esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Calibration and Validation for Each Model
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Land Cover Types**} & \multicolumn{2}{c}{_m\_AVM_} & \multicolumn{2}{c}{_m\_GR_} & \multicolumn{2}{c}{_m\_TG_} & \multicolumn{2}{c}{\(\varepsilon_{\text{max}\_VPM}\)} & \multicolumn{2}{c}{\(\varepsilon_{\text{max}\_MOD\_ALG}\)} \\  & **(gC/m\({}^{2}\)/Eight-Day)** & **(gC/MJ/Eight-Day)** & **(gC/m\({}^{2}\)/Eight-Day)** & **(gC/MJ/Eight-Day)** & **(gC/MJ/Eight-Day)** & **(gC/MJ/Eight-Day)** \\ \hline MF & 255.34 & 11.39 & 123.90 & 11.11 & 9.32 \\ Croplands & 325.65 & 20.85 & 171.92 & 23.74 & 17.66 \\ ENF & 130.29 & 7.37 & 74.24 & 8.08 & 7.41 \\ EBF & 130.16 & 7.65 & 87.57 & 9.59 & 5.03 \\ Grasslands & 222.39 & 6.82 & 87.02 & 8.83 & 6.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters of light use efficiency (LUE) (\(\varepsilon_{max}\)_VPM and \(\varepsilon_{max}\)_MOD_ALG) and the amount of carbon fixed in photosynthesis (_m_GR, _m_AVM_, and _m_TG) in different models for each land cover type. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{Model} & \multicolumn{2}{c}{_MF_} & \multicolumn{3}{c}{_Croplands_} & \multicolumn{3}{c}{_ENF_} & \multicolumn{3}{c}{_EBF_} & \multicolumn{3}{c}{_Grasslands_} \\ \cline{2-13}  & Bias & RMSE\% & R\({}^{2}\) & Bias & RMSE\% & R\({}^{2}\) & Bias & RMSE\% & R\({}^{2}\) & Bias & RMSE\% & R\({}^{2}\) & Bias & RMSE\% & R\({}^{2}\) \\ \hline \multicolumn{13}{c}{Calibration} \\ AVM & \(-\)5.28 & 25.98 & 0.97 & \(-\)9.89 & 67.78 & 0.64 & \(-\)16.14 & 48.91 & 0.72 & \(-\)16.78 & 58.55 & 0.29 & \(-\)0.64 & 55.22 & 0.90 \\ GR & 7.70 & 51.72 & 0.82 & 13.13 & 61.31 & 0.77 & \(-\)14.64 & 46.31 & 0.69 & \(-\)16.55 & 58.90 & 0.26 & 6.50 & 114.92 & 0.83 \\ TG & \(-\)1.76 & 25.58 & 0.94 & \(-\)2.30 & 57.56 & 0.70 & \(-\)12.98 & 43.66 & 0.65 & \(-\)7.47 & 46.42 & 0.36 & 1.22 & 59.69 & 0.89 \\ VPM & \(-\)5.30 & 41.23 & 0.87 & \(-\)1.06 & 45.48 & 0.81 & \(-\)16.29 & 48.52 & 0.75 & \(-\)14.23 & 53.40 & 0.36 & 1.19 & 59.80 & 0.89 \\ MOD_ALG & \(-\)6.46 & 58.43 & 0.73 & 3.98 & 51.81 & 0.76 & \(-\)18.32 & 57.87 & 0.51 & \(-\)27.33 & 77.42 & 0.17 & 1.30 & 58.57 & 0.89 \\  & & & & & & &
# _Thuja occidentalis_ L. (Cupressaceae): Ethnobotany, Phytochemistry and Biological Activity
## 4 Biological Activities
### Anticancer Activity  
In homeopathy, _T. occidentalis_ is used in the treatment of cancer, but its mechanism of action is not known. [PERSON] et al. (2016) studied the effect of \(\alpha\)/\(\beta\)-thujone on glioblastoma, using in vitro and in vivo models. They have observed that \(\alpha\)/\(\beta\)-thujone has the ability to diminish cell viability and has antiproliferative, proapoptotic, and antiangiogenic properties in vitro. In in vivo studies, \(\alpha\)/\(\beta\)-thujone has been reported to induce regression of neoplasia and inhibited angiogenic markers of VEGF, Ang-4, and CD31 inhibitors in the tumor [42]. The antitumoral effect of the extract obtained from thuja leaves has been evaluated on numerous cancer cell lines [41; 44; 63; 84].  
The in vivo study performed by [PERSON] and [PERSON] (2011b) demonstrated the antitumor effect of thujone in the malignant ascites lymphoma model ([PERSON]). Thujone, a monoterepne naturally found in _T. occidentalis_, has been shown to increase the number of leukocytes and bone marrow cells. This increased the proliferation of splenocytes and thymocytes, both in the presence and absence of specific mitogenes. Thujone stimulated cell mediated immune response and production of IL-2 and IFN-\(\gamma\)[92]. Thujone also showed the ability to inhibit metastasis in melanoma in vivo [83]. Thujone obtained from _T. occidentalis_ ethanolic extract has been shown to have anticancer properties on the malignant melanoma cell line A375. In the same study, thujone was shown to have an antiproliferative effect and the ability to induce apoptosis [41].  
Thuja's antitumoral activity has been highlighted in breast cancer [85]. Polysaccharides obtained from _T. occidentalis_ L. leaf extract have been shown to reduce mice-induced inflammation. They have the ability to prevent metastasis by diminishing inflammatory cytokines, such as IL-1\(\beta\), IL-6, granulocyte-macrophage colony stimulating factor (GM-CSF), and TNF-\(\alpha\). Moreover, these polysaccharides stimulated the activity of natural killer (NK) cells, cell-mediated antibody-dependent cytotoxicity (ADCC), and complement-mediated cytotoxicity (ACC) and stimulated the activity of antitumor factors, IL-2, and TIMP [43].
## 2 Materials and Methods
The absorption peak of SBII is localized at \(\lambda=604\) nm and significantly enhanced absorption spreads also to the \(\lambda=532\) nm wavelength, as presented in Figure 1f; the absorption spectra of 6 CHBT NLC doped with a dye of different weight concentrations are 0.1%, 0.2%, and 1.0%--light to dark blue lines, respectively. To verify the repeatability of our experimental results, measurements were conducted on six distinct cells, with each cell subjected to beam exposure in eight different dye-doped areas to assess variations in threshold power values. This methodology thoroughly evaluated the consistency across multiple samples and conditions, emphasizing the robustness of the observed optical limiting effects. The light and dark blue lines in Figure 1f represent the average T value for cells of 12 \(\upmu\)m and 30 \(\upmu\)m thicknesses, respectively. We observed significantly lower threshold powers for TN cells filled with a dye-doped 6 CHBT (1% SBII \(w/w\)), as shown in Figure 1g. The black dots indicate experimental data, while the shaded regions represent a standard deviation of the mean value. The narrow range of the area corresponding to the deviation from the mean value indicates the high repeatability of the results in various cells. Consequently, the use of a dye-doped liquid crystal led to a considerable reduction in the threshold power \(P_{\text{th}}\) to roughly 300 mW and 160 mW for the 12 \(\upmu\)m and 30 \(\upmu\)m NLC layer widths, respectively. Notably, the difference in the transmission level for the 6 CHBT NLC with increased absorption level compared to the pure nematic, for the same cell thickness, does not exceed 10%. This suggests that this type of solution could prove beneficial in effectively safeguarding photodetectors and CCDs.
# Thermal Mapping of Power Semiconductors in H-Bridge Circuit +
## 1 Introduction
The dominant factors (e.g., power factor, current amplitude, fundamental frequency, switching frequency) are considered, which can serve as the guideline of the loading condition for the desired thermal stress. The rest of the paper is organized as follows. Section 2 introduces the basic operation principle of the H-bridge circuit. The loss and thermal models of the power semiconductor are described in Section 3. Section 4 verifies the loss dissipation and junction temperature in both the simulation and experimental setup. Some conclusions are drawn in Section 5.
## 2 VDES Overview and Related Work  
In this section, we provide a brief overview of VDES, including its components, channels, and frame structure. We also review related studies on MAC protocols for VDES networks in MIoT, focusing on collision avoidance strategies and addressing their limitations to enhance maritime communications.
# PFC Single-Phase AC/DC Boost Converters: Bridge, Semi-Bridgeless, and Bridgeless Topologies
## 3 Categorization of Semi-Bridgeless Boost Converter Topologies
### Semi-Bridgeless Boost Converter with Clamped Diodes and Switches  
SBBC based on a clamped topology with diodes and switches is depicted in Figure 12. The authors in [107] reported that this topology presents high efficiency. This topology considers two additional switches (\(Q_{3}\) and \(Q_{4}\)); consequently, each boost converter works by using two switches per semi-cycle. The main advantage corresponds to the reduction of conduction losses due to the current return path being shortened; in addition, \(Q_{3}\) and \(Q_{4}\) contribute to the reduction of CM noise. Please note that power losses do not increase for this topology when compared with a topology with only clamped switches; basically, clamped diodes (\(D_{3}\) and \(D_{4}\)) only operate for the pre-charge of the DC bus, being excluded from the current path in normal operation conditions or in steady state.  
Figure 11: Semi-bridgeless boost converter with clamped switches.
## 2 Materials and Methods
### Data Analysis
#### 2.3.3 Performance Evaluation  
The coefficient of determination (\(R^{2}\)), root mean square error (RMSE), and residual predictive deviation (RPD) are used to comprehensively evaluate the effect of the trained model on the prediction of N, P\({}_{2}\)O\({}_{5}\), and K\({}_{2}\)O content in fertilizers. \(R^{2}\) represents the goodness of fit between the predicted value and the experimental value. \(R^{2}_{c}\) and \(R^{2}_{p}\) are used as the coefficients of determination for the calibration set and the prediction set, respectively. The closer the value of \(R^{2}\) is to 1, the better the fit of the model. RMSE is used to measure the deviation between the predicted value and the experimental value. RMSECV and RMSEP are the root mean square error of the calibration set and the predicted set, respectively. The smaller the value of RMSECV and RMSEP, the smaller the deviation between the predicted value and the experimental value, and the higher the accuracy of model prediction. RPD is defined as the standard deviation of the predicted value divided by the RMSEP, which is a measure of the effectiveness and overall predictability of the regression model. The larger the RPD, the better. The calculation of these parameters is as follows:  
\[R^{2}=1-\frac{\sum_{i=1}^{N}(\hat{y}_{i}-y_{i})^{2}}{\sum_{i=1}^{N}(y_{i}- \overline{y})^{2}}, \tag{2}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}(y_{i}-\hat{y_{i}})^{2}}{N-1}}, \tag{3}\]  
\[RPD=\frac{\sigma_{P}}{RMSEP}, \tag{4}\]  
where \(y_{i}\) and \(\hat{y_{i}}\) are the experimental value and predicted value of the nutrient content of the \(i\)-th sample, \(\overline{y}\) is the average value of the experimental value, \(N\) is the number of samples, and \(\sigma_{P}\) is the standard deviation of the predicted value. A model with excellent predictive performance has a higher \(R^{2}\) and RPD.
# Model Reference Adaptive Control of Vehicle Slip Ratio Based on Speed Tracking
## 4 Simulations Results and Analysis  
To verify the effectiveness and evaluate the braking performance of the proposed MRAC method, simulations under different conditions are considered. The main parameters of the vehicle are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Quantity** & **Value** \\ \hline \(m\) & \(\frac{1}{4}\) Car vehicle mass & 350 kg \\ \(J\) & Inertia & 0.65 kg m\({}^{2}\) \\ \(r_{w}\) & Wheel radius & 0.31 m \\ \(c_{f}\) & Friction coefficient & 0.4 \\ \(c_{v}\) & Air resistance coefficient & 0.595 N/(m\({}^{2}\) s\({}^{2}\)) \\ \(n_{w}\) & Number of brake wheel & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vehicle Parameters.  
The vehicle speed and road adhesion coefficient observer gains are selected as \(k_{v}=0.5\), \(c=-0.1\), and the reference model adaptive controller parameters are shown in Table 2. The vehicle is expected to achieve good braking performance via controlling the slip ratio at the optimal slip ratio \(\lambda^{*}(t)\). The reference slip ratio is chosen as  
\[\lambda^{*}=-10\lambda^{*}+10\lambda_{0},\ \lambda_{0}=-0.18 \tag{36}\]  
The slip ratio control problem is transformed into the vehicle speed tracking control, \(v_{w}=(1+\lambda^{*})v\). The vehicle starts braking at an initial speed of 80 km/h and continues to a full stop under different road conditions.
# Resource Indexing and Querying in Large Connected Environments
###### Abstract  
The proliferation of sensor and actuator devices in Internet of things (IoT) networks has garnered significant attention in recent years. However, the increasing number of IoT devices, and the corresponding resources, has introduced various challenges, particularly in indexing and querying. In essence, resource management has become more complex due to the non-uniform distribution of related devices and their limited capacity. Additionally, the diverse demands of users have further complicated resource indexing. This paper proposes a distributed resource indexing and querying algorithm for large connected environments, specifically designed to address the challenges posed by IoT networks. The algorithm considers both the limited device capacity and the non-uniform distribution of devices, acknowledging that devices cannot store information about the entire environment. Furthermore, it places special emphasis on uncovered zones, to reduce the response time of queries related to these areas. Moreover, the algorithm introduces different types of queries, to cater to various user needs, including fast queries and urgent queries suitable for different scenarios. The effectiveness of the proposed approach was evaluated through extensive experiments covering index creation, coverage, and query execution, yielding promising and insightful results.  
IoT; resource indexing; resource querying; date engineering +
Footnote †: journal:  
0000-0001-  
uncovered zones. Following this pattern, devices will be able to receive and forward queries between each other directly. Our approach considers the limited capacities of devices to increase the network life cycle. In this paper, we extend a previous study [2] where global index generation was detailed. Here, we consider different complex scenarios, introduce the implementation of four types of queries along with experiments, and also explain the adopted architecture, as will be seen in the upcoming sections.  
The rest of the paper is organized as follows. Section 2 presents a motivating scenario and highlights some needs and challenges. Section 3 details the categories of indexing approaches and presents related works, while showing their limitations. Section 4 defines preliminaries and assumptions. Then, we introduce the proposed indexing approach along with different querying techniques in Section 5. Section 6 presents the set of experiments conducted to validate our approach. The last section concludes this study and describes several future directions.
# An Overview of Indoor Positioning and Mapping Technology Standards
## 2 Related Work
### Indoor Positioning Technologies
[34] present an indoor positioning platform to support the development of foreseen location-based 5G network functionalities and services. However, indoor positioning in 5G IoT networks is still a very new research area, and a recent 5 GPPP technical report [35] set out the detailed indoor 3D positioning requirements and challenges. Although recent technologies have shown significant advances in terms of accuracy and speed, they have been used in new algorithmic arrangements to improve the quality of indoor positioning systems; and, there are continuous open efforts for guiding readers, researchers, and developers to select reliable, user-friendly, and accurate solutions for indoor positioning and navigation applications that are suitable for different scenarios. Nevertheless, the problem of positioning in indoor environments is far from being solved. There is still no satisfactory solution of an IPS capable of being used in all indoor scenarios with standard behavior [7]. One of the significant challenges is a lack of standardization; in other words, there is no standard that can serve as a guide for designing localization and proximity techniques and indoor positioning systems [5], although several international standards for RTLS and ITS have been published by ISO, which will be discussed in Section 2.3.
# Shear Wave Velocity Estimation Using the Real-Time Curve Tracing Method in Ultrasound Elastography
## 4 Results  
### Evaluation of Shear Wave Velocity  
In the experiment, the real-time curve tracing method is used to estimate the transverse propagation velocity of a shear wave in the selected homogeneous region, and the results are compared with those of the time-to-peak method and the lateral peak method. According to the experimental method in Section 3.2, these three methods selected 50 points in each transverse direction for data analysis to ensure consistency and fairness. In the longitudinal range of 25 \(\lambda\) to 85 \(\lambda\), 61 groups of transverse data were selected at intervals of 1 \(\lambda\). With regard to the real-time curve tracing method, linear regression is performed on each group of transverse data, as shown in Figure 3, and then the average value of the 61 groups is obtained as the shear wave velocity in the homogeneous region. With regard to the time-to-peak method and the lateral peak method, the shear wave velocity is calculated by the peak information of the shear wave. The confidence interval is 95%, and the results are shown in Figure 9. The expression of velocity is average \(\pm\) standard  
Figure 8: Gaussian noise experiment with equal noise standard deviation.  
Figure 7: Gaussian noise experiment with equal noise mean.  
deviation, and the results are shown in Table 2. In this paper, the calculation formula of relative error \(X_{R}\) is shown in Equation (9).  
\[X_{R}\ =\ \frac{V_{T}-V_{E}}{T_{T}}\times 100\% \tag{9}\]  
where \(V_{T}\) is the theoretical velocity and \(V_{E}\) is the estimated velocity.
## 3 Results and Discussion
### Evaluation of Flame Segmentation Performance
The overall flame segmentation results revealed that the YOLO-YCbCr model was more effective at detecting irregular and complex-shaped flames, whereas the YOLO-seg model was better suited for detecting large and structured flame patterns. The YOLO-YCbCr model uses a pixel-based segmentation approach that leverages flame color data, enabling it to accurately segment small and complex-shaped flames. However, this model is limited in accurately segmenting flames that exhibit a wide range of colors. In contrast, the YOLO-seg model is based on the fundamental structure of YOLO, which is optimized for real-time detection; however, it tends to focus on capturing the overall outline of the flame rather than performing pixel-level precise object segmentation, simplifying complex flame patterns and predicting them more broadly.
# Structural Information Reconstruction of Distorted Underwater Images Using Image Registration
## 4 Discussion
### Analysis of Patch Fusion  
Figure 5 shows the registration results of using the mean frame or the fused frame as the reference frame after the guided filter. It can be seen that there are some distortions and double image (marked by the red rectangle) that affect the recognition and even the overlapping of letters in the mean-to-frames registration. However, the results of our proposed method can express more accurate structural information. The results show that the patch fusion step must be carried out to eliminate the seriously distorted parts before the guided filtering, so as to enhance the correct boundaries of the objects as much as possible, which could decrease the errors that occurred in the registration step.
## 3 Results and Discussion
### Comparison and Discussion of Simulation Results  
The effectiveness of the ICS-based ELM BOD soft sensing model was verified based on the data attribute reduction results; \(S_{\text{s}}\), \(X_{\text{i}}\), \(X_{\text{s}}\), \(X_{\text{bh}}\), \(S_{\text{nh}}\), and \(S_{\text{nd}}\) of the influent and the flow rate \(Q\) are used as the auxiliary variables for the network input, so the ICS-based ELM network structure had 7 input nodes, 40 hidden nodes and one output node (7-40-1). The 1344 groups of data are randomly divided into training datasets (500 groups), verifying datasets (460 groups), and testing datasets (384 groups) for the simulation. The training accuracy and prediction accuracy of the soft sensor model are represented by mean square error (MSE).  
\[MSE=\frac{1}{N}\sum_{i=1}^{N}\left(Y(i)-Y^{*}(i)\right)^{2} \tag{14}\]  
where, \(Y(n)\) is the predicted output of the model; \(Y^{*}(n)\) is the actual measured value; \(N\) is the sample size number.  
Figure 3: Membership degrees of each conditional attribute to biochemical oxygen demand (BOD).  
Parameters are set as follows: For the ICS algorithm, the population size \(n\) is 25, the maximum and the minimum probability to be discovered by the host bird are 0.75 and 0.1 respectively; for the adaptive step length control parameters \(m=0.8\), \(k=0.2\), \(p=25\), \(S_{\min}=0.01\).  
When the number of iterations \(t=100\), the iteration will be terminated; the curve of the optimization process is shown in Figure 4. The prediction results and errors of ICS-based ELM soft sensor for effluent BOD are shown in Figure 5.  
It can be seen from the results, the ICS-based ELM has a better prediction accuracy than the basic ELM. To verify the advantage of the ICS-based ELM, a comparison studies are simulated with the other five models, which are extreme learning machine (ELM), cuckoo search (CS)-based ELM, relevance vector machine (RVM), back-propagation (BP) neural network and least squares support vector machines (LS-SVM), with the same influent data under dry weather condition. The MSE results are shown in Table 3, and the prediction results are shown in Figure 6. Clearly, the MSE from ICS-based ELM are much smaller than the other five models. But the training process will take a slightly longer computer time than for the basic ELM because of the optimization process of the input weights \(\mathbf{w}_{i}\) and the hidden layer biases \(b_{i}\).  
Figure 4: Optimization process.  
Figure 5: Compared prediction results of improved cuckoo search algorithm-based extreme learning machine (ICS-ELM) and basic ELM under dry weather condition, (**a**) effluent BOD concentration, (**b**) error in predicting the measured effluent BOD.  
As can be seen from the results shown in Figures 7 and 8, no matter how the weather condition changes, a well-trained ICS-based ELM still can predict the effluent BOD with smaller errors compared to other five models considered in this paper.