esa-sceva/satcom-chunk-collection huggingface dataset:
# Deploying CPU-Intensive Applications on MEC in NFV Systems: The Immersive Video Use Case
## 5 Experimental Results  
The ME system proposed in this paper has been implemented in a prototypal version running on a real-life testbed, to verify its effectiveness. The testbed, and some experimental results obtained with the i-EVS framework, are presented and briefly discussed in the following sub-sections.
# Solid-State Lithium Battery Cycle Life Prediction Using Machine Learning
## 2 Materials and Methods
### Battery Cycle Data Generation: Dataset Description  
The dataset used in this work was composed of charge/discharge tests from 12 solid-state lithium polymer batteries. The cathode materials of 9 batteries were composed of lithium iron phosphate (LFP). The cut-off voltage range of the charge/discharge test was 2.0-3.65 V. The assembled LFP batteries were first charged with a constant current of 0.1 C, and the cutoff voltage was 3.65 V. When the voltage reached 3.65 V, they would charge at constant voltage with a cutoff current of 0.05 C. Then, they would discharge with a constant current of 0.1 C and a cutoff voltage of 2.0 V. The cycle tests were repeated until the battery capacity decayed to 80% of the nominal capacity. The cathode materials of the other 3 batteries were lithium nickel cobalt manganese oxide (NCM). The assembled NCM batteries were cycled in the same way as the LFP batteries were, while the cut-off voltage range was 3.0-4.2 V. The cross-linked nanocomposite polymer electrolytes (CNPEs) based on polyethylene oxide were prepared as described in our previous work from reference [34]. The LFP or NMC cathode was made of active materials with acetylene black and PVDF in the weight ratio of 8:1:1. Then, the CNPE films were sandwiched between the lithium anode and cathode film. All of the assembly processes were conducted under an argon environment in a glove box. These batteries were packaged in pouch cells and cycled in constant current using LAND Electronics with the temperature controlled at 25 \({}^{\circ}\)C. The tests were finished when the discharge capacity of the battery decreased to 80% of the initial discharge capacity. Due to the high-dimensional test data forms and intricate degradation mechanism of solid lithium batteries, we employed a data-driven approach using a series  
Figure 1: Summary of causes of battery degradation.  
of descriptors extracted from the full voltage curves of each cycle. The specific feature selection and algorithm design are discussed in the next section.
# A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model
###### Abstract  
Images captured by sensors in unpleasant environment like low illumination condition are usually degraded, which means low visibility, low brightness, and low contrast. In order to improve this kind of images, in this paper, a low-light sensor image enhancement algorithm based on HSI color model is proposed. At first, we propose a dataset generation method based on the Retinex model to overcome the shortage of sample data. Then, the original low-light image is transformed from RGB to HSI color space. The segmentation exponential method is used to process the saturation (\(S\)) and the specially designed Deep Convolutional Neural Network is applied to enhance the intensity component (\(I\)). At the end, we back into the original RGB space to get the final improved image. Experimental results show that the proposed algorithm not only enhances the image brightness and contrast significantly, but also avoids color distortion and over-enhancement in comparison with some other state-of-the-art research papers. So, it effectively improves the quality of sensor images.  
Keywords:image enhancement; Retinex model; color model; feature learning; convolutional neural network; batch normalization +
Footnote †: journal: Sensors
## 3 Results and Discussion
### Semi-Quantitative Analysis and Standard Curve
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Factor and Level Combination** **(CTBN Content /wt. \%)**} & \multirow{2}{*}{**MV**} & \multirow{2}{*}{**Difference**} & \multicolumn{2}{c}{**Confidence Interval**} & \multirow{2}{*}{**Significant or Not?**} \\ \cline{4-5} \cline{7-7}  & & & **Lower Limit** & & **Upper Limit** \\ \hline
1\({}^{1)}\) & 0.0044 & – & – & – & – \\
5 & 0.0183 & \(-\)0.0138 & \(-\)0.0191 & \(-\)0.0086 & Y \\
10 & 0.0303 & \(-\)0.0259 & \(-\)0.0317 & \(-\)0.0207 & Y \\
15 & 0.0524 & \(-\)0.048 & \(-\)0.0532 & \(-\)0.0428 & Y \\
5\({}^{2)}\) & 0.0183 & – & – & – & – \\
1 & 0.0044 & 0.0139 & 0.0086 & 0.0191 & Y \\
10 & 0.0303 & \(-\)0.0121 & \(-\)0.0173 & \(-\)0.0068 & Y \\
15 & 0.0524 & \(-\)0.0341 & \(-\)0.0393 & \(-\)0.0289 & Y \\
10\({}^{3)}\) & 0.0303 & – & – & – & – \\
1 & 0.0044 & 0.0259 & 0.0207 & 0.0312 & Y \\
5 & 0.0183 & 0.0121 & 0.0068 & 0.0173 & Y \\
15\({}^{1}\) & 0.0524 & \(-\)0.022 & \(-\)0.0273 & \(-\)0.0168 & Y \\
15\({}^{2)}\) & 0.0524 & \(-\) & – & – & – \\
1 & 0.0044 & 0.048 & 0.0428 & 0.0532 & Y \\
5 & 0.0183 & 0.0341 & 0.0289 & 0.0393 & Y \\
10 & 0.0303 & 0.022 & 0.0168 & 0.0273 & Y \\ \hline \hline \end{tabular} \({}^{1)}\) Comparison between the virgin bitumen with 1 wt. % CTBN and those with 5 wt. %, 10 wt. %, and 15 wt. % CTBN in terms of the mean value of \(\mathrm{I_{CTBN}}\); \({}^{2)}\) comparison between the virgin bitumen with 5 wt. % CTBN and those with 1 wt. %, 10 wt. % and 15 wt. % CTBN in terms of the mean value of \(\mathrm{I_{CTBN}}\); \({}^{3)}\) comparison between the virgin bitumen with 10 wt. % CTBN and those with 1 wt. %, 5 wt. % and 15 wt. % CTBN in terms of the mean value of \(\mathrm{I_{CTBN}}\); \({}^{4)}\) comparison between the virgin bitumen with 10 wt. % CTBN and that with 1 wt. %, 5 wt. % and 10 wt. % CTBN in terms of the mean value of \(\mathrm{I_{CTBN}}\).  
\end{table} 
Table 10: Multiple comparison results for the MV of \(\mathrm{I_{CTBN}}\) for the virgin asphalt mixtures. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**CTBN Content**} & \multicolumn{3}{c}{**\(\mathrm{I_{CTBN}}\)**} \\ \cline{2-4}  & **MV** & **SD** & **CV** \\ \hline
1 wt. \% & 0.0044 & 0.0001 & 0.0988 \\
5 wt. \% & 0.0183 & 0.0012 & 0.0678 \\
10 wt. \% & 0.0303 & 0.0019 & 0.0637 \\
15 wt. \% & 0.0524 & 0.0032 & 0.0611 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: CTBN indices of the virgin asphalt mixtures with different CTBN contents.
## 3 Results
### Chondrocytes  
HPCs cultured in spheroids were left untreated (CTL) or treated with either 10 uM nNAG or 10 uM NAG for 72 h. At the end of the exposure period, the spheroids were analyzed by immunofluorescence to highlight the collagen type II production. The untreated cells (CTL) showed only a slight red fluorescence, as did the NAG-treated cells, whereas the nNAG-treated cells showed a very intense red staining (Figure 1).  
Because collagen type II production decreases and MMP production increases during OA progression, leading to the loss of tissue homeostasis and cartilage degradation, we measured the amounts of MMPs in the spheroid culture media after 72 h of treatment.  
Figure 1: Effects of NAG and nNAG on collagen II production. HPCs, cultured in spheroids, were left untreated (CTL) or treated with either 10 uM NAG or nNAG for 72 h and then analyzed by immunofluorescence using the anti-Coll-II primary antibody and Alexa Fluor 594 (red) secondary antibody. Nuclei were stained with DAPI (original magnification: 40\(\times\)). The bar graph represents the pixel intensities in the region of interest, obtained using Image. * \(p\) < 0.01 vs. CTL.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Va** & **Vb** & **Yv** \\ \hline C3 & 8880 & 5984 & 28.96 \\ \hline C3 + 1\% NAG & 6826 & 3984 & 28.42 \\ \hline C3 + 1\% nNAG & 4288 & 3184 & 11.04 \\ \hline G3 & 9840 & 8176 & 16.64 \\ \hline G3 +1\% NAG & 33,546 & 21,960 & 55.86 \\ \hline G3 +1\% nNAG & 15,120 & 13,248 & 18.72 \\ \hline G1 & 33,760 & 20,248 & 135.12 \\ \hline G1 + 1\% NAG & 32,133 & 19,808 & 123.25 \\ \hline G1 + 1\% nNAG & 26,666 & 17,600 & 90.66 \\ \hline \end{tabular} \(\forall\)a = viscosity at the slowest viscometer speed; Vb = viscosity at the next-to-slowest viscometer speed; Yv = the yield value  
\end{table}
Table 3: _Cont._In particular, MMP-3, MMP-13, and ADAMTS-5 were analyzed, and the results showed that 10 \(\upmu\)M NAG and nNAG were able to inhibit their release, with nNAG being more effective (Figure 2).
# Performance Comparison of H.264 and H.265 Encoders in a 4K FPV Drone Piloting System
## 2 Related Work
H.264 video encoder parameter optimization in real-time wireless transmission systems has been presented in [13], and it was concluded that PSNR and bitrate show a direct and proportional relation. Furthermore, the optimal trade-off between QP and GOP size has been presented with minimal processing latency and nodal power consumption. In the area of specialized video quality assessment for drone devices, ref. [14] presented the findings of a user study that assessed the QoE (Quality of Experience) of a video stream displayed to pilots using an FPV-based drone system. The video-encoding parameters had a significant impact on the perceived QoE. The study also noted the influence of simulator sickness in FPV systems, which may impair the QoE and severely influence the results of the study. In our research, we limited our comparison to objective methods, thus not requiring an additional pool of real pilots. One of the goals of this work is to provide motivation for designing an ad hoc FPV video encoder. The researchers in [15] have described step-by-step implementation details when converting an HEVC encoder into a customized VVC (Versatile Video Coding) encoder. It was shown that reusing parts of the coding structure is a valid and effective approach. A similar methodology could be utilized to design an FPV encoder.
# Enhancement of Sentinel-2A Images for Ship Detection via Real-ESRGAN Model
## 1 Introduction
Even though the impact of super-resolution on the performance of detectors to detect multi-scale objects on remotely sensed images has been investigated in previous studies, the majority of them made an effort only to enhance high-resolution (HR) satellite images or proved the contributory impact of super-resolution on object detection results rather than enhancing low-resolution (LR) satellite images. The trade of Sentinel-2A images is free of charge and offers medium spatial resolution. A limited number of studies have addressed the enhancement of Sentinel-2A images at medium spatial resolution and the evaluation of super-resolution techniques for ship detection. The main aim is to quantitatively and visually assess super-resolution's contributory impact on ships' detectability from Sentinel-2A images. The research questions intending to be answered are as follows:  
* To what degree do super-resolution practices effect the detectability of ships from Sentinel-2A images? * Can Real-ESRGAN enhance Sentinel-2A images to the point where ship detection with higher metric values is achievable as it can be in HR images? This study's first and foremost objective is to demonstrate the positive impact of super-resolution on ship detection with fewer false positive occurrences and, thus, higher confidence values. The second objective is to investigate the ability of GANs, specifically, Real-ESRGAN, to enhance Sentinel-2A images. The detection of ships was implemented in two phases in this study. Ships were detected from Sentinel-2A images in the first phase before they were detected from enhanced Sentinel-2A images in the second phase. The visual and statistical comparison of detections from Sentinel-2A and enhanced Sentinel-2A assists in revealing the degree of positive or negative change that super-resolution manifests. Only large container ships were aimed to be detected throughout the phases, meaning smaller ships were excluded from the study.
## 4 Discussion
### Far-Field Exposure  
Far-field exposures from radio and TV broadcast transmitters and mobile phone base stations were estimated using geospatial propagation modelling. We did not find any influence of building characteristics on the personal measurements taken at home and in school. This is in contrast to our previous study where metal window frames and concrete walls resulted in a significant exposure reduction [14]. However, our finding is in line with a recent study on modelled mobile phone downlink exposure in the city of Amsterdam, Netherlands, where none of the building characteristics could explain additional variance of the modelled values [26]. We found the availability of WLAN at home and not switching off the base station during night and the availability of WLAN in school being relevant exposure predictors. Furthermore, the time spent in trains explained part of the measured WLAN exposure. Because of the increase of WLAN in public spaces and public transport this part of WLAN exposure may become even more important in the future. The number of smartphones being used at home was the strongest predictor for the far-field uplink exposure followed by the time spent in trains and buses. A considerable part of the uplink exposure however remained unexplained. Previous studies have also demonstrated high uplink exposure in public transport [27, 28, 29] or investigated the influence of small cells in trains on the exposure of mobile phone users [30]. The relevance of mobile phones in stand-by mode for exposure is still quite unclear. [PERSON] _et al._ demonstrated that personal RF-EMF exposure was affected by one's own mobile phone in stand-by mode because of its regular location updates and push functions implemented in applications [29]. This finding may explain why the number of smartphones at home is one of the exposure relevant predictors. And, additionally, this finding led us to include passive mobile phone data traffic for the near-field exposure estimate. Carrying a mobile phone on the body contributed on average 0.56% to the total whole-body exposure of the HERMES participants.  
The contribution of the far-field exposure is small compared to the contribution of the near-field exposure (1.6% of the brain dose and 6.0% of the whole-body dose originated from far-field sources). Nevertheless, far-field exposure is relevant: There are public concerns about potential health effects related to mobile phone base stations [31] and exposure from broadcast transmitters and mobile phone base stations is not lifestyle related which complicates the investigation of soft outcomes (e.g., symptoms and behaviour). Furthermore, far-field exposure is long-term and continuous and people are exposed during night as well, which might be a critical time window. Therefore we think it is worth the effort to investigate far-field exposure as well.
## 1 Introduction
### Our Contributions  
In this research, we focus on minimizing the delay and the latency of fog computing to avoid incidents discussed above. Though fog gives responses to VANET requests in less time than the cloud, there are many challenges, such as delays in responses, irregularities in connectivity, security, heterogeneity, a vast amount of mobility, etc. [15]. To the best of our knowledge, no previous work has compared cloud and fog computing in the light of VANETs by using a 5G network. This is what we have tried to solve in this research work. We have focused on one of the most critical challenges of VANETs, which is a delay in responses. We first compared the performance of cloud and fog computing in terms of delay and latency in VANETs, then utilized our priority-based proposed model in the fog and found that our model can reduce the delay and latency by 20% and 35%, respectively, compared to cloud. Our work aims to make the smart city safe and sustainable with the innovation of fog computing, rectify the disadvantages in fog computing like latency and delay, and provide a high-speed data transmission rate by using a 5G cellular network.  
Our research work contributes the following:  
* A novel algorithm is developed to reduce fog computing delay and latency in smart city transportation.
* A fog computing model is proposed in which the delay and the latency of data transfer have been calculated by implementing several mathematical equations.
* For faster communication between vehicles and RSUs, we used 5G SDN.  
The rest of this paper is structured as follows. In Section 2, we present the concepts of delay and latency of fog computing, 5G SDN, and IoV in smart city transportation, as discussed in the literature. We discuss fog computing architecture in detail in Section 3. The proposed model, algorithm, and workflow diagram are presented in Section 4. The model evaluation and results are discussed in Section 5. Finally, we present our conclusions in Section 6.
## 2 Data and Methods  
### SIF Data  
We used the GOME-F version 28 (V28) terrestrial chlorophyll fluorescence data retrieval, produced by Dr. [PERSON] ([24; 25; 26]) as part of a NASA Making Earth System Data Records for Use in Research Environments program. V28 retrievals use GOME instrument channel 4 with \(\sim\)0.5 nm spectral resolution and wavelengths between 734 and 758 nm. The Level 3 data are available ([[https://avdc.gsfc.nasa.gov/pub/data/satellite/MetOp/GOME_F/v28/MetOp-A/level3/](https://avdc.gsfc.nasa.gov/pub/data/satellite/MetOp/GOME_F/v28/MetOp-A/level3/)]([https://avdc.gsfc.nasa.gov/pub/data/satellite/MetOp/GOME_F/v28/MetOp-A/level3/](https://avdc.gsfc.nasa.gov/pub/data/satellite/MetOp/GOME_F/v28/MetOp-A/level3/)), accessed on 15 June 2021) at 0.5\({}^{\circ}\) spatial resolution and at monthly temporal resolution and cover February 2007 to March 2019.  
GOME-F products are inherently noisy due to low signal levels. We used the estimated monthly SIF values contained in the products. Because the GOME instrument has a relatively large observation footprint, clouds and aerosols are present in nearly every daily observation. Various filtering techniques were applied ([26]), but cloud-contaminated data are still present in a significant portion of the monthly mean data. To address the noisy data and cloud cover problems, we applied a seven-point median box filter in space and a three-month running mean filter in time to generate a gap-free monthly time series of SIF (Figure 1). Such smoothing degrades the time and space resolution of features. For example, small-scale vegetation changes over steep terrains cannot be resolved in the smoothed imagery, nor can the effects of events having time scales of days to weeks be fully resolved. Nevertheless, the data processing approach adopted herein is justified because the focus of this study is the global, continental-scale features that occur on interannual to decadal timescales.
# A Precise Modeling Method for PMSM Servo System of Spacecraft
## 3 Nonlinear Precise Modeling  
### Modeling of Elastic Characteristics  
The elastic characteristics of the spacecraft servo system can be represented by the two-mass elastic model shown in Figure 3. The transmission link connects the motor and the actuator, with a certain stiffness \(K_{s}\) and damping coefficient \(B_{s}\), When the transmission shaft undergoes torsional deformation, a torque \(T_{s}\) is generated. The electromagnetic torque \(T_{e}\) of the motor and the torsional torque \(T_{s}\) of the transmission shaft act together on the motor shaft with a rotational inertia of \(J_{M}\) and a damping coefficient of \(B_{M}\). The equivalent rotational inertia of the actuator is \(J_{L}\), and the damping coefficient is \(B_{L}\). The torsional torque \(T_{s}\) of the transmission shaft and the load torque \(T_{L}\) act together on the actuator, ultimately determining the load speed.  
Based on the theoretical analysis above, the system dynamic equation is established as follows:  
\[\left\{\begin{array}{l}J_{m}\ddot{\theta}_{m}=T_{e}-B_{m}\dot{\theta}_{m}-T_{ s}\\ J_{L}\ddot{\theta}_{L}=T_{s}-B_{L}\dot{\theta}_{L}-T_{L}\\ T_{s}=K_{s}(\theta_{m}-\theta_{L})+B_{s}(\dot{\theta}_{m}-\dot{\theta}_{L})\\ \omega_{m}=\dot{\theta}_{m}\\ \omega_{L}=\dot{\theta}_{L}\end{array}\right. \tag{2}\]  
By performing the Laplace transform on the above equation, the block diagram of the two-mass elastic system is obtained as shown in Figure 4.
## Materials and Methods
### Data  
Observed meteorological data comes from 175 gauge stations in the Yangtze River basin during 1982-2013 ([[http://data.cma.cn](http://data.cma.cn)]([http://data.cma.cn](http://data.cma.cn))). Observed NPP data from 17 sites in the Yangtze River basin was from National Ecosystem Research Network of China ([[http://www.cnern.org.cn/](http://www.cnern.org.cn/)]([http://www.cnern.org.cn/](http://www.cnern.org.cn/))). The simulated NPP data from 1982 to 2013 at a spatial resolution of \(0.5^{\circ}\times 0.5^{\circ}\) comes from LPJ model.  
The MODIS data came from the NASA Land Processes Distributed Active Archive Center with a spatial resolution of 1 km \(\times\) 1 km ([https://e4 ftl01.cr.usgs.gov/](https://e4 ftl01.cr.usgs.gov/)). The NDVI dataset at a spatial resolution of 8 km \(\times\) 8 km and 15-day interval were from GIMMS (Global Inventory Modelling and Mapping Studies group, [[https://ecocast.arc.nasa.gov/data/pub/gimms/3g.v1/](https://ecocast.arc.nasa.gov/data/pub/gimms/3g.v1/)]([https://ecocast.arc.nasa.gov/data/pub/gimms/3g.v1/](https://ecocast.arc.nasa.gov/data/pub/gimms/3g.v1/))). The GIMMS NDVI 3g.v1 is generated from NOAA's Advanced Very High Resolution Radiometer (AVHRR) data, and the spatial resolution is 1/12\({}^{\circ}\). The dataset spanned from July 1981 to December 2015. It has been calibrated for sensor shift, cloud test and removed the effects of solar zenith angles and other factors [26]. The annual AVHRR GIMMS NDVI dataset was obtained by using the maximum value composite (MVC) method.  
The land-use and land-cover change (LUCC) data came from Resource and Environment Data Cloud Platform ([[http://www.resdc.cr/](http://www.resdc.cr/)]([http://www.resdc.cr/](http://www.resdc.cr/))). The resolution of the LUCC data in 1990, 1995, 2000, 2005 and 2010 was 1 km. The land-use data included six classifications: croplands, forests, grassland,  
Figure 1: Location of the observed meteorological station and NPP (Net Primary Productivity) sites over the Yangtze River basin and the forest distribution map in the year 2000.  
waters, barren and urban. The vegetation map adopted the national 1:1,000,000 map of vegetation types in 2000 which was provided by \"Cold and Arid Regions Science Data Center at Lanzhou\" ([[http://westdc.westgis.ac.cn](http://westdc.westgis.ac.cn)]([http://westdc.westgis.ac.cn](http://westdc.westgis.ac.cn))). Moreover, the vegetation map adopted the thirty-meter resolution forest map for China in 2013 which was provided by \"Department of Earth Sciences, Tsinghua University\" ([[http://data.ess.tsinghua.edu.cn/](http://data.ess.tsinghua.edu.cn/)]([http://data.ess.tsinghua.edu.cn/](http://data.ess.tsinghua.edu.cn/))).