esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Observation Sufficiency to Distinguish the Convective Transition Relationship  
Despite demonstrating separation by tropospheric temperature, the number of precipitation-moisture profile observations (hereafter referred to simply as observations) needed to distinguish between convective transition relations has not been quantitatively assessed. This section quantifies the minimum number of observations of joint precipitation and moisture profiles needed to assure that the transition curves for different  
Figure 4: Using vertical profile information to assess convective conditions in CMIP6 models. (**a**) Vertical profiles of equivalent potential temperature \(\theta_{e}\) (solid curves) and saturation equivalent potential temperature \(\theta_{es}\) (dashed curves) at the Nauru island ARM site for the ARM Best-Estimate observations (ARMBE) and two examples of CMIP6 climate models (denoted NASA-GISS and MPI-ESM1-2-LR). The vertical axis is in units of pressure (1 hPa = 100 Pa). The profiles are for conditions 3 h prior to precipitation exceeding 0.5 mm h\({}^{-1}\), as a measure of typical profiles associated with onset of deep convection. For the observed curves, the orange shaded area gives a measure of conditional instability for a non-entraining parcel, and the gray area indicates the subsaturation (\(\theta_{es}-\theta_{e}\)) in the lower troposphere (750–900 hPa) that reduces conditional instability when entrained into the convective plume. (**b**) Precipitation conditionally averaged on this subsaturation (\(\theta_{es}-\theta_{e}\)) in the lower troposphere (750–900 hPa layer) for ARMBE observations and several CMIP6 models.  
temperature ranges can be clearly distinguished. To carry out this analysis requires a large number of joint observations of the precipitation rate \(P\), conditioned on the (\(T,p,q\)) profile. For this analysis, existing RO soundings from the abundant Formosat-3 COSMIC period (2006-2020) ([PERSON] et al., 2018) are combined with the nearest 30-min precipitation estimate from IMERG. This information will then be with considerations from a constellation simulation in Section 4. The global tropical (\(\pm\)20-degree) latitudes are the focus of this investigation, where precipitation is predominantly associated with deep convective systems driven by conditional instability. The relationship between buoyancy and precipitation applies to convective precipitation: If environmental air in the lower free troposphere is sufficiently moist, a convective air parcel is more likely to survive entrainment of environmental air in its ascent ([PERSON] et al., 2018) yielding strong updrafts and precipitation. The focus on the tropics largely excludes precipitation from extratropical cyclones that obey different dynamics associated with forced uplift in frontal systems--for which evaluation of factors affecting buoyancy from adjacent RO constellation vertical profiles (discussed in Section 5 below) is less relevant. Distinguishing extratropical conditions associated with deep convection is possible in principle, but is more complex and beyond the scope of this paper.
# An Optimized Graphene-Based Surface Plasmon Resonance Biosensor for Detecting SARS-CoV-2
## 3 Results and Discussions
### Comparison with Previous Reports
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Sensor Architecture** & **Sensitivity (\({}^{\circ}\)/RIU)** & **Stability** & **Linearity (Concentration Range, mM)** & **Responsivity (\({}^{\circ}\)/mM)** & **Detection Limit (mM)** \\ \hline \multirow{2}{*}{GMC Santos et al., 2021 [33]} & Gold-antibody-based sensor for HAV & & & & \\  & & & & & \\ \hline \multirow{2}{*}{[PERSON] et al., 2021 [34]} & Nano-layered & & & & \\  & & & & & \\ \multirow{2}{*}{[PERSON] et al., 2021 [34]} & SPR for & 298.00 & Moderate & 200–500 & 4.0\({}^{\circ}\)@450 & 200 \\  & SARS-CoV-2 & & & & & \\ \hline \multirow{2}{*}{[PERSON] et al., 2014 [35]} & Gold-graphene & & & & \\  & SPR for porcine & 280.00 & Moderate- & 50–300 & 3.6\({}^{\circ}\)@300 & 50 \\  & cicrovirus & & & & \\ \hline \multirow{2}{*}{[PERSON] et al., 2021 [36]} & Ag-based & & & & \\  & multilayer & & & & \\ \multirow{2}{*}{2021 [36]} & sensor for & 320.50 & Moderate & 150–400 & 4.1\({}^{\circ}\)@400 & 150 \\  & SARS-CoV-2 & & & & \\ \hline \multirow{2}{*}{This work} & Graphene- & & & & & \\  & based & & & & \\ \multirow{2}{*}{2021 [36]} & multilayer & 315.91 & High & 150–400 & 4.2\({}^{\circ}\)@400 & 150 \\  & sensor for & & & & \\ \multirow{2}{*}{2021 [36]} & SARS-CoV-2 & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of SPR biosensors for viral detection, showing sensitivity, stability, linearity, responsivity, and detection limits. The optimized \(\text{Sys}_{8}\) configuration is highlighted alongside similar systems.
## 4 Redundant Transmission Control Clustering Approach  
To explain RTC clustering approach, we divided its function into time steps (rounds). Then each of the round is further divided into four steps such as; (1) initialization phase, (2) cluster head selection phase, (3) data collection at cluster heads and (4) network data collection. The detail information about RTC approach is explained in the next subsections.
# Planning of High-Power Charging Stations for Electric Vehicles: A Review
## 2 Target Identification
### Modes of Transport
#### 2.2.2 Buses  
The second type of vehicle that research focuses on is buses. Electrification of this mode of transport may be of interest to public entities or companies running public transportation services, as it can reduce the vehicle's operational costs and environmental footprint. Compared to light-duty vehicles (LDVs), battery electric buses (BEBs) violations of schedules and/or paths are generally more costly [70] and usually characterized by pre-specified duration constraints [41]. In addition, given the fact that buses are required to operate up to 16 h per day, compared to just 1 h for LDVs [71], en-route fast-charging stations are a necessity, as are higher power capabilities [42]. As described in [72], several charging strategies can be implemented specifically for buses: conductive charging through pantograph charging or overnight depot charging are some of the most innovative and favorable options for buses operating in urban areas. Of the literature reviewed concerning AS, only one study includes both private and public transport [25]. In this case, the authors investigate the use of centralised charging coupled with battery swapping to supply both private electric vehicles (EVs) and buses, analyzing the statistical data of moving daily distances. In all other cases, the problem is examined solely for EVs, probably due to the above-noted differences that exist. Several articles investigate the conversion of bus fleets from using conventional fuels to electricity in urban contexts. In their case study analyzing the transit network of Davis, authors in [43] conclude that it is not only more environmentally friendly to utilize electric buses, but also economically more beneficial compared to ICEV. In [42], authors find that of 143 bus routes, 42 can be electrified in a cost-optimal way for the city of Stockholm. The resolution process uses ArcGIS to analyze the length of each route and produce an optimal set of FCSs for en-route charging. Furthermore, in [39], authors aim to minimize the cost of replacing a certain number of conventional buses and also consider in-depot charging strategies. In this case, terminals are potential locations for en-route CSs and garages are potential locations for in-depot CSs. The authors of [44] also account for simultaneous optimization of bus battery size and allocation of two different FCS technologies along routes to bus stations. Finally, the authors of [69] state that scheduling and location planning for public transport should be further coupled as integrated problems, given their high inter-dependency.
# Study on the Position Deviation between the Iron Roughneck's Spin-Rollers and the Drilling Tool
## 2 Materials and Methods
### Analysis of Position Deviation between the Spin-Roller and the Drilling Tool  
#### 2.2.1 Analysis of Contact Mode between Spin-Rollers and Drilling Tool  
It is evident from Figure 1 that when the wedge plate drives the clamping arm and the follower roller inward, two situations may result: one is that the driving roller contacts the drilling tool first, and the follow-up roller contacts the drilling tool later; second, the follow-up roller contacts the drilling tool first, and the driving roller contacts the drilling tool later. Due to the larger aspect ratio of the drill-pipe stand used in drilling, it is easy to cause a certain curvature of the drill pipe [23]. To realize the full contact between the spin-rollers and the drill pipe, a small range of swing freedom is often reserved in the spinner mechanism to adapt to the position deviation of the drill pipe.  
The deviation of the drill pipe may occur in \(x\) direction (lateral direction), \(y\) direction (longitudinal direction), or both. For the deviation in \(y\) direction, since it is consistent with the direction of the force of the floating mechanism, it can be reduced or eliminated under the action of the clamping process of the spin-rollers or the dynamic load during spin; in addition, the deviation in the \(y\) direction can be reduced by adjusting the initial position of the cross beam. Therefore, for the spin mechanism with good floating performance, the conventional deviation in \(y\) direction will not have a negative impact on the working performance of the spinner mechanism. Whereas the deviation in the \(x\) direction will make the spinner mechanism have an eccentric load and affect the floating sensitivity of the floating mechanism. Therefore, the deviation in \(x\) direction has an obvious impact on the spin performance. The deviation of the drill pipe in \(x\) direction is mainly analyzed in this paper.  
When the driving roller makes contact with the drill pipe first, due to the symmetrical arrangement of the two spin-rollers, the position of the drill pipe can be limited, and the spinner mechanism can maintain a good centering and holding capacity. When the follow-up roller makes contact with the drill pipe first, due to the axis deviation of the drill pipe, the impact force, and the approximate line of contact between the roller and drill pipe, it easily causes the drill pipe axis to offset to one side relative to the long body. The deviation in this case is analyzed below.
## 1 Introduction
They also introduced additional factors to the retrieval process for a more realistic simulation of ET\({}_{0}\) or AET, such as soil structure [29], terrain effect [9], stomatal conductance [10; 13], diurnal temperature difference [24; 30], and vegetation greenness [12; 24]. Additionally, some previous studies estimated ET\({}_{0}\) or AET using satellite-derived vegetation index and net radiation data with a statistical model like linear regression [30; 31]. Recently, artificial intelligence (AI) approaches using the techniques like neural network (NN) or machine learning (ML) have tried to take account of the nonlinearity between ET and the variables for meteorological and land surface conditions, showing a possibility for the retrieval of the gridded ET\({}_{0}\) or AET [32; 33; 34; 35]. Because the PM and PT equations and the AI techniques are already in a stable status, the construction of a reasonabledatabase for the meteorological and land surface conditions is one of the critical points for the effective retrieval of gridded ET\({}_{0}\) and AET. Currently, the operative gridded ET products are available from the Climatic Research Unit (CRU), TerraClimate, and Moderate Resolution Imaging Spectroradiometer (MODIS). The ET\({}_{0}\) products by the CRU and the TerraClimate are the monthly reanalysis data suitable for long-term climate researches. So, they cannot support examining daily or weekly changes for the applications in agriculture, hydrology, and meteorology. On the other hand, the MODIS product (MOD16A2 GF) has a reasonable spatial resolution of 500 m and a temporal resolution of 8 days. However, the applicability to the local-scale variabilities due to complex and heterogeneous land surfaces in countries like South Korea is not sufficiently validated. Meanwhile, the AI approaches have rarely demonstrated a substantial product for the daily gridded ET\({}_{0}\) on the local scale, although they showed a useful functionality for the ET\({}_{0}\) retrieval on the local scale. In this paper, we present ML-based modeling of daily local-scale ET\({}_{0}\) that can be used for estimating the crop AET, with the consideration of the complexity and nonlinearity between ET\({}_{0}\) and input variables, and thereby produce a daily 500 m ET\({}_{0}\) in the croplands of South Korea. Vegetation data from MODIS, precipitation data from Global Precipitation Measurement (GPM), and meteorological data from Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) were used as input features for the ML-based daily ET\({}_{0}\) model. The in situ point-based ET\({}_{0}\) data taken by the Korea Meteorological Administration (KMA) were used for training and blind testing of our model for the period between March and November 2013-2019. We also present the accuracy statistics followed by the characteristics of meteorology, hydrology, and vegetation in the croplands of South Korea in recent years using the daily ET\({}_{0}\) retrieved over the 500 m grid.
## 3 Materials and Methods
### Proposed Architectures
#### 3.1.3 Basic Architecture with Time Attention (Approach 1)  
Based on the baseline architecture in Section 3.1.2, an attention layer was included after the \(BiRL\) to provide the contribution of each time period \(t\) to the outcome through an attention weights vector, \(\mathbf{\alpha}_{T}\in\mathbb{R}^{T}\). We compared four kinds of attention layers, as formulated in Equations (1) [19], (2) [39], (3), and (4).  
\[\mathbf{\alpha}_{T}=sf(\mathbf{w}_{1}^{\top}\mathbf{H}_{T}+\mathbf{b}_{1});\quad\mathbf{c}_{T} =\mathbf{H}_{T}\mathbf{\alpha}_{T} \tag{1}\]  
\[\mathbf{\alpha}_{T}=sf(\mathbf{w}_{1}^{\top}\tanh(\mathbf{H}_{T}));\quad\mathbf{c}_{T}= \tanh(\mathbf{H}_{T}\mathbf{\alpha}_{T}) \tag{2}\]  
\[\mathbf{\alpha}_{T}=sf(\mathbf{w}_{1}^{\top}\mathbf{H}_{T}+\mathbf{b}_{1});\quad\mathbf{c}_{T} =\tanh(\mathbf{H}_{T}\mathbf{\alpha}_{T}) \tag{3}\]  
\[\mathbf{\alpha}_{T}=sf(\mathbf{w}_{1}^{\top}\tanh(\mathbf{H}_{T}));\quad\mathbf{c}_{T}= \mathbf{H}_{T}\mathbf{\alpha}_{T} \tag{4}\]  
Figure 1: General scheme of the architectures that were evaluated. Input data processing and output generation (top and bottom boxes) were common to all the tested architectures. The purple box represents any of the four approaches shown in the bottom boxes. The shadowed boxes identify which parts in each approach compared different options. Coloured arrows indicate the input (blue) and output (red). Notation can be found in the Supplementary Materials.  
\(sf\) denotes the well-known softmax normalization function, and \(\tanh\) is the hyperbolic tangent. As detailed in Supplementary Materials, in Equations (1) to (4), matrix \(\mathbf{H}_{T}\in\mathbb{R}^{2 Ul\times T}\), where \(U\) is the number of neurons of the \(BiRL\), contains the BiRL output sequence (forward and backward), and vector \(\mathbf{c}_{T}\in\mathbb{R}^{2U}\) is the so-called context vector needed for output prediction. Finally, vectors \(\mathbf{w}_{1}\in\mathbb{R}^{2U}\) and \(\mathbf{b}_{1}\in\mathbb{R}^{T}\) are learned during the training process.
# Dynamic Routings in Satellite Networks: An Overview
## 3 System Model and Characteristics  
Satellite networks are an essential part of the space-air-ground integrated network. They not only provide coverage for remote and off-land regions, but they also provide emergency communications. It is crucial for seamless global transmissions, the global Internet of Things, and emergency communications.
## 2 Data and Methodology
### Verification of Simulation Results  
#### 2.3.1 Evaluation Statistics  
To verify the accuracy of the simulation results, the Pearson correlation coefficient (R) is used to measure the linear correlation degree between the simulation sequence and the observation sequence. The closer R is to 1, the higher the correlation degree. The mean bias error (MBE) is used to verify the deviation between the simulation sequence and the observation sequence. The closer the result is to 0, the smaller the deviation. The root mean square error (RMSE) is used to verify the average deviation between the simulation sequence and the observation sequence. The closer the result is to 0, the better the simulation effect. The relevant calculation formulas are as follows:  
\[\text{R}=\frac{\sum\limits_{i=1}^{n}{(X_{i}-\overline{X}_{i})(X_{0}-\overline{ X}_{0})}}{\sqrt{\sum\limits_{i=1}^{n}{(X_{i}-\overline{X}_{i})}^{2}\sum \limits_{i=1}^{n}{(X_{0}-\overline{X}_{0})}^{2}}}, \tag{11}\]  
Figure 4: Vertical stratification of region \(1\) (**left**) and region \(2\) (**right**).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **Case1** & **Case2** & **Case3** & **Case4** \\ \hline mp\_physics & Thompson & Purdue-Lin & Purdue-Lin & Thompson \\ bl\_pbl\_physics & YSU & MRF & YSU & MRF \\ ra\_sw\_physics & RRTM & RRTM & RRTM & RRTM \\ ra\_la\_physics & Dudhia & Dudhia & Dudhia & Dudhia \\ sf\_surface\_physics & Noah & Noah & Noah & Noah \\ sf\_clay\_physics & MM5 & MM5 & MM5 & MM5 \\ cu\_physics & / & / & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 3: Schemes of physical parameters for WRF simulation.  
\[\text{MBE}= \frac{1}{n}\sum_{i=1}^{n}\left(X_{i}-X_{0}\right), \tag{12}\] \[\text{RMSE}= \sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(X_{i}-X_{0}\right)^{2}}, \tag{13}\]  
where \(X_{i}\) and \(X_{0}\) are the simulated and observed value, \(\overline{X}_{i}\) and \(\overline{X}_{0}\) are the simulated and observed average values, and \(n\) is the number of records.
## 4 Discussion
Its robust characteristic extraction capability and learning ability are not acquired by conventional machine learning algorithms. Moreover, the problem of class imbalance in classification typically causes the learning algorithm to be dominated by the majority classes, and the minority class features are sometimes ignored, resulting in misclassification bias. As a result, careful consideration must be given to the learning algorithm to improve minority class accuracy, as well as resort to hybrid techniques based on misclassification analysis. Another challenge encountered during the application of the DL optimizers for example is that the SGD is consistently slow to converge as it requires a forward and backward propagation for each record. Then, the path to attain the global minima gets very noisy. Moreover, it is mandatory to tune the learning rate manually, by running several experiments since the suggested value is often not appropriate for every task. This way, the Adagrad optimizer surmounts the drawbacks of SGD as there is no necessity to manually tweak the learning rate; however, there is a tremendous downside, due to the monotonic decline of the learning rates, at some point in the time step, the model will cease learning because the learning rate is approaching 0. A major drawback of using the sigmoid is the vanishing gradient problem. For a very high or low value of x sample, the sigmoid derivative is very small. This may lead to a failure of the network in learning more information, as well as it is computationally demanding since it contains an exponential term. In light of these stated limitations, we briefly recap all the restrictions and benefits of deep learning applied during model training in Table 10.  
Other guidelines might also be considered to address these shortcomings as we mentioned in our prior work [68], such as the use of a new dataset, which contains many labeled images of leaves captured in a real environment; or as an alternative to create synthesized and generated images by hybrid data augmentation techniques based on the Generative Adversarial Network (GAN) architecture [69] to enhance crop leaf disease detection in real-world images. Finally, the comparison results reveal that there is no single tailored technique to meet all research challenges. Therefore, the appropriate method is selected based upon the applications and the dataset size.
# Monitoring Technologies for Truck Drivers: A Systematic Review of Safety and Driving Behavior
## 3 Results
### Monitored Variables
Connectivity and communication systems emphasize monitoring behavioral variables such as driver compliance with connected vehicle warnings, responses to advisory speed limits, and interactions with cooperative systems. They also track environmental variables, including real-time weather updates, road closures, and traffic conditions. By integrating real-world data with simulation-based training, these systems ensure adaptability across diverse operational environments, delivering dynamic and context-sensitive safety recommendations. By systematically integrating physiological, behavioral, and environmental metrics, these monitoring technologies provide a holistic approach to improving road safety and driver performance. Each technology offers specialized capabilities, contributing valuable data for targeted interventions. Together, they form a robust framework for enhancing the safety, efficiency, and sustainability of modern transportation systems.
# A Novel Mosaic Method for Spaceborne ScanSAR Images Based on Homography Matrix Compensation
## 3 Traditional Feature-Based Mosaic Process for SAR Images
### Image Preprocessing  
Before mosaicking, to suppress speckle noise, SAR images were filtered to enhance edges and denoise. In regard to ScanSAR, the intrinsic periodic scalloping in the azimuth and the effect of the roll angle error on the range, which are caused by the multiple-beam  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ScanSAR Mode** & **Incidence Angle (\({}^{\circ}\))** & **Nominal Resolution (m)** & **Nominal Swath (km)** & **Polarization** & **Adjacent Beams** \\ \hline NS & 17–50 & 50 & 300 & Dual & 3 \\ WS & 17–50 & 100 & 500 & Dual & 5 \\ G & 17–53 & 500 & 650 & Dual & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: ScanSAR modes of GF-3 satellite.  
Figure 1: Spaceborne ScanSAR working mechanism of GF-3.  
Figure 2: The traditional feature-based image mosaic process.  
scanning strategy in swath, should also be corrected [29; 30; 31]. In addition, the radiation intensity of adjacent images has to be balanced to avoid obvious differences after stitching.