esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
In this work, we focussed on implementing a kd-tree in Gaussian process regression. However, this technique could also be used in classification scenario's or when working with non-stationary data. Further work is required to establish the viability in these settings. Furthermore, it is an open question whether or not the approximations can be used with non-stationary data, as large nodes tend to hide underlying local trends like a change in the frequency spectrum of the underlying latent function. In our dataset, the number of dimensions remained restricted to three. Moreover, points on a polygon mesh are locally almost coplanar. This makes the kd-tree a reasonable choice. A comparison to other tree-like structures could confirm this.  
We implemented a fixed kd-tree that is constructed beforehand. Alternatively, the kd-tree could be adaptive to sequentially incoming data points. We permanently flag the decision for a node to descend further into its children. Future iterations use this flag to bypass the calculations for the criteria. In our experiments, we gradually introduce new data points one by one. This results in an ever growing set of retained test points. Alternatively, one could implement Bayesian filtering techniques explained in [48; 49] to combine the previously calculated posterior with a new one based on only one data point and a new set of test points.  
Figure 5: The Car model. (**a**) three different hotspots (on the back of the roof left, at the front of the back tire and between the bonnet and the front window). This is the ground truth to be found via GPR. The posterior belief after sequentially adding a new data point 100 times; (**b**) without kd-tree; (**c**) with kd-tree.  
Figure 6: Results for the Car model as 100 data points are added sequentially. (**a**) NRMSE between the posteriors and the ground truth; (**b**) average 2\(\sigma\) value for all points.  
In our experiments, we restricted the dataset to 100 data points. The issue of large datasets is beyond the scope of this paper. Further studies regarding the incorporation of the techniques presented in [14] would be worthwhile. This would lower both the amount of data points and test points simultaneously, resulting in even larger computational gains.  
This work can be extended to points on graphs such as representations of computer networks or links between people both in real life or on social media. An application of this is monitoring the spread of contagious diseases like for example Covid-19 among a population with only limited testing.
# TTH-Net: Two-Stage Transformer-CNN Hybrid Network for Leaf Vein Segmentation
## 4 Experiment
### Comparisons with the State of the Art  
In this section, we evaluate the performance of the proposed model. For comprehensive comparison, we select CNN models including FCN [10], U-Net [12] GhostNet [13], MobileNetv3 [41], and DeepLabv3+ [42] Transformer models including ViT [15], Segmenter [16], Swin Transformer [43], SETR [17], DeiT [44], as well as two-stage models, such as ALSNet [45], TSUNet [46], DDNet [47], APCNet [48], CMMANet [49], and PCDNet [50] as benchmark methods. For the GhostNet and ViT models, we preserve their encoding parts and replace the original classification head with a \(1\times 1\) convolution. For training and testing, all models are conducted under the same settings. As shown in Table 1, the results of variants of CNN and transformer, as well as the outcomes of the two-stage algorithm are recorded. For leaf vein segmentation, the transformer has significant advantages over CNNs due to its global modeling capability. In addition, the two-stage algorithms based on the deep learning methods also perform well, and our proposed TTH-Net achieves the best scores in all the indicators. Specifically, compared to the DeepLabv3+ algorithm, our proposed TTH-Net surpasses it by 2.73%, 1.05%, 2.30%, and 1.89% in terms of IoU, Spe, Sen, and Acc metrics, respectively. Additionally, TTH-Net achieves an improvement of 2.1%, 1.19%, 0.61%, and 1.15% in IoU, Spe, Sen, and Acc indicators, respectively, compared to the Swin Transformer. We also record the mean (standard deviation) of the 5-fold cross-validation of each metric for the proposed method. As shown in Table 1, transformer variants such as Segmenter and DeiT exhibit stronger dependency on the data compared to CNNs. Additionally, the proposed TTH-Net, which integrates the advantages of CNN and transformer, shows a lower dependency on the data. Thus, we can conclude that the proposed TTH-Net has state-of-the-art performance on the LVN dataset for the leaf vein segmentation task and lower dependency on the data.
# Low-Complexity Hybrid Optical OFDM with High Spectrum Efficiency for Dimming Compatible VLC System
###### Abstract  
In visible light communications (VLC), dimming control constitutes an indispensable technique to comply with various illumination necessities and with different energy consumption constraints. Therefore, a novel dimming compatible hybrid optical orthogonal frequency division multiplexing (DCHO-OFDM) is conceived in this paper to fulfil the requirements from communications and illuminations. Explicitly, the signal branch of the unclipped asymmetrically clipped O-OFDM (ACO-OFDM) and the down/upper-clipped pulse-amplitude-modulated discrete multitone (PAM-DMT) are adaptively amalgamated in order to increase the spectrum efficiency. For the sake of precisely achieving dimming control, the chromaticity-shift-free and industry-preferred pulse width modulation (PWM) is further invoked to the hybrid signal, assisted by a time-varying biasing scheme to mitigate the non-linear distortion. As the different signal components in DCHO-OFDM are beneficially combined in an interference-orthogonal approach, the transmitted symbols are able to be readily detected upon relying on a standard OFDM receiver, as that of ACO-OFDM. Our simulations demonstrate that a high spectrum efficiency of the conceived DCHO-OFDM scheme can be achieved with less fluctuation in a wide dimming range.  
visible light communications (VLC); optical orthogonal frequency division multiplexing (O-OFDM); dimming control; pulse width modulation (PWM) Article
## 2 Fault Signal and Wavelet Transform
### Wavelet Transform  
Fault signals from EMTP/ATPDraw were decomposed by WT of MATLAB. High frequency components were caused by abnormalities in the electrical system. WT extracted and analyzed the fault signals. Three-phase current signals and zero sequence were decomposed by Daubechies (db2) of DWT for the high frequency component in scale 1. The coefficients from DWT were squared to focus on the changes in the coefficients and find the maximum coefficients, as shown in Figure 4.  
Figure 4 shows the characteristics of coefficients from the DWT of phase A--short circuit to ground at 30% of the length of the transmission line and at an angle of fault of 0\({}^{\circ}\). Before the fault occurred, the value of the coefficient of the three phase and zero sequence was almost zero. When the fault occurred, the value of the coefficient of phase A and zero sequence became higher than that of phases B and C. Then, because of the steady state current after the fault occurred, the value of the coefficient decreased. The coefficient of the three-phase current, the phase in which the fault occurred, had a higher coefficient than the phase in which no fault occurred. The coefficient of the case in which short circuit to ground coefficients of zero sequence occurred was higher than that in the case of short circuit underground fault.  
Figure 3: Fault signal from EMTP/ATPDraw of the primary and secondary currents: Phase A short circuit to ground fault at 30% of the length of transmission line.  
The behavior of coefficients from DWT varies depending on various factors. The behavior of the values of the coefficients was previously analyzed to create a fault classification algorithm [7]. Moreover, there are studies of the behaviors of the coefficient value of positive sequence. This is confirmed in paper [9].
# Physical Properties of _Jatropha curcas_ L. Fruits and Seeds with Respect to Their Maturity Stage
## 3 Results and Discussion
### Geometric Characteristics of _J. curcas_ fruits and Seeds  
Geometric properties of _J. curcas_ fruits and seeds from each maturity stage are presented in Table 1.  
Figure 3: Detachment force of _J. curcas_ fruit at different maturity stages. Different small letters (a, b, and c) indicate significant differences at \(p<0.05\).  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Fraction**} & **Moisture** & \multirow{2}{*}{
\begin{tabular}{c} **Content** \\ **(\%)** \\ \end{tabular} } & **Unit Mass** & **Length \(L\)** & **Width \(W\)** & **Thickness \(T\)** & **Sphericity** & **Arithmetic** & **Geometric** & **Volume \(V_{a}\)** & **Surface** & **Size** \\  & **Content** & **(g)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** \\ \hline \hline \multicolumn{1}{c}{_Faults_} & & & & & & & & & & & \\ \multicolumn{1}{c}{Green} & \(85.2^{\pm}\pm 1.1\) & \(82.3^{\pm}0.28\) & \(26.61^{\pm}\pm 0.33\) & \(25.71^{\pm}\pm 0.32\) & \(26.38^{\pm}\pm 0.30\) & \(0.99^{\pm}\pm 0.01\) & \(26.23^{\pm}\pm 0.29\) & \(26.22^{\pm}\pm 0.29\) & \(9.50^{\pm}\pm 0.31\) & \(2164^{\pm}\pm 47\) & \(1.2^{\pm}\pm 0.04\) \\ \multicolumn{1}{c}{Vellow} & \(79.5^{\pm}\pm 3.6\) & \(5.25^{\pm}0.27\) & \(24.64^{\pm}\pm 0.34\) & \(24.55^{\pm}0.25\) & \(25.14^{\pm}\pm 0.34\) & \(0.98^{\pm}\pm 0.02\) & \(24.78^{\pm}\pm 0.28\) & \(24.79^{\pm}\pm 0.27\) & \(8.00^{\pm}\pm 0.26\) & \(1931^{\pm}\pm 42\) & \(1.4^{\pm}\pm 0.07\) \\ \multicolumn{1}{c}{Black} & \(8.6^{\pm}\pm 0.5\) & \(2.51^{\pm}\pm 0.11\) & \(26.90^{\pm}\pm 0.38\) & \(21.99^{\pm}\pm 0.21\) & \(24.59^{\pm}\pm 0.27\) & \(0.91^{\pm}\pm 0.01\) & \(24.50^{\pm}\pm 0.24\) & \(24.40^{\pm}\pm 0.24\) & \(7.64^{\pm}\pm 0.22\) & \(1873^{\pm}\pm 36\) & \(1.6^{\pm}\pm 0.07\) \\ \multicolumn{1}{c}{_Sends_} & & & & & & & & & & \\ \hline \multicolumn{1}{c}{\({}^{\prime}\)Collow\({}^{*}\)} & \(58.3^{\pm}\) \(5.5\) & \(0.41^{\pm}\pm 0.03\) & \(16.98^{\pm}\pm 0.14\) & \(10.56^{\pm}\pm 0.21\) & \(8.17^{\pm}\pm 0.07\) & \(0.67^{\pm}\pm 0.01\) & \(11.90^{\pm}\pm 0.09\) & \(11.30^{\pm}\pm 0.13\) & \(0.77^{\pm}\pm 0.02\) & \(468^{\pm}\pm 11\) & \(4.5^{\pm}\pm 0.31\) \\ \multicolumn{1}{c}{\({}^{\prime}\)Black\({}^{*}\)} & \(6.8^{\pm}\) \(0.5\) & \(0.57^{\pm}\pm 0.02\) & \(18.33^{\pm}\pm 0.12\) & \(10.78^{\pm}\pm 0.11\) & \(8.32^{\pm}\pm 0.07\) & \(0.64^{\pm}\pm 0.00\) & \(12.48^{\pm}\pm 0.09\) & \(11.80^{\pm}\pm 0.09\) & \(0.87^{\pm}\pm 0.02\) & \(503^{\pm}\pm 9\) & \(4.1^{\pm}\pm 0.19\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Moisture content and geometric characteristics of \(J\). _curas_ fruits and seeds at different maturity stages.
## 2 Materials and Methods
### Detecting River Pollution through Machine Learning
#### 2.4.4 Model Training
For the RF model, hyperparameter tuning involves selecting the optimal number of trees in the forest, the maximum depth of the trees, and the minimum number of samples required to split a node. The number of trees in the forest determines the robustness and stability of the model. More trees lead to better performance, but also increase the computational cost. The testing values included a range from 100 to 500 trees with increments of 100. This range allowed us to evaluate the impact of different numbers of trees on the model performance. The maximum depth of trees controls the complexity of individual trees in the forest. Deeper trees capture more complex relationships in the data, but also lead to overfitting. The testing values included a range from 10 to 50 with increments of 10. This range covered a spectrum of tree depths, from relatively shallow to deeper trees, allowing for the exploration of different levels of complexity. The minimum number of samples required to split a node determines the minimum number of samples required to split an internal node during the tree-building process. Increasing this value helps to prevent overfitting by ensuring that nodes with insufficient samples are not split. The testing values included a range from 5 to 50 with increments of 5 that reflected a gradual increase in the minimum number of samples required to split a node. This range allowed us to test the impact of different levels of node splitting criteria on the model generalization. After thorough hyperparameter tuning, the final decision for the RF model's parameters consisted of a forest of 300 trees that yielded the best balance between accuracy and computational efficiency. After assessing the model's performance with varying tree depths, it was found that setting the maximum depth of the trees to 20 resulted in optimal generalization performance without overfitting. Through experimentation with different values for the minimum number of samples required to split a node, it was concluded that setting this parameter to 10 achieved the most robust and stable model performance. These parameter values were chosen based on their ability to maximize the model accuracy and generalization while minimizing overfitting. They represented the optimal configuration for the RF model in classifying the images captured by the drone over the Shatt al-Arab River region.
###### Abstract
The model uses, amongst other parameters, the view factors which measure the fraction of the total radiative heat leaving a given radiating surface that arrives at a given receiving surface, implying a multi-dimensional consideration. The model relies on several successive steps: the evaluation of the fire geometry and its position in the compartment, the evaluation of the flame temperature, the evaluation of the heat fluxes in the compartment, and finally, the evaluation of the temperature of a steel structural member located in the compartment. To evaluate the heat fluxes, different zones are defined depending on the location of the target and on whether the flame impacts or not the ceiling. In addition, the transient evaluation of steel temperatures of a structural member placed in the compartment is computed following an iterative procedure from Eurocode 3. The effect of the ventilation is considered through a simple concept: the burning rate (\(\mathrm{kW/m^{2}}\)) in the compartment may be (momentarily) modified during the course of the fire when the fire is air controlled. The latter situation is evaluated following an approach initially proposed by [PERSON] [12] which considers that the fire power is limited by the oxygen which can enter in the compartment through the openings, using the opening factor to assess the ventilation limitation. Then, this paper presents a comparison of the temperatures measured during a travelling fire test in a column made of a hot-rolled steel profile with the ones obtained through the analytical model: the similitudes and differences are discussed. This paper suggests that the developed analytical model can allow for an acceptable representation of the travelling fire in terms of fire spread and of steel temperatures, despite limitations inherent to the analyticity of the model, making it potentially usable for the pre-design stage of a building structure.
## 6 Conclusions  
The implications of the shortening of winter seasons for frost and thaw penetration depths and frost heave in Canadian airports is presented in this research. Three commonly used methodologies proposed by the Ministry of Transportation of Ontario (MTO), the Ministere des Transports du Quebec's (MTQ), and Transport Canada Civil Aviation (TCCA) were evaluated. The following conclusions can be drawn based on the results of this case study:  
From comparing the frost penetration predictions with the actual field measurements, it can be concluded that both the empirical (MTO and TCCA) and mechanistic (MTQ) models performed considerably better in cases of finer grained soil types than coarse subgrade soils. The MTQ model resulted in the highest accuracy, which can attributed to its mechanistic-based approach that accounts for material properties.  
The climate change simulation using RCP8.5, for horizon times 2041 and 2061, shows that the warming climate impacts on areas underlain by permafrost should be considered differently than those not underlain by permafrost, since changes to the active layerthickness becomes the main concern, due to the projected thaw penetration, in the former case.  
Both the MTO and TCCA methods provide consistent trends of frost penetration prediction for the airport sites investigated in this study when compared with the measured field data. However, due to climate change impacts and the empirical nature of these methods, moving towards more mechanistic approaches that can account for actual soil properties would be a necessity to avoid overdesigning pavements in non-permafrost areas, or under-designing in the case of airports underlain by permafrost.  
It is suggested that future research on this matter broaden the number of sites evaluated to include more divers soil types and climate regimes outside North America, as well as simulate more climate change scenarios. The results of this study also indicate the need for including more specific soil properties, and ideally the utilization of measured values to enhance the reliability of climate change impact projections through the use of mechanistic models.  
Conceptualization, P.S.R.B., P.T. and S.L.T.; methodology, P.S.R.B. and P.T.; software, P.S.R.B.; validation, P.T. and P.S.R.B.; formal analysis, P.S.R.B.; investigation, P.S.R.B. and P.T.; data curation, P.S.R.B. and P.T.; writing--original draft preparation, P.S.R.B.; writing--review and editing, P.S.R.B., P.T. and S.L.T.; visualization, P.S.R.B. and P.T.; supervision, P.T and S.L.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Results
### Classification Results and Accuracy Assessment
#### 3.1.2 The Classification Difference of Regions  
In order to more specifically show the classification differences of SVM, RF, and CART in different regions and classes (i.e., Wheat (WHE)- Other (OTH), WHE-Urban (URB), WHE- Vegetation (VEG), and WHE-Water (WAT)), four 5 \(\times\) 5 km sub-regions (i.e., a, b, c, and d) were randomly selected from the classification results of the three algorithms to discuss the misclassification of winter wheat pixels (Figure 5).  
By carefully comparing the mapping results of SVM, RF, and CART in the four sub-regions, it is obvious that the three algorithms are affected by the other classes when identifying winter wheat, resulting in misclassification. In region a, the spectrum of vegetation is more similar to winter wheat than the other classes, resulting in misclassification. Compared to the RF and CART algorithms, SVM can effectively distinguish vegetation and winter wheat pixels. In region b, we analyzed the differences between winter wheat and urban pixels. It can be seen from the figure that the three algorithms can distinguish the urban pixels well, but there are differences when classifying roads with vegetation growth. SVM and RF can effectively classify wheat and urban pixels, while CART classifies such pixels as winter wheat pixels. It is difficult for CART to distinguish winter wheat pixels from winter wheat pixels when classifying mixed pixels. In region c, we analyzed the misclassification of winter wheat and water pixels. Obviously, due to the strong absorption of solar radiation in the visible and near-infrared bands, the spectrum of water is significantly different from that of winter wheat. Therefore, the three algorithms can distinguish the boundary between water bodies and winter wheat well. For region d, it is obvious that SVM is more accurate than RF and CART in classifying wheat class and other class.  
Through the analysis of the misclassification of the four different sub-regions, we determined that the SVM and RF algorithms can distinguish winter wheat pixels from the other classes. However, RF is not as effective as SVM in identifying vegetation and winter wheat pixels. According to Figure 5, it can be found that except for water bodies, vegetation and urban pixels could not be accurately classified. Compared to SVM and RF, the classification ability of CART is relatively inferior. This indicates that SVM and RF have strong performance in classifying winter wheat from the other classes in regions with complex classes.
# Link Connectivity and Coverage of Underwater Cognitive Acoustic Networks under Spectrum Constraint
## 3 System Model
### Network Model  
In our network model, we assume that PUs are distributed according to homogeneous Poisson point process (HPPPs) with intensity \(\lambda_{p}\)[19]. Each PU radiates the identical power, denoted by \(P_{p}\). Since we are concerned with the link connectivity and the probability of coverage of an SU pair, we consider that this SU pair is located at the center of this network within a 2-D plane; this assumption is consistent with the sophisticated stochastic geometric method proposed in [20]. We consider that a secondary transmitter sends a packet, with the power denoted by \(P_{s}\).
## 4 Field Data Examples
According to the comparison with CWT, GST, and SSCWT, we can find that SSGST can provide higher time-frequency resolution and can more accurately extract the abnormal response characteristics of seismic signals. Therefore, the SSGST method can locate hydrocarbon reservoirs effectively and depicts the reservoir boundary accurately with higher precision.