esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Data
### Crop Type Maps  
Crop type maps are primarily used as the label data for the deep learning model for model training and accuracy verification. In this study, three crop-type maps were used according to the situation in different regions. For the Mississippi Aluvial Plain (Region A) and Sacramento Valley (Region B), crop data layer (CDL) data, produced by the National Agricultural Statistics Service (NASS), United States Department of Agriculture (USDA), were employed [37]. The year of the CDL data used in this study was 2019 with a resolution of 30 m.  
For the Songnen (Region D), Liaohe (Region E), and Sanjiang plains (Region F), the 10 m crop type map of year 2017 in Northeast China produced and released by [PERSON] et al. [12] was used. The user accuracy and producer accuracy of the rice mapping product reached 0.87 and 0.92, respectively [38].  
For the Shuangcheng area (Region C), Sentinel-1 and Sentinel-2 satellite images were used as remote sensing image data sources, high-resolution satellite images and ground surveys were used to obtain sample points; the random forest method was used for crop classification. Crop maps of the Shuangcheng area from 2017 to 2020, with a resolution of 10 m, were produced and used in this study. The relevant classification method can be found in [2], which will not be repeated here.
## 4 Application Example  
The proposed motion-based design method under uncertainty conditions was validated herein via the design of three passive damping devices when they are used to control the wind-induced vibrations of the longest cable of a real bridge. For this purpose, the Alamillo bridge (Seville, Spain) was considered (Figure 4). The length of the deck of this bridge is 200 m. Unlike most cable-stayed bridges, the Alamillo bridge has not back-stays. An inclination of its pylon of 32\({}^{\circ}\) with respect to the vertical axis compensates the lack of the back-stays [55]. A total of 26 stays (13 parallel pairs) with a longitudinal separation of 12 m guarantees an adequate connection between the deck and the pylon.  
Figure 4: Illustrative scheme of the Alamillo bridge.  
Figure 3: Reference coordinate system, drag force component, lift force component, and wind velocity components.  
Previous research reported that the longest stay cable of this bridge, which has both a low damping and mass ratio, was prone to vibrate due to the wind action. Concretely, it was detected that the main sources of vibration of this cable were the rain-wind interaction phenomenon and the turbulent component of the wind action [56]. Therefore, this stay cable was considered as a benchmark to validate the performance of the proposed design method. For this purpose, three different passive damping devices (viscous, elastomeric and friction dampers) were designed according to the proposed method, and the results obtained were compared with the ones provided by a conventional method adopted by the Standards [6]. Additionally, the uncertainty associated with the variation of the modal properties of the cable due to the modifications of the operational and environmental conditions was taken into account in this design process. The development of this case-study was organized in the following steps: (i) a FE model of the cable was built and its numerical modal properties were obtained via a numerical modal analysis; (ii) a transient analysis was performed to evaluate the vibration serviceability limit state of the structure; (iii) as this limit state was not met, the three passive damping devices were designed according to both methods (the new proposal and the conventional one); and (iv) finally, the results obtained were compared and some conclusions were drawn to close the section.
## 4 Numerical Simulation Results
### Strength Parameters
#### 4.3.2 Relationship Between Cohesion and RFCA  
Figure 10 reveals that there is a positive linear relationship between \(c\) and \(H\). As \(H\) increases from 0.1 to 0.7, \(c\) also increases from 0.359 MPa to 0.530 MPa, representing a 32.5% improvement. This highlights the significance of RFCA in affecting the cohesion of the numerical specimen. The cohesion of the similar material is influenced by the cementation process of the interstitial cementing particles. As \(H\) increases, the fine aggregate particles begin to fill the spaces among the skeleton of the coarse aggregate particles, resulting in a denser model with a higher degree of particle involvement in the cementation process. This triggers a gradual increase in \(c\) of the similar material as \(H\) increases.
# A Survey on Traffic Prediction Techniques Using Artificial Intelligence for Communication Networks
## 9 Linear Time Series  
The use of linear predictive models is successfully demonstrated in several papers for short-term traffic prediction. These include Auto-Regressive (AR), Moving Average (MA), Autoregressive Moving Average (ARMA), and Autoregressive Integrated Moving Average (ARIMA). Autoregressive models are created on the idea that we can try to predict future values based on past values of this same variable. Alternatively, it could be said that the current value can be described as a linear function of past values [55]. The 'auto' portion of autoregressive indicates that we are examining the relationship of the variable against itself. For Moving Average models, past forecast errors are used for regression rather than past values. ARIMA combines these two models but includes an integrated component where differences between periods must be modeled, rather than data from the period itself [56].
# Driver Fatigue Detection Based on Residual Channel Attention Network and Head Pose Estimation
## 5 Conclusions  
This study proposes a driver fatigue detection system based on the RCAN and head pose estimation. First, we use Retinaface to locate the face and eye/mouth regions. Then, the RCAN is used to classify the states of eyes and the mouth. Experiments show the superiority of the RCAN in classifying eye and mouth states. Meanwhile, we use EPnP to estimate the head pose based on five landmarks output by Retinaface. Then we compare the Euler angle output by EPnP with the over-angle of the head pose to judge whether it is in the state of over-deflection of the head. We use PERCLOS and POM to judge whether the driver is in a state of drowsiness. The experimental results show that if PERCLOS is greater than 0.32 or POM is greater than 0.37, the driver has already entered the state of fatigue driving and should stop driving in time, and if the yaw angle is greater than 20\({}^{\circ}\), the pitch angle is greater than 21\({}^{\circ}\), and the roll angle is greater than 20.5\({}^{\circ}\), the driver's head posture has deviated excessively and a safety warning shall be given to the driver. This driver fatigue detection system has high detection accuracy and robustness.  
There are still some limitations of this framework. The position of the camera affects head pose estimation. Ideally, the camera is facing the driver. We have envisaged a solution, such as using the Euler angle gradient, to judge whether the driver's head pose has changed suddenly. However, the discrimination conditions based on the gradient are too complex to design, Therefore, the future improvement of the framework may involve calibrating the initial pose of the camera based on other references in the vehicle, so as to optimize the accuracy of head pose estimation.  
This framework has been tested for fatigue detection in a real driving environment. There are three future directions: 1. Continue to optimize the head pose estimation module. 2. Further increase the test data in the real driving environment. 3. Study how this framework applies to drivers with conversational or acquired disabilities.  
Conceptualization, M.Y. and W.Z.; methodology, M.Y.; software, M.Y.; validation, M.Y. and P.C.; formal analysis, M.Y.; investigation, M.Y.; resources, M.Y.; data curation, M.Y.; writing--original draft preparation, M.Y.; writing--review and editing, M.Y.; project administration, K.L.; funding acquisition, M.Y. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the National Natural Science Foundation of China (No. 51805312) and in part by the Shanghai Sailing Program (No.18 YF1409400). The authors would like to express their appreciation to the developers of Pytorch and OpenCV and the authors of EPnP. The authors declare that they have no conflicts of interest.  
Figure 10: Threshold of PERCLOS and POM.
# Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data
## 4 Results
### Detection of Frozen/Unfrozen Plots
#### 4.2.1 Freezing Detection Results over Site A
Indeed, the algorithm detected minimal anomalies during the period between November and February, and most of the detected anomalies appear after rainfall events. However, also due to the lower threshold used for VV, our algorithm detected a higher percentage of plots with anomalies in comparison to VH.
# The Role of Extracellular Matrix (ECM) Adhesion Motifs in Functionalised Hydrogels
### Hydrogels as Sophisticated, Biomimetic Materials
#### 1.2.1 Polymer-Based Hydrogels  
A common example of a synthetic hydrogel-forming polymer is Poly(lactic-co-glycolic acid) (PLGA), which consists of glycolic acid and lactic acid monomers connected by ester bonds [15]. This FDA approved polymer is advantageous for tissue engineering applications due to its biocompatibility and controllable degradation rate [15]. In 2006, PLGA scaffolds coupled with adipose-derived stem cells were investigated for the generation of muscle tissue [16]. PLGA has also been shown to promote cell viability and differentiation, demonstrating its suitability as a cell culture scaffold [17].  
Poly(ethylene glycol) (PEG) is another polymer commonly used for hydrogel formation, with a unique ability to resist protein absorption [15]. PEG scaffolds are normally polymerised using chemical or photoinitiators. These scaffolds have been noted for their suitability as candidates for stem-cell-based tissue engineering in tissues such as liver, heart and nerve. Extensive research has also investigated the use of PEG scaffolds seeded with stem cells to generate bone and cartilage [18,19].
applied sciences  
A Statistical Assessment of Water Availability for Hydropower Generation in the Context of Adequacy Analyses [PERSON] [PERSON], [PERSON]; [PERSON], [PERSON] A Statistical Assessment of Water Availability for Hydropower Generation in the Context of Adequacy Analyses [PERSON], [PERSON]; [PERSON], [PERSON] A Statistical Assessment of Water Availability for Hydropower Generation in the Context of Adequacy Analyses A.  
Although the installed capacity of hydropower had only a slight increase in the decade 2009-2019 [8], wind and hydropower made up two-thirds of the total electricity generated from renewable energy sources (RES) in 2019 [7]; the remaining one-third was covered by PV (13%), solid biofuels (8%) and other RES (9%). Although the 13% (125.7 TWh) share of generation by PV as of 2019 may seem small, it must be considered that this generation technology started from a 1% (7.4 TWh) share in 2008; this sharp percentage increase over a decade makes PV technology the one with the fastest growth rate among all renewables in EU.  
The data cited above testify to a trend in the penetration of renewables, in the generation mixes of EU countries' electric power systems (EPSs), that is set to increase further to meet the environmental targets of net zero GHGs emissions by 2050. Indeed, in 2019 the EU released the European Green Deal [9], an agreement among the member countries to achieve sustainable development goals, such as GHGs emission reductions of 55% below 1990 levels by 2030, and climate neutrality by 2050. To achieve these challenging goals, the EU plans to focus heavily on PV and wind power installations. It is worth mentioning a recent energy plan, also released by the EU, namely the REpowerEU Plan [10], which aims to increase the installation rates of wind and PV power plants already envisioned by [11]. Energy savings, heat pumps, renewable hydrogen, biomethane, and electrification of industrial sectors currently fossil-fueled, are the other linchpins of the European strategy.  
An ever-increasing amount of renewable generation sources in the power systems of EU countries (and beyond) poses risks to system adequacy, and therefore the flexibility of other generation sources already present in the EPSs of the respective countries, along with storage, is crucial to the proper functioning of present and future power systems, as is illustrated in the following subsections.
## 3 Antenna Design
### Performance Analysis with State-of-the-Art of Printed THz Antennas  
In this part, comparisons between proposed optimized Ant. C design with previously reported antennas has been performed for the terahertz frequency band. The comparison is conducted based on performance parameters such as gain, size of the antenna elements, and resonance frequency. Moreover, the MIMO parameters such as ECC, DG, and CCL are tabulated in Table 2. Based on the presented data, we can conclude that the proposed graphene plasmonic MIMO antenna has the best isolation and ECC. Although the proposed design presents a high gain with all compared references, the design in [40] has a higher gain, but gives low isolation and CCL. Finally, the proposed antenna demonstrates a superior MIMO performance to that of the other listed designs in Table 2. So, it is a good candidate to be used in 6G for mobile generation and medical applications. It has a suitable compact size to easily integrate with on-chip integrated circuits.
## 2 Preliminary Design of SOEs
### KOD-Type SOE  
The design concept of KOD-type SOE comprises law of refraction, law of reflection, marginal ray, equal-optical path design and imaging optics [1,2,8,9,15]. Figure 1 illustrates the KOD-type SOE, comprising an ellipsoidal-top and a middle cone part. Total internal reflection (TIR) occurs at the internal surface of the bottom section, which is placed in front of the solar cell. The initial designed incidence angle \(I_{i}\) of the KOD-type SOE was 1.2\({}^{\circ}\).  
\begin{table}
\begin{tabular}{c c} \hline
**Size of Fresnel Lens** & **40 mm \(\times\) 40 mm** \\ \hline Thickness of Fresnel lens & 2 mm \\ Material & Silicone-on-Glass (BK7) \\ Light source & divergence angle of 0.265 degrees \\ Radian flux & 1.36 W \\ Wavelength & 550 nm \\ Index of Refraction of BK7 & 1.518 \\ Index of Refraction of Urethane & 1.510 \\ Size of solar cell & 1.25 mm \(\times\) 1.25 mm \\ Geometrical concentration ratio & 1024 \\ Focal length & 130.1 mm \\ Prism pitch of Fresnel lens & 0.2 mm \\ \hline \end{tabular}
\end{table}
Table 1: Design parameters of the concentrated photovoltaic (CPV) unit with a flat Fresnel lens.  
Figure 1: Illustration of KOD-type SOE **(a)** design parameters; **(b)** solid model.  
Figure 2 shows the optical efficiency of the flat Fresnel lens with the KOD-type SOE and the effect of incidence angle on the optical efficiency. According to the simulation results shown in Figure 2, the optical performance can be determined as follows: the optical efficiency under normal incidence is 84.6%, the acceptance angle is 1.2\({}^{\circ}\) with an optical efficiency of 76.2%. Moreover, the optical efficiency drops to only 5% at an incidence angle of 2\({}^{\circ}\). When the incidence angle is smaller than 1.2\({}^{\circ}\), most of the incident rays refracted from the POE will be refracted at the top elliptical surface of the KOD-type SOE and then be reflected (due to TIR) at the bottom surface to the solar cell. Figure 3a demonstrates the ray-tracing results when the incidence angle is 0.4\({}^{\circ}\), where only a few rays will not enter the solar cell. On the other hand, when the incidence deviation is larger than 1.2\({}^{\circ}\), more light rays will not have second reflection after TIR at the bottom section of the SOE, and those rays will not enter the solar cell, as shown in Figure 3b. Consequently, the optical efficiency drops largely after the incidence angle is larger than 1.2\({}^{\circ}\).  
Figure 4 shows the irradiance distribution on the solar cell under normal incidence. The irradiance distribution is uniform by using the KOD-type SOE, and the peak value of irradiance shown in Figure 4 is 0.9291 W/mm\({}^{2}\) over an average irradiance of 0.7372 W/mm\({}^{2}\).  
Figure 3: Ray tracing inside the KOD-type SOE (**a**) incidence angle = 0.4\({}^{\circ}\); (**b**) incidence angle of 1.2\({}^{\circ}\).  
Figure 2: Optical efficiency of the Fresnel lens with KOD-type SOE.
## 2 Materials and Methods
### Overview of vePOD  
vePOD aims to (1) protect a vehicle's sensor data from being exposed to or used by edge servers, and (2) protect the object-detection results from being obtained by edge servers. To achieve the first goal, a vehicle creates \(N\) randomized secret shares \(\left\{R_{i}\right\}\) from an image (or a feature map) \(M\) following an additive relation, that is, \(M=\sum R_{i}\). Each edge server processes one secret share, which contains random data. To protect against possible attacks in the public network between a vehicle and edge servers, a secret share \(R_{i}\) is encrypted using an encryption key \(K\) retrieved from a trusted server \(T\), before being sent to an edge server. For the second goal, an edge server runs the vePOD CNN on a secret share \(R_{i}\) and produces only partial results. We assume there is no conspiracy attack, that is, the \(N\) edge servers do not exchange secret shares or partial results.  
Each edge server executes an object-detection CNN on the received secret share. Figure 1 plots the workflow. The key challenge is _how to design an object-detection CNN that can process secret shares containing random data and still generate correct (partial) detection results_. To tackle this challenge, we examine every layer in an object-detection CNN (e.g., convolutional, activation, pooling, fully-connected layers, and others), and design secure functions for each layer to handle secret shares. The design details of vePOD will be described in the next section.  
To illustrate where and how vePOD can be applied, we present an application scenario. Please note that this is one of many scenarios where vePOD can be used to protect a vehicle's data privacy. In this scenario, two or more vehicles exchange sensor data to expand their perception range and enhance perception accuracy. In cooperative perception, a vehicle needs to process not only its only sensor data, but data from other vehicles as well. To avoid overloading a vehicle, the sensor data from another vehicle can be offloaded to nearby edge servers, in which vePOD can be applied to protect data privacy while processing the vehicle's data on the edge. To apply vePOD, a vehicle partitions its sensor data to create secret shares, which are offloaded to edge servers. Each edge server runs a secret share through the secure layers in a vePOD CNN. The outputs from participating edge servers are combined on the destination vehicle to obtain the object-detection results. In this way, the privacy of the vehicle's sensor data is protected in both network transportation and edge computation.
# Modeling DGNSS Pseudo-Range Correction Messages by Utilizing Satellite Repeat Time
## 3 GNSS Orbit Repeat Period
### Gps  
A GPS satellite has an orbital period of 11 h 58 min 2 s, and thus, orbits Earth twice a day. The length of a sidereal day is 23 h 56 min 4 s, which is shorter than a solar day by 3 min 56 s. Due to this time difference, satellites should be observed in the same direction at a slightly earlier time on the next day. Even though GPS constellations were designed with a regular orbit period, an exact repeat period of each satellite is slightly different due to a number of factors. In order to calculate a repeat time for each satellite, a time lag with which the cross-correlation becomes the highest should be determined in the X, Y, and Z directions using three-day SP3 files prior to the desired date, and the results are averaged. The reason for selecting a three-day dataset is to minimize errors by securing as much data as possible and to shorten processing at the same time.  
A repeat time for GPS satellites calculated using cross-correlation was within the range of 220-248 s. These values differed slightly, according to the date and satellite. On average, GPS repeat time was found to be around 240 s. This value is similar to those published by [PERSON] et al. [24] and [PERSON] [21], who used repeat time in analyzing and removing multipath error.
## 2 Materials and Methods
### Extraction of VOCs and GC-MS Analysis  
Random pieces of citrus fruit, either non-infested or infested, were placed individually into a 500 mL glass jar and sealed to equilibrate gases for 8, 16 and 20 h for mandarin, orange and lemon, respectively. The differences in the equilibration time for extracting VOCs for each fruit could be attributed to species characteristics and many other factors such as growing area, weather conditions and non-homogeneity of the fruit ripening stage. After this, VOCs were extracted by inserting and exposing a solid-phase microextraction (SPME) fibre with 50/30 \(\upmu\)m Carboxen/DVB/PDMS (2 cm) (Sigma-Aldrich, Bellefonte, PA, USA) to the headspace of the glass jar over the fruit samples at room temperature at \(25\pm 2\)\({}^{\circ}\)C and 45-55% RH for 2 h. The fibre was then withdrawn into the needle and transferred to the injection port of the GC-MS system as described by [22]. The desorption time of the SPME fibre at GC-MS injector was 10 min [16]. Six replicates were conducted for each treatment. The VOCs were analysed by GC-MS using an Agilent 7820A GC (Mulgrave, Victoria, Australia), equipped with a DB-35 ms (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) fused-silica capillary column (Agilent Technologies, Santa Clara, CA, USA), with a mass spectrometer detector 5977E (Agilent Technologies, USA) under splitless mode. The carrier gas was 99.99% helium supplied by BOC (Sydney, Australia). The operation conditions of GC-MS were 250 \({}^{\circ}\)C injector temperature; the oven temperature was initially programmed at 60 \({}^{\circ}\)C and increased to 270 \({}^{\circ}\)C (by 5 \({}^{\circ}\)C/min); the column flow rate and splitless were 20 mL/min at 1.5 min and 1:1; the total GC-MS run time was 45.4 min.  
Compound peaks were deconvoluted by AMDIS version 2.72 and identified by searching the NIST 2014 MS database (US National Institute of Standards and Technology, Gaithersburg, MD, USA) with retention index confirmation. The experiment was repeated twice.