esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In summary, using large-area and high quality CNT and graphene films, we successfully fabricated flexible and semitransparent solar cells with all-carbon electrodes. The current densities of all devices with all-carbon electrodes, including CNT-G, G-G and MG-G, are comparable to those reported for carbon-based devices with conventional anodes (ITO) and cathodes (Ag or Al). The best performance achieved for the devices with all-carbon electrodes is 0.63%, comparable to the performance of OPVs using pristine CVD graphene films as anodes. There is great room for improvement in film hydrophilicity, smoothness, and conductivity to ultimately obtain higher efficiencies. This development demonstrates the potential of graphitic nanomaterials as all-carbon electrodes for future flexible carbon-based photovoltaics and other thin-film solar cells.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/8/2/152/s1](http://www.mdpi.com/2076-3417/8/2/152/s1)]([http://www.mdpi.com/2076-3417/8/2/152/s1](http://www.mdpi.com/2076-3417/8/2/152/s1)). Figure S1: TEM images of ZnO nanoparticles, Figure S2: Photographs of as-grown graphene, Figure S3: Optical images of as-grown graphene, Figure S4: SEM images of as-grown few-layer graphene, Figure S5: Raman spectrum and transmittance spectrum of multi-layer graphene, Figure S6: AFM topography images of few-layer graphene, Figure S7: AFM topography images of CNT films, Figure S8: Dark \(J\)-\(V\) characteristics of solar cells with CNT (or G) as anodes and G as cathodes.  
**Acknowledgments:** We acknowledge the support from the 973 program of China (Nos. 2015 CB932500, 2014 CB932401), National Natural Science Foundation of China (Nos. 51372131, 51722207, and 51372133), Beijing Nova Program (No. Z161100004916099), and Tsinghua University Initiative Scientific Research Program (No. 20151080367). Z.Z. gratefully acknowledges the financial support from the National Natural Science Foundation of China (No. 11364043).  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON], [PERSON], and [PERSON] contributed reagents/materials/analysis tools; [PERSON] and [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Samples** & \(V_{\text{oc}}\) **(V)** & \(J_{\text{sc}}\) **(mA/cm\({}^{2}\))** & **FF (\%)** & \(\eta\) **(\%)** \\ \hline CNT-G & 0.47 & 3.7 & 28 & 0.48 \\ CNT-ITO & 0.50 & 10.9 & 32 & 1.71 \\ G-G & 0.62 & 3.6 & 28 & 0.63 \\ G-ITO & 0.48 & 5.1 & 30 & 0.73 \\ MG-G & 0.20 & 4.8 & 23 & 0.22 \\ MG-ITO & 0.50 & 3.6 & 29 & 0.50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Photovoltaic parameters of different solar cell devices.
# Synthetic Fog Generation Using High-Performance Dehazing Networks for Surveillance Applications
## 4 Experiments and Results Analysis
### Results Analysis
#### 4.2.2 Quantitative Performance  
Table 1 summarizes the dehazing performance of each model on the O-HAZY evaluation set. ModelU, trained on GridNet-generated fog, achieves the highest PSNR (17.6018 dB) and SSIM (0.7697), outperforming ModelS (14.8382 dB, 0.7186), ModelDA (14.6777 dB, 0.7125), and ModelDC (14.5749 dB, 0.7007). This result validates our hypothesis that a network-driven fog generation approach improves dehazing efficacy by providing more realistic and challenging training data.  
Figure 5: Loss curve of the Fog Generator Model during training, illustrating convergence over 100 epochs.  
Figure 6: Loss curves of dehazing models (ModelS, ModelU, ModelDA, and ModelDC) over 30 epochs, highlighting ModelU’s superior convergence.
## 4 The Proposed AAC-IoT System Model
### Registration and Authentication
#### 4.2.3 DO Authentication  
The authentication of the DO is based on the credentials identity, certificate and signature. The sequential process followed in the DO authentication is represented in Figure 5. The DO is authenticated whenever a data upload is needed. In this work, the DO will upload meta-data into the HLF including the attributes for generating access control policies. On the other hand, the main-data of the DO are stored in a cloud server using a lightweight PRESENT block cipher algorithm. The steps followed for the DO authentication are illustrated as follows.  
Step 1: The DO \(\mathcal{D}_{1}\) submits the authentication request consisting of \(R_{1}\)\(\rightarrow\)\(\{H(Id_{1})\),\(T_{0}\}\), i.e., hashed identity with timestamp, to the CA. The hashing is performed using a QUARK algorithm which is lightweight [49].  
Step 1.1: Construction of sponge by padding at initialization until the specified length is reached.  
Step 1.2: An XOR operation is performed in the absorbing phase and returns permutation from this phase.  
Step 1.3: Next, in the squeezing phase, the bits to be hashed are extracted, and the process of extraction is completed.  
Step 1.4: The hashed security credentials in the HLF are verified from the given DO credentials.  
Step 2: The CA checks timestamp, \(T_{0}\)\(<\)\(\Delta T\) and then the hash identity in the HLF blockchain is extracted from the block. and if valid, the signature and certificate are requested for the DO as \(R_{2}\)\(\rightarrow\)\(\{H(SO_{1}\bigoplus h(C_{1})\),\(T_{1})\}\) to HLF blockchain via the CA.  
Figure 4: Workflow of the DU registration.  
Step 3: The CA checks timestamp \((T_{1}-T_{0})<\Delta T\). If valid it forwards the \(DO\to\{H(SO_{1}\bigoplus h(Ct_{1})\}\) to the HLF blockchain for validation. On completion of this authentication, the DO can submit meta-data to the HLF. The HLF blockchain sends \(R_{3}\)\(\to\) {S,T\({}_{2}\)} to the DO, where \(\mathbb{S}\) denotes successful authentication. Step 4: The DO verifies the timestamp \((T_{2}-T_{1})<\Delta T\), and then the meta-data and attributes for the data \(\{a_{1}\),\(a_{2}\),\(a_{3}\),....\(\}\) are uploaded to the HLF blockchain and the complete data are uploaded into a cloud server using the \(\mathbb{S}\) which assures the DO is legitimate.
# Failure Identification Method and LMI-Based Control System for Small Spacecraft
## 5 Simulations Results
Firstly, there is a transitional phase, where the spacecraft moves from the initial configuration to the desired configuration. Then, a scientific phase occurs, in which the spacecraft moves according to the mission-chosen path. Finally, the spacecraft returns to its initial configuration after a second transitional phase. The proposed algorithm was able to accomplish the desired tasks in all of the considered scenarios. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value/Range** \\ \hline Inertia matrix [kg/m\({}^{2}\)] & diag ([25 50 50]) \\ RW inertia [kg/m\({}^{2}\)] & 0.022 \\ Maximum torque [Nm] & \(\pm\)0.1 \\ External disturbances [Nmm] & [\(-\)0.1, 0.1] \\ Initial attitude [\({}^{\circ}\)] & [\(-\)25, 25] \\ Initial angular velocity [mrad/s] & [\(-\)5, 5] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. Figure 4: In this figure, the performance of the controller designed while considering the failures are compared with the augmented design: attitude time variation with the second reaction wheel failing (**left**); attitude time variation with the fourth reaction wheel failing (**right**). Figure 3: In this figure, the performance of the controller with the two design approaches are compared with the augmented design: attitude time variation with no failure (**left**); attitude time variation with the third reaction wheel failing (**right**).
# An Efficient Model-Based Clustering via Joint Multiple Sink Placement for WSNs
## 4 Our Proposal
### Deployment Sensors  
In our model, we assumed that N nodes are spread randomly throughout the environment and that each node i has a unique identity (ID). S(i) is the set of node i's neighbours. Each node i can adopt one of the two types of communication: single-hop, in which a node near the sink sends its data directly as a direct node, and multihop, in which a distant node uses intermediate nodes to send its data to the sink. Below are the assumptions made in our model:  
Assumptions:  
1. Sensor nodes are stationary and are deployed randomly in the environment.
2. Multiple sinks (base stations) should be in fixed positions.
3. The sensor nodes in the base station's communication range transmit data directly to the sink.
4. The sensor nodes are aware of their neighbours' node locations.
5. The sensor nodes can send and receive data from nodes in the communication range.
6. Each sensor node's total energy consumption cannot exceed its initial energy.
7. All sensor nodes have different energies, communications, and sensing ranges.
## 3 Results and Discussion  
### Physico-Chemical Characteristics of the Rice Flours  
The chemical composition of the three rice flours is presented in Table 1. The ash, protein, fat, and fiber contents of NRF were 1.30%, 11.16%, 3.42%, and 4.75%, respectively, much higher than BRF and ARF. At the same time, BRF has higher contents of protein and fiber compared the ARF. Black rice flours usually have higher contents of protein, fiber, ash, and fat compared to the white rice flours [2; 8], but differences also appear between Indica and Japonica varieties, Indica being reported to have a higher content of proteins [1]. Based on the amylose content, the investigated flour samples can be classified, according to [PERSON] and [PERSON] [3], as follows: NRF (6.87% amylose) as rice flour with very low amylose content (3-9%), while BRF and ARF (amylose content of 17.40% and 11.17%, respectively) are rice flours with low amylose content (10-19%). The firmness module varied within quite large limits, from 1.99, in the case of NRF, to 2.10 and 2.15 for BRF and ARF, respectively. NRF had a higher percentage of particles smaller than 160 \(\mu\)m (15.57%), followed by BRF (with 8.57%) and ARF (with 5.83%).
# A Method of Free-Space Point-of-Regard Estimation Based on 3D Eye Model and Stereo Vision
## 3 Methods  
The proposed gaze estimation method is consisted by four main parts: (1) multi-camera image acquisition; (2) eye feature detection; (3) personal calibration process; and (4) 3D PoR (point-of-regard) estimation. The diagram of the proposed gaze estimation system is shown in Figure 1.  
Two pairs of stereo cameras installed in the device capture eye region images and extract necessary eye features, and then directly solve the spatial coordinates of these features in real-time. A 3D eye model and gaze estimation model are proposed in Section 3.3, and also a calibration process. The proposed 3D eye model is a model which is simplified by a classical eye model, and the 3D PoR is considered to be the middle point of the nearest point of the binocular visual axes.  
The calibration process is used to estimate the position of the eyeball center and the angle between the visual axis and the optical axis. The spatial coordinates of the pupil, the inner eye corners and the calibration target point are firstly calculated by stereo cameras. We assume that the pupil center normal is the initial value of the optical axis of the eye, and the spatial coordinates of target point are also involved to the estimate the eyeball features.
# Development of a New Sound Quality Parameter for Road Noise Perception Inside Vehicle Cabinet
## 4 Discussion  
The results of the derived sound quality (SQ) metrics indicate that car5 produces the least comfortable and most annoying sound, while car1 provides the most comfortable and least annoying sound perception, as illustrated in Figure 8. The Figure uses different colors to represent various cars, with subjective evaluations shown as full circles and SQ metric results represented by full diamonds.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Subjective Evaluations**} & \multicolumn{2}{c}{**SQ Metrics**} \\ \hline  & **Comfort** & **Satisfaction** & **Comfort** & **Satisfaction** \\ \hline Car1 & 6.71 & 6.65 & 7.09 & 6.89 \\ \hline Car2 & 5.74 & 6.02 & 5.51 & 5.81 \\ \hline Car3 & 6.54 & 6.36 & 6.24 & 6.05 \\ \hline Car4 & 5.56 & 5.99 & 5.27 & 6.28 \\ \hline Car5 & 5.34 & 5.54 & 5.19 & 5.33 \\ \hline Car6 & 6.33 & 6.37 & 6.75 & 6.39 \\ \hline Avg & 6.04 & 6.16 & 6.01 & 6.13 \\ \hline Range & 1.37 & 1.11 & 1.90 & 1.56 \\ \hline Std dev & 0.52 & 0.36 & 0.74 & 0.49 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Subjective evaluations vs. new SQ metrics.  
Figure 8: SQ metrics for road noise perception.  
The results indicate that car1 performs best in both aspects of the new road noise SQ metrics, excelling particularly in AI, OAI, and SIL. Although SPL ranks second, this suggests that SPL can still be considered a useful indicator of sound perception. However, the superior performance of the SQ metrics demonstrates their added value in more accurately reflecting human perception.  
On the other hand, car5 ranks the lowest according to the new road noise SQ metrics. This distinction would not have been clear using SPL alone, as its value of 74.1 dBA is close to the average of 73.8 dBA, making it indistinguishable from other cars. The SQ metrics provide much more relevant information regarding subjective evaluations and, thus, the perception of road noise in vehicles.  
This highlights a key finding: While SPL remains a standard tool for providing basic acoustic measurements, its correlation with road noise perception is relatively weak. Instead, spectral variations play a far more significant role in how noise is perceived. This study demonstrates that subjective evaluations are closely aligned with OAI, AI, and SIL, which emerge as the most influential factors in perceived road noise.  
This relationship is better captured by a new sound quality (SQ) model that combines these key SQ metrics to reflect subjective experiences more accurately. This model will be pivotal in the target-setting phase of future vehicle development, offering deeper insights into customer expectations regarding road noise. By applying this model, it will be possible to design vehicles that more effectively meet these expectations, ultimately enhancing acoustic comfort and overall customer satisfaction.
## 3 Seizure Detection Performance and Power Consumption Evaluation
### Channel Error Analysis  
So far, the seizure detection performance was assessed under the assumption that the wireless transmission from the sensor unit to the data server had perfect channel conditions, such that the data at the server side was received without errors. However, the environments in which the seizure detection system is to be deployed, including hospitals and homes, may contain factors that can affect the quality of the wireless communications and, hence, the integrity of the received data. These factors include structural obstacles and interference from WiFi networks, Bluetooth devices and cordless phones. While the use of the forward error correction (FEC) technique can reduce the effective bit error rate (BER) of a wireless link, this was not used in our system. This is because the transmission of the additional redundant bits introduced by the FEC results in increases in the power consumption of the transmitter and is generally not favorable in the context of a WSN. To study the effects of imperfect channel conditions on the seizure detection performance and the sensitivity of different data reduction schemes, we simulated channel errors and measured the detection performance as a function of the BER. Specifically, we used a 16-bit uniform encoder and converted the data to be transmitted (raw EEG, compressed EEG or feature vectors) into a binary stream. The wireless channel, modeled as a black box with a specified BER, introduced errors into the binary stream by probabilistically flipping bits based on the BER value. The resulting binary stream represents the signals received at the server side. This stream was then decoded and fed to the seizure detection algorithm.
###### Abstract  
A virtual TV set combines actors and objects with computer-generated virtual environments in real time. Nowadays, this technology is widely used in television broadcasts and cinema productions. A virtual TV set consists of three main elements: the stage, the computer-system and the chroma-keyer. The stage is composed by a monochrome cyclorama (the background) in front of which actors and objects are located (the foreground). The computer-system generates the virtual elements that will form the virtual environment. The chroma-keyer combines the elements in the foreground with the computer-generated environments by erasing the monochrome background and inserting the synthetic elements using the chroma-keying technique. In order to ease the background removal, the cyclorama illumination must be diffuse and homogeneous, avoiding the hue differences that are introduced by shadows, shines and over-lighted areas. The analysis of this illumination is usually performed manually by an expert using a photometer which makes the process slow, tedious and dependent on the experience of the operator. In this paper, a new calibration process to check and improve the homogeneity of a cyclorama's illumination by non-experts using a custom software which provides both visual information and statistical data, is presented. This calibration process segments a cyclorama image in regions with similar luminance and calculates the centroid of each of them. The statistical study of the variation in the size of the regions and the position of the centroids are the key tools used to determine the homogeneity of the cyclorama lighting.  
chroma-keying; cyclorama; illumination; virtual TV set; mixed reality 2020
## 2 An Overview of Recent Advances in Design Optimization of Electromagnetic Devices
### Optimization Methods
#### 2.3.4 System-Level Multidisciplinary Design Optimization  
Besides the optimization problems discussed above, there are two emerging and challenging research topics in the design optimization of electromagnetic devices, system-level design optimization and topology optimization.  
The system-level design optimization is very important for electrical machines and drive systems, e.g., the in-wheel motor drive systems for EVs. The conventional component-level (e.g., the motor) optimization cannot guarantee optimal performance of the whole system. To design and optimize this kind of drive systems, electromagnetic analysis, thermal analysis, mechanical analysis, power electronics, and control systems have to be investigated in the optimization [102; 103; 104; 105; 106; 107; 108; 109; 110]. Therefore, multidisciplinary design optimization methods should be investigated. Another example is the design of high-speed electrical machines, where utilizing a multi-physics analysis is crucial to obtain accurate and good optimization results [8; 9; 111].
# Data-Driven Safe Deliveries: The Synergy of IoT and Machine Learning in Shared Mobility
## 4 System Design and Implementation
### Component Design
#### 4.1.2 Control and Management Layer  
The control and management layer consists of the following components:  
1. Behavior Monitoring and Tracking Component: It analyzes data collected by the Data Collection Component using predefined rules to track and monitor vehicle behavior. It depends on the Rides Management Component to update vehicle statuses.
2. Decision-Making Model Component: It utilizes the analyzed data from the Behavior Monitoring and Tracking Component as input for machine learning models, facilitating behavior classification.
3. Rides Management Component: It records ride details, including analyzed data and decisions made during trips. It also maintains a comprehensive history of all rides and associated vehicle behavior analysis. It depends on the Vehicle Management Component to update vehicle statuses.
4. User Management Component: It manages the user usage of vehicles, recording decisions made, ride reports, and the vehicles used by each user. It relies on the Users Database Component to store and retrieve user data.
5. Vehicle Management Component: It manages vehicle-related data, including registration details, sensor status, and availability. It interfaces with the Vehicle Database Component to update and retrieve vehicle data. It depends on the Rides Management Component to keep track of vehicle statuses during rides.
6. Rides Database Component: It stores and retrieves ride information and generates reports, supporting historical analysis and reporting.
7. Users Database Component: It stores and retrieves user data, enabling user management functionalities.
8. Vehicle Database Component: It stores and manages vehicle-related data, including registration details, sensor status, and availability.
## 3 Regression Models
### Methods for Analyzing the Random Components of the Model
#### 3.3.5 The Hypothesis of Homoskedasticity of the Random Components of the Residuals  
The Breusch-Pagan test (19) was used to determine the presence of equality of variance of random components (homoskedasticity).  
\[\chi^{2}=n\cdot R_{\epsilon}^{2} \tag{19}\]  
where \(R_{\epsilon}^{2}\)--fit of the regression model residuals.  
The hypotheses posed were as follows: H\({}_{0}\): Homoskedasticity is present \(\chi_{2}<\chi_{2\alpha}\) (constancy of variance); H\({}_{1}\): Heteroskedasticity is present \(\chi^{2}>\chi^{2\alpha}\) (no constancy of variance).
# Biomechanical Task-Based Gait Analysis Suggests ReWalk Gait Resembles Crutch Gait
###### Abstract  
Current gait rehabilitation strategies rely heavily on motor learning principles, which involve facilitating active patient participation, high-doses of biomechanical task-related motor activities and accurate feedback. Furthermore, appropriate muscle groups need to be recruited for the joint movements that constitute the biomechanical task-related activities in order to effectively promote motor learning. Recently, exoskeleton-type robots utilizing crutches have been incorporated into overground gait rehabilitation programs. However, it is unclear which gait-related tasks are being trained because the joint movements and muscle recruitment patterns deviate from those of natural gait. This raises concerns because repetitive training with these devices may not lead to desirable rehabilitative gains. In this study, we compare the lower limb joint angles and electromyography patterns of healthy subjects walking with and without ReWalk in accordance with the three major biomechanical tasks required by bipedal gait: weight acceptance (WA), single-limb support, and limb advancement. Furthermore, we investigate whether the physical constraints of ReWalk, most noticeably the use of crutches and fixed ankle joints, were responsible for the specific changes by conducting additional walking sessions with either crutches or ankle foot orthoses. The results from the six healthy male volunteers suggest that the gait patterns observed with ReWalk deviate significantly from those of natural gait, particularly during the WA, and closely resemble those of crutch gait.  
exoskeleton; gait; electromyography; rehabilitation; kinematics Article