esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Performance Metrics for GBPTs  
The performance of the GBPT can be characterized with respect to different features, quantified by a corresponding metric. In turn, each performance feature of the terminal is quantified by a corresponding metric. In the road application domain, the most relevant performance features are availability, accuracy, and integrity.  
Availability is the percentage of time during which the system can be used for the required function in a given scenario [5]. An example of a relevant metric for the availability feature is the number of epochs with a position output divided by the total number of epochs for a given operational scenario.  
The accuracy can be measured by the error between the position provided by the positioning terminal, when this position is available, and the user's \"true\" position, generally estimated by a reference measurement system. This error, which is a random variable, is fully characterized by its cumulative distribution function (CDF). In 2D, the error is called horizontal position error (HPE). Some relevant metrics for HPE are the 50 th, 75 th, and 95 th percentiles.  
The integrity is a measurement of the confidence the user can have in the position supplied by the system. For civil aviation, it is expressed in the form of a probability (or risk) of failure over the period during which the positioning service is provided [5]. However, the applicability of the aviation-born integrity to other transportation fields is not straightforward due to the limitation of the urban contexts. Some \"local integrity\" concepts, suitable to automotive applications in urban scenarios, have already been proposed [22].  
A summary of the metrics for the performance characterization of the positioning terminal was proposed by the CEN-CENELEC standardization organization to [23]. However, in this work the statistical assessment of the GBPT performance evaluation was carried out by considering the horizontal accuracy only (i.e., HPE), defined as follows:  
\[HPE=\sqrt{(x^{rx}_{East}-x^{ref}_{East})^{2}+(y^{rx}_{North}-y^{ref}_{North})^{2}}, \tag{1}\]  
where:  
* \(x^{rx}_{East}\) and \(x^{ref}_{East}\) are the east coordinates estimated respectively by the receiver under test and the reference receiver, at a specific time instant;
* \(y^{rx}_{North}\) and \(y^{ref}_{North}\) are the north coordinates estimated respectively by the receiver under test and the reference receiver, at a specific time instant.
# Viscosity Prediction of Different Ethylene Glycol/Water Based Nanofluids Using a RBF Neural Network
## 3 Modeling Implementation of a RBF Neural Network
### Configuration of a RBF Neural Network  
Considering the nonlinear characteristics of the ethylene glycol/water based nanofluid viscosity ratio with different factors, a three layer RBF neural network is developed in the present investigation. Temperature, nanoparticle diameter, nanoparticle volume fraction, nanoparticle density, and viscosity of the base fluid are selected as the input variables. The objective output of the RBF neural network is the viscosity ratio between the nanofluids and the base fluid. Therefore, the basic structure of the developed RBF neural network for predicting the viscosity ratio of ethylene glycol/water based nanofluids is 5-m-1, as illustrated in Figure 3. For the neurons, the numbers in the hidden layer (m) and other parameters are determined in the training process.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Nanofluids** & **TiO\({}_{2}\)-EG** & **SiO\({}_{2}\)-EG/W 1 [12]** & **SiO\({}_{2}\)-EG/W 2 [13]** & **SiO\({}_{2}\)-EG/W 3 [14]** & **SiO\({}_{2}\)-EG [15]** & **CuO-EG/W 4 [16]** & **SiC-EG/W 5 [19]** \\ \hline \(d_{p}\) (nm) & 25 & 10 & 10 & 30/45/50 & 18.1/28.3/45.6 & 29 & 30 \\ \(\phi_{p}\) (\%) & 0.1–1.8 & 0.1 & 0.1 & 1–6 & 0.6–8.4 & 1–4 & 0.1–0.5 \\ \(p_{p}\) (kg/m3) & 4230 & 2650 & 2650 & 6310 & 2650 & 6310 & 110 \\ \(T\) (\({}^{\circ}\)C) & 20.1–60.2 & 28.45–59 & 28–59 & \(-\)35–50 & 25–59 & 0–40 & 10–50 \\ \(\mu_{f}\) (m/a.s) & 3.87–23 & 0.98–1.68 & 1.6–3.11 & 2.33–99.5 & 4.08–18.5 & 4.35–11.5 & 9.2–11.34 \\ \(\mu_{nf}\)/\(\mu_{f}\) & 0.81–1 & 1–1.15 & 1.05–1.13 & 1.1–4.65 & 1.04–2.02 & 1.14–2.09 & 1.13–1.29 \\ No. of data & 27 & 11 & 10 & 80 & 31 & 12 & 45 \\ \hline \hline \end{tabular}  
* \({}^{\text{a}}\) EG/W: 25:75 by volume ratio; \({}^{\text{b}}\) EG/W: 50:50 by volume ratio; \({}^{\text{c}}\) EG/W: 60:40 by weight ratio; \({}^{\text{d}}\) EG/W: 40:60 by weight ratio.  
\end{table}
Table 1: Viscosity information of ethylene glycol/water based nanofluids.
# Injection Molding of Biodegradable Deciduous Teeth Dental Post
## 3 Results and Discussion
### Integrated Summary of Material Properties and Molding Performance  
To provide a consolidated overview of the material behavior and processing outcomes, Table 8 summarizes the key mechanical, thermal, rheological, and biological properties of the PLA/PCL/ACP composite, alongside listing metrics related to its injection molding performance. The composite exhibited a flexural strength of 21.9 \(\pm\) 2.7 MPa and a modulus of 2.08 \(\pm\) 0.13 GPa, indicating sufficient mechanical integrity for pediatric dental post applications. Thermal analysis revealed a glass transition temperature (T\({}_{\text{g}}\)) of around 65 \({}^{\circ}\)C and a melting temperature (T\({}_{\text{m}}\)) of 172.6 \({}^{\circ}\)C, suggesting a stable thermal profile during molding. Rheological evaluation demonstrated moderate viscosity under shear, supporting processability via micromolding, as characterized by the Modified Cross model parameters (Table 1) and shear-dependent viscosity profiles (Figure 5). The contact angle of 73.73\({}^{\circ}\)\(\pm\) 1.59\({}^{\circ}\) indicated moderate hydrophilicity, while cell viability exceeding 70% confirmed acceptable biocompatibility. Molding performance, assessed through linear shrinkage and dimensional precision, showed consistent quality under optimized conditions derived from Taguchi analysis. This integrated presentation highlights the interplay between material formulation, biological response, and processing precision, providing a foundation for evaluating the clinical feasibility of the proposed system.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Control Factor** & **Variance (S)** & **DoF (f)** & **Mutations (V)** & **Pure Change (S)** & **Contribution (\(\rho\))\%** \\ \hline A (Melt Temperature) & 35.621 & 2 & 17.811 & 35.582 & 52.932 \\ B (Injection Speed) & 8.635 & 2 & 4.317 & 8.595 & 12.786 \\ C (Holding Speed) & 22.609 & 2 & 11.305 & 22.570 & 33.575 \\ e (Error) & 0.357 & 18 & 0.020 & 0.476 & 0.708 \\ \hline Total & 67.222 & 24 & & 67.222 & 100.000 \\ \hline \hline \end{tabular}
\end{table}
Table 7: ANOVA results for the Taguchi experiment, indicating the relative influence of each control factor on dimensional shrinkage.
# Q-RPL: Q-Learning-Based Routing Protocol for Advanced Metering Infrastructure in Smart Grids
## 1 Introduction
Since it does not require datasets for training, successful implementation of Q-learning within RPL implies limitless applicability for this innovative approach across various AMI deployments. To assess the effectiveness of our proposed approach, we conducted simulations using actual smart meter locations in the cities of Montreal and Barcelona, with traffic patterns representative of Smart Grid applications. Our new RL-based routing mechanism provided notable improvements in the packet delivery ratio (PDR), the end-to-end delay, and the compliant factor compared to the standard RPL implementation as well as to three other routing algorithms RPL+, ML-RPL, and RI-RPL. The comparison was made across a range of traffic loads and in different deployments. Contribution and OrganizationWe highlight the following key contributions based on our findings in this paper:  
* We propose a novel routing strategy based on a Reinforcement Learning technique, Q-learning, to improve the routing decisions of RPL in AMI deployments. * Our approach balances the use of RL and traditional routing metrics, while the parent selection is guided by the Q-learning algorithm, traditional routing metrics like the Expected Transmission Count (ETX) and Received Signal Strength Indicator (RSSI) are used to enhance the Q-learning policy and the exploration-exploitation strategy. * We have conducted simulations using smart meter locations from two real deployments of smart meters in the cities of Montreal and Barcelona to evaluate the performance of our proposed routing strategy and compare it to other benchmark protocols. The results show a significant improvement in key performance metrics such as PDR, average end-to-end delay, and compliant factor. * Q-RPL bridges the gap between traditional routing methods and advanced Machine Learning techniques, offering insights into how these two domains can be effectively combined for improved network performance and reliability. Our approach, although used here with RPL, could potentially be adapted to other routing protocols used in the same context. The rest of the paper is organized as follows. Section 2 discusses some related works. Section 3 provides a background to RPL. The design of the proposed solution is presented and described in detail in Section 4. The performance of the new routing strategy is evaluated and compared to other routing protocols in Section 5. Section 6 provides a technical and critical analysis of the new routing protocol along with some recommendations for its deployment. Lastly, Section 7 addresses the conclusions and future work.
# Experimental Study on Mechanical Behavior of Shear Connectors of Square Concrete Filled Steel Tube
## 3 Test Results and Theoretical Analysis
### Load-Displacement Relationships and Parametrical Study
#### 3.2.1 Width to Thickness Ratio of Steel Tube  
The load-displacement curves of different width to thickness ratios of the specimens are shown in Figure 7a. The width to thickness ratios of Specimens P8-4-150-120, P8-6-150-120, P8-8-150-120, P8-14-150-120 are 50, 33.3, 25 and 14.3, respectively. The ultimate bearing capacities of these four specimens are 527.9 kN, 709.3 kN, 921.0 kN, 2284.0 kN, respectively. Comparing them with that of specimen P8-4-150-120, the ultimate bearing capacities have increased by 34.4%, 74.5% and 332.7%,  
Figure 7: The load-displacement curves. (**a**) Effect of width to thickness ratio of steel tube; (**b**) Effect of thickness of steel plate; (**c**) Effect of length of steel plate; (**d**) Effect of concrete strength; (**e**) Effect of welding condition; (**f**) Effect of interlayer spacing; (**g**) Effect of type of shear connectors.  
respectively. Hence, both the ultimate bearing capacity and the initial stiffness increase with decreasing width to thickness ratio of the steel tube. This is due to the increasing restraint ability of the steel tube to the core concrete as the width to thickness ratio of the steel tube decreases.
## 1 Introduction
The relationship between good regulation (based on correctly established and reliable risk assessment methods) and measurements is therefore evident; some of the main fields where the best possible measurements are especially relevant are shown in Figure 1.  
It can then be stated that a metrological characterization of all the parameters involved has a key role in the occupational exposure to vibrations. There are different approaches on how to assess occupational risks in overall processes [12]; however, many studies on how to analyze critical occupational risks include the measuring modules as a complete functional unit to be assessed, where safety allocation methods show that it has an impact on the risk assessment of the overall process [13]. As it is stated in [14], despite the proven need of using metrological techniques (e.g., detailed uncertainty estimation), their application is not properly defined. Unfortunately, current regulations and standards do not always cover all the metrological aspects required for a correct risk assessment, leaving some unresolved issues. As a result, the risk of overestimating or underestimating exposure to vibrations is increasing [15], and it can affect workers wellbeing. The control strategies necessary to reduce this type of risk require new measuring instruments, tools, and standards to be able to better assess vibration at the workplace [16]. In this way, the authors of [17; 18] have studied the risk assessment process for exposure to vibrations, considering such a new and emerging context. From these works, important challenges arise in metrological matters applied to the study of vibrations. Among these challenges, the lack of detailed enough methodologies and procedures stands out proposed by current regulations. This limitation can be considered one of the causes that configure the exposure to vibrations as a NER. Thus, this work aims to critically review the different alternatives for the assessment of occupational vibrations from a metrological point of view. For this, first a metrological study of the regulatory framework on the occupational exposure to vibrations is approached. Next, a critical analysis of the metrological requirements regarding European standards on how to estimate exposure to vibrations is developed. To this end, the estimation method and measure method are investigated, considering in both cases the main factors related to uncertainty, reliability, and traceability.
# Kiwifruit Detection Method in Orchard via an Improved Light-Weight YOLOv4
## 4 Results and Analysis
### Performance Metrics  
In order to select a suitable model, accuracy (Precision), recall (Recall), mean average precision (mAP), average frame rate (fps), weight size (weights), and F1-Score (F1), are used as model performance evaluation metrics, while performance evaluation is performed using Precision-Recall curves. The calculation formula is as follows:  
\[\text{Precision }=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{6}\]  
\[\text{Recall }=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{7}\]  
\[\text{F}_{1}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{8}\]  
\[\text{mAP }=\frac{1}{\text{C}}\sum_{\text{K=i}}^{\text{N}}\text{P(k)}\Delta \text{R(k)} \tag{9}\]  
where, TP is the number of positive samples judged to be true, FP is the number of positive samples judged to be false, FN is the number of negative samples judged to be false, P(k) represents the accuracy, and R(k) represents the recall rate.
# Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre
## 5 Results
### Preliminary Modeling Results
#### 5.1.3 CNN+LSTM Model  
This model is expected to perform better than both individual models since it is considering critical information on weather and previous water surface elevations to produce a prediction. The month of June 2008, when there were no storms hitting the Laguna Madre area, can be seen in Figure 7.  
Figure 5: Preliminary CNN-only model prediction for the month of July 2008.  
Figure 6: Preliminary LSTM-only model prediction for the month of June 2010.  
It can be seen from this plot that the model is able to follow the trends closely; however, it still struggles in some places by underestimating both highs and lows. The CNN+LSTM model prediction for the month of June 2010 when the impact of Hurricane Alex was felt in the Laguna Madre is a very good example of the performance of the machine learning model, as shown in Figure 8.  
In this case, the model is able to follow the surge correctly but still struggles at some points where normal conditions were expected. However, as it can be seen in the figures, the RMSE was reduced from a maximum of 0.1555 in the LSTM-only model in the month of June 2010 (when Hurricane Alex hit) to 0.0860 during the same period of time in the preliminary model that combines CNN and LSTM.
# Quantum-Key-Distribution (QKD) Networks Enabled by Software-Defined Networks (SDN)
## 3 Architecture of QKD Network Enabled by SDN  
According to the division of functions, the architecture of QKD networks enabled by SDN is introduced in this section. As shown in Figure 3, the architecture of QKD networks enabled by SDN consists of three layers: application layer, control layer, and QKD layer. Users send their requests though northbound interfaces to the controller in control layer, and then controller dynamically controls QKD devices to construct transmission through southbound interfaces. The specific details from the top down are shown below.  
Figure 2: The architecture of QKD networks with cloud/NFV orchestration and SDN control plane [18].
# Advances in the Biosynthesis and Molecular Evolution of Steroidal Saponins in Plants
## 3 Molecular Regulation of Steroidal Saponin Biosynthesis in Plants
The BRI1-EMS-SUPPRESSOR1 (BES1) and BRASSINAZOLE-RESISTANT1 (BZR1) families are the core TFs [53; 54]; they can not only regulate BR synthesis, but also interact with other TFs to regulate BR synthesis. RD26 is an NAC transcription factor induced by drought and inhibiting the BR response [52]. WRKY46, WRKY54, and WRKY70, which are WRKY transcription factors, have the same effect as RD26 [51], and MYB TFs have been reported to regulate BR; for example, _GntMYB14_ overexpression decreases the BR content in soybean [55]. _DWF4_, which is a C-22 hydroxylase, is a target gene downstream of the regulatory pathway of BR biosynthesis. The PIF4-BES1 complex activates the promoter of the _DWF4_ gene to increase the accumulation of BRs and serve as a rate-limiting step of the BR biosynthetic pathway in _Arabidopsis_[53; 56].
# Cooperative Transmission Mechanism Based on Revenue Learning for Vehicular Networks
###### Abstract  
With the rapid development of science and technology and the improvement of people's living standards, vehicles have gradually become the main means of travel. The increase in vehicles has also brought about an increasing incidence of car accidents. In order to reduce traffic accidents, many researchers have proposed the use of vehicular networks to quickly transmit information. As long as these vehicles can receive information from other vehicles or buildings nearby in a timely manner, they can avoid accidents. In vehicular networks, the traditional double connection technique, through interference coordination scheduling strategy based on graph theory, can ensure the fairness of vehicles and obtain suitable neighborhood interference resistance with limited computing resources. However, when a base station transmits data to the vehicular user, the nearby base station and the vehicular network user may be in a state of suspended communication. Thus, the resource utilization of the above double connection vehicular network is not sufficient, resulting in a waste of resources. To solve this issue, this paper presents a study based on earnings learning with a vehicular network multi-point collaborative transmission mechanism, in which the vehicular network users communicate with the surrounding collaborative transmission. We use the Q-learning algorithm in the reinforcement learning process to enable vehicular network users to learn from each other and make cooperative decisions in different environments. In reinforcement learning, the agent makes a decision and changes the state of the environment. Then, the environment feeds back the benefit to the agent through the related algorithm so that the agent gradually learns the optimal decision. Simulation results demonstrate the superiority of our proposed approach with the revenue machine learning model compared with the benchmark schemes.  
vehicle networks; cooperative transmission; reinforcement learning; interference coordination; driverless technology +
Footnote †: journal: Applied  
**Keywords:** vehicular networks; cooperative transmission; reinforcement learning; interference coordination; driverless technology +
Footnote †: journal: Applied
## 3 Results and Discussion
### Scanning Electron Microscopy and X-ray Microanalysis (EDS)  
Scanning electron microscopy (SEM) images depicting the fracture morphology of BGFT-0 ceramics, as well as the corresponding ceramics doped with gadolinium ions, are illustrated in Figure 4 for two different magnifications, namely 1000 times and 8000 times.  
As already noted in previous publications [20; 21], the grains of the base BGFT-0 ceramics exhibit a plate-like shape consistent with the characteristic morphology of compounds containing Aurivillius phases. The closely packed plates, overlapping each other, contribute to the formation of fractures that traverse through the grains during the fracture process. This phenomenon indicates the presence of well-sintered and robust grains with distinctly defined grain boundaries. Incorporating gadolinium Gd\({}^{3+}\) admixture induces a significant transformation in the microstructure appearance of the examined fractured ceramic materials. In the case of BGFT-02, there is a noticeable alteration in the plate shapes, transitioning to rounded forms, and the distribution of grains becomes rather chaotic. Notably, the introduction of gadolinium results in a gradual reduction in the number of pores within the doped ceramics, correlating with the increased dopant content. The grain size across all ceramics exhibits heterogeneity. Particularly in the BGFT-02 material, the presence of individual large plates becomes apparent, with their proportion in the total grain count escalating as the Gd\({}^{3+}\) modifier content increases.  
Figure 4: _Cont._  
The examined ceramic materials underwent energy-dispersive X-ray analysis (EDS). The measurements ruled out the presence of any foreign elements or impurities. An exemplary EDS spectrum, illustrated for the BGFT-06 ceramics sample, is presented in Figure 5.  
The percentage composition of individual elements (calculated as oxides) in the examined materials is detailed in Table 3. For all obtained materials, a microanalysis of the chemical composition validated the theoretical stoichiometry assumed at the onset of the technological process. Minor deviations of the marked (actual) values from theoretical values fall within the measurement uncertainty range of the employed method.  
Figure 5: Exemplary EDS spectrum on the BGFT-06 ceramics.