esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of CYGNSS Ocean Surface Wind Speed Products
## 3 Results
### Sampling Properties
#### 3.3.2 Sampling of Tropical Cyclone Winds  
The MRG product is reported on a 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) grid and on a storm-by-storm basis every 6 h from the start to the end of a storm's lifecycle, as defined by Best Track data for storms occurring in the Northern Atlantic and Eastern Pacific basins and by the Joint Typhoon Warning Center (JTWC) for storms occurring in the Western North Pacific, North Indian Ocean, and the Southern Hemisphere basins. Six-hourly reports are only provided if sampling of the storm by the CYGNSS constellation is sufficient.  
Table 4 shows the temporal coverage of the MRG product, calculated as the percentage of 6-hourly increments per day that produced wind field reports, averaged over all days throughout the lifecycles of the 360 storms in the dataset covering August 2018-November 2023. The most likely number of reports is three times per day (43%), followed by twice per day (34%), no reports that day (12%), and once per day (11%).  
In addition to wind speed, the MRG product also includes R34 estimates in each storm quadrant, calculated directly from the MRG wind fields. In order to produce an R34 estimate, there must be (1) a crossover from > to <34 knots in the azimuthally averaged radial wind speed profile and (2) sufficient spatial coverage to produce a gap-free radial profile of the wind speed. These conditions are not always met, so R34 estimates are not always available in every quadrant. Table 5 summarizes the frequency of R34 production by specifying what fraction of the time R34 is reported in a given number of quadrants. The table also breaks up R34 production by storm intensity--tropical depression (TD), tropical storm (TS), or typhoons and hurricanes (TY/HU). Table 5 demonstrates that stronger storms tend to have better R34 sampling, with only 3% of wind fields corresponding to TD strength storms producing any R34 values vs. 67% of wind fields corresponding to TY/HU category storms. Among wind fields that report R34 values for at least one quadrant, the wind fields corresponding to TD and TS storm categories are most likely to produce an R34 value for only one quadrant, while wind fields corresponding to TY/HU storm status are most likely to produce R34 values for all four quadrants.  
\begin{table}
\begin{tabular}{l l l l l l} \hline Number of reports per day & 0 & 1 & 2 & 3 & 4 \\ \hline \% of dataset & 12.1 & 11.1 & 33.7 & 42.6 & 0.52 \\ \hline \end{tabular}
\end{table}
Table 4: Average number of MRG wind fields reported per day from a 360 storm dataset.  
Figure 15: Mean revisit time of FDS wind speed product as a function of latitude.
# Efficient Communication Model for a Smart Parking System with Multiple Data Consumers
## 2 Data Consumers in an SPS
### Over-the-Internet Data Consumers  
Off-site data consumers are remote devices that access the parking lot occupancy data combined with other systems such as maps or payments. Figure 4a,b show mobile applications for booking parking lots or viewing parking lots available in a particular area. In contrast, Figure 5 presents a web page that provides a passive view of the availability information from the parking sensors. These are good examples for remote data consumers that need Internet access to receive data from an SPS. In an IPv6 mesh, the BR advertises a global prefix, and hence, the nodes become accessible over the Internet. The IoT devices are capable of hosting a web page, and hence, it can supply the occupancy data to a web browser upon an HTTP request. The example for such a web page is as shown in Figure 5, where a laptop is connected to the BR through a Serial Line Internet Protocol (SLIP). SLIP allows IP datagrams to be encapsulated and exchanged over serial ports. The web page can be accessed through the IPv6 address of the data collator.  
Such direct BR to Internet connections work for a limited number of devices. However, if the number of simultaneous users increases, it is preferential to host the application in the cloud, as it provides elasticity in meeting user demand. In addition, web and mobile applications in the cloud have the privilege of collating data from multiple parking lots to show area-wide or city-wide car park availability. They can integrate the parking system data with other systems to offer seamless services.  
Figure 4: Mobile application with multiple services. (**a**) Booking individual parking slot. (**b**) Searching for available parking slots in a map.  
Figure 5: Over-the-Internet data consumer, a web browser showing parking occupancy.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analyses were conducted using the GraphPad-Prism software (GraphPad Software, La Jolla, CA, USA) and SPSS (23.0, SPSS Inc., Chicago, IL, USA). The data distribution was assessed by applying a Kolmogorov-Smirnov test prior to all subsequent analyses. Normally distributed data were expressed as mean \(\pm\) standard error of the mean (SEM) and not normally distributed data were depicted as median \(\pm\) interquartile range (IQR). Medians between the investigated groups were analyzed by applying a Kruskal-Wallis test with [PERSON]'s post hoc test. A \(\chi^{2}\)-test was used to assess differences between expected and observed frequencies in the investigated groups. [PERSON]'s rank correlation coefficient was used for correlation analysis, whereas binary logistic regression analysis was used to investigate the predictive ability of biomarkers for detecting sepsis. ROC analysis and AUC measurement were calculated for all investigated biomarkers. Using [PERSON]'s index (Youden's J statistic), cut-off values for sST2, GDF-15, suPAR and H-FABP were calculated based on coordinates obtained from the ROC-curves. In order to combine AUC measurements of biomarkers, binary logistic regression analysis was calculated, followed by further ROC analyses of biomarker combinations. A \(p\)-value \(<\) 0.05 was considered statistically significant.
# A Membrane Modified with Nitrogen-Doped TiO2/Graphene Oxide for Improved Photocatalytic Performance
## 2 Experimental
### Modified Membrane Characterization  
The surface morphology of the modified membrane was characterized by scanning electron microscopy (SEM) (Carl Zeiss AG, Oberkochen, Germany, www.zeiss.com.cn). The crystal structure of the nanoparticles on the surface of the modified membrane was determined by X-ray diffractometry (XRD) (Bruker, Santa Barbara, CA, USA, www.bruker.com). The chemical bonds on the surface of the membrane were characterized by Fourier transform infrared spectroscopy (FT-IR) (Thermo Scientific, Waltham, MA, USA, www.thermofisher.com). The hydrophilicity of the membrane surface was investigated by water contact angle meter (Micaren, Xiamen, China, www.micaren.com).
## 2 Sparse Representation
### Construction of Redundant Dictionary  
For the sparse representation of signals, the choice of dictionary \(D\) is very important. There are generally two ways to construct redundant dictionaries: one is a fixed dictionary, such as discrete cosine transform (DCT), wavelet basis, Gabor dictionary, and so on. This kind of dictionary is fast in calculation, but an important defect is that it can only be sparsely represented for a certain kind of vibration signal. Another method is to construct an adaptive dictionary, such as the K-SVD algorithm [PERSON] (1996), through learning and training according to the structural characteristics of the signal itself. Compared with the fixed dictionary, the K-SVD algorithm constructs an adaptive learning dictionary, which is not limited to a certain kind of signal data, has stronger adaptive ability, and the method is more efficient, and it can obtain more sparse signal expression. Its core idea is to alternately update dictionary atoms and sparse coefficients by solving sparse constraints and singular values of dictionaries. The specific steps of the K-SVD algorithm are as follows: (Algorithm 1).  
```
Step 1: Initialize the dictionary \(D\), and randomly select \(x\) column vectors from the original sample \(Y\) as the atoms of the initial dictionary. Then, set the relevant parameters: sample length \(W\), number of iterations \(J\), and sparsity \(K\). Step 2: Fixed dictionary \(D\), using a sparse coefficient algorithm to solve \(\alpha_{k}\): \[\min\lVert Y_{k}-D\alpha_{k}\rVert^{2}_{2}\,s.t.\forall i\,\left\lVert\alpha_{k }\right\rVert_{0}\leq\varepsilon\] (4) where \(Y_{k}\) is the original signal vector and \(\alpha_{k}\) is the sparse coefficient vector. Step 3: Fixed sparse coefficient \(\alpha_{k}\), update the dictionary column by column (\(D=\{d_{1},d_{2},d_{3},\ldots,d_{N}\}\)); when the \(d_{k}\) is updated, the residual matrix is calculated \(E_{k}\): \[E_{k}=Y-\sum_{j\
eq k}d_{j}\alpha_{T}^{j}\] (5) where \(Y\) is the original sample, \(d_{j}\) is the \(j\) column vector of the dictionary \(D\), and \(\alpha_{T}^{j}\) is the \(j\) row vector of the \(d_{j}\) corresponding sparse matrix.Remove the value of \(0\) in \(\alpha_{T}^{j}\), and similarly take out the column with the value of \(0\) corresponding to \(E_{k}\). \(E_{k}\) is decomposed by singular value decomposition \(E_{k}=U\sum V^{T}\), the first column of \(k\) is taken to update the \(k\) column of the dictionary \(D\), and the updated \(d_{k}\) is obtained. At the same time, the product of \(\sum(1,1)\) and the first column is used as the updated sparse coefficient. Step 4: Repeat steps 2 and 3 until the iterative conditions are met.
```  
**Algorithm 1**The specific steps of the K-SVD algorithm  
The flow chart is shown in Figure 1.  
Figure 1: K-SVD algorithm flow.
## 3 Results and Discussion  
### Structure and Micomorphology of Raw Materials and Composites  
Figure 3a shows the microscopic morphology of the PLA particles under SEM (50\(\times\)). PLA particles had irregular shapes and large size. Figure 3b shows the microscopic morphology of CB particles under SEM (5000\(\times\)). CB particles had a high specific surface area and surface energy with nanometer diameter, so agglomeration and adsorption occurred. It contributes to the formation of conductive networks, thus increasing the conductivity and dielectric constant of the material. Figure 3c is the microscopic topography of CB/PLA composites. In general, the CB particles were uniformly distributed in the matrix with good compatibility, which is beneficial to improving the microwave absorption performance.  
The XRD patterns of the CB/PLA composites with different CB contents are shown in Figure 4. With the increase of CB, the crystal diffraction peak of the CB/PLA composite was still the crystal diffraction peak of PLA. Although the crystal diffraction peak intensity of the composite material was slightly weakened, the position of the diffraction peak remained basically unchanged. It shows that the increased CB content had little effect on the crystal structure.  
Figure 3: SEM micrographs of (**a**) PLA; (**b**) CB; (**c**) composite.  
Figure 2: Schematic diagram of the coaxial transmission line measurement.  
The TG curves of the CB/PLA composites are shown in Figure 5. From room temperature to 270 \({}^{\circ}\)C, the curve was almost horizontal, and no mass loss occurred. It indicated that the composites had good thermal stability at this stage. The residual rate of the pure PLA sample was 0.68%, indicating that it was almost completely decomposed. The CB residual rate of 5-25% CB-PLA samples were 4.83%, 8.74%, 13.23%, 16.37%, and 20.19%. These are coincident with the ratio of CB to total composite that is listed in the last column of Table 2 (4.76, 9.09, 13.04, 16.67, 20). It proved that the control of various contents of loading CB was precise.
# Unlicensed Spectrum Allocation for LTE and Wi-Fi Coexistence with HAP
## 4 The Proposed Scheme
### Time Complexity  
We analyze the time complexity of the coexistence mechanism proposed in this paper. According to the optimization process for \(\delta\), as shown in Figure 5, we list the time complexity of each step.  
First, we will determine the throughput threshold \(P_{th}\) and transmission probability threshold \(Throughput_{th}^{WiFi}\) of Wi-Fi based on the load of LTE eNB and Wi-Fi AP. Assuming that there are \(n\) LTE users in the environment, the time complexity of calculating the load of LTE eNB is \(O(n)\), and the time complexity of determining the threshold of Wi-Fi stations is \(O(1)\). Therefore, the time complexity of the step is \(O(n)\). The next step is to compute the lower bound of \(\delta\) according to the throughput threshold \(P_{th}\) of Wi-Fi. To do this, we need to calculate the successful transmission probability of Wi-Fi stations, \(P(d<D\mid N_{STA})\). Suppose that the maximum retry limit is \(R\) and the number of STA is \(m\), the loop that calculates the successful transmission probability will execute \(\sum_{i=0}^{R}\sum_{j=0}^{\text{CN}_{0}*(2^{i+1}-1)}\sum_{l=0}^{c}\sum_{k=0}^{ j}1\) times, where \(CW_{0}\) represents the initial size of the contention window, and \(c\) denotes a constant count for sampling the probability in (5). The complexity of calculating the lower bound of \(\delta\) is equal to the complexity of calculating the successful transmission probability of Wi-Fi stations. In computing \(P(d<D\mid N_{STA})\) of (3), the time complexity is \(O\big{(}R2^{R}\big{)}\) as in (5).  
The procedure of finding out the upper bound of \(\delta\) is to sum up the throughput of all LTE users. Therefore, the complexity of this part is \(O(n)\). On the other hand, when considering the user filtering mechanism, there are two parts to the procedure. The first part is to sort all the LTE users according to their assigned priorities using the Quicksort algorithm. Therefore, the complexity is \(O(n\log n)\). The second part is simply filtering the sorted users in which the complexity is \(O(n)\). Thus, the complexity of performing the user filtering mechanism is \(O(n\log n)\).  
Finally, we can get the list of all feasible solutions between the upper and lower bounds. All these feasible solutions will be substituted into the utility function and find out the optimal \(\delta\) which maximizes the utility function. The complexity of finding the optimal \(\delta\) is \(O(n)\).  
According to the above analysis, the most time-consuming procedure is the calculation of the successful transmission probability of Wi-Fi stations and performing the LTE user filtering mechanism. Therefore, the time complexity of the coexistence mechanism proposed in this paper is \(\max\{O\big{(}R2^{R}\big{)},O(n\log n)\}\).
## 2 Datasets and Methods
### Intercalibration over the Amazon Rainforest
Only HY-2C and HY-2D provide data at all LST. In this study, the relative calibrations are derived by comparing the data at local time around 6:00 and 18:00 in the following steps:  
1. Average the \(\sigma^{0}\) data for the local time in range of [5:00, 7:00] for each polarization. Then form the same average for \(\sigma^{0}\) data in the local time range of (17:00, 19:00). The results are denoted as \(\sigma^{0}_{\text{HH,6}}\), \(\sigma^{0}_{\text{HH,18}}\), \(\sigma^{0}_{\text{VV,6}}\), and \(\sigma^{0}_{\text{VV,18}}\). 2. Compute the difference of average \(\sigma^{0}\) values between two of HSCAT instruments at 6:00 and 18:00 separately for each polarization. The results are denoted as \(\Delta\sigma^{0}_{\text{HH,6}}\), \(\Delta\sigma^{0}_{\text{HH,18}}\), \(\Delta\sigma^{0}_{\text{VV,6}}\), and \(\Delta\sigma^{0}_{\text{VV,18}}\). 3. Compute the mean difference for the same polarization; the results are denoted as \(\Delta\sigma^{0}_{\text{HH}}\) and \(\Delta\sigma^{0}_{\text{VV}}\). \[\Delta\sigma^{0}_{\text{HH}} =\left(\Delta\sigma^{0}_{\text{HH,6}}+\Delta\sigma^{0}_{\text{HH,18}}\right)/2, \tag{1}\] \[\Delta\sigma^{0}_{\text{VV}} =\left(\Delta\sigma^{0}_{\text{VV,6}}+\Delta\sigma^{0}_{\text{VV,18}}\right)/2, \tag{2}\]  
Figure 6: Hourly average \(\sigma^{0}\) over the Amazon as measured by HSCAT-B, HSCAT-C, HSCAT-D, ISS/RapidScat, and QuikSCAT/SeaWinds for different seasons (different lines with the same color). Noting that only summer is available for HSCAT-D so far.
## 3 Results and Discussion  
### Characteristics of Nano-Silver Dispersion  
The basics of nano-silver dispersion used in this study were investigated as shown in Figure 3. After removing the water from the dispersion, a large matrix made of polyvinylpyrrolidone (PVP) polymer which was water soluble and originally included as a dispersant in the nano-silver dispersion was observed, and countless nanoparticles were contained in the PVP as shown in Figure 3a. According to EDX analysis, these nanoparticles were indeed silver (Figure 3b,c). TEM images showed that the silver nanoparticles were spherical and varied in diameter from a few nm to about 10 nm (Figure 3d). In addition, individual nanoparticles were dispersed and did not aggregate to form large clumps that would prevent entry into the pore. It is thought that the silver nanoparticles originally existed in a dispersed state in the nano-silver dispersion liquid without aggregation due to the effect of PVP and that the removal of water from the dispersion fixed the nanoparticles in a dispersed state in the dried PVP film. The particle size distribution chart showed that the mode was 3.5 nm and the average diameter was 4.6 \(\pm\) 2.0 nm (Figure 3e). This indicates that the silver nanoparticles were sufficiently smaller than the nanopores, which were approximately 100 nm in diameter. Therefore, the silver nanoparticles are fully capable of entering the nanopore.  
Figure 3: (**a**) TEM image of dried nano-silver dispersion, (**b**) HAADF-STEM image of dried nano-silver dispersion, (**c**) EDX spectra of silver nanoparticle in PVP matrix, (**d**) TEM image of silver nanoparticles separated from PVP matrix, (**e**) size distribution of silver nanoparticle.  
It is necessary to investigate whether the nanoparticles adsorb on the substrate after dropping the dispersion on the YSZ surface. The driving force for the adsorption of nanoparticles on the YSZ substrate could be the Coulomb force due to the surface charge of the YSZ and silver nanoparticles. Figure 4 shows the zeta potential of each sample. At the expected dilution ratio, the pH of the dispersion was about 5.0-5.8 (Figure 4a). In this pH range, the YSZ surface after nanopore generation showed a slightly positive zeta potential, and nano-silver dispersion showed a negative zeta potential (Figure 4b). The polarity of the surface charge of silver nanoparticles in the dispersant and YSZ surface was opposite, indicating that the nanoparticles were in a desirable state to adsorb onto the sample.
## 6 Contribution to Sensor and Actuator Networks  
The field of sensor and actuator networks plays a pivotal role in modern technological advancements, enabling real-time data collection and remote control across various domains. In this section, we explore how our research on polarization division multiplexing-based underwater optical wireless communication (UWOC) with on-off keying (OOK) modulation format can contribute to the enhancement of sensor and actuator networks.  
1. Integration with Sensor and Actuator Networks Our proposed UWOC system offers several features that align with the requirements of sensor and actuator networks. One of the primary strengths of our system is its ability to transmit data at high rates with low latency, making it particularly suitable for real-time data collection and control applications. By seamlessly integrating our UWOC system into sensor networks, we can achieve faster and more efficient data transmission, enabling enhanced decision-making processes and real-time monitoring of remote environments.
2. Use Cases and Applications 1. Remote Environmental Monitoring In sensor and actuator networks for environmental monitoring, the timely collection of data from remote sensors is critical. Our UWOC system can enable high-speed data transmission from underwater sensors to central monitoring stations, facilitating the rapid assessment of environmental conditions. This can have applications in monitoring oceanographic data, assessing water quality, and tracking marine life. 2. Underwater Robotics and Actuation In underwater robotics and actuation systems, real-time communication is vital for remote vehicle control and data retrieval. By employing our UWOC technology, underwater robots can benefit from improved data rates and reduced communication delays, leading to more precise control and efficient exploration of aquatic environments.
3. Potential Impact The integration of our proposed UWOC system into sensor and actuator networks can yield significant benefits. It can enhance the capabilities of remote monitoring system: 1. Expand the scope of underwater data collection and analysis. 2. Improve the reliability of underwater communication networks. 3. Enhance real-time control and actuation in aquatic environments.  
Thus, our research on polarization division multiplexing-based UWOC with OOK modulation format holds promise for advancing sensor and actuator networks, particularly in underwater and aquatic environments. The high-speed data transmission, low latency, and enhanced security aspects of our system can contribute significantly to the efficiency and effectiveness of sensor and actuator-based applications in challenging underwater settings.