esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 LPD Signal Design and Detection Method  
### LPD Signal Waveform Design  
The basic strategy of LPD signal design is to reduce the level of radio frequency energy; the DSSS signal is an example. In this section, an LPD signal waveform based on multi-carrier modulation is designed. The differences between our design and the traditional multi-carrier modulation method lie in the following aspects: signal structure and receiving method. In our design, signals modulated by the sub-carriers are the same, and the sub carriers should be equally spaced. Furthermore, under-sampling method based on active aliasing is employed for signal detection. The designed LPD signal can be expressed as  
\[x(t)=\sum_{k=L}^{L+N-1}s(t)\cdot\alpha_{k}\exp(k\cdot j\omega_{c}t) \tag{1}\]  
where the scaled factor \(\alpha_{k}\) satisfied power constraint as \(\sum_{k=1}^{N}|\alpha_{k}|^{2}=1\), \(N\) implies the total number of sub-carriers. Thus, the mean power of signal \(x(t)\) is equivalent to that of signal \(s(t)\), and \(L\) is the number of null subcarriers from the zero frequency to the first signal carrier. \(s(t)\) is the original modulated signal with bandwidth \(\omega_{B}\). The carrier spacing can be given by \(\omega_{c}=D\cdot\omega_{B}\), where \(D\) is the ratio between the carrier spacing and baseband width of signal \(s(t)\). The parameter \(D\) should be no less than 2, or aliasing may occur between adjacent channels. Moreover, artificial noise can be added over the gaps among useful sub-band signals to enhance the covertness of the transmitted signal. In such a case, \(D\) should be determined cautiously to avoid aliasing between artificial noise and useful signals.  
The comparison diagram of the spectrum structure between the modulated signal \(s(t)\) and the proposed LPD signal \(x(t)\) is shown in Figure 1. The bandwidth of \(x(t)\) is \(N\) times of \(s(t)\) while the power is consistent. As a result, the power spectrum density of signal \(x(t)\) will be significantly reduced, which may be even lower than the background noise provided if \(N\) is large enough. Furthermore, \(x(t)\) also performs sparsity in the frequency domain when \(\omega_{c}\gg\omega_{B}\). These two characteristics are similar to that of direct sequence spread spectrum and frequency hopping signals.
# Recharging Schedule for Mitigating Data Loss in Wireless Rechargeable Sensor Network
## 5 Simulation Evaluations
### Simulation Environment  
We consider an event-driven sensor network deployed in a 100 m \(\times\) 100 m square. The network consists of 100 sensor nodes, which are uniformly randomly distributed in the network. A base station is located at the center of the area. The node's communication range is 25 m to form a multi-hop network topology and [PERSON]'s shortest path dynamic routing algorithm is used. In this routing algorithm, each node out of the one-hop distance to the base station would find an active node which is in its transmission range and closer to the base station, then sends sensory data to the base station via it. We assume that each node's maximum battery capacity is 1000 J by equipping a 138 mAH battery with a typical voltage of 2 V. The transmitting and receiving costs of nodes are set based on the MICA2's datasheet: the node's energy consumption rates on transmitting and receiving are 25 mA \(\times\) 2 V = 50 mW and 8 mA \(\times\) 2 V = 16 mW, respectively [19]. With the sensory data packet is in the size of 120 bytes and the bit rate of 9.6 Kbps [31], each node approximately consumes 5 mJ and 1.6 mJ in transmitting and receiving one sensory data packet, respectively. In the event-driven network, whenever events occur in the nodes' sensing range, these nodes would consume 0.15 mJ energy to capture the event [19], then generate a sensory packet and transmit it to the base station via the routing path. The relay nodes on the routing path would apply the data aggregation technique to combine the correlated sensory data. We assume that the mobile charger travels at a constant speed of 1 m/s [19]. Considering the advance in the WPT technology based on the strongly magnetic resonances [3; 26], we assume the mobile charger can replenish energy to the sensor nodes at a rate of 5 W. The mobile charger's traveling distance constraint is 600 m [20]. To simplify the presentation, we set \(E_{min}\) equals to 0. We set the simulation time to 100,000 s and record each node's disjointed time and sleep time. We repeat the simulation 50 times with different network distribution and calculate the mean of total disjointed duration, inactive duration and data loss rate as the results. The total disjointed duration is defined as the total time of the nodes which become disjointed and can't send any sensory data to the base station via a dynamic routing path. Although the node in sleep mode is also disjointed from the base station, we only consider the active node's disjointed time. Thus, the total disjointed duration is positively correlated with the high criticality nodes' sleep time, which can reflect the charging priority of the high criticality nodes in the scheduling algorithm. Considering the node's sleep time, the node's total inactive time is the union of disjointed time and sleep time, which can reflect the charging effect to all of nodes in the scheduling algorithm. The default parameters set in the simulation is shown in Table 2.
## 3 Results
### Assessment of the Simple GPP Model with MOD17 GPP and Climate  
As a broader evaluation of the use of a simple approach to estimate annual GPP, we then applied equation 4 to MOD15 LAI product globally and compared it to MOD17 GPP (both subset for forested areas, aggregated to 0.5 degrees, and calculated as a 2001 to 2006 climatology). We used the monthly 0.25 degree Collection 5 MOD15 LAI product from Boston University [37] and 1 km global MOD17 Collection 5.1 annual GPP from the University of Montana [69]. We compared the single-metric GPP with MOD17 GPP for Evergreen Needleleaf Forest (ENF), Evergreen Broadleaf Forest (EBF), Deciduous Needleleaf Forest (DNF), Deciduous Broadleaf Forest (DBF), and mixed forest. Figure 6 shows that the best-performing single-product GPP model (Equation (4)) and the more complex MOD17 GPP produced comparable climatological GPP estimates with similar standard deviations, with the average LAI-based GPP (Equation (4)) only slightly lower than MOD17 GPP, except for deciduous broadleaf forest.  
Finally, to assess whether our LAI-based GPP estimates were consistent with expectations relatively to climate, we plotted global annual GPP against mean temperature, annual precipitation, and mean diurnal temperature range (DTR) derived from the CRU TS 3.1 datasets. Our results for global forested areas show that the LAI-based GPP behaves as expected, with the highest values found in conditions of high temperature and precipitation and moderate DTR, while the lowest values are found in cold and dry climates (Figure 7).  
Figure 5: Scatterplot of annual mean MOD15 LAI (**a**) and FPAR (**b**) against flux tower annual GPP. LAI and FPAR were processed to a 32-day maximum value composite (MVC) before calculating the annual mean value. The solid line represents the reduced major axis regression between the variables. The dashed lines are 95% confidence interval lines.
# Quality Assessment of HDR/WCG Images Using HDR Uniform Color Spaces
## 3 Methodology for the Quality Assessment Metrics Evaluation
### Databases Presentation  
Five image databases are used for carrying out the comparison. Three were already available online and two were created to handle WCG image quality assessment. To describe objectively the images of each database, we use four indicators:  
Figure 2: Different perceptually uniform luminances as a function of the linear luminance: (**a**) for the range 0–1000 cd/m\({}^{2}\), (**b**) for the range 0–150 cd/m\({}^{2}\).  
* the image dynamic range (DR): \[\text{DR}=\log(Y_{max})-\log(Y_{min})\] (24) where \(Y_{min}\) and \(Y_{max}\) are the minimum and the maximum luminance (in the \(XYZ\) linear domain), respectively. They are computed after excluding 1% of the brightest and darkest pixels to be more robust to outliers.
* the key of the picture is a measure of its overall brightness (in the range [0, 1]) : \[\text{key}=\frac{\overline{\log(Y)}-\log(Y_{min})}{\log(Y_{max})-\log(Y_{min})}\] (25) \(\overline{\log(Y)}\) is computed as follows: \[\overline{\log(Y)}=\frac{1}{N}\sum_{i=1}^{I}\sum_{j=1}^{J}\log(Y(i,j)+\delta)\] (26) where \(Y(i,j)\) is the luminance of pixel \((i,j)\), \(N\) the total number of pixels and \(\delta\) a small offset to avoid a singularity when the luminance is null. \(Y_{min}\) and \(Y_{max}\) are calculated as for the dynamic range.
* the spatial information (SI) [32] describes the complexity of an image. It corresponds to the standard deviation of the image luminance plane which has been filtered by a Sobel filter: \[\text{SI}=\text{std}[\text{Sobel}(Y)]\] (27) On SDR images, this metrics is used after the OETF, usually a gamma function, and, thus, making the luminance approximately perceptually linear. To be as similar as values in SDR, the PU function is applied on the luminance of the HDR images.
* the colorfulness is a metrics of the perceived saturation of an image [33]. The \(\widehat{\text{M}}^{(1)}\) version of the metrics is used because the image is first converted in the CIELab space, a space that can be adapted to HDR (cf. Section 2.1). This metrics is computed as follows: \[\widehat{\text{M}}^{(1)}=\sigma_{ab}+0.37\times\mu_{ab}\] (28) where \[\sigma_{ab}=\sqrt{\sigma_{a}^{2}+\sigma_{b}^{2}}\] (29) and \[\mu_{ab}=\sqrt{\mu_{a}^{2}+\mu_{b}^{2}}\] (30) where \(\sigma_{a}\) and \(\sigma_{b}\) are the standard deviations along the \(a\) and \(b\) axis, respectively. \(\mu_{a}\) and \(\mu_{b}\) are the means of the \(a\) and \(b\) component, respectively.  
Before calculating these indicators, the image luminance is limited to the display available dynamic range used in the respective subjective tests.  
Table 2 presents the main characteristics of the studied databases and a representation of each image of each database can be found in Appendix A.
## 2 Materials and Methods
### UPLC Analysis of Phenolic Compounds in Spice Extracts  
Extract samples (0.20 g) were placed in sealed 17 mL culture test tubes, where first alkaline and then acid hydrolysis was run. In order to run alkaline hydrolysis 1 mL of distilled water and 4 mL of 2 M aqueous sodium hydroxide were added to test tubes. Tightly sealed test tubes were heated in a water bath at 95 \({}^{\circ}\)C for 30 min. After cooling (during 20 min) test tubes were neutralized with 2 mL of 6 M aqueous hydrochloric acid solution (pH = 2). Next, samples were cooled in water with ice. Flavonoids were extracted from the inorganic phase using diethyl ether (\(2\times 2\) mL). Ether extracts were transferred to 8 mL vials. Then, acid hydrolysis was run. For this purpose, the aqueous phase was supplemented with 3 mL of 6 M aqueous hydrochloric acid solution. Tightly sealed test tubes were heated in a water bath at 95 \({}^{\circ}\)C for 30 min. After being cooled in water with ice the samples were extracted with diethyl ether (\(2\times 2\) mL). Produced ether extracts were continuously transferred to 8 mL vials. Then, these extracts were evaporated to dryness under nitrogen stream. Prior to analysis all samples were dissolved in 1 mL of methanol. Analysis was performed using an Aquity H class UPLC system equipped with a Waters Acquity PDA detector (Waters, USA). Chromatographic separation was performed on an Acquity UPLC\({}^{\circledR}\) BEH C18 column (100 mm \(\times\) 2.1 mm, particle size 1.7 \(\mu\)m) (Waters, Ireland). The elution was carried out gradient using following mobile phase composition: A: Acetonitryl with 0.1% formic acid, B: 1% aqueous formic acid mixture (pH = 2).  
Concentrations of flavonoids were determined using an internal standard at wavelength of 320 nm. Concentrations of phenolic acids were determined using an internal standard at wavelength of 280 nm. Compounds were identified based on a comparison of retention times of the analyzed peaks with the retention times of the standards and by adding a specific amount of the standards to the analyzed samples and a repeated analysis. Detection level was 1 \(\mu\)g/g.
## 3 Materials and Methods
### Materials
#### 3.1.1 Knee Dataset  
This dataset was introduced by [PERSON] et al. [20] for rheumatoid arthritis patients. The dataset targets synovitis areas in the human knee joint, containing 3D T1-weight contrasted MRI images. The dataset contains 205 cross-section 2D images annotated by three radiologists, yet only 48 of them were annotated due to a clear view of epiphyses of the tibia and femur. The MRI images were obtained by a 3.0 Tesla Achieva MRI scanner from Philips Healthcare. The MRI images were acquired in a coronal plane using an eight-channel knee coil. As one of these 48 images was used to establish manual annotation procedure between different annotators, the dataset's final version consists of 47 images (with 141 segmented objects) of which 31 images were used for training, and the remaining 16 images were used for testing. Figure 1 shows a sample from a Knee dataset with segmentation by three experts.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Feature 1 1** & **Feature 2 2** & **Feature 3 3** & **Feature 4 4** & **Feature 5 5** \\ \hline
[9] & NO & NO & NO & NO & NO \\
[7] & NO & NO & NO & NO & NO \\
[10] & NO & NO & NO & NO & NO \\
[8] & NO & NO & NO & NO & NO \\
[12] & NO & YES & NO & NO & NO \\
[3] & NO & NO & NO & YES & YES \\
[16] & NO & NO & NO & YES & NO \\
[6] & NO & NO & NO & NO & NO \\
[1] & YES & NO & NO & NO & YES \\
[5] & YES & YES & YES & NO & NO \\
[19] & NO & NO & YES & NO & YES \\ Our Work & YES & YES & YES & YES & YES \\ \hline \hline \end{tabular}  
* Multiple medical image types, 2 multiple anatomic locations, 3 multiple ground truths, 4 dropout rate influence, 5 model comparison.  
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Properties** & **KNEE** & **SIJ** & **LUNG** & **HEART** \\ \hline Total Num. & 47 & 345 & 1338 & 114 \\ Training Num. & 31 & 267 & 1029 & 65 \\ Testing Num. & 16 & 78 & 309 & 49 \\ Image Size & 448 by 448 & Vary & Vary & 512 by 512 \\ Image Type & MRI & MRI & CT & X-ray \\ Num. Objects 1 & 1 & 1 & 1 & 2 \\ Segmented objects & Synovitis & Bone Oedema & Lung Nodules & Lung and Heart \\ Num. Radiologists & 3 & 3 & 3 or 4 & 3 \\ Body Part & Knee & Sacroiliac Joint & Chest & Chest \\ Image Dimension & 2D & 2D & 3D & 2D \\ \hline \hline \end{tabular}  
* In addition to background.  
\end{table}
Table 2: Summarize the datasets property utilized in this paper.
## 3 Results
### In Vitro Drug Release Kinetics
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Formulations**} & **Temperature** & \(E_{\mathbf{a}}\) & \(\Delta\)S & \(\Delta H\) & \(\Delta G\) \\  & \({}^{\circ}\)**C/K** & **(kJ mol\({}^{-1}\))** & **(kJ mol\({}^{-1}\) K\({}^{-1}\))** & **(kJ mol\({}^{-1}\))** & **(kJ mol\({}^{-1}\))** \\ \hline  & 37/310.15 & & & & & 75.415 \\ PLGA-CS-PEG\_PGS5 & 41/314.15 & 2.656 & \(-\)0.243 & 0.050 & 76.387 \\  & 44/317.15 & & & & & 77.116 \\ PLGA-CS-PEG\_PGS8 & 41/314.15 & 3.245 & \(-\)0.242 & 0.638 & 76.662 \\  & 44/317.15 & & & & & 77.388 \\  & 37/310.15 & & & & & 75.622 \\ PLGA-CS-PEG\_PTX5 & 41/314.15 & 5.653 & \(-\)0.234 & 3.047 & 76.558 \\  & 44/317.15 & & & & & 77.260 \\ PLGA-CS-PEG\_PTX8 & 37/310.15 & & & & & 75.720 \\ PLGA-CS-PEG\_PTX8 & 41/314.15 & 3.890 & \(-\)0.240 & 1.284 & 76.680 \\  & 44/317.15 & & & & & 77.400 \\ \hline \end{tabular}
\end{table} 
Table 5: Values for thermodynamic parameters for PLGA-CS-PEG microspheres. Figure 5 shows a plot of \(\Delta G\) (kJ mol\({}^{-1}\)) vs. temperature (K) for PLGA-CS-PEG microspheres. A positive value was obtained for \(\Delta\)H, which shows that the release of drug from PLGA-CS-PEG microspheres was an endothermic process. Additionally, as the drug diffused in the medium to try to reach equilibrium, there was reduction in system disorder. This was clearly indicated by the negative values for \(\Delta S\) for all PLGA-CS-PEG formulations. Such nonspontaneous release may be due to the controlled release and more likely to promote the release of the drug at a controlled rate during a period of one month. The \(E_{\text{a}}\) is the energy required to move the drug molecule from within the polymer matrix to the medium. A positive \(E_{\text{a}}\) value was obtained for the drug release from PLGA-CS-PEG formulations, with values < 10 kJ/mol indicating that the in vitro drug release was mainly by diffusion-driven processes.
# Application of Multi-System Combination Precise Point Positioning in Landslide Monitoring
## 5 Application of Multi-System Combination PPP in Landslide Monitoring  
Using the landslide monitoring data of 18 June 2019-18 January 2020 in the research area, the monitoring data were processed and analyzed by static relative positioning technology, and the displacement of the monitoring point was obtained. The relative accuracy of the baseline vector is better than 0.1 ppm, the position accuracy of the plane direction is better than 2 mm and the position accuracy of the elevation direction is better than 5 mm. The traditional measurement methods through precision level measurement validate the results of the static relative positioning. The results show that the static relative positioning monitoring results and precision level measurement results are basically identical and have high precision and reliability. In this paper, the coordinates of monitoring points obtained by static relative positioning technology are taken as reference values, and the convergence time, positioning accuracy and stability of multi-system combined PPP technology in landslide monitoring are emphatically discussed. To be concise, GPS, GLONASS, Galileo and BDS are represented by the letters G, R, E and C, respectively.
# The Application of Fine Sand in Subgrades: A Review
## 2 Materials
### Physical and Mechanical Properties of Fine Sand Subgrades  
The engineering application of sand-filled subgrades has predominantly focused on gravel, river sand, and aeolian sand, while fewer studies focus on the practical application of fine sand in existing subgrades. A notable instance is the utilization of sand dunes in the subgrade of Interstate 1-94 in northern Indiana [6]. The soil in this case is classified as poorly graded sand (SP) in the Unified Soil Classification System and as A-3 in the AASHTO classification, indicating poorly graded gravel or sand, or sandy gravel with little or no fines. [PERSON] [11] et al. conducted non-destructive testing of the existing sand soil subgrade using a falling weight deflectometer combined with core drilling tests to determine the particle size distribution of the sand soil and the thickness of the asphalt pavement structure. Additionally, the research analyzed the nonlinear characteristics of the sand soil subgrade's resilient modulus and its relationship with the particle size distribution of the sand soil. The obtained sand soil particle size distribution curve is shown in Figure 1.  
Sand from the Osorio region in southern Brazil is classified as non-plastic homogeneous fine sand, characterized by spherical particle shapes (Figure 2) with a specific gravity of 2.63. Mineralogical analysis indicates that the predominant mineral in the sand grains is quartz. The average effective diameter D50 of the fine sand is 0.16 mm, with a uniformity coefficient of 1.9 and a curvature coefficient of 1.2. The minimum void ratio and maximum void ratio are 0.6 and 0.9, respectively [12].  
[PERSON] et al. [13] conducted sieve analysis experiments on six representative sand samples from Shanghai, China. The particle sizes of the sand samples were mostly in the range of 0.075-0.3 mm, with the coefficient of uniformity for each sand sample generally being less than 5. Although some differences were observed, fine sand samples exhibited a relatively uniform particle size distribution, indicating poorly graded sand.
## 2 Materials and Methods
### Chromatographic Analysis of C. sativa Leaf: LC-PDA and RP-LC-QTOF-MS
#### 2.2.3 Analysis by RP-LC-QTOF-MS  
For metabolic analysis, 5 mg of the crude extract of C. _sativa_, which contains a high cannabidiol (CBD) content (>85% of the total phytocannabinoids extracted) [18], was dissolved in methanol to a final concentration of 250 mg/L for subsequent analysis via reverse-phase liquid chromatography coupled with mass spectrometry (RP-LC-QTOF-MS).  
Samples were analyzed in a liquid chromatography system (Agilent Technologies 1260) coupled with a quadrupole time-of-flight (Q-TOF) mass analyzer (Agilent Technologies 6545B) with an electrospray ionization source (ESI). Separation was conducted in a C18 column (InfinityLab Poroshell 120 EC-C18 (100 \(\times\) 3.0 mm, 2.7 \(\upmu\)m) at 30 \({}^{\circ}\)C with a gradient elution consisting of 0.1% (\(v/v\)) formic acid in Milli-Q water (Phase A) and 0.1% (\(v/v\)) formic acid in acetonitrile (Phase B) at a constant flow rate of 0.4 mL/min. Mass spectrometric detection was performed initially in positive mode, followed by a subsequent analysis in negative mode using the same set of acquired data at full scan from 70 to 1100 \(m/z\). The QTOF instrument was operated in 4 GHz (high resolution) mode. The data acquisition parameters were configured as follows: ion source temperature of 325 \({}^{\circ}\)C, gas flow of 8 L/min, nebulizer gas pressure at 50 psi, and capillary voltage of 2800 V. MS/MS acquisition mode was performed in data-dependent acquisition (DDA) mode in the range of \(m/z\) 50 to 1100 with a scan sweep rate of 3 spectra/s and under chromatographic and spectrometric conditions identical to those employed in the initial analysis. For each sample, analysis was performed at different collision energies 20 eV, 40 eV, and equation mode was used (CE = 3.6 \(\times\) (\(m/z\))/100 + 4.8) [59; 60], using 3 precursors per cycle. During the analysis, several reference masses were used for mass correction: \(m/z\) 121.0509 (C\({}_{5}\)H\({}_{4}\)N\({}_{4}\)), \(m/z\) 922.0098 (C\({}_{18}\)H\({}_{18}\)O\({}_{6}\)N\({}_{3}\)P\({}_{3}\)F\({}_{24}\)) in positive mode and \(m/z\) 112.9856 [C\({}_{2}\)O\({}_{2}\)F\({}_{3}\) (NH\({}_{4}\))], and \(m/z\) 1033.9881 (C\({}_{18}\)H\({}_{18}\)O\({}_{6}\)N\({}_{3}\)P\({}_{3}\)F\({}_{24}\)) in negative mode.