esa-sceva/satcom-chunk-collection huggingface dataset:
# Exact Relationship between Black Phosphorus Thickness and Behaviors of Field-Effect Transistors
## 4 Discussion
### Effect of the \(l\) on \(I_{ds}\)  
By studying the electrical characteristics of these devices, we can conclude that the \(I_{ds}\) of BP-FET was directly related to the \(l\) of the BP material under the same test environment. The greater the \(l\) is, the greater the \(I_{ds}\) under the same gate voltage (\(V_{g}\)) regulation is, but the modulation effect of the electric field will weaken with the increase of the \(l\). When \(V_{g}\) = 0, under different but fixed \(V_{gs}\), there is a very good rule between \(I_{ds}\) and \(l\), which can be expressed by a very accurate empirical formula. An approximate thickness \(l_{o}\) (21.7 nm) can be deduced from this formula, at this point, the slope of the log\(I_{ds}\)-\(l\) takes the maximum value. Namely, the influence of \(l\) on log\(I_{ds}\) is obvious in the vicinity of \(l_{o}\). The thickness region with the most significant effect on the \(I_{ds}\) of device is between 16.7 nm and 27.3 nm. Then, if BP thickness is less than 16.7 nm, the influence of thickness on device characteristics will increase as the \(l\) increases; but if BP thickness is greater than 27.3 nm, the influence of thickness on characteristics will be weak with the increasing \(l\). When the thickness reaches or exceeds 50 nm, the \(l\) has almost no effect on log\(I_{ds}\).  
Figure 6: Transport characteristics of a FET with 30 nm BP etched from 50 nm, (**a**) the output characteristic curve, (**b**) the transfer characteristic curve.
# Crescent Microstrip Antenna for LTE-U and 5G Systems
## 6 Simulations and Measurements Results
### Input Impedance  
The antenna design assumes that the impedance of the feed line should be 50 \(\Omega\). In the case of large discrepancies, it is possible to use a matching system, but it is another system that introduces additional losses, and in financial terms generates additional costs. The input impedance as a function of frequency for the proposed antenna is shown in Figure 9. The continuous lines show the results of the simulations obtained in CST Microwave Studio, while the dashed lines show the results of the measurements made for the physical model of the antenna.
## 3 Cooperative Schemes
### Cooperative Schemes for VLC  
#### 3.2.1 Frequency Reuse (FR)  
FR techniques are straightforwardly applied to VLC systems by simply allocating different frequency sub-bands to neighbouring APs. Note that FR is not strictly a cooperative transmission scheme for providing communication to the same user from multiple APs, but it is a technique that significantly improves the overall system performance by allocating different resources among multiple APs, and therefore, reducing the inter-cell interference. For the sake of completeness, FR techniques must be considered in this survey. Two optical APs that transmit at the same frequency band are separated by the reuse distance. As users located at the cell-edge areas are the ones subject to strong inter-cell interference, more efficient FR techniques were proposed traditionally in RF multi-cell systems, such as fractional frequency reuse (FFR), which employs different frequency reuse factors in cell-center and cell-edge areas to keep the inter-cell interference under control [107; 108]. FFR is evaluated in VLC scenarios by the authors of [109; 110], demonstrating that the inter-cell interference can be reduced notably for those users located at the cell-edge areas. An example of FFR network in a VLC scenario is shown in Figure 13. Note that users located at the cell center are all served by the same frequency band, assuming that the inter-cell interference among them is low. Then, cell-center area has a frequency-reuse factor of 1. However, users located at the borders of the cell are served by different frequency bands, with a frequency reuse factor of 3 in the example.  
Figure 13: Example of FFR.  
A potential limitation of this technique is notable in ultra-dense VLC networks, where users located at the cell-center areas also suffer from a high inter-cell interference level coming from adjacent lamps [111]. That is, managing the transmission resources, e.g., allocating frequency sub-bands in an O-OFDM scheme, is more complex and less efficient as the network becomes more dense.
# Applications of GNSS-RO to Numerical Weather Prediction and Tropical Cyclone Forecast
### Advantages of RO  
RO measurements have many advantages, including global coverage, high accuracy and precision, superior vertical resolution, and a full-time and all-weather observation. Near the top of the tropopause, the departure of the RO soundings with respect to the temperature/pressure/ geopotential temperature is approximately 0.7 K/0.15%/1.4 K. The departure further decreases toward the lower troposphere because of the increased information in the background [31]; its vertical resolutions in the stratosphere and troposphere are approximately 1.5 km and 0.2 km, respectively, even reaching 0.1 km near the surface [32]. RO measurements have a global coverage, particularly including sights over the polar region and oceans, which are blind spots of other detection approaches such as radar and radiosonde [33]. Additionally, RO signals loaded on the L1 (1575.42 MHz) and L2 (1227.60 MHz) bands, whose wavelengths are sufficiently long to avoid appreciable attenuation by rain [34]. Therefore, RO measurements are minimally contaminated by aerosol, clouds, and precipitation, thus ensuring a full-time and all-weather observation. RO is a self-calibrated remote sensing technique. Once deployed on an orbiting satellite, the RO sensor requires no further calibration. Hence, RO can provide long-term steady data. Furthermore, an RO measurement is considered a calibration standard [35] and can complement other observations. Given that RO data are independent of the missions and satellites, the data obtained from the different missions always have a high degree of consistency. Owing to this unified benchmark, RO data complement each other well [15; 36].  
This paper focuses on the impacts of RO measurements on the NWP and TC forecasts. The rest of this paper is organized as follows. In Section 2, we introduce the status quo of the application of RO to NWP, including a sensitivity analysis and discussions on the amount of RO data. Section 3 presents the positive effects of RO data on TC forecasting through some typical studies. The advances in RO and its prospects are discussed in Section 4. Finally, Section 5 presents the conclusions drawn from our review.
# A Photoplethysmogram Dataset for Emotional Analysis
## 2 Materials and Methods
### Deep Network Architecture  
Figure 3 represents the overall architecture of emotion recognition, where statistical and convolutional neural network (CNN) features were used to classify valence and arousal. The left module extracts features automatically from the time-domain signals. Two network modules (central and right) were placed to emphasize more important features. The central module was to utilize the statistical frequency domain features with a single dense layer of 7 units. Seven features were selected among the thirteen frequency domain features and were used as the input of the central module. The right module consists of a single dense layer with 4 units to utilize statistical time-domain features. Four out of the six time-domain features were selected and were used as the inputs for the right module.  
The outputs of the three modules were concatenated and connected to a module of two dense layers. The number of units of the dense layer were set to 32 and 1, respectively. All the activation functions in the network were rectified linear units (ReLUs), except the last layer which should be the sigmoid function to predict the level of the emotional states.  
The left module for time-series data starts from convolutional blocks. A convolutional block consists of two one-dimensional convolutional layers connected to a max pooling layer, which is repeated four times to precisely extract features. The filter numbers of the first and second convolutional blocks were set to 32, and the filter numbers of the third and fourth blocks were set to 64. The kernel sizes of the filter masks were all set to 3. The output of the last convolutional block was connected to a variable block which we changed to compare the different network structures. The output of the variable block was connected to two dense layers via a flattened layer. The number of units in the first dense layer was set to 16, and the second to 8. A dropout layer was attached to each dense layer of the time-series module with a dropout ratio of 0.2 to prevent overfitting.  
Figure 3: The overall architecture of the proposed deep learning model.  
In this study, seventeen different network structures were validated by substituting the variable block. The variable blocks consist of recurrent neural networks (RNNs) such as long short-term memory (LSTM) and gate recurrent unit (GRU). Table 5 lists the sorts of variable blocks. B1 represents a network structure without the variable block, the variable network blocks from B2 to B9 utilize a single memory layer with different number of nodes, and the blocks from B10 to B17 consist of two memory layers with a dropout layer in the middle.
###### Abstract  
In conventional flight control design, the autopilot and the autothrottle systems are usually considered separately, resulting in a complex system and inefficient integration of functions. Therefore, the concept of aircraft energy control is brought up to solve the problem of coordinated control using elevator and throttle. The goal of this study is to develop an optimal energy control system (OECS), based on the concept of optimal energy for fixed-wing unmanned aerial vehicles (UAVs). The energy of an aircraft is characterized by two parameters, which are specific energy distribution rate, driven by elevator, and total specific energy rate, driven by throttle. In this study, a system identification method was employed to obtain the energy model of a small UAV. The proposed approach consists of energy distribution loop and total energy loop. Energy distribution loop is designed based on linear-quadratic-Gaussian (LQG) regulator and is responsible for regulating specific energy distribution rate to zero. On the other hand, the total energy loop, based on simple gain scheduling method, is responsible for driving the error of total specific energy rate to zero. The implementation of OECS was successfully validated in the hard-in-the-loop (HIL) simulation of the applied UAV.  
Keywords:total energy control system; hard-in-the-loop simulation; unmanned aerial vehicle; system identification; linear-quadratic-Gaussian regulator +
Footnote †: journal: Applied Sciences
## 3 Materials and Methods
### Experimental Setup and Implementation  
#### 3.3.1 Definition of Objectives  
The objective behind applying each method is to assess the effects of factors related to scalability, traffic conditions and routing security attacks on the performance metrics of the AODV routing protocol in VANETs. Secondly, we build regression models to model the performance metrics of AODV routing protocol. Finally, we analyze the obtained results using graphical, mathematical and statistical techniques.
## 5 Discussion  
This Section reviews calibration results of Section 4 in perspective of other Cal/Val works. It also elaborates upon future research and infrastructure upgrading, scheduled to take place at the PFAC. More specifically, the Sentinel-3 Mission Performance Center (S3 MPC) has recently assessed the performance of Sentinel-3A and Sentinel-3B using independent processing of the CDN1 transponder data along with sea-surface calibration results using the Corsica, France permanent calibration facilities [32]. These S3 MPC calibration results are compared against the range and sea-surface calibration results obtained at the PFAC. Furthermore, the on-going upgrading of the PFAC as a range and sigma-naught transponder facility is also presented.
## 2 Materials and Methods  
### Data Acquisition  
This research utilized an OCT image database comprising data from 30 healthy subjects. Eyes with history of previous intraocular intervention (e.g., intravitreal injection) or surgery (e.g., vitrectomy), intraocular implants, other ocular diseases that may influence the normal contour of retina and choroid layer (such as intraretinal fluid, subretinal fluid, large drusens or tumor) were excluded. The database included only one OCT image sequence from each patient. All patients underwent SD-OCT scan (Heidelberg Engineering Inc., Heidelberg, Germany) with enhanced depth imaging (EDI) methods which provided a more detailed image of the choroid layer compared with conventional SD-OCT [7]. The scan protocol composed a 6 \(\times\) 6 mm area, which was determined by 25 scans. Each scan was separated by 24 \(\upmu\)m. One pixel represented 4 \(\upmu\)m in this study. The quality of the scan was assessed by the physicians. Scans with low signal strength or severe motion artifacts  
Figure 1: (**a**) An original OCT image with a contrast-enhanced area. (**b**) Location of the three desired layers, namely, the ILM, CiB, and CoB.  
were repeated until an adequate quality was achieved. Mydriatic agents were used if necessary. Each monochrome OCT imaging was quantized into eight bits with 256 gray levels. The entire database was supplied by coauthors, Dr. [PERSON] and Dr. [PERSON], from the Department of Ophthalmology, Taichung Veterans General Hospital, Taiwan.  
Each OCT image sequence contained 25 two-dimensional (2D) scans, and the spatial resolution of each scan was 1008 \(\times\) 596 pixels. In order to discard redundant image contents, every 2D scan was cropped to form a 480 \(\times\) 480 image slice. Figure 2a illustrates the relationship between an original 2D scan and the extracted image slice. The inner border of the choroid was deemed to be [PERSON]'s membrane, while the choroid-scleral interface (CSI) was considered to represent the outer border of the choroid.
## 3 Numerical Simulation
### Numerical Simulation Results  
The simulation results of the crack propagation path are shown in Figure 8. As three loading rates are applied in the numerical simulation, the crack propagation path performs some universality appearances as well as some difference in detail. It is clear that the main crack runs straight after pre-crack initiation and across the set area before it stops at the back of it. Meanwhile, secondary cracks emerge on the holes, which present different fracture phenomena, as the secondary cracks on the back surface of both holes have a larger fracture area than the front ones, and the crack propagation path of the hole below is longer than the above. Also, the transverse crack emerges on the two sides of the hole, as HT-2 and HT-3 in Figure 8 show.  
Figure 7: Mesh of the ISCSC specimen.  
When comparing those fracture phenomena with the experimental results, the simulation results show a significant similarity with the results of the specimens in the experiment shown in Figure 8. It can be clearly seen that their main cracks all cross the set area along with the secondary cracks emerging on both sides of the holes. Especially with the high loading rate and the secondary impact, the transverse cracks on both sides of the holes are generated sufficiently within the expected time.  
Despite the points discussed above, the fracture phenomenon in the specimens, where the running crack rushes directly into the hole below (shown by HT-1 in Figure 5a), is hardly reproduced by the numerical simulation. This is because the sandstone employed in the experiment is not the perfect homogeneous material which contains many irregular fissures and particles, offering a lot of tunneling opportunities when the cracks initiate and propagate. During the propagation process, the running crack might deviate from the symmetry axis of the specimen and approach the hole, resulting in the influence of the hole leading the running crack to rush directly into the hole. However, the material given in the numerical simulation is the ideal homogeneous material, so the cracks could run straight along the symmetry axis of the specimen, leaving no chance for the running crack to approach the hole.  
Therefore, the comparative analysis is conducted between the numerical simulation, using the experimental results specimen as the calibration reference, to investigate the crack propagation behaviour and mechanism of arrest in the set area.
## 3 System Design
### Illumination and Communication Constraints
To simplify the expression, we let \(m_{i}=\frac{\Delta_{i}^{2}(M+1)}{3(M-1)}\). To satisfy the communication performance, we set the minimum SINR requirement of the \(i\)th receiver as \(\chi_{i}\). Thus, the communication quality constraint can be expressed as:\[\frac{m_{i}\rho^{2}i_{ii}^{2}l_{ii}^{2}}{\sum_{j=1,j\
eq i}^{N}m_{i}\rho^{2}i_{ij}^ {2}l_{ij}^{2}l_{jj}^{2}+\sigma^{2}}\geq\chi_{i}. \tag{25}\]  
After the transformation of Equation (25), we have:  
\[m_{i}\geq\frac{\chi_{i}\sigma^{2}}{\rho^{2}t_{ii}^{2}h_{ii}^{2}-\sum_{j=1,j \
eq i}^{N}\chi_{i}\rho^{2}t_{ij}^{2}h_{jj}^{2}}. \tag{26}\]  
For the simple expression, we let \(\Gamma_{i}^{2}=\chi_{i}\sigma^{2}/(\rho^{2}t_{ii}^{2}h_{ii}^{2}-\sum_{j=1,j \
eq i}^{N}\chi_{i}\rho^{2}t_{ij}^{2}h_{jj}^{2})\) where \(\Gamma_{i}\) is a nonnegative value, and \(\beta=\sqrt{\frac{3(M-1)}{M+1}}\). Thus, the SINR constraint can be transformed as:  
\[\mathbf{\Delta}\geq\beta\mathbf{\Gamma}, \tag{27}\]  
where \(\mathbf{\Gamma}=[\Gamma_{1},\Gamma_{2},\cdots\Gamma_{N}]^{T}\).
## 3 Results and Discussion
### Effect of Adaptation to Cd Stress on Biomass of the Middle European Ecotype of S. nigrum
#### 3.3.2. RuBisCO Activities  
Activities of RuBisCO proteins representing both large RbcL and small Rbc5 subunits (Figure 2a,b) responsible for converting atmospheric CO\({}_{2}\) into organic carbon forms, showed different response to oxidative stress in the non-adapted N0 and adapted A50 varieties. Opposite to photosynthetic pigments, no difference in the activity of both subunits at T0 (control) was observed. Increase of abiotic oxidative stress caused by the growing content of Cd in the soil resulted in the regular decline of RbcL and Rbc5 in N0. It was more pronounced with respect to Rbc5 that showed complete disappearance at T50 (Figure 2b).  
Figure 1: Effects of different Cd treatments on (**a**) chlorophyll \(a\), (**b**) chlorophyll \(b\), (**c**) carotenoids content in the non-adapted N0 and adapted A50 varieties of _S. nigrum_ L. (1) 0 to 50 means control and soil treatments with Cd (mg kg\({}^{-1}\)); (2) Data for the same treatments marked by the same capital letters over bars are not significantly different at \(p\) < 0.05. Data for different treatments marked by the same lowercase letters over bars are not significantly different at \(p\) < 0.05.  
It is believed that expression of RbcS regulates the size of the RuBisCO pool and therefore affects the overall catalytic efficiency of the RuBisCO complex mostly located in the RbcL. This well correlates with regularly declining RbcL activity in the N0 variety, up to 78% at T50 compared to T0 (Figure 2a).  
In contrast, in the A50 variety both RbcS and RbcL activities display much bigger resistance to Cd stress (Figure 2a,b), showing decline of RbcS by 78%, and RbcL by 44% only at T50, while at T30 the RbcL decline did not exceed 24% (Figure 2a,b).
# Multi-Head Self-Attention-Based Fully Convolutional Network for RUL Prediction of Turbofan Engines
## 4 RUL Prediction Experiment  
### Data Description  
The Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) turbofan engine dataset [35] is specifically designed to evaluate models for predicting the performance degradation and RUL of turbofan engines. This dataset collects data by simulating the degradation process of commercial turbofan engines under various operating conditions and failure modes, aiming to provide a comprehensive and realistic platform that assists researchers in accurately understanding and simulating engine behavior during actual operation. The C-MAPSS dataset utilizes advanced simulation techniques to mimic the performance degradation of turbofan engines. During the simulation, various operating conditions are adjusted, and multiple failure modes, such as turbine blade wear and sensor failures, are introduced to simulate different scenarios that an engine may encounter in real-world operation. The data are collected over multiple flight cycles, with each cycle fully simulating every stage from takeoff to landing, including takeoff, climb, cruise, descent, and landing.  
To reflect the complexity of turbofan engines in real-world operation, the following operating conditions were simulated during the data acquisition process:  
1. Different flight stages: The dataset covers all stages of flight, and the operating parameters such as thrust, airflow, temperature, and pressure for each stage were adjusted based on actual conditions.
2. Environmental variables: Different environmental conditions, such as atmospheric temperature, humidity, and flight altitude, were also considered during the simulation to explore the potential impact of these external factors on engine performance.  
Figure 5: Flowchart of the RUL prediction procedure using MSA-FCN.  
3. Failure modes and operational settings: Additionally, various failure modes were deliberately introduced into the simulation, and engine performance data under these failure modes were recorded. Meanwhile, different pilot operational settings, such as throttle position and flight speed, were also taken into account to enable a more comprehensive analysis of the impact of various factors on engine performance and degradation.  
The C-MAPSS dataset consists of raw monitoring data generated by multiple sensor signals, which meticulously documents the performance changes in turbofan engines. The raw data include readings from 21 sensors, capturing various critical physical indicators that change over time, such as pressure, temperature, and rotational speed. In this paper, in order to investigate RUL prediction models in the situation of multiple operational conditions and fault modes, FD002 and FD004 of the C-MAPSS dataset are utilized to evaluate the performance of MSA-FCN. Both FD002 and FD004 contain corresponding historical datasets (for training) and real-time datasets (for testing), and information about both can be found in Table 1.