esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In order to quantify the level of SQI for each PPG pulse, the error percentage of measured SV for each beat was used to define the level of SQI for each PPG pulse. The morphologies of PPG and DPPG pulses were combined into an image, which was used to determine the quality level of each PPG pulse. The proposed VGG-19 and ResNet-50 models can be used to successfully determine the SQI of each PPG pulse. Thus, we did not need to explore the characteristics of the PPG pulse to determine the pulse SQI when using the 2D DRNN. Moreover, comparing with the results of our study, the performance of the 2D DRNN was better than that of a traditional rule-based method. Noticeably, the main limitation of the study is the small number of PPG pulses. If more PPG pulses are used in the training process of the 2D DRNN, better results can be expected.  
Conceptualization, S.-H.L. and [PERSON]-[PERSON]-W.; methodology, S.-H.L. and R.-X.L.; software, R.-X.L.; validation, C.-H.S. writing--original draft preparation, S.-H.L. and [PERSON]-[PERSON]; writing--review and editing, S.-H.L., [PERSON]-J.W., and W.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the Ministry of Science and Technology under the grants MOST 108-2221-E-324-015 and MOST 108-2221-E-214-007-MY3, and I-Shou University in Taiwan under the grant ISU-109-MCRP-05. The authors would like to acknowledge Chaoyang University of Technology for the administrative support and Z.-Y.Z. for technical support in the development of the proposed method. The authors declare no conflict of interest.
## 5 Conclusions  
In this study, an energy management strategy based on the ECMS was proposed for a plug-in SHEV equipped with an SI/HCCI dual-mode combustion engine. The intention was to improve fuel economy under the NVH constraints of the ICE at low vehicle speeds. A dual-loop PSO was designed to obtain an optimal EF during offline optimization and solve the ECMS problem for online applications. A simulation based on a well-calibrated Simulink model of the target SHEV powertrain system was performed for different driving cycles to investigate the impact of NVH constraints on fuel economy and evaluate the improvement achieved by the proposed algorithm. The main findings of this study are as follows:  
* The dual-mode engine and the proposed EMS can reduce fuel consumption by 1.3%-9.9% for different driving cycles on the target SHEV. The largest improvement was achieved in the MANHATTAN and NYCC cycles, which represent traffic conditions. This is due to a significant improvement in fuel efficiency when using the HCCI mode in comparison with the SI mode at a low load range under NVH constraints.
* The dual-mode combustion option offers two optimal engine operating ranges: a high load in SI mode or a low load in HCCI mode. When combined with the proposed ECMS, the engine can achieve an average BSFC close to the global optimal value under all the testing driving cycles. In the cycles with lower vehicle speeds, the HCCI mode is the dominant mode; as the vehicle speed increases, the engine has more opportunities  
Figure 14: Simulation results under the WLTC driving cycle.  
to operate in the SI mode, such as via the US06 cycle and the ultra-high-speed period of the WLTC cycle.
* The dual-mode engine and the proposed EMS also reduced the average battery power by 9.2% to 64.1% for different driving cycles. This will further reduce the battery heat losses and the future power needed for battery cooling, as well as extend the lifespan of the battery. Future work will focus on the implementation of the proposed control strategy in the real-time hardware-in-the-loop (HIL) simulation and experimental validation on the test bench.  
Conceptualization, S.Z. and H.W.; methodology, S.Z.; modeling and coding, C.Y., Z.O. and S.Z.; simulation and validation, Z.O. and X.W.; writing--original draft preparation, S.Z.; writing--review and editing, H.W. All authors have read and agreed to the published version of the manuscript. This research was supported by the project \"Modeling and Control of New Energy Vehicle Powertrain Systems\" funded by Shenzhen Technology University with grant number 2018010801007.  
Simulation and experiment data which do not reflect confidential information about the target engine and vehicle are available from the corresponding author upon reasonable request. We would like to thank our lab technicians and students who supported the experiments related to this paper. The authors declare no conflicts of interest.
# Advancements in the Intelligent Detection of Driver Fatigue and Distraction: A Comprehensive Review
## 3 Intelligent Detection Methods for Driver Fatigue and Distraction
### Intelligent Detection Methods Based on Multimodal Fusion Feature  
Driver fatigue or distraction is influenced by various physiological and psychological factors and exhibits individual differences, presenting diverse manifestations and behaviors. Therefore, it is challenging to make a comprehensive judgment based solely on single-modality signal features. Consequently, many scholars have integrated features such as driver image information, physiological signals, and vehicle data to study detection methods based on multimodal feature fusion, aiming to improve the accuracy and robustness of the detection and explore the potential connections between features and driver fatigue and distraction.  
[PERSON] et al. [63] integrated physiological signals collected by biometric instruments, such as heart rate and brain waves, with facial features and head postures extracted by cameras and trained with an RBF neural network, to achieve deployment and application at a lower cost. [PERSON] and others [64] addressed the issue of distracted driving caused by drivers conversing with others, by introducing the analysis and detection of voice signals. They proposed a dataset comprising three modalities: driver facial features, voice recognition, and vehicle signals, applied to the training of a multi-layer fusion model, achieving better detection results than single-modality models. [PERSON] and others [65] developed a multimodal detection system that integrates physiological and facial features for training a Deep Residual Neural Network (DRNN) model. They classified the driver states into five categories: normal, fatigued, visually distracted, cognitively distracted, and drowsy. The hardware setup is shown in Figure 6.  
Detection methods based on multimodal feature fusion, by integrating various modal characteristics, can establish a more comprehensive assessment system. This approach compensates for the shortcomings of single-modality methods, collectively enhancing the accuracy and stability of detection and enabling more flexible and refined state monitoring. However, the requirement for a greater variety and number of sensors leads to increased hardware and software costs, and the processing and fusion algorithms for data from different sensors become more complex. Overall, multimodal fusion represents a research trend in the detection of driver fatigue and distraction states.
## 4 Simulation Results
### Comparison of the Network Coverage  
The network coverage reflects the proportion of monitored targets covered in the area out of the total number, reflecting the network's monitoring capability. A decline in the network coverage means that the monitoring capability of the network begins to decline. As shown in Figure 16, among the five protocols, the decline of the network monitoring capability under LEACH is the earliest, followed by DEEC and GSEN, while EECRP-HQSND begins to experience a decline in monitoring  
Figure 15: The number of live nodes varies with the iteration round. DEEC— Distribute Energy-Efficient Clustering; EECRP-HQSND—energy-efficient clustering routing protocol based on a high-QoS node deployment; EECRP-HQSND-ICRM—energy-efficient clustering routing protocol based on a high-QoS node deployment with an inter-cluster routing mechanism; GSEN— and Group-Based Sensor Network; LEACH—Low-Energy Adaptive Cluster Hierarchical.  
capacity in the 1333 rd round. Through an effective ICRM, the EECRP-HQSND-ICRM delays the monitoring capability decline to the 1367 th round, thus ensuring that the network can perform more rounds of monitoring during this period to achieve higher monitoring requirements. Meantime, with a reasonable CH selection mechanism through partition based on the uniformity of the CH distribution, compared with LEACH, DEEC, and GSEN, the round when the network coverage of EECRP-HQSND and EECRP-HQSND-ICRM begins to decline can be greatly delayed.
## 4 Factors to Study in VANETs
### Message Priority  
The 802.11p MAC layer is based on the IEEE 802.11e Enhanced Distributed Channel Access (EDCA), and Quality of Service (QoS) extensions. Therefore, application messages are categorized into different Access Classes (ACs), where AC0 has the lowest and AC3 the highest priority.  
In our experiments, \(warning\)\(messages\) (which contain information about abnormal situations such as accidents) have always the highest priority (AC3) at the MAC layer, while \(beacons\) (containing information such as vehicles' positions and speeds), which are not propagated by other vehicles, change their priority from the lowest (AC0) to the highest (AC3) priority in the \(2^{k}\) factorial analysis.
# Effects of Ethanol-Diesel on the Combustion and Emissions from a Diesel Engine at a Low Idle Speed
## 3 Results and Discussion
### Emissions Characteristics
#### 3.2.2. Soot Morphology  
The size and morphology of the soot particles are changed by the operating conditions of the engine, for example, the engine load, the speed of rotation, the injection timing and the effect of the fuel used. The sizes of the soot particles emitted under each fuel condition can be identified by TEM images, as shown in Figure 9. As the blend ratio of ethanol increases, the size of the particles decreases. As shown in Figure 10, the mean size of the particles was 33.9 nm for DE_0, down 12.8% to 29.6 nm for DE_3, down 20.0% to 27.1 nm for DE_5, and down 26.7% to 24.9 nm for DE_10. The sizes of particles is between 10 nm and 60 nm, and the distribution of small particles increases with the increase in the ethanol blending ratio. This is believed to be due to the promotion of soot oxidation due to the introduction of more oxygen from the blending of ethanol. [PERSON] et al. (2011) applied ethanol in the same way as this test, and reported that the oxidation of the soot nuclei produced during combustion decreases with the increase in the ethanol blending ratio. Likewise, [PERSON] et al. (2012) experimented by applying methanol 5%, 10% and 15% (by vol.%) to diesel fuel. The study reported that, due to the increase in the methanol blending ratio, the fractal dimension of soot particles decreased, because the methanol promoted branches of soot aggregates, and the soot activation occurred in the outer area, thus inhibiting soot maturity. [PERSON] et al. (2013) applied oxygenated fuel in a methanol blend, a dimethyl carbonate blend and a dimethyl methane blend to confirm the distribution of the primary particle diameter for each fuel condition. The distribution for diesel fuel was 9.5 nm to 50 nm. For the methanol blend, the size range was 7.5 nm to 42.5 nm; for the dimethyl carbonate blend, it was 12.5 nm to 43.5 nm; and for the dimethyl methane blend, the range was 6.5 nm to 41.0 nm. When using an oxygenated fuel, the soot has a smaller particle size distribution. Also, the mean primary particle meter of the methanol blend was the smallest, at 21.66 nm.
## 1 Introduction
They have proposed a simple Oil-Slick Discrimination Algorithm based on SAR backscatter signature, i.e., sigma-naught (\(\sigma^{\text{o}}\)), beta-naught (\(\beta^{\text{o}}\)), and gamma-naught (\(\gamma^{\text{o}}\)) [23; 24; 25], along with the geometry, shape, and dimension of the oil slicks. Their best outcome is reached with optimal Overall Accuracies of approximately 70%, based on the oil slicks' areas and perimeters. We report on analyses to refine the ability to discriminate the petrogenic oil-slick category (seeps versus spills) proposed in our previous investigations [19; 20; 21; 22]. Exploiting the same dataset, but with expanded Data Processing Segments, we extend our earlier studies onto a firmer basis. Based on our methodical data mining exercise, we seek to improve the seep-spill discrimination accuracy, as well as to answer three scientific questions:  
1. Among the several Data Transformation Approaches we tested, which one provides the most accurate oil-slick category discrimination? 2. Is there a specific Attribute Selection Process that excels at choosing variables to discriminate seeps from spills? 3. Which combination of Oil-Slick Information Descriptors promotes the best discrimination between seeps and spills?
# Towards a Digital Twin Warehouse through the Optimization of Internal Transport
## 2 Materials and Methods
### RFID Used  
Each of the bags has been characterized and identified individually by means of a passive RFID tag during the unloading process (prior to the storage process). This type of tag allows the reflection and modulation of a carrier signal [46].  
The system used for the management of the storage bins is configured as a beacon system. The beacons are passive RFID tags located in the aisles in front of each of the storage bin columns. The \"beacon\" tags are installed in floor cavities and protected with epoxy resin to prevent deterioration and to form a completely flat surface that does not hinder transit. The forklift incorporates two short-range RFID readers (Figure 4). One of them is located at the bottom of the forklift, which will detect the beacons, and the other at the front, which will detect whether the forklift is loaded or not, as well as which particular bag is being moved.  
This low-cost system, through the sequencing (Figure 5) of the readings of both RFID readers, makes it possible to know the position of each of the bags in the warehouse at any given moment, making it relatively easy to readapt the beacon system to a new warehouse layout.  
Figure 3: Nodes and paths.  
The particular process under analysis requires processing a quantity of raw material that generally involves about 10 bags (previously selected). Each combination of bags will determine manufacturing times, quantities of additives, and energy inputs. The choice of a given combination is made on the basis of criteria of optimization of the available resources. The way to transfer this choice to the simulation model is by means of a random process in which 10 bags are selected randomly.  
Figure 4: Forklift RFID readers.  
Figure 5: Sequence of the readings of RFID readers: (**a**) bag unloading; (**b**) bag extraction; (**c**) sequences of readings in the unloading of bags; (**d**) sequences of readings in the extraction of bags.  
The location of these bags may correspond to interior positions (Figure 6) within each of the storage zones.
# A High Full Quality CMOS Image Sensor for Space Applications
## 3 Experimental Results
### MTF Measurement Results  
The MTF measurements have been made using the ISO 23333 slanted-edge method. The maximum output voltage of the bright pixel near the edge was limited to 50% of the saturated pixel output and the knife-edge was used as a target with a 5 degree tilting angle [24,44]. The MTF was measured in the vertical and horizontal directions and was expressed at Nyquist frequency.  
Two different CISs utilizing pixels with and without metal shielding have been fabricated in order to compare the MTF performance. Table 2 shows the MTF measurement results, and it clearly shows that the horizontal MTF has been increased from 0.131 to 0.402, resulting in a 300% improvement. The measured MTF curves with spatial frequency are also shown in Figure 9.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Technology & 0.11 μm CMOS \\ Pixel Pitch & 6.5 μm \(\times\) 6.5 μm \\ Resolution & 3000 \(\times\) 3000 pixels \\ Chip size & 22 mm \(\times\) 22 mm \\ Fill factor & 67\% \\ Output voltage swing V\({}_{\text{sat}}\) (gain = 1) & 1 V \\ Modulation transfer function (MTF) at Nyquist & 0.4 \\ Conversion gain & 8.55 μm/e\({}^{-}\) \\ FD capacitance & 19.6 fF \\ Full well capacity & 103,448 e\({}^{-}\) \\ CIS read noise & 24.7 e\({}^{-}\) \\ Dynamic range & 72.4 dB \\ Quantum efficiency @ 550 nm & 68.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the CIS.  
Figure 8: Measured pixel characteristic: (**a**) Readout voltages with a different amount of charge transfer; (**b**) Quantum efficiency chart with various wavelengths.
## 4 Discussion
### Applications Potential of Empirical Models of GPP, Re, and NEP  
There are more potential applications of empirical models of GPP, Re, and NEP in the future; for example, good relationships between the calculated GPP and satellite SIF were found and used to compute GPP as another method with large space coverage and better GPP and Re simulations from 2003-2017 and 2013-2016. From the simulations of GPP, Re, and NEP from 2013-2016 to 2003-2017, empirical models displayed reasonable performance from the hourly to daily, monthly, and annual time scales. PAR energy method showed potential advantages to deal with the multiple and complex interactions in the processes associated with CO\({}_{2}\) balance and is recommended to be further studied. PAR transfers in the atmosphere and PAR balance above a canopy level can objectively describe PAR absorption and scattering caused by atmospheric GLPs (including CO\({}_{2}\)) ([40,41], and this study). Thus, the transformation/application of PAR balance to the empirical models of GPP, Re, and NEP exhibited acceptable short-term and long-term simulations (e.g., 2013-2016 and 2003-2017). The system of PAR-atmosphere-biosphere-land should be studied as a whole, and the PAR balance is an objective and useful tool for studying CO\({}_{2}\) exchange.  
We strongly suggest measuring direct and diffuse solar radiation at solar radiation monitoring stations in order to fully understand the scattering roles/mechanisms of GLPs in the CO\({}_{2}\) exchange processes associated with the ecosystem [40,41,102], such as diffuse PAR roles on GPP [83,104-108].
## 4 Experiment and Result Analysis  
### Experimental Environment and Parameter Settings  
The experiment is based on OpenCV and PCL, the programming language is C++, and the experiment is implemented in the windows operating system. In terms of the experimental hardware, the CPU is AMD Ryzen5 5600U. Its frequency is 2.30 GHz, and the memory space is 16 GB. The width of the stixel is 5 pixels and the thresholds of \(PR_{\mathit{PNV}}\), \(PR_{\mathit{SPH}}\) and \(PR_{\mathit{LSF}}\) are set to 0.1, 0.15 and 0.45, respectively. The above parameters are the optimal parameters obtained during the experiment. There are also some parameters that have little influence on the experimental result, so we empirically selected an appropriate threshold; for example, the error threshold for determining inliers/outliers in RANSAC is set to 0.1, the prior probability \(P_{\mathit{pr}}\) is 0.5, and the disparity optimization threshold is set to 5. All of the methods in the experiment use the same disparity data.  
The specific implementation process of the OD-LSF method is as follows: first input the stereo image pair in the data set, calculate the disparity map, and further calculate the point cloud information, and generate the superpixel point cloud through the segmentation result of the SLIC algorithm and the point cloud information. In the second step, the superpixel point cloud information is input into the OD-PNV method, and the probability that the superpixel belongs to an obstacle is calculated through the local plane normal vector feature. In the third step, the detection results of the OD-PNV method and the superpixel point cloud are input into OD-SPH, and the road plane is fitted to obtain the local height features. The OD-SPH method calculates the probability that the superpixel belongs to an obstacle through the local height feature. In the fourth step, the detection results of the two methods are fused using the Bayesian framework. Finally, SGA-SO is used to generate the stixel and output obstacle detection result.
# Estimating Parameters of Photovoltaic Models Using Accurate Turbulent Flow of Water Optimizer
## 2 Problem Formulation and Objective Function
### Analysis of TDM  
Figure 3 illustrates the TDM equivalent circuit related to the PV solar cell. The mathematical equations to compute the output current of the TDM are as follows:  
\[I=I_{ph}-I_{d1}-I_{d2}-I_{d3}-I_{sh} \tag{5}\]  
\[I=I_{ph}-I_{s1}\Big{[}e^{\frac{\delta(V+I_{sh})}{\delta_{2}^{2}I_{c}}}-1\Big{]} -I_{s2}\Big{[}e^{\frac{\delta(V+I_{sh})}{\delta_{2}^{2}I_{c}}}-1\Big{]}-I_{s3} \Big{[}e^{\frac{\delta(V+I_{sh})}{\delta_{2}^{2}I_{c}}}-1\Big{]}-\frac{V+IR_{s}}{ R_{sh}} \tag{6}\]  
where \(I_{d3}\) is the dark saturation current of the third diode in the TDM, \(a_{3}\) is the ideality factor of the third diode. In this model, nine parameters should be estimated, which are \(\Big{(}I_{ph},\ I_{s1},\ a_{1},\ R_{s}\,\ R_{sh}\,\ I_{s2}\,\ a_{2}\,\ I_{s3}\,\ a_{3}\Big{)}\).
## 3 Actual Platform Benefits
### Reduce Total Cost of Care for Care Coordination  
With a robust data analytic component, the organization was able to prioritize opportunities for improvement and to improve the way care is coordinated and delivered throughout its network of hospitals and medical facilities. The results include a considerable increase in financial results in just six months.  
The organization uses the platform to generate timely, meaningful, and actionable data to drive change and improve the quality of care for patients. The organization uses the data for risk-stratification of the network's population, prioritization of the care coordination activities, and prevention activity's interventions. Risk stratification was completed for all patients, enabling care managers to identify individuals at various risk levels for unnecessary services and high-cost utilization, improving patient outcomes and experience. The analytical component also reduces unnecessary visits, facilitates access to specialty care and community-based services, and achieves healthcare outcomes. Other benefits include 3% increase in the detection of high-risk patients with primary care, 20% increase in the number of patients with ongoing care managed, and 10% percent reduction in emergency department utilization per member among care managed patients.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Qualitative Maternal Infections Variables**} & \multirow{2}{*}{**Categories**} & \multicolumn{2}{c}{**Cases**} & \multicolumn{2}{c}{**Controls**} & \multirow{2}{*}{**X2**} & \multirow{2}{*}{\(p\)**-Value**} \\ \cline{3-4} \cline{6-7}  & & **N** & & & & & \\ \hline \multirow{2}{*}{**Maternal Fever**} & Yes & 67 & 36.0 & 40 & 10.8 & \multirow{2}{*}{50.38} & \multirow{2}{*}{0.0001} \\  & No & 119 & 64.0 & 329 & 89.2 & \\ \hline \multirow{2}{*}{**Yeast Infections**} & Yes & 31 & 16.7 & 15 & 4.1 & \multirow{2}{*}{25.83} & \multirow{2}{*}{0.0001} \\  & No & 155 & 83.3 & 354 & 95.9 & \\ \hline \multirow{2}{*}{**Sexualy transmitted disease history**} & Yes & 27 & 14.5 & 7 & 1.9 & \multirow{2}{*}{34.24} & \multirow{2}{*}{0.0001} \\  & No & 159 & 85.5 & 362 & 98.1 & \\ \hline \multirow{2}{*}{**Uinary Tract Infections**} & Yes & 11 & 5.9 & 9 & 2.4 & \multirow{2}{*}{4.29} & \multirow{2}{*}{0.0381} \\  & No & 175 & 94.1 & 360 & 97.6 & \\ \hline \hline \end{tabular}
\end{table}
Table 10: Qualitative maternal infections variables in cases (186) and controls (369).