esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
A total of 26 subjects participated in this study [24,27], with the subject demographic information shown in (Table 1).  
Table 2 summarizes the results of the intra- and inter-rater reliability of each muscle. The intra-rater reliability was good to excellent (ICC: 0.781-0.938) for most of the muscles and positions with one exception for GM at RST position (ICC: 0.696). Inter-rater reliability ICC ranged from 0.707 to 0.895. In general, the intra-rater reliability was better than inter-rater reliability.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Gender (Female:Male)** & **Age (year)** & **Height (m)** & **Weight (kg)** & **Body Mass Index (BMI)** \\ \hline
12:14 & 33.5 \(\pm\) 5.9 & 1.66 \(\pm\) 0.10 & 60.3 \(\pm\) 10.8 & 21.4 \(\pm\) 2.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographic of the subjects (mean \(\pm\) Standard Deviation).  
Figure 3: Illustration of applying the novel probe placement procedure on tibialis anterior muscle. (**a**) Mark the body landmarks of the fibular head and lateral malleolus for TA; (**b**) draw a transverse line on the upper one-third of the line between landmarks for TA; and (**c**) locate the middle point between the muscle boarder, which was confirmed with ultrasound B-mode image.  
It was found that there was a significant dependence of the shear modulus on the muscle position for the three muscles (\(p<0.001\) for RF, TA and GM). Table 3 summarizes the shear modulus of both groups. There was significantly larger shear modulus at MRL position than that of at RST position for all the three muscles (\(p<0.001\)).
# Severity Prediction of Traffic Accidents with Recurrent Neural Networks
## 4 Experimental Results and Discussion
### Network Depth Analysis  
Generally, in a deep learning model, several modules and multilayers can be stacked on top of each other so that it is significant to analyze the network depth (number of hidden layers) to understand the network behavior. Table 5 shows the accuracy of the RNN model with various numbers of dense layers used on top of the LSTM layer. The best accuracy was achieved by using two dense (fully-connected) layers with 64 hidden units. This model achieved training and validation accuracies of 71.79% and 71.77% respectively. When another dense layer was added to the network, the training and validation accuracies were reduced to 70.09% and 71.24 respectively. When using five dense layers, the validation accuracy (70.35%) was found to be better than the training accuracy (67.26%); however, it could not perform better than using only two dense layers. The model started over-fitting when more than five dense layers were used. The validation accuracy of the network with eight dense layers was 56.37%, i.e., less by almost 10% than the training accuracy.  
In addition, the effect of the number of LSTM layers on model accuracy was assessed. Table 6 shows that the best training (71.79%) and validation (71.77%) accuracy could be achieved with a network of one LSTM layer. The accuracy was slightly reduced with another LSTM layer which was added to the network. Finally, when three LSTM layers were used, the accuracy of the model was gradually decreased and overfitted the training data.  
Figure 5: The sensitivity of the RNN model for different learning rate and sequence length configurations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Dense Layers** & **Training Accuracy (\%)** & **Validation Accuracy (\%)** \\ \hline
2 & 71.79 & 71.77 \\
3 & 70.09 & 71.24 \\
5 & 67.26 & 70.35 \\
8 & 65.27 & 56.37 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The training and validation accuracy of the proposed RNN model with a different number of dense layers.
# Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning
###### Abstract  
Accurate and timely prediction of crop yields is crucial for ensuring food security and promoting sustainable agricultural practices. This study developed a winter wheat yield prediction model using machine learning techniques, incorporating remote sensing data and statistical yield records from Henan Province, China. The core of the model is an ensemble voting regressor, which integrates ridge regression, gradient boosting, and random forest algorithms. This study optimized the hyperparameters of the ensemble voting regressor and conducted an in-depth comparison of its yield prediction performance with that of other mainstream machine learning models, assessing the impact of key hyperparameters on model accuracy. This study also explored the potential of yield prediction at different growth stages and its application in yield spatialization. The results demonstrate that the ensemble voting regressor performed exceptionally well throughout the entire growth period, with an R\({}^{2}\) of 0.90, an RMSE of 439.21 kg/ha, and an MAE of 351.28 kg/ha. Notably, during the heading stage, the model's prediction performance was particularly impressive, with an R\({}^{2}\) of 0.81, an RMSE of 590.04 kg/ha, and an MAE of 478.38 kg/ha, surpassing models developed for other growth stages. Additionally, by establishing a yield spatialization model, this study mapped county-level yield predictions to the pixel level, visually illustrating the spatial differences in land productivity. These findings provide reliable technical support for winter wheat yield prediction and valuable references for crop yield estimation in precision agriculture.  
machine learning; winter wheat; growth stage; yield prediction; food security +
Footnote †: journal: _agronomy_  
0
Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Nonlinear Analysis and Closed-Form Solution for Overhead Line Magnetic Energy Harvester Behavior
## 1 Introduction
Figure 1 shows an OLMEH as a part of a simple power processing scheme operating with a constant voltage load (CVL). The circuit employs a full-wave bridge rectifier to perform the AC to DC conversion. The rectifier's action imposes a square wave voltage (whose amplitude equals the CVL voltage) across the OLMEH's secondary winding. This may drive the magnetic core into the saturation region of the BH curve. While saturated, the OLMEH can provide no output current for a considerable portion of a line cycle. Thus, predicting the output power of OLMEHs is a challenging problem. Furthermore, the increased power comes at the expense of increased volume and weight. To minimize the mentioned physical parameters while attaining the desired output power, OLMEHs' core material, geometry, size, and number of turns have to be properly selected. Therefore, the aim of this article is to reexamine the workings of OLMEHs and obtain a complete analytical description of device operation, which can further serve as a  
Figure 1: Overhead line energy harvester under constant voltage load. theoretical foundation to develop a reliable set of engineering design rules. The applied methodology is as follows.
# Handover Scheme in LEO Satellite Networks Based on QoE for Streaming Media Services
## 2 System Model
### Downlink Budget Model  
When user terminals can only use satellite multimedia services, they are usually in areas without terrestrial base stations, such as deserts, wildernesses, and oceans, so the free space propagation loss model is considered. Referring to the International Telecommunication Union (ITU) recommendation, when the frequency is within 100 GHz, the signal will also be affected by oxygen and water vapor attenuation, cloud attenuation, rainfall attenuation and tropospheric scintillation in the atmosphere [22]. These attenuations are related to the elevation angle \(\theta\). Therefore, the loss factors in this paper are summarized as follows:  
To throw out other effects such as coding, we consider [PERSON]'s theorem to measure the effect of signal loss on the communication rate. The transmission losses listed in Table 1 are included in [PERSON]'s law, as shown in Equation (1):  
\[C=B\text{log}_{2}(1+\frac{P_{T}G_{T}G_{R}}{L_{f}A_{g}A_{c}A_{r}A_{s}N}), \tag{1}\]  
where \(P_{T}\) is the satellite transmit power, \(G_{T}\) is the transmit antenna gain, \(G_{R}\) is the receive antenna gain, \(N\) is the noise power, \(B\) is the signal bandwidth, and \(C\) is the channel capacity. \(A_{g}\) is the attenuation due to atmospheric gases, and for signals with frequencies below 100 GHz, it is mainly affected by oxygen and water vapor; thus, \(A_{g}=A_{o}+A_{w}\). Note that the attenuation coefficients, gain coefficients, etc. are not in dB form here. When \(G_{T}\), \(G_{R}\), \(B\), frequency \(f\), noise \(N\), and other factors in the atmosphere are constant, and the channel  
Figure 1: Multi-user LEO satellite communication system.  
capacity is a function that varies with the elevation angle \(\theta\) and the satellite transmit power \(P_{T}\), that is  
\[C=f(P_{T},t)=B\text{log}_{2}(1+\frac{G_{tot}}{N}\cdot\frac{P_{T}}{L_{t}(t)}), \tag{2}\]  
where \(G_{tot}\) is the total gain that includes the transmit antenna gain \(G_{T}\) and receive antenna gain \(G_{R}\), and \(N\) is the noise power. They are constants independent of \(\theta\) and \(P_{T}\). \(L_{t}(t)\), which includes free space propagation loss and other attenuation such as atmospheric oxygen, water vapor, clouds, etc., is the value of the loss affected by the elevation angle \(\theta(t)\). And the prediction of the elevation angle \(\theta(t)\) as a function of the satellite service time \(t\) can be referred to the literature [23]. Therefore, the link loss \(L_{t}(t)\) here is a function of the satellite service time \(t\). If other effects such as coding are subsequently added for consideration, the coding efficiency can be expressed by multiplying a factor on the channel capacity \(C\).
## 3 Experimental Results
### Influence of Layer Orientation Effect on Crack Growth and Fracture Mode
#### 3.3.2 Influence of Layer Orientation Effect on Fracture Mode  
To sum up, layer orientation has a significant effect on the fracture mode of continental shale. In this paper, Table 4 shows the typical fracture morphology of continental shale with different layer orientations under the Brazilian test conditions. The classification standards of Tavallai and Vervoort are used to classify the influence of layer orientation on the fracture mode of continental shale [10], as shown in Table 5.  
It can be seen from Tables 4 and 5 that the samples with different layer orientations show compound fracture modes, such as through-center or deviating-center, in different morphologies and positions. We noticed that when \(\theta=0^{\circ}\), \(\theta=45^{\circ}\), and \(\theta=60^{\circ}\), obvious secondary cracks were produced. Among them, when \(\theta=0^{\circ}\) and \(\theta=45^{\circ}\), the secondary cracks only connect the adjacent main cracks, and the secondary cracks propagate along the lamination direction and deviate from the lamination direction, respectively. When \(\theta=60^{\circ}\), the secondary cracks can propagate along the lamination direction and connect the two adjacent main cracks. It is, particularly, noteworthy that when \(\theta=0^{\circ}\) and \(\theta=90^{\circ}\), the cracks have only a single fracture mode, while the samples of the other three-layer orientations all have compound fracture modes. When \(\theta=30^{\circ}\), there are no obvious secondary cracks along the lamination direction.  
The results of previous studies have shown that the degree of cementation of lamination is the main factor that influences the anisotropy of shale strength [11]. In addition, layer orientations and primary cracks have a significant influence on the crack morphology and fracture mode of continental shale. It is very important to analyze the mechanics mechanism of the fracture mode, by integrating the cohesive force and layer orientation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Orientation \(\theta\) (\(\circ\))** & **Typical Fracture Morphology** & **Characteristics of Secondary Cracks** & **Fracture Mode** \\ \hline
1. The secondary crack connects to the single main crack. & 1. The secondary cracks propagate along the lamination direction. & Straight through-center fracture mode. \\ \hline
1. The secondary crack connects to the single main crack. & Straight and crescent-shaped deviating-center compound fracture mode. \\
2. The secondary cracks deviate from the lamination direction and propagate vertically. & Straight, crescent-shaped and folded-shape deviating-center compound mode. \\
1. Secondary cracks connect adjacent double main cracks. & Straight and crescent-shaped, through-center, and deviating-center compound mode. \\
2. Secondary cracks propagate along the lamination direction. & Straight and crescent-shaped, through-center, and deviating-center compound mode. \\
2. Secondary cracks propagate along the lamination direction.
# Toward Vulnerability Detection for Ethereum Smart Contracts Using Graph-Matching Network
## 2 Background
### Four Types of Vulnerabilities in Smart Contracts
#### 2.2.1. _Reentrancy_  
Reentrancy vulnerability happens when a built-in token transfer function, i.e., _call.value_, can call back to itself from an external invocation. In other words, such a function can be re-entered to perform unexpected token transfers.  
Figure 2 shows an example code of reentrancy vulnerability. The attack occurs when a function makes an external call to another untrusted contract (e.g., _msg.sender.call.value_ at line 7). Then, the untrusted contract can make a recursive call back to the original function (e.g., _withdraw_()) to drain funds. The underlying cause of reentrancy vulnerability is that the vulnerable contract allows external calls to take over the control flow and make unexpected changes to internal variables. The notorious DAO attack is the most famous example of a reentrancy hack [5].
## 7 Conclusions and Future Work  
Buoy geometry can significantly impact a point absorber's dynamic response due to the static and dynamic Froude-Krylov forces. The hourglass-shaped buoy's static and dynamic force expressions have nonlinearities of the form \((\eta-\zeta)^{3}\) and \(\cdot\eta\left[e^{-x(\eta-\zeta)}+x(\eta-\zeta)\right]\), respectively, as seen in Equation (8). Although the nonlinear dynamic forces are less than the static, their shape results in a total force that excites the buoy's vertical motion more than the static force alone. This additional motion results in the nonlinear terms, both static and dynamic, having an even more significant effect. Exploiting buoy shapes to create synergy between the static and dynamic forces can be used to increase the efficiency of a point absorber. While this effect is evident in the hourglass buoy, it may be possible to find shapes that are easier to implement physically, with similar synergy between their static and dynamic forces.  
Model-based control usually requires reduced-order models that can execute in real time. The nonlinear dynamic forces, though less than the static, were found to impact the energy extraction of the closed-loop system. This effect will vary under different wave conditions, but they should be included when possible.  
Experimental validation of static and dynamic Froude-Krylov force expressions for the hourglass buoy is one of the next steps. Incorporating the effects of significant motion on added mass and radiation damping into the control law should be explored. Shape  
\begin{table}
\begin{tabular}{c c} \hline \hline
**First State Gain** & **Displacement Error (m)** \\ \hline
10 & 0.130 \\
20 & 0.098 \\
50 & 0.064 \\
100 & 0.015 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Displacement tracking error due to \(Q_{11}\) LQR gain value.  
Figure 9: Comparison of the controller performance when using the exact model parameters and perturbed values.  
adaptation, and its effect on the Froude-Krylov forces, is yet another feature that could be exploited to increase point absorber efficiency.  
Conceptualization, H.Y., G.P. and D.W.; Methodology, H.Y.; Validation, G.P.; Formal analysis, H.Y. and T.D.G.; Writing--original draft, H.Y.; Writing--review & editing, T.D.G., G.P. and D.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author.  
Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in the paper do not necessarily represent the views of the U.S. Department of Energy or the United States Government.  
The authors declare no conflict of interest.
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Iceberg
#### 2.1.4 Altimeter  
[PERSON] et al. [45] proved that existing targets on the ocean surface, such as ships and icebergs, were detectable in the thermal noise section of the waveform echoes. These targets can be identified based on radar equations by demining their impact on the waveform echo. Icebergs can significantly affect the altimeter waveform echo and can be detected through their signatures. However, iceberg detection performance by altimeters is negatively affected by the presence of SI, which requires high caution [11]. Considering the Gaussian antenna pattern and altimeter pulses, specific radar equations can be applied to delineate the iceberg signature [46]. The iceberg signature is deterministic and is in parabolic shape in the altimeter waveform echo. Therefore, automatic methods can be developed for their delineation. For instance, [PERSON]. [47] introduced an automatic method based on a convolution product and filtering method to distinguish the parabolic signature of icebergs in the thermal section of altimeter waveform echo. This method was then applied to estimate the minimum height of approximately 8000 icebergs using one-year Jason data. Furthermore, [PERSON]. [48] created a database (e.g., position, size, and volume) of small icebergs using archives of nine altimeters between 1992 and 2014. Intercalibrated altimetric data were merged to obtain reliable monthly iceberg volumes. Finally, the correlation between global small and large iceberg volumes revealed that the smaller icebergs were dominantly generated by the disruption of larger ones. Furthermore, [PERSON]. [49] implemented eight ML algorithms to discriminate icebergs and ships using Jason-2 satellite altimetry data. The reference samples were generated using ENVISAT-ASAR images, and the results indicated the superiority of the SVM algorithm for iceberg-ship discrimination.
## 4 Results and Discussion
### Analysis of Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Node vs. Network Lifetime (Sec)**} & \multicolumn{2}{c}{**\% Improvements of TM2 RP as Compared with Existing Techniques**} \\ \hline
**Nodes** & **DBR** & **MRP** & **TM2 RP** & \
\begin{tabular}{c} **\% Improvement of TM2 RP as** \\ **Compared with MRP** \\ \end{tabular} &
\begin{tabular}{c} **\% Improvement of TM2 RP as** \\ **Compared with DBR** \\ \end{tabular} \\ \hline
25 & 720 & 1100 & 1430 & 30.00 & 98.61 \\
50 & 710 & 1150 & 1320 & 14.78 & 85.92 \\
100 & 680 & 1200 & 1340 & 11.67 & 97.06 \\
250 & 590 & 1130 & 1250 & 10.62 & 111.86 \\
400 & 570 & 1220 & 1270 & 4.10 & 122.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The obtained results of TM2 RP with regard to network lifetime. Figure 11: Comparison of underwater network lifetime with increasing number of nodes. The average end-to-end delay of TM2 RP, DBR, and MRP is measured in the following figure. Figure 13 proves that TM2 RP achieved a lower delay than DBR and MRP. This is because DBR and MRP employ holding time based on residual energy. This holding time resulted in increasing the delay if all neighbors did not overhear and was acknowledged from its neighbor. In this case, the second elected node forwards the data after the holding time is finished. As a result, the delay occurred. On the other hand, TM2 RP did not employ the holding time technique as the electing node directly forwarded the data packet. This is because the sender node chose the receiver node. Only the sender node holds the data packet for a second until acknowledged by its neighbor, resulting in less delay than DBR and MRP. A more comprehensive discussion of the end-to-end delay average obtained results of TM2 RP is given in Table 5. For MRP and DBR, respectively, the average obtained results by TM2 RP were 50% and 69%. This can indicate that the underwater characteristics are not considered in existing algorithms, depending on the location information of sensor nodes. On the other hand, TM2 RP  
Figure 12. The statistical outcomes of the comparative protocols with regard to network lifetime. Figure 13. Comparison of underwater end-to-end delay performance with increasing number of nodes. employed direct forwarding of the data packet without holding time, leading to significant results. This feature is demonstrated in Figure 14 clearly, where observations of the average obtained results of end-to-end delay are displayed comprehensively. The outcomes provided in Table 5 and Figure 13 were verified and demonstrate that TM2 RP overwhelmed other state-of-the-art techniques regarding the end-to-end delay.
###### Abstract  
Internet of Things (IoT) ecosystem in smart cities demands fast, reliable, and efficient image data transmission to enable real-time Computer Vision (CV) applications. To fulfill these demands, an Orthogonal Frequency Division Multiplexing (OFDM)-based communication system has been widely utilized due to its higher spectral efficiency and data rate. When adapting such a system to achieve fast and reliable image transmission over fading channels, noise is introduced in the signal which heavily distorts the recovered image. This noise independently corrupts pixel values, however, certain intrinsic properties of the image, such as spatial information, may remain intact, which can be extracted as multidimensional features (in the convolution layers) and interpreted (in the top layers) by a Deep Learning (DL) model. Therefore, the current study analyzes the robustness of such DL models utilizing various OFDM-based image communication systems for CV applications in an Intelligent Transportation Systems (ITS) environment. Our analysis has shown that the EfficientNetV2-based model achieved a range of 70-90% accuracy across different OFDM-based image communication systems over the Rayleigh Fading channel. In addition, leveraging different data augmentation techniques further improves accuracy up to 18%.  
artificial intelligence; image processing; intelligent transportation systems; IoT technologies; machine learning; OFDM; wireless communication +
Footnote †: journal: Electronics Electronics