accuracy of the first stage directly affected the operation of the second stage, so the mask that used the U-Net model to calculate the icing area was better than the other networks. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Camera Perspective** & **Origin** & **Ground Truth** & **SegNet** & **DeepLab** & **PSPNet** & **U-Net** \\ \hline Left camera & & & & & & \\ Right camera & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Qualitative comparison of ice segmentation tasks for stay cables.
## 4 Methods
### Soil Moisture Estimation  
#### 4.2.1 Alpha Approximation  
By using a change detection approach on a dense time series of SAR data, the changes in backscattering signal between two consecutive observations can be related to a change in soil moisture, assuming that the temporal variability of this parameter is highest compared to other surface parameters, such as soil roughness, vegetation height, vegetation water content, and vegetation geometry, and the recording parameters frequency and incidence angle are constant within the time series [81]. Thus, the ratio of two consecutive co-polarized backscattering signals \(\left(\sigma_{0}^{(2)}/\sigma_{0}^{(1)}\right)\) can be expressed as function of soil dielectric constants \(\left(\varepsilon^{(1,2)}\right)\) and incidence angle \(\left(\theta\right)\)[82]:  
\[\left(\frac{\sigma_{0}^{F2}}{\sigma_{0}^{F1}}\right)^{detr.}\approx\left|\frac {\alpha_{PP}^{(2)}\left(\varepsilon^{(2)},\theta\right)}{\alpha_{PP}^{(1)} \left(\varepsilon^{(1)},\theta\right)}\right|^{2} \tag{13}\]  
with  
\[\left|\alpha_{VV}(\varepsilon,\theta)\right|=\left|\frac{(\varepsilon-1) \left(sin^{2}\theta-\varepsilon\left(1+sin^{2}\theta\right)\right)}{( \varepsilon\cos\theta+\sqrt{\varepsilon-sin^{2}\theta)^{2}}}\right| \tag{14}\]  
For solving the equation, it can be resolved to zero, giving [81]:  
\[\left|\alpha_{PP}^{(1)}(\varepsilon,\theta)\right|-\sqrt{\frac{\sigma_{0}^{(1 )}}{\sigma_{0}^{(2)}}}\times\left|\alpha_{PP}^{(2)}(\varepsilon,\theta)\right| =0 \tag{15}\]  
Applying this equation to all consecutive observation pairs of the SAR time series, an underdetermined linear equation system can be created, with N unknown variables and N\(-1\) equations, depending on an initial soil moisture value to be analytically solvable [83]:  
\[\left[\begin{array}{ccccc}1&-\sqrt{\left(\frac{\sigma_{0}^{F1}}{\sigma_{0}^{ F2}}\right)^{detr.}}&0&\vdots&0&0\\ 0&1&-\sqrt{\left(\frac{\sigma_{0}^{F2}}{\sigma_{0}^{F3}}\right)^{detr.}}&\vdots &0&0\\ \ldots&\ldots&\ldots&\iddots&\ldots\\ 0&0&0&\vdots&-\sqrt{\left(\frac{\sigma_{0}^{FN-2}}{\sigma_{0}^{FN-1}}\right) ^{detr.}}&0\\ 0&0&0&\vdots&1&-\sqrt{\left(\frac{\sigma_{0}^{FN-1}}{\sigma_{0}^{FN}}\right) ^{detr.}}\end{array}\right]\times\left[\begin{array}{c}\left|\alpha_{PP}^{(1) }(\varepsilon_{1},\theta)\right|\\ \left|\alpha_{PP}^{(2)}(\varepsilon_{2},\theta)\right|\\ \ldots\\ \left|\alpha_{PP}^{(N)}(\varepsilon_{N},\theta)\right|\end{array}\right]= \left[\begin{array}{c}0\\ 0\\ \ldots\\ 0\end{array}\right] \tag{16}\]  
In addition, in this study, it is proposed to rescale the backscattering signal to dB, as this is expected to reduce possible biases in the final soil moisture estimates.
# Study of Damage Mechanism and Evolution Model of Concrete under Freeze-Thaw Cycles
## 4 Experimental Results and Analysis
### The Variation in P-Velocity  
The changes in P-wave velocity, before and after F-T cycles, are presented in the Figure 6. The P-wave velocity of the samples decreases as the F-T cycles increase. The internal pores of the sample developed after 10 F-T cycles, and the P-wave velocity decreased from 4.61 km/s to 4.44 km/s, a decrease of 3.68%. After 20 F-T cycles, the P-wave velocity of the samples decreased from 4.5 km/s to 4.15 km/s, a decrease of 7.77%. After 30 F-T cycles, the P-wave velocity decreased from 4.37 km/s to 3.81 km/s, a decrease of 12.81%. With the increase in the number of F-T cycles, the pores continued to develop and began to connect, and the total number increased significantly. After 40 F-T cycles, the P-wave velocity of the samples decreased from 4.29 km/s to 3.44 km/s, a decrease of 19.81%, After 50 F-T cycles, the wave velocity of the samples decreased from 4.2 km/s to 3.19 km/s, a decrease of 24.04%.  
This is due to the fact that the F-T cycle effect creates a huge number of pores in the concrete samples, altering the internal structure and ultrasonic wave propagation mode and path. Constantly forming new pores, the existing pores also keep growing and spreading as the F-T cycle times rise. The sample's mechanical strength and P-wave velocity significantly  
Figure 5: The change in the porosity of concrete samples under F–T cycles.  
Figure 6: P-velocity of concrete samples before and after F–T cycle treatment.  
decrease as a result of the initial pores continuing to expand with each cycle due to the irreversible breakdown of the pore structure within the sample. The changes in both the porosity and the P-wave velocity were in good agreement, suggesting that material deterioration and damage buildup occur as the frequency of F-T cycles increases.
###### Abstract  
The vehicular ad hoc network (VANET) is a method through which Intelligent Transportation Systems (ITS) have become important for the benefit of daily life. Real-time detection of all forms of attacks, including hybrid DoS attacks in IEEE 802.11p, has become an urgent issue for VANET. This is due to sporadic real-time exchange of safety and road emergency message delivery in VANET. Sporadic communication in VANET has the tendency to generate an enormous amount of messages. This leads to overutilization of the road side unit (RSU) or the central processing unit (CPU) for computation. Therefore, efficient storage and intelligent VANET infrastructure architecture (VIA), which includes trustworthiness, are required. Vehicular Cloud and Fog Computing (VFC) play an important role in efficient storage, computation, and communication needs for VANET. This research utilizes VFC integration with hybrid optimization algorithms (OAs), which also possess swarm intelligence, including Cuckoo/CSA Artificial Bee Colony (ABC) and Firefly/Genetic Algorithm (GA), to provide real-time detection of DoS attacks in IEEE 802.11p, using VFC for a secure intelligent vehicular network. Vehicles move ar a certain speed and the data is transmitted at 30 Mbps. Firefly Feed forward back propagation neural network (FFBPNN) is used as a classifier to distinguish between the attacked vehicles and the genuine vehicles. The proposed scheme is compared with Cuckoo/CSA ABC and Firefly GA by considering jitter, throughput, and prediction accuracy.  
Cuckoo/CSA (ABC); Firefly/Genetic Algorithm (GA); Vehicular Cloud and Fog Computing (VFC); DoS attacks; IEEE 802.11P; VANET; ITS 2019 8 776 10.3390/electronics8070776
## 1 Introduction
They tested the fine-tuned VGGNet, ResNet, and GoogLeNet models and a modified model, which was based on VGGNet for automatic BAA. The best result was achieved for modified VGGNet with hierarchical features, which had an MAE of 4.856 months. [PERSON] et al. proposed a model for predicting bone age based on the VGG16 with an attention mapping-focused architecture. Using the RSNA dataset [13] they achieved an MAE equal to 11.45 months for both genders. [PERSON] et al. evaluated a proposed two-stage bone age assessment network with ranking learning using the same dataset. They achieved an average MAE of 6.05 months [14]. [PERSON] and [PERSON] introduced a two-stage approach for bone age assessment using segmentation and ridge regression [15]. The MAE of the proposed solution was equal to 6.38 months. Here, we focused on age prediction for children and young adults from dual-energy X-ray images. This could be important in many applications, including in the proper assessment of a child's development. The agreement between chronological age and developmental advancement is an important indicator of the health condition of children. However, under pathological conditions, age predictions based on other methods (e.g., bone age) may better reflect biological maturity [16]. In this work, our contributions were as follows:  
* We built a dataset of low-resolution, dual-energy X-ray absorptiometry images with annotations performed by specialists. This was probably one of the first datasets containing whole-body, dual-energy X-ray absorptiometry images (to the best of our knowledge). * We proposed a CNN-based age prediction framework for low-resolution, whole-body, dual-energy X-ray absorptiometry images. * As a result of our experiments, we demonstrated that age could be successfully predicted from low-resolution images, so potentially even less radiation energy could be used at the data-acquisition phase. The rest of the paper is structured as follows. In Section 2, the methodology used in the study is described. The results are presented in Section 3 and discussed in Section 4. The study is concluded in Section 5.
# Short-Term Travel Demand Prediction of Online Ride-Hailing Based on Multi-Factor GRU Model
## 1 Introduction
[20] proposed an hourly passenger-flow prediction model based on deep learning by using stacked autoencoders (SAE) and a deep neural network (DNN). Travel characteristics, defined as time characteristics, scene characteristics, and passenger flow characteristics are inputs in this model. Additionally, experimental results showed that this method can provide a more accurate and general passenger-flow prediction model for bus rapid transit (BRT) stations with different passenger-flow distributions. Table 1 summarizes the current literature on online ride-hailing travel demand forecasting. There are not much research in the field of online ride-hailing travel demand prediction, and there is no deep-learning model based on multi factors in the prediction of online ride-hailing travel demand. Based on the research of the above scholars, in order to fill the gap in travel demand prediction of online ride-hailing and multi-factor prediction, this paper proposes a GRU model with multi-factor consideration to predict travel demand of online ride-hailing. Based on analysis of the data characteristics, this paper proposes a GRU model considering multiple factors. The weight parameters are adjusted to the optimal by K-fold cross validation method, and then the model proposed in this paper is evaluated by various evaluation functions and compared with other models. The purpose of this paper is to optimize the supply relationship of online car-hailing and make full use of online car-hailing resources according to the prediction of online car-hailing travel demand. This \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author, Year** & **Model** & **Consider Influence Factor** & **Consider Multi-Factor** \\ \hline [PERSON] et al. [21] (2020) & RNN & No & No \\ [PERSON] et al. [22] (2020) & Stacking ensemble learning & Yes & No \\ [PERSON] et al. [23] (2020) & CNN-LSTM & Yes & No \\ [PERSON] et al. [24] (2020) & ST-transformer & Yes & No \\ This paper & GRU & Yes & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature on online ride-hailing travel demand forecasting. can reduce road traffic pressure and traffic pollution, which is of great significance to the sustainable development of the city.
## 2 Materials and Methods  
### Site Description  
Carajas railway (Estrada de Ferro Carajas--EFC) is the highest transport capacity railway in Brazil, covering 972 km, as seen in Figure 2. The EFC, located in Carajas, Brazil (Southeast Para), connects the largest surface mining iron ore operation in the world with the Port of Ponta da Madeira in Sao Luis, Maranhao Figure 2, and currently tops the list of most productive railways in Brazil according to National Land Transportation Agency (ANTT) [20].  
EFC has a throughput of more than 120 million tons and transports 350,000 passengers per year. Among the largest trains in operation in the world, about 35 of them run in parallel along its tracks, including one comprising 330 wagons and extending for 3.3 km. The construction of 559 km that will connect the 54 switching yards of the EFC is still underway, which will further increase the transportation volume of the EFC, requiring platforms with higher performance.  
The EFC is in a region of high precipitation and elevated temperatures. The railway track is continuously exposed to high precipitation, which leads to the premature deterioration of the permanent way. The rising humidity levels accelerate the degradation process, expediting the deterioration of the railway track.  
Due to the abundance of humid tropical climates, Brazil has a high prevalence of tropical soils. According to [PERSON] and [PERSON] [9], these soils possess distinctive characteristics and properties that are notably distinct from non-tropical soils due to the operation of geological and/or pedological processes that are common in regions with humid tropical climates.  
Given that laterite soil is common as a substructure material in tropical regions, the standard that guides the sizing of railway pavements soil, the American Railway Engineering and Maintenance-of-Way Association (AREMA) [3], does not provide criteria for this type of material, thus it is necessary to carry out studies focused on the behavior of tropical regions.  
Unlike the stone materials used in international railways, lateritic soil is susceptible to the impact of water, and seepage can jeopardize the railway platform, leading to a decline in the resilient modulus, culminating in permanent deformations.  
Figure 2: EFC location map, ANTT [20].  
The resilient modulus is an indicator of the elastic behavior of the material, which plays an important role in assessing its suitability for use as one of the layers of the pavement. Thus, examining the soil's water response is imperative to make a well-informed choice of materials for railway pavement construction.  
Given this context, the objective of this research is to evaluate the variation of humidity over time on the railway platform when subjected to the action of rainwater.
# On End-to-End Intelligent Automation of 6G Networks
###### Abstract  
The digital transformation of businesses and services is currently in full force, opening the world to a new set of unique challenges and opportunities. In this context, 6G promises to be the set of technologies, architectures, and paradigms that will promote the digital transformation and enable growth and sustainability by offering the means to interact and control the digital and virtual worlds that are decoupled from their physical location. One of the main challenges facing 6G networks is \"end-to-end network automation\". This is because such networks have to deal with more complex infrastructure and a diverse set of heterogeneous services and fragmented use cases. Accordingly, this paper aims at envisioning the role of different enabling technologies towards end-to-end intelligent automated 6G networks. To this end, this paper first reviews the literature focusing on the orchestration and automation of next-generation networks by discussing in detail the challenges facing efficient and fully automated 6G networks. This includes automating both the operational and functional elements for 6G networks. Additionally, this paper defines some of the key technologies that will play a vital role in addressing the research gaps and tackling the aforementioned challenges. More specifically, it outlines how advanced data-driven paradigms such as reinforcement learning and federated learning can be incorporated into 6G networks for more dynamic, efficient, effective, and intelligent network automation and orchestration.  
6G networks; intelligent automation; data-driven opportunities 1
## 3 High-Efficiency Wideband Envelope Amplifier
### Wideband Linear Envelope Amplifier
The test signal frequency representation shows how the components extend up to frequencies of tens of MHz, which leads to a high demand on the power bandwidth of the WLEA. An arbitrary signal generator (Agilent 81150A) controlled by a PC running MATLAB was used to support signal generation. In view of these data and compared with the graphs in Figure 10, it seems optimistic to think that the implemented WLEA can cover the linearity needs of a multicarrier system with up to a 10 MHz bandwidth.
# Inter-Unit Consistency and Validity of 10-Hz GNSS Units in Straight-Line Sprint Running
## 5 Conclusions  
In team sports, high consistency between different GNSS units is critical as coaches compare the movement characteristics across players in a game or training. This study revealed that there exist variations in distance and speed measurements among eight GNSS units worn by participants at the same time. In general, GNSS units may lead to an overestimation of total distance and underestimation of peak speed during high-speed sprint running. Practitioners should be aware of the window of errors associated with GNSS measurements and interpret the results with caution. This is especially important for data collected during sport competitions or training which involve movement demands at high speeds. When making comparisons over a season, players should wear the same GNSS unit every time if logistically possible.  
Despite some limitations, the use of GNSS/GPS technology is still widespread, and it offers practical insights to players' movements characteristics and playing demands. In view of the rapid advancement in technology, it may be possible to improve current GNSS/GPS systems so as to enhance their inter-unit consistency and measurement accuracies across different movement types including high-speed sprinting.  
Conceptualization, J.Z.L. and P.W.K.; methodology, J.Z.L., A.K.C. and P.W.K.; validation, A.K.C.; formal analysis, A.K.C., J.-W.P. and P.W.K.; writing--original draft preparation, A.K.C., J.Z.L. and J.-W.P.; writing--review and editing, A.K.C., J.Z.L., J.-W.P. and P.W.K.; supervision, P.W.K. All authors have read and agreed to the published version of the manuscript.  
We wish to acknowledge the funding support for this project from Nanyang Technological University under the URECA Undergraduate Research Programme.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Nanyang Technological University Institutional Review Board (IRB-2020-09-033).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The authors would like to acknowledge all participants for taking their time to participate in this study.  
The authors declare no conflict of interest.
## 1 Introduction
In reality, all the above estimation methods must first extract the vibration signal of the strong point target in the coarsely focused image. The extracted vibration signal will be disturbed by the surrounding clutter signal, and the signal-to-clutter ratio may be fairly low. Although it is biased in estimating instantaneous frequency (IF), STFT is comparatively robust against noise effects [25]. Thus, a novel parameter estimation method of multi-component high-frequency vibration error based on the STFT is presented in this paper. After IF estimates using STFT, least square (LS) regression is performed on the IF estimates to estimate the high-frequency vibration parameters. The estimated parameters are insensitive to the noise effects. However, the method accuracy is influenced by the window width. To overcome the problem, the parameters are estimated for various window widths in the STFT. Then, a method based on the maximum likelihood function (MLF) is developed for determining the optimal window width by a one-dimensional search of these window widths. Here, the multi-dimensional search for a set of parameters is avoided, thus reducing the computing time. In addition, the method accuracy is also influenced by the accuracy of the IF-estimate samples in the LS regression. If the noise is significant, the estimation error is aggravated by the IF estimates that deviate significantly from the true value. To avoid singular IF estimates in the LS regression, a random sample consensus (RANSAC) [26; 27] is introduced to randomly select the IF-estimate samples for multiple times. The previous MLF can also be used as a measure of the selection results. Finally, to show the superiority of the proposed method, it is compared with the original STFT-based method. The rest of this paper is organized as follows. Section 2 establishes the high-frequency vibration error model and the THz-SAR echo-signal model. The proposed estimation and compensation method is described in detail in Section 3. Section 4 gives the simulation results, and conclusions are drawn in Section 5.