esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Using measurements of the column-averaged CO\({}_{2}\) dry air mole fraction (XCO\({}_{2}\)) from GOSAT and biosphere parameters, including normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), leaf area index (LAI), gross primary production (GPP), and land surface temperature (LST) from MODIS, this study proposes a data-driven approach to assess the impacts of terrestrial biosphere activities on the seasonal cycle pattern of XCO\({}_{2}\). A unique global land mapping dataset of XCO\({}_{2}\) with a resolution of 1\({}^{\circ}\) by 1\({}^{\circ}\) in space, and three days in time, from June 2009 to May 2014, which facilitates the assessment at a fine scale, is first produced from GOSAT XCO\({}_{2}\) retrievals. We then conduct a statistical fitting method to obtain the global map of seasonal cycle amplitudes (SCA) of XCO\({}_{2}\) and NDVI, and implement correlation analyses of seasonal variation between XCO\({}_{2}\) and the vegetation parameters. As a result, the spatial distribution of XCO\({}_{2}\) SCA decreases globally with latitude from north to south, which is in good agreement with that of simulated XCO\({}_{2}\) from CarbonTracker. The spatial pattern of XCO\({}_{2}\) SCA corresponds well to the vegetation seasonal activity revealed by NDVI, with a strong correlation coefficient of 0.74 in the northern hemisphere (NH). Some hotspots in the subtropical areas, including Northern India (with SCA of 8.68 \(\pm\) 0.49 ppm on average) and Central Africa (with SCA of 8.33 \(\pm\) 0.25 ppm on average), shown by satellite measurements, but missed by model simulations, demonstrate the advantage of satellites in observing the biosphere-atmosphere interactions at local scales. Results from correlation analyses between XCO\({}_{2}\) and NDVI, EVI, LAI, or GPP show a consistent spatial distribution, and NDVI and EVI have stronger negative correlations over all latitudes. This may suggest that NDVI and EVI can be better vegetation parameters in characterizing the seasonal variations of XCO\({}_{2}\) and its driving terrestrial biosphere activities. We, furthermore, present the global distribution of phase lags of XCO\({}_{2}\) compared to NDVI in seasonal variation, which, to our knowledge, is the first such map derived from a completely data-driven approach using satellite observations. The impact of retrieval error of GOSAT data on the mapping data, especially over high-latitude areas, is further discussed. Results from this study provide reference for better understanding the distribution of the strength of carbon sink by terrestrial ecosystems and utilizing remote sensing data in assessing the impact of biosphere-atmosphere interactions on the seasonal cycle pattern of atmospheric CO\({}_{2}\) columns.  
Keywords:GOSAT; MODIS; XCO\({}_{2}\); seasonal cycle pattern; biosphere absorption +
Footnote †: journal: Remote Sens. **2017**, 9, 251; doi:10.3390/rs9030251
## 3 Latency-Classification-Based Deadline-Aware Task Offloading Algorithm (LCDA)
### Task Selection for Offloading  
Selecting tasks to be offloaded to an edge cloud server has a significant impact on overall system performance. This is because offloading an inappropriate task will result in a severe offloading time penalty and will not guarantee the satisfaction of the deadline of a latency-sensitive task. As mentioned previously, when offloading a latency-sensitive task, degradation will occur, and the deadline for the task may not be satisfied. Therefore, the core concept of task selection is to offload tasks in the latency-tolerant queue preferentially, as shown in Algorithm 1.  
When selecting a task for offloading, only tasks in the latency-tolerant queue can be candidates. The task with the lowest weight calculated by task analysis function in the latency-tolerant queue is given the highest priority for offloading. This is done to minimize the impact of the performance degradation that is incurred when a task is offloaded on the execution time of any current tasks. If there are multiple tasks with the same weight value, the task with a smaller task size is selected preferentially.  
If there are only a few remaining deadlines to be satisfied or if it is determined that offloading a task is more damaging than waiting to execute it on the current user device, then we conclude that there are no appropriate tasks for offloading in the latency-tolerant queue. In this case, or in the case where a user device has only latency-sensitive tasks, we search for tasks to be offloaded in the latency-sensitive queue. In such cases, tasks with the lowest weight calculated by task analysis are selected for offloading. If there are multiple tasks with the same weight value, the smallest task is selected for offloading. Finally, if it is determined that there are no appropriate tasks for offloading in the latency-sensitive queue, then the device will conclude that it is not possible to offload any tasks.  
```
1:for all task\({}_{i}\) in task\({}_{latency-tolerant}\), \(\forall_{i}\in\{1,2,\dots\ n\}\);
2: Find the task\({}_{i}\) with lowest weight calculated by task analysis;
3: if There are tasks with weight of the same value
4: Find the task\({}_{i}\) with the smallest size;
5: end if
6: if 'offloading time of task is greater than local execution time of task' |'remaining deadline is lower than threshold'
7: for all task\({}_{i}\) in task\({}_{latency-sensitive}\), \(\forall_{i}\in\{1,2,\dots\ n\}\);
8: Find the task\({}_{i}\) with lowest weight calculated by task analysis;
9: if There are tasks with weight of the same value
10: Find the task\({}_{i}\) with the smallest size;
11: end if
12: if 'offloading time of task is greater than local execution time of task' |'remaining deadline is lower than threshold'
13: Continue to run on the current user device;
14: end if
15: end for
16: end if
17: end for
```  
**Algorithm 1**_Task_Selection ()_
## 3 Results
### FTIR Analysis  
Two samples of 2.5% carboxymethyl cellulose solution in water were analyzed; the first was unused liquid (red curve) and the second was the medium used for 50 cooling cycles (black curve) (Figure 6). The liquid was placed between two potassium bromide tablets and irradiated in the infrared spectrum (4000-400 cm\({}^{-1}\)). The two curves of the absorption bands overlapped when looking for the areas with peaks (local maxima and minima) and were highlighted with the help of the deconvolution performed by the device. There was a substantial difference between the two curves; the areas with spectral changes were 990 cm\({}^{-1}\), 929 cm\({}^{-1}\), 876 cm\({}^{-1}\) and 777 cm\({}^{-1}\).  
The FTIR analysis showed the main signals on the spectrum between the wavelengths of 700 cm\({}^{-1}\) and 1900 cm\({}^{-1}\). The analyzed bands showed the characteristic spectrum of the bonds of the asymmetric and symmetrical groups of carboxymethyl cellulose at 1582 cm\({}^{-1}\) and at 1390 as well as the C-H bond at 1409 cm\({}^{-1}\) of organic compounds with the hydroxyl group O-H. Signals also appeared at 1320 cm\({}^{-1}\) and 1010 cm\({}^{-1}\), indicating the presence of C-H and C-O bonds, which are characteristics of the polymer structure of carboxymethyl cellulose. The C-O bond appeared at 1730 cm\({}^{-1}\), characteristic of the group of pectic compounds specific to cellulose plant cells.  
The peaks at 1638 cm\({}^{-1}\), 1640 cm\({}^{-1}\) and 1648 cm\({}^{-1}\) presented the bonds H-O-H, C-O-O and C-OH (the presence of the hydroxyl group). Also, the group of signals at 1415 cm\({}^{-1}\) indicated the presence of bonds of C-O-C. Unlike the absorption band of the freshly prepared carboxymethyl cellulose solution, the one used in multiple heating and cooling thermal cycles showed some variations that indicated the presence of iron and silicon oxides and other compounds present as residues after cooling during the heat treatment of tempering. On the curve of the multiple medium-used CMC 50, there was the bond of Si-O-Si at 980 cm\({}^{-1}\), the presence of FeOH at 920 cm\({}^{-1}\), FeS at 800 cm\({}^{-1}\) Si-O-H and, at 605 cm\({}^{-1}\), Fe-O.  
The new bonds of compounds that appeared after the thermal decomposition of the CMC, established by FTIR analysis, influenced the surface tension of the liquid, decreased the viscosity and provided a contact angle characteristic of strongly hydrophilic substances.
## 3 Results  
The results of each variable measured are presented on Table 1. Data from the first and second investigators were analyzed using a \(t\)-test, and no statistical differences were found. In addition, the data collected from each investigator had a high degree of reproducibility, indicating that the airway variables measured were reliable and accurate for analysis.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Variable**} & **Control** & **JIA** & \(p\)**-Value** & **ODDS Ratio** \\  & **Mean (SD)** & **Mean (SD)** & **Mean (SD)** & **(Confidence Range)** \\ \hline Age (years) & 13.75 (2.91) & 13.59 (2.73) & 0.825 & 1 \\ SNA (Degrees) & 83.06 (3.45) & 81.53 (4.27) & 0.12 & 1.00 (0.42–2.35) \\ SNB (Degrees) & 79.29 (3.65) & 78.43 (4.42) & 0.398 & 0.88 (0.32–2.44) \\ ANB (Degrees) & 3.69 (2.84) & 3.21 (2.15) & 0.448 & 0.45 (0.16–1.25) \\ Anterior Face Height(mm) & 113.12 (9.46) & 109.16 (8.51) & 0.083 & 0.72 (0.23–2.23) \\ Posterior Face Height (mm) & 75.39 (7.05) & 71.06 (7.87) & 0.024 * & 5.40 (1.66–17.56) * \\ PFH/AFH Ratio & 66.58 (5.67) & 65.12 (5.08) & 0.281 & 1.29 (0.48–3.44) \\ SN-MP (Degrees) & 32.45 (7.30) & 33.51 (6.12) & 0.529 & 0.85 (0.28–2.59) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean and standard deviation of each variable in control and JIA study groups, as well as \(p\)-value for statistical significance of unpaired Student \(t\)-test. In addition, the ODDS ratio was also calculated. * Indicates statistical significance.  
Figure 2: Showing boundaries of airway segmentation performed in Dolphin Imaging for (**A**) oropharynx, (**B**) nasopharynx, and (**C**) total upper airway.
## 4 Results and Discussion
### Data Analysis  
Linear correlation analysis is performed on every measured variables in the database. As described in Section 2, we also perform an additional MI analysis to ensure all possible relationships are captured between measured variables, either linear or nonlinear. Pearson correlation coefficient (PCC) is used here representing linear correlation method whereas mutual information (MI) acts as a nonlinear method. To make both methods comparable, as described in [PERSON] et al. (2016), we take absolute values of PCC because MI does not capture the correlation direction. In this case, the correlation direction is insignificant to be analysed because the air pollutant proxy will be modelled based on a black block approach (i.e., ML). Figure 5 shows the correlation analysis in the form of matrix plots. It can be seen that the linear correlation method (a) does not find strong correlation between O3 and several variables that are known to be connected to O3 formation, such as NO2 and NOx. On the other hand, MI method (b) demonstrates that O3 presents good connection to these two variables.  
From the MI scores, the variables that share the highest correlation to O3 are NOx, wind speed (WS), _m,p_-Xylene (TNX) and NO2. It is well known from previous studies [(58; 20; 59)] that O3 is strongly connected to NO and NO2 as well as NOx concentrations. Furthermore, TNX was found as the largest contributor to O3 formation [(55)]. However, the relationship between these concentrations cannot be captured well by PCC analysis as shown in Figure 5.  
Figure 6 presents the scatter plots of some selected variables. It can be seen that the three variables (e.g., NO, NOx and TNX) are shaped to a specific pattern (i.e., exponential functions), indicating that they correlate nonlinearly to O3 concentration (these variables also behave nonlinearly in logarithmic scale). Their linear correlation coefficients and MI scores are also not similar. Nevertheless, both methods are able to capture good linear correlation between O3 and wind speed (WS) at R\({}^{2}=0.76\) and MI = 0.79. WS has been known to affect O3 concentration in urban area as described in previous studies, such as explained by [PERSON] [(60)] and [PERSON] et al. [(61)]. In summary, the use of MI allows  
Figure 5: Matrix plot correlation analysis between ozone (O3) and other measured variables. It can be seen that linear correlation (a) does not capture well the relationship between O3 and other measured variables, such as _m,p_-xylene (TNX), NO and NOx.  
the detection of linear and nonlinear correlation between variables of interest where the correlated variables can then be used as potential inputs for the pollutant proxy.
# Motor Vector Control Based on Speed-Torque-Current Map
## 5 Conclusions  
In this paper, the mechanical and electrical energy conversion mechanism and dynamic control of PMSM are studied in order to analyze the impact on the performance of pure electric vehicle. Considering the influence of bus voltage fluctuation on motor performance, a vector control strategy based on the speed-torque-current map is designed. In this study, three working conditions were formulated, including the 0-100 km/h acceleration condition, speed coupling condition, and torque  
Figure 21: Demand power corresponding to different target speeds.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**MG1 Target Speed/rpm** & **MG1 Power/kW** & **MG2 Power/kW** & **Demand Power/kW** \\ \hline \(-295\) & 7.615 & 16.291 & 23.906 \\ \(1867\) & 14.347 & 7.745 & 22.092 \\ \(3590\) & 21.53 & 2.013 & 23.543 \\ \(5600\) & 31.56 & \(-2.965\) & 28.595 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Demand power of the electric drive system at different MG1 target speeds.  
coupling condition. They have been used to study the dynamic characteristics and energy consumption characteristics of the double motor coupling mode. The simulation results show that compared to the static control model, the dynamic control model can better reflect the actual working process of the motor. In the torque coupling mode, the minimum demand power of the system, corresponding to the optimal MG1 target torque, is obtained from the dynamic characteristics of the motor. This power is 1.27% lower than that obtained from the static model and 20.6% lower than the maximum demand power of the system. In the speed coupling mode, the minimum demand power of the system, corresponding to the optimal MG1 target speed, is also obtained from the dynamic characteristics of the motor. This power is 6.16% lower than the demand power corresponding to the MG1 target speed obtained by the static characteristics and 22.74% lower than the maximum demand power of the system. The above results show that the proposed vector control strategy based on the dynamic model of the speed-torque-current map achieves optimal power distribution of the electric drive system, and effectively reduces the energy consumption of the electric drive system and extends the mileage of the car. This research results in this article lay a theoretical foundation for further improving the dynamic performance of electric vehicles.  
[PERSON] wrote the first draft of the manuscript, proposed the design method. [PERSON] designed the process of control strategy and the performance simulation method, completed the verification of the strategy, and dealt with the simulation data. [PERSON], [PERSON], and C.F. provided insights and additional ideas on presentation. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Chongqing Key Research and Development Program (Project No. cstc2018 jszx-cyztzx0047), and the National Key Research and Development Program of China under Grant 2018 YFB0106100.  
The authors declare no conflict of interest.
## 2 Background
### WBASN in Healthcare  
Healthcare is facing a challenge as remote monitoring will be needed for a population of 761 million people in 2025 [56]. Additionally, the number of patients with chronic diseases has increased, so there is a need to provide quality of life in terms of healthcare.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Requirements** \\ \hline Lifetime & Long for wearable sensors and ultra-long for implanted sensors \\ Covered Area & Inside and around the body \\ Data Rate & Application dependent \\ Setup Time & Fast \\ Security & Simple and light mechanisms required \\ Customisation & Configurable sensor nodes \\ Fault Management & Detection mechanisms for the case of the node failure \\ Quality of Service & Application dependent \\ Power and Energy & Efficient energy and power mechanisms \\ Medium Access Control & Controllable, scalable and reliable \\ Frequency Bands & Medical bands and compatible with human tissues \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics and requirement analysis of WBASNs [55; 56].  
Patient data monitoring is one of the most important applications in healthcare. Further, continuous monitoring of patients in indoor and outdoor environments proves to be very useful for doctors to extract useful information for treatment and care. Hence, WBASNs are used for remote healthcare and monitoring in various environments such as hospitals, ambulatory, emergency and elderly care centres, etc.  
For a chronic disease patient, the formal procedure of routine visits is required to monitor the progress, development of complications or relapse of the disease. Questions such as what, how and when to monitor are crucial for treatment. In this context, various biosensors are used for monitoring the patient's physiological conditions in order to receive relevant information regularly. Table 3 provides examples of diseases with measurable physiological parameters and usable sensor types.
## 2 Proposed SALDL Based on GCM
### Residual Layer Design for Baser Layer Generation  
Unlike the residual layer design based on the additive model of Equation (1), a new GCM is proposed herein to generate the residual of the base layer.  
\[\mathbf{x}^{s_{b}}=\mathbf{x}^{o}\otimes\mathbf{k}^{g}-\mathbf{x}^{i}\otimes\mathbf{k}^{g}=\left( \mathbf{x}^{o}-\mathbf{x}^{i}\right)\otimes\mathbf{k}^{g} \tag{4}\]  
where \(\mathbf{x}^{s_{b}}\) denotes the residual layer corresponding to the base layer. Herein, the base layer is defined as the Gaussian blurring of the input halftoned image, \(\mathbf{x}^{i}\otimes\mathbf{k}^{g}\). Here, \(\otimes\) denotes the convolution operation, and \(\mathbf{k}^{g}\) indicates the Gaussian smoothing filter. Therefore, Equation (4) indicates that the residual layer corresponding to the base layer is defined as the image difference between the blurred original image and blurred halftoned image through Gaussian filtering. Compared with Equation (1), the proposed residual layer is the filtered version of \(\mathbf{x}^{o}-\mathbf{x}^{i}\). Hereinafter, the proposed model expressed as Equation (4) is referred to as GCM to differentiate it from the additive model expressed in Equation (1).  
The main objective of residual learning is to narrow the output range. Whether the residual layer generated based on the GCM yields a narrow output range is yet to be elucidated. The histogram distribution for one sample image was analyzed to verify this. Figure 2 shows four images for generating two types of residual layers. The original, halftoned, blurred original, and blurred halftoned images are shown from left to right. Figure 3 shows a comparison of the histogram distributions for the two types of residual layers. One is the residual layer generated using the additive model, which subtracts the original image from the halftoned image. The other is the residual layer generated using the proposed GCM, which subtracts the blurred original image from the blurred halftoned image. As shown in the histogram distributions, the residual layer generated using the proposed GCM yielded a narrow output range compared with the conventional additive model, which yielded a wider output range. This is because the residual layer generated based on the additive model tends to exhibit textures that resemble dot patterns. Meanwhile, the proposed GCM utilizes Gaussian filtering to smooth out sudden changes that appear in halftoned images, thereby enabling the output range of the residual layer to be narrow.  
Figure 1: Concept of image decomposition based on proposed SALDL for inverse halftoning.  
Figure 2: Original, halftoned, blurred original, and blurred halftoned images (left to right).
# SENTINEL: A Secure and Efficient Authentication Framework for Unmanned Aerial Vehicles
## 7 Experiments
### Evaluation--Lightweight Digital Certificate  
To see how efficient our lightweight certificate is, we compared the size of our certificate with a conventional X.509 v3 certificate with necessary fields only and its compressed version using gzip. As shown in Table 2, the size of our certificate was reduced by 72.73% and 71.56% compared to the conventional X.509 v3 certificate and its gzip-compressed version, respectively, under the same cryptographic settings of ECDSA with SHA-256 and a 256-bits public key.
# Electrically Tunable Propagation Properties of the Liquid Crystal-Filled Terahertz Fiber
## 3 Results and Discussion
### Electrically Tunable Confinement Loss of the Designed THz LC-MOF  
For realistic MOFs, the fundamental modes are inherently leaky as a result of the finite number of air-hole rings in the cladding region. So, confinement loss inevitably occurs. The addition of more cladding hole rings could reduce mode confinement loss (proportional to the imaginary part of the effective-mode index), but also increase the outer diameter of the fiber. In order to be flexible and compact, the THz fiber should be as thin as possible. The transmission loss of the solid core THz MOF is also mainly derived from material absorption loss rather than confinement loss. Thus, only three rings of cladding holes are adopted for the designed THz LC-MOF.  
Confinement losses (normalized to \(\alpha\Lambda\), in decibels) of the fundamental modes with and without external electric field applied across the fiber are studied and shown in Figure 5. The two fundamental modes are degenerate for the THz LC-MOF without an external electric field applied, and confinement loss as a function of normalized frequency is shown by the dotted black line. However, the external electric field caused bandgap splitting. The confinement loss for the HE\({}_{11\mathrm{x}}\) within the first and second x-polarization PBGs and HE\({}_{11\mathrm{y}}\) within the first y-polarization PBG are shown by the solid red lines and dashed blue line, respectively. It can be seen that confinement loss becomes minimal around the center of PBGs and increases as approaching the band edges. The second x-polarization PBG provides lower confinement loss than the first one. The high confinement loss peaks of the HE\({}_{11\mathrm{y}}\) around the normalized frequency F = 3.5 is corresponding to the cut-off frequency of the second modes of x-polarization bandgap. We attribute the appearance of high confinement loss peaks of HE\({}_{11\mathrm{y}}\) to the coupling between the y-polarization core mode and the second x-polarization band of the cladding structure [39]. An external electric field applied across the fiber can broaden the effective transmission bandwidth and achieve single-polarization guidance for the designed THz LC-MOF. The electrically tunable single-mode single-polarization guidance can be utilized for applications of the THz LC-MOF for dynamic polarization control.  
Figure 4: Fraction of core confined energy as a function of normalized frequency and single-mode single-polarization operation regions of the designed THz LC-MOF.