esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions
## 3 Results and Discussions
### Evaluation of Performance Metrics
#### 3.2.5 Performance (ANN)  
Figure 13a shows the progression of performance for a given machine learning model over epochs, i.e., looking specifically at Mean Squared Error (MSE) for the training, validation, and test datasets. The first graph shows the optimal validation performance (approx. 118.81) at epoch 250, and that the error curves for all three datasets (train, validation, and test) are all close together toward the latter epochs, indicating that the model learned effectively from the training data and generalizes the well to unseen data (as seen by the small differences between train and validation errors). The flattening error after epoch 250 suggests that the model plateaued, and, with additional training, only marginal improvements would occur.  
Figure 12: Training state for ANN: (**a**) (LUCIA Building, nZEB-HVAC), (**b**) (FUHEM Building-Heating).  
In line with the above, we see in Figure 13b that the model has a more dynamic learning phase, with its best validation performance achieved at epoch 188. In this case, the MSE values on all datasets saw a rapid decline in values in the early epochs and followed a close track overall. The sharp decrease indicates that the model was achieving decreased error values across datasets, showing that the learning rate is potentially appropriate. What is most notable is that it has already decreased a significant amount of error values on the first few epochs of training. This indicates that the model learned to an efficient degree in the early training stages, which lead to a strong foundation in the early phases of its training. Overall, both figures show that the model can learn and adapt over time, and both have elements that lend themselves nicely to learning, but Figure 13b offers a strong case in favor of a more effective stage of early learning.
# Performability Evaluation and Sensitivity Analysis of a Video Streaming on Demand Architecture
## 2 Methodology Overview  
The main objective of this work is to develop Petri net models to evaluate the performance of a video-on-demand infrastructure. An initial model was created to replicate the operation of this type of architecture, and it was possible to identify aspects that directly impact the operation of the architecture. Different scenarios were designed to evaluate the model. The flowchart of the work methodology is shown in Figure 1.  
**Application overview:** Explains how video-on-demand streaming infrastructures function, including data flow, how many, and which components are involved. **Definition of metrics:** Metrics of interest for evaluating the model's performance will be selected, and metrics will be created to aid in analyzing elements that might affect system performance, such as system availability. **Definition of parameters:** The model's parameters will be defined. These parameters will define the model's capacity and functioning for each component. The parameters utilized for this study are the number of machine cores, service rate, service rate, and queue size. **Generation of analytical model:** A model will be created to analyze performance using stochastic Petri nets, as this analytical modeling approach is useful for simulating the functioning of a system with multiple requests being handled in sequence. The parameters and metrics given above will be used to build such a model. During this phase, the model may need to be adjusted. **Sensitivity analysis:** The model will be subjected to a sensitivity analysis utilizing the DoE (Design of Experiments) approach to find the model's most influential component. **Scenario selection:** The scenarios to be investigated will be specified to improve the model's availability and allow simulation in various settings. **Conducting scenario assessment:** Finally, simulations will be run on the model using the previously established scenarios.
# Traffic Congestion Detection System through Connected Vehicles and Big Data
## 1 Introduction
To meet the objective set, the developed system monitors, processes and stores large amounts of data reported from vehicles and fixed infrastructure, communicates to a big data cluster employing Location Routing Algorithm With Cluster-Based Flooding (LORA_CBF) which, through a series of algorithms, accurately identifies traffic congestion. The OMNeT++ discreet event network simulator was used to validate and evaluate the proposed system and the big data cluster [12]. The SUMO traffic simulator [13] was then coupled with the Veins vehicular network framework [14] to recreate a case study, based on a real traffic issue, to detect traffic congestion and the impact of the proposed system in terms of reducing traffic congestions.
## 1 Introduction
Therefore, the fuzzy logic technique and nonsingular terminal SMC method can be both integrated in the investigated adaptive backstepping control strategy for better control performance of LIM. Motivated by the above investigation on control methods of LIM, a projection-based adaptive command filtered fuzzy nonsingular terminal sliding mode backstepping (PACFTB) control strategy is proposed for better speed control performance of LIM. The controlled plant is the LIM with unknown end effects and the control objective is to make the speed tracking have high precision and quick response against variable trajectory. The main contributions of this paper are given as follows. * The nonsingular terminal sliding mode control method is integrated with the adaptive fuzzy backstepping control to enhance the robustness of the system and ensure to reach the equilibrium point within a limited time. * The fuzzy logic technique is introduced to estimate the nonlinear part of the system model to make the controller design process more clear and easy, and the stability proof of the proposed PACFTB control strategy is provided concretely. * The simulations and experiments are both carried out and discussed to further prove the effectiveness of the proposed PACFTB control strategy. The structure of the article is arranged as follows. Section 2 describes the mathematical model of LIM with unknown end effects and introduces the FLS and projection operators. In Section 3, an FLS-based adaptive command-filtered backstepping control strategy combining nonsingular terminal SMC is developed for LIM while detailed design process is given step by step, and the stability analysis is given in Section 4. Section 5 provides the simulation and experimental results and discussions to verify the progressiveness of the proposed control strategy. Finally, we summarize the whole paper and draw conclusions in Section 6.
## 5 Performance Evaluation
RAR considers connected paths towards sea surface and computes packets delivery probability over a selected route. This leads to more advancement of packets towards sea surface than REEP. Consequently, RAR's energy consumption becomes higher than REEP that, in turn, leaves less residual energy with RAR. The pattern with which nodes remain alive in the network is exhibited in Figure 6. The least residual energy left with the nodes in RACAA makes the least number of nodes alive. In other words,  
Figure 4: Number of packets successfully reached to the water surface. Figure 5: Total residual energy. RACAA has the most dead nodes due to the least residual energy left with the nodes as the network operation proceeds. Likewise, the lower residual energy left with the nodes in RAR than REEP leaves fewer alive nodes with the former than the latter. Alternatively, RAR has more dead nodes than REEP as the network operates. Figure 7 shows the latency pattern. The highest latency of RACAA is due to cooperative routing in which reception of source broadcasted information is accomplished by relay and destination. The relay node further sends the information to the destination. This almost doubles the transmission path over which packets are transmitted. The destination further combines these packets and consumes extra processing time. Cooperative routing is absent in RAR and REEP. As a result, latency becomes the highest in RACAA. RAR has a shorter latency than REEP due to the difference in the transmission manner of hello packets. Before information transmission, every time a node in REEP sends a hello packet to the neighbors for acquiring the information necessary for forwarder selection. This introduces extra latency and computation. On the other hand, obtaining neighbors' information is accomplished only in regular intervals of time in RAR. As a result, RAR has a shorter latency than REEP. Figure 6: Active nodes. Figure 7: Delay.
## 2 Materials and Methods
### Aging Test and Strength Test  
#### 2.5.1 Fluorescent UV Lamp Aging Test  
The UV aging test was conducted in accordance with the geotextile aging standard [29]. Specimens measuring 75 mm \(\times\) 200 mm were prepared according to the fixture size requirements. The aging conditions were set as follows based on the testing standard: continuous UV exposure with a constant wavelength of 340 nm and irradiance of 0.76 W/m\({}^{2}\), temperature maintained at (60 \(\pm\) 3) \({}^{\circ}\)C, for a duration of 144 h. These test parameters (irradiance and temperature) were established according to the testing standard and represent relatively extreme environmental conditions. The testing equipment used was model LU-8047-TM.
###### Abstract  
The introduction of new digital technologies represents an important advance to fabricate metal-ceramic restorations. However, few studies have evaluated the influence of these technologies on the fit of the restorations. The aim of this study was to evaluate the effect of different manufacturing techniques and pontic design on the vertical marginal fit of cobalt--chromium (Co-Cr) posterior fixed partial dentures (FPDs) frameworks. Methods: Eighty stainless-steel dies were prepared to receive 3-unit FPDs frameworks with intermediate pontic (n = 40) and cantilever pontic (n = 40). Within each design, the specimens were randomly divided into four groups (n = 10 each) depending on the manufacturing technique: casting (CM), direct metal laser sintering (LS), soft metal milling (SM), and hard metal milling (HM). The frameworks were luted, and the vertical marginal discrepancy was assessed. Data analysis was made using Kruskal-Wallis and Mann-Whitney U tests (\(\alpha\) = 0.05). Results: The vertical marginal discrepancy values of all FPDs were below 50 \(\upmu\)m. The HM frameworks obtained the lowest misfit values in both designs. However, no differences were found among intermediate pontic groups or cantilevered groups. Likewise, when differences in a marginal discrepancy between both framework designs were analyzed, no differences were observed. Conclusions: The analyzed digital technologies demonstrated high precision of fit on Co-Cr frameworks and on both pontic designs.  
marginal adaptation; fixed partial denture; dental technology; cobalt-chromium alloys; scanning electron microscopy
## 3 Optimum Design of SOE
### SP II-Type SOE  
The SP-type SOE is an open-truncated tetrahedral pyramid with specular walls, and its two basic design parameters are height H and inclination angle \(\theta\) of the walls, similar to the illustration shown in Figure 5a. A parametric design process with various values of H and \(\theta\) were performed to determine the optimum SP-type SOE, designated as SP II.  
Figure 18(a,b) shows the optical efficiency with various values of H and \(\theta\) when the incidence deviation are of 0\({}^{\circ}\) and 1\({}^{\circ}\), respectively. As Figure 18a shows, the optical efficiency increases when the  
Figure 16: Optical efficiency of the Fresnel lens with KFTS SOE and KFTS II SOE.  
Figure 17: Irradiance distribution on the receiver for the Fresnel lens with KFTS II-type SOE (**a**) under normal incidence; (**b**) incidence angle = 1.1\({}^{\circ}\).  
inclination angle \(\theta\) is larger than \(9^{\circ}\). Meanwhile, Figure 18b revealed that the best value of \(\theta\) is \(10^{\circ}\) to attain a peak optical efficiency under an incidence deviation of \(1^{\circ}\). Moreover, when the inclination angle \(\theta\) is \(10^{\circ}\), the optical efficiencies were 69.826% at H = 52 mm, 69.828% at H = 53 mm, 69.827% at H = 54 mm, and 69.826% at H = 55 mm, respectively. Therefore, the optimum values of H and \(\theta\) were selected as 53 mm and \(10^{\circ}\), based on the parametric design process shown in Figure 18.  
The optical efficiency of the CPV unit with the original SP and optimum SP II SOEs under various incidence angle deviations is shown in Figure 19. The optimum SP II SOE increases the acceptance angle to only \(0.7^{\circ}\) and the corresponding optical efficiency at this acceptance angle is 79.9%. The optical efficiency under normal incidence is 88.7%, and it drops gradually to 64% at an incidence angle of \(1^{\circ}\).  
Figure 20a,b displays the irradiance distribution on the solar cell when the incidence angle deviation is \(0^{\circ}\) and \(0.7^{\circ}\) (acceptance angle), respectively. The irradiation distribution under normal incidence shown in Figure 20a was not as uniform as those shown in Figure 17a from KFTS II and Figure 14a from KOD II. This phenomenon indicates that the SP II SOE does not have good solar energy  
Figure 19: Optical efficiency of the Fresnel lens with SP SOE and SP II SOE.  
Figure 18: Optical efficiency for the Fresnel lens with SP-type SOE with various design values of H and \(\theta\). (**a**) under normal incidence; (**b**) incidence angle = \(1^{\circ}\).  
redistribution on the receiver under normal incidence. Furthermore, the irradiance distribution on the receiver was affected when the incidence angle deviation was 0.7\({}^{\circ}\), as shown in Figure 20b.
# Fault Inference of Electronic Equipment Based on Multi-State Fuzzy Bayesian Network
## 1 Introduction
Firstly, each of test indicators is divided five fault states base on the normal membership function, the expert gives the weight of test indicator, and then we can calculate the membership degree of test point. Secondly, the prior probability and conditional probability are performed by using triangular membership function. Finally, through fuzzy multi-state BN inference, we can speculate on the fault state of leaf nodes and calculate posterior fault probability of root nodes. Moreover, the model was verified to be effective through examples. The structure of the research is arranged as follows: Section 2 establishes FRM and elaborated definitions of each variable and process in detail. Section 3 conducts inference based on multi-state fuzzy BN. The verification is carried out through examples in Section 4, and the conclusions are made in Section 5.
# Automatic Failure Modes and Effects Analysis of an Electronic Fuel Injection Model
## 4 Case study: Electronic Fuel Injection System
### Electronic Fuel Injection Analysis Using Automatic FMEA
#### 4.2.2 Step 2: Definition of Failure Mode  
In this step, we determine the failure mode of the sensor unit of the EFI model. The four sensors of the EFI may cause open-circuit (or short-circuit) faults in sensor connector pins due to factors such as wearing out, vibration, etc., in the vehicle driving environment [44; 45]. These faults cause sensor failure modes such as drift, hard-over, and stuck [46; 47; 48; 49]. Automatic FMEA analyzes the characteristics of these three sensors' failure modes, and defines the failure mode as follows:  
First, the drift failure mode outputs higher or lower than the value of the normal signal. Automatic FMEA sets the drift failure mode using the \"More Than Requested\" or \"Less Than Requested\" failure mode. For example, the \"signal output degradation due to drift\" failure mode of the EGO sensor is explained in the top two rows of Table 3. The first row represents the failure mode of \"EGO sensor signal output is lowered to 50%\". In this failure mode, the EGO sensor signal output is lowered to 50%, as shown in Figure 10a. Next, in the second row, we illustrate the failure mode of \"EGO sensor signal output increased to 150%\" in Figure 10b.  
Figure 10: EGO sensor failure modes: (**a**) drift 50%; (**b**) drift 150%; (**c**) hard-over; (**d**) stuck.
## 3 Results and Discussion  
In order to understand the stability of the bR-graphene complex and to elucidate the structure-function relationship for the bR protein adsorbed on graphene, a detailed quantification of the interactive events at the bR-graphene interface is carried out and a role of water molecules is proposed. Typically, when a protein is adsorbed onto a hydrophobic surface such as graphene, it undergoes many conformational changes to adjust itself to the surface while maintaining its function. In fact, compared to those in the aqueous environment, proteins adsorbed on a surface have a greater potential to retain their stability and, hence, their function. This is primarily because of the fact that in an aqueous environment, a protein is constantly being targeted by the water molecules and ions from all sides of its largely hydrophilic surface. In the case where a protein is adsorbed on a surface, however, the protein exposes its core hydrophobic residues to strongly interact with the surface. This results in a less solvent-accessible surface area and, hence, a more stable protein. This binding between the protein and the hydrophobic surface is primarily due to the hydrophobic interactions and van der Waals forces, but it is envisaged that such interactions are also governed by the number of water molecules at the interface through the electrostatic energies. In this study, the stability of the protein and of the complex with graphene, as a whole, is quantified based on the root mean square deviation (RMSD), number and lifetimes of the hydrogen bonds at the interface, number and lifetimes of the salt bridges within the protein and the conformational changes of the protein via secondary structure analysis before, during and after the formation of the complex.
### State of the Art in Proximal Sensing Sensors for SWC
#### 1.1.3 ElectroMagnetic Induction  
ElectroMagnetic Induction (EMI) devices were commonly used in agriculture to map significant soil variability or changes in soil type. EMI can also estimate soil salinity, texture, and clay content [16; 26]. In any case, the demand for this technology is increasing in recent years due to interest in mapping SM in large areas [12]. The increasing interest in this technology relates to the use of multifrequency and multi-coil EMI sensors and recent calibration algorithms [26; 27; 28; 29].  
EMI devices rely on the strong correlation between soil electrical conductivity and soil water content, unlike other approaches [30]. These approaches apply an electric current to the soil using electromagnetic induction and then read the EM field that the ground generates [14]. The amplitude and phase shift of the EM field created by the soil depends mainly on the moisture content and the concentration of ions in the soil. The space between the transmitter and the receiver coil and the distance to the ground also greatly influences the EM field created by the soil [14].  
EMI devices cannot directly measure SWC, and several soil parameters such as soil composition, temperature, salinity, and density influence the readings [12]. Because electrical conductivity is correlated with several parameters, the relation between electrical conductivity and SWC is very complex and non-linear. Based on these points, solutions using only EMI in agriculture are not very likely to succeed [12]. That is why many solutions tend to use EMI with GPR to measure soil moisture better [30; 31].
## 4 Discussion
### The Effect of the Temporal Distance between the Input and the Target on the Model Results  
The ESTARFM uses three basic images to calculate the target image. The closer the acquisition time is, the higher the accuracy of the result. Figure 11 shows the changes in the statistical indicators with different time intervals. As the interval increases, the _RMSE_ showed obvious upward trends, while the CC showed downward trends. The two models performed similarly before 130 days. Both ESTARFM indicators fluctuated and degraded after 130 days, with the average _RMSE_ increasing by about 47% and _CC_ decreasing by about 14%. The average ESTARFM_p _RMSE_ degraded within a smaller range (18%), and its CC did not show a significant decrease. Single-date results have similar trends. The _CC_ values were also more stable than those of the ESTARFM. Therefore, the new model in this study is more robust when there are limited input data. The reason for this is that the modified similar-pixel-obtaining process increases the weight of the GOCI data on the target date, and it therefore collects more valid information than the original model does.
# Penetration Analysis of SAR Signals in the C and L Bands for Wheat, Maize, and Grasslands
## 3 Data Analysis  
In this section, the potential of SAR signals in the C and L bands to penetrate the canopy cover of wheat and grasslands was investigated. For this investigation, the sensitivity of SAR data (C and L bands) to SSM was studied according to three classes of NDVI (NDVI! 0.4, 0.4! NDVI! 0.7, and NDVI! 0.7), and the temporal evolution of SAR data according to SSM and NDVI variations was analyzed. Furthermore, the potential of SAR data in the C-band to penetrate the maize canopy was assessed by analyzing the temporal evolution of the C-band according to SSM and NDVI variations.
# Improved Sea Ice Image Segmentation Using U\({}^{2}\)-Net and Dataset Augmentation
## 3 Results
### SC-U\({}^{2}\)-Net Network  
The SC-U\({}^{2}\)-Net (Training set 1) and U2-Net (Training set 1) network was trained using the same dataset (Training dataset 1). The accuracies of SC-U\({}^{2}\)-Net (Training set 1) and U\({}^{2}\)-Net (Training set 1) were tested using the test set, and the accuracy of SC-U\({}^{2}\)-Net (Training set 1) and U\({}^{2}\)-Net (Training set 1) tests are shown in Table 4.  
Figure 16 shows some experimental results, where figures (a) to (h) show the sea ice images of different regions, and (1) to (4) show the original image, labeled image, U\({}^{2}\)-Net segmentation result, and SC-U\({}^{2}\)-Net segmentation result, respectively. U\({}^{2}\)-Net was less effective in segmenting some sea ice, such as the broken ice area at the land edge. SC-U\({}^{2}\)-Net performed better and could extract the outline and some details of sea ice, as shown in (a) of Figure 16.  
The accuracy of SC-U\({}^{2}\)-Net (Training set 5) was examined using an extended test set (1500 images) and compared to U\({}^{2}\)-Net (Training set 5). The IoU, F1-score, and recall of U\({}^{2}\)-Net (Training set 5) and SC-U\({}^{2}\)-Net (Training set 5) on the extended test set are shown in Table 5.  
Using the transformed test sets to test U\({}^{2}\)-Net (Training set 1) and SC-U\({}^{2}\)-Net (Training set 5), the statistics of IoU, F1-score, and recall per image for each test set were calculated (Table 3; Figure 17). SC-U\({}^{2}\)-Net (Training set 5) had a much better segmentation effect than U\({}^{2}\)-Net (Training set 1) on each test set (Table 6). The resul  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicators** & **Imou** & **F1** & **Recall** \\ \hline U\({}^{2}\)-Net (Training set 1) & 0.842 & 0.897 & 0.889 \\ SC-U\({}^{2}\)-Net (Training set 1) & 0.857 & 0.913 & 0.920 \\ \hline \hline \end{tabular}
\end{table}
Table 4: SC-U2-Net (Training set 1) vs. U2-Net (Training set 1) test accuracy.  
Figure 16: Comparison of SC-U\({}^{2}\)-Net and U\({}^{2}\)-Net segmentation results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Indicators** & **Imou** & **F1** & **Recall** \\ \hline U\({}^{2}\)-Net (Training set 5) & 0.834 & 0.886 & 0.884 \\ SC-U\({}^{2}\)-Net (Training set 5) & 0.836 & 0.897 & 0.898 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy of U\({}^{2}\)-Net and SC-U\({}^{2}\)-Net on augmented test set.  
the simultaneous use of data augmentation and network improvements can improve the accuracy and generalization of the model.