esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparison of Land Cover Categorical Data Stored in OSM and Authoritative Topographic Data
## 5 Discussion
### Validity and Applicability of Data Comparison  
Decisions to use fit-for-purpose geospatial datasets are heavily based on data quality, and in particular, information volume, which is understood as the number of geographical features captured. OSM data are rich and heterogeneous, and their quality strongly depends on the degree of urbanization, as mentioned by [34; 57]. On the contrary, BCDT10k is perceived as very reliable as authoritative data [58]. It is updated every 2-3 years [56]. Both datasets are used in many applications in Poland by commercial companies and administrations, and in science and education.  
Risk assessment and risk management, spatial planning, and environmental protection and monitoring applications often require detailed data when every object is important. In loss assessment (e.g., due to flood or fire), indicating access roads and planning the locations of investments and many other applications, every building, road, railway and object that constitute some kind of barrier, such as water or forest, is important. When the analyzed sets are characterized by high data agreement, the choice of the set is definitely less important than when the two sets vary from each other. It is then necessary to analyze the quality of the datasets based on the indicators described, inter alia, by [PERSON] and [PERSON] [37], and to make a choice supported by the cartographic visualization of data quality [38].  
It is worth mentioning that OSM data are made available alongside official data in the form of the WMS (Web Map Service) in the national web portals (Geoportals), providing access to geographic information and spatial data services, e.g., in Poland, Germany, France and Greece [59; 60; 61; 62].
# Satellite Monitoring of Italian Vineyards and Spatio-Temporal Variability Assessment
## 2 Materials and Methods
### Statistical Analysis  
All the RS images were summarised by vineyard and vintage. Then, two statistical tools were performed in three steps using RStudio (version 1.2.1335 (c) 2009-2019 RStudio). ANOVA was performed for the categorical features (Topography, Location, Training System, Cardinal Direction), whereas a PCA was carried out on the numerical features (Area, Perimeter, N\({}^{\circ}\) of Vertices, Row Space, Headland, Slope, Elevation, L-W, and Orientation). An autocorrelation matrix was plotted among the numerical features to identify any correlation between them. Only one variable was chosen among the solid autocorrelated ones to avoid any predominance due to redundant information. Because a wide range of records came from different sources, the dataset was scaled and centred before the PCA, which balanced the comparison between variables. Each step was focused on a different assessment: (I) to evaluate the VIs distribution and variance along the Italian peninsula, (II) to assess the VIs evolution during the vineyard biomass accumulation temporal window, and (III) to assess the within-field variability of the vineyard's structure and stability along the six-year monitoring. To solve the first and the second research questions, an ANOVA and a PCA were carried out to evaluate the vintage and the vineyard's structure effect on the average VIs, the VIs' CV, the VIs regression line slope's average values and the slopes' CV. Moreover, [PERSON]'s multiple comparisons test was carried out to define the significative differences between classes in each variable. Finally, the average VIs measured during the monitoring period in each vineyard and vintage were used to define the specific experimental variogram to solve the third research question. The semivariograms were calculated with RStudio, using the autofitVariogram function of \"Automap\" packages with the spherical model [38]. These experimental semivariograms were used to describe the within-field variability of the vineyard by extracting specific spatial parameters such as nugget, silk and range. The silk and the nugget explain the maximum variance and the variance not spatially correlated, respectively. On the other hand, the range estimates the threshold distance at which ground data are no longer spatially correlated. Then, the Cambardella Index (CI) was assessed. The CI aims to explain the weight of the not spatially correlated variability compared to the whole vineyard variability [39]. Equation (7) describes the CI.  
\[CI=\frac{Nugget}{Sill}*100 \tag{7}\]
# An Image Segmentation Method Using an Active Contour Model Based on Improved SPF and LIF
## 2 Related Work
### The C-V Model  
The C-V model is proposed based on the assumption that the original image intensity is homogeneous. The energy functional of the C-V model [34] is expressed as  
\[E^{CV}=\lambda_{1}{\int_{inside(c)}{|I(x)-{c_{1}}|}^{2}dx}+\lambda_{2}{\int_{ outside(c)}{|I(x)-{c_{2}}|}^{2}dx}, \tag{6}\]  
where \(\lambda_{1}\) and \(\lambda_{2}\) are positive constants that regulate image driving force inside and outside the contour, \(c_{1}\) represents the mean gray value of the target area and the background area in the evolution curve \(C\), and \(c_{2}\) represents the mean gray value of the target area and the background area outside the evolution curve \(C\).  
By minimizing Equation (6), one has \(c_{1}\) and \(c_{2}\), which are described, respectively, as  
\[c_{1}=\frac{\int{I(x)H(\varphi(x))dx}}{\int{H(\varphi(x))dx}}, \tag{7}\]  
\[c_{2}=\frac{\int{I(x)(1-H(\varphi(x)))dx}}{\int{(1-H(\varphi(x)))dx}}, \tag{8}\]  
where \(H(\varphi)\) is the Heaviside function.  
In practice, the Heaviside function \(H(\varphi)\) and the Dirac delta function \(\delta(\phi)\) must be approximated by smooth functions \(H_{\epsilon}(\phi)\) and \(\delta_{\epsilon}(\phi)\) when \(\epsilon{\rightarrow}0\), which are typically expressed as follows, respectively  
\[H_{\epsilon}(x)=\frac{1}{2}[1+\frac{2}{\pi}\arctan(\frac{x}{\epsilon})], \tag{9}\]  
\[\delta_{\epsilon}(x)=\frac{1}{\pi}\cdot\frac{\epsilon}{\epsilon^{2}+x^{2}}, \tag{10}\]  
By incorporating the length and area energy terms into Equation (6) and further minimizing the length and area of the level set curve, the corresponding partial differential equation is described as  
\[\frac{\partial\varphi}{\partial t}=\delta(\varphi)\bigg{[}\mu\
abla(\frac{ \
abla\varphi}{|\
abla\varphi|})-v-\lambda_{1}|I-c_{1}|^{2}+\lambda_{2}|I-c_{ 2}|^{2}\bigg{]}, \tag{11}\]  
where \(\mu\), \(\
u\), \(\lambda_{1}\), and \(\lambda_{2}\) denote the corresponding coefficients, all of which are positive constants; \(\
abla\) is the gradient operator; \(\mu\) controls the smoothness of the zero level set; \(\
u\) increases the propagation speed; and \(\lambda_{1}\) and \(\lambda_{2}\) control the image data driving force inside and outside the contour, respectively.  
Because \(c_{1}\) and \(c_{2}\) are related to the global information inside and outside the curve, this model can segment blurred images and images with weak gradients more effectively than the edge-based model can, and it is insensitive to the initialization location [22; 35]. However, when the internal and external intensities of the curve are inhomogeneous, \(c_{1}\) and \(c_{2}\) cannot express the local information precisely, which leads to the failure of image segmentation [2].
# Transmission Delay-Based Uplink Multi-User Scheduling in IEEE 802.11 ax Networks +
## 6 Performance Evaluation  
We conducted performance evaluations of uplink multi-user scheduling using MATLAB. In the network topology where an AP and multiple stations are deployed, multiple stations participate in the uplink multi-user transmissions. As shown in Figure 1, the AP broadcasts an MU-RTS, and stations respond by transmitting a CTS. After collecting the transmission information, including buffer status and channel state, the proposed uplink multi-user scheduling method divides the stations into multiple clusters so that stations with similar expected delays are grouped together to transmit uplink data. The clusters are selected based on a proportional fair strategy or in a round-robin manner. We compared the proposed method with delay-based clustering with round-robin selection method, delay-based clustering with random selection method, and random selection method without clustering. In the proposed method, network throughput and fairness performance can be managed by adjusting \(\alpha\) and \(\beta\), and three kinds of \(\alpha\) and \(\beta\) combinations were compared in the simulation. Parameters \(\alpha\) and \(\beta\) are decided depending on the operation policy of Wi-Fi service providers. Table 1 shows the simulation parameters for the uplink transmission scenarios in IEEE 802.11 ax networks as in [14]. The required duration for channel sounding process is also considered as in [14]. The aggregated MAC protocol data unit (A-MPDU) pre-EOF padding (APEP) sizes of stations deployed in the network are randomly selected between 0 and 4097 bytes, and the MCS is set between 0 and 11 following the channel state between each station and the AP. In the simulations, channel status between stations and the AP were assumed to be asymmetric, which means that uplink transmissions may experience different channel conditions. This is more realistic than the symmetric channel scenario where all the stations experience the same channel conditions. Note that in the network scenario where all the stations experience a similar channel environment, the performance may predominantly depend on the data lengths.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline SIFS & 16 \(\mu\)s & Service field length & 16 (bits) \\ \hline AIFS & 34 \(\mu\)s & MPDU delimiter length & 32 bits \\ \hline Legacy preamble & 20 \(\mu\)s & MAC header length & 320 bits \\ \hline HE preamble & 168 \(\mu\)s & Tail length & 18 bits \\ \hline Max APEP size & 4096 Bytes & RTS & 160 bits \\ \hline Max buffer size & 256,000 bits & CTS & 112 bits \\ \hline \hline \end{tabular}
\end{table}
Table 1: IEEE 802.11 ax simulation parameters.
## Materials and Methods
### Calculation Model and Hourly Simulation  
Figure 3 shows the simplified simulation scheme of the calculation model, consisting of five major steps that are executed hourly for an entire year. A detailed description and input data are described in Appendix B, Tables A.4-A.6.  
1. Electricity consumption and production (yellow; see description in Appendix B.1)
2. Road transport hydrogen demand (blue; see description in Appendix B.2)
3. Electricity and hydrogen hourly balance (red; see description in Appendix B.3)
4. Hydrogen tube trailer and tractor fleet (grey; see description in Appendix B.4)
5. Wind hydrogen production and seasonal storage balance (green; see description in Appendix B.5)  
Two sets of energy balances are calculated, on both an hourly and an annual basis (Figure 3 in red and green) for both hydrogen and electricity energy carriers. Energy consumption takes place in buildings and for mobility. Energy production is by roof-top solar and wind turbines and covers all energy consumption needs, taking into account all efficiencies of the different energy conversion and storage processes.  
The amount of rooftop area available for solar electricity systems is fixed in both scenarios and locations for ease of comparison of the system performance between the two climates. The amount of installed wind capacity is the degree of freedom in the calculation model and completes the annual energy balance.  
The system is simulated for five years using weather data from 2012 to 2016, which results in varying hourly electricity production consumption profiles, as well as electricity production per installed capacity. For ease of comparison between the years, the annual building electricity demand is kept constant.
## 6 Results and Discussions
### 69-Bus System
#### 6.1.1 Results without Reducing the Search Space  
The search space in this case includes all the system's buses (excluding the slack bus); i.e., 68 buses. PODESCA decides the optimal locations and sizes of the added SCs, where only two locations are considered. The obtained results of full-load level are given in Tables 3 and 4. As shown in those two tables, the proposed algorithm's results are also compared to those from other methods in [27; 30; 46; 47; 48]. The optimal locations are 20 and 61 with total installed capacity equal to 1400 kVar, which is less than those from the other methods. Moreover, by applying PODESCA, \(V_{min}\) and VD are improved to 0.9302 p.u. and 0.0622 p.u., respectively. The APL is also reduced to 147.78 kW with a loss reduction (LR) percentage of 34.3%. Those values are better than those obtained from particle swarm optimization (PSO), fuzzy genetic algorithm (FGA), heuristic method (HM), and DE. However, although the APL obtained from improved harmony algorithm-power loss index (IHA-PLI) (145.32 kW) is still lower than that from PODESCA, the annual cost (ann. cost) is higher (86,122.10 $/year), while the annual cost by the proposed algorithm reaches 85,913.48 $/year. Consequently, the net savings (net sav. = 32,356.5 $/year) are also better than that from IHA-PLI (32,082.7 $/year). Therefore, the compromised results obtained by applying the proposed PODESCA are better than most of the other results considered in the comparison, which proves the algorithm's effectiveness. Additionally, the small SD value (0.01133) shown in Table 4 validates the method's consistency, especially when it is compared to the SD value of IHA-PLI method (832.160). The optimal results for different load levels are also obtained as given in Table 5, where, according to those levels, only a fixed SC of 550 kVar is needed at bus 61, whereas two switched SCs of 250 kVar at bus 20 and 600 kVar at bus 61 are required to cover the three load levels. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **APL** & \multirow{2}{*}{**LR (\%)**} & \multirow{2}{*}{**\(\mathbf{V_{min}}\)**} & **VD** & **Ann. cost** & **Net sav.** & **Net sav.** & **SD** \\  & **(kW)** & & & **(p.u.)** & & **(p.u.)** & & **(\$/year)** & **(\$/year)** & **(\%)** & **SD** \\ \hline Without SCs & 225.02 & - & 0.9092 & 0.0993 & 118,270.5 & - & - & - \\ \hline PSO [46] & 152.48 & 32.2 & - & - & 90,108.50 & 28,096.3 & 23.77 & - \\  & FGA [47] & 156.62 & 30.4 & 0.9369 & - & 92,179.50 & 26,025.3 & 22.02 & - \\ With SCs & HM [48] & 148.48 & 34.0 & 0.9305 & - & 88,901.10 & 29,303.7 & 24.79 & - \\  & DE [30] & 151.38 & 32.7 & 0.9311 & - & 89,913.40 & 28,291.4 & 23.93 & - \\  & IHA-PLI [27] & 145.32 & 35.4 & 0.9370 & - & 86,122.10 & 32,082.7 & 27.14 & 832.160 \\  & PODESCA & 147.78 & 34.3 & 0.9302 & 0.0622 & 85,913.48 & 32,356.5 & 27.36 & 0.01133 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of results between PODESCA and other methods for the 69-bus system.
## 5 Conclusions  
By merging the Monte Carlo method and the POD technique into the 4 DVar method, the POD4 DVar method retains the main strengths of traditional 4 DVar and avoids the need for an adjoint or tangent linear model of the forecast model in data assimilation. Compared with the EnKF-based strategy, this method has the advantage of higher computational efficiency in operational monitoring of regional crop yields. Significant improvements in the accuracy and efficiency of crop yield estimation were observed when the measured LAIs or the remotely sensed LAI maps were assimilated into the CERES-Wheat model with POD4 DVar. In addition to crop yield estimation, the potential applications of the POD4 DVar-based CERES-Wheat model may include the estimation of other critical variables in a crop growing season, such as the biomass, harvest index, evapotranspiration, and soil organic carbon. These potential applications suggest that our data assimilation strategy is a promising approach for operationally monitoring crop growing states and predicting critical crop variables. However, successful application in an operational system for regional crop monitoring must address several issues of uncertainty, such as the crop-growing mechanism in the crop model, model inputs, data assimilation algorithm, and assimilated observations. These issues will be examined in future studies.
# A Study on the Softening Shear Model of the Energy Pile-Soil Contact Surface
## 2 Materials and Methods
### Modeling Process  
The geometric model of the coupled thermal-hydro-mechanical numerical model of energy piles under multilayered geological conditions arranges a cylinder containing curves inside in a multilayered rectangular body. As shown in Figure 3, the curve represents the heat exchanger pipe, the cylinder represents the concrete pile, and the multilayer rectangular body represents the multilayer soil body, which is as follows: heterogeneous fill, silty powdery clay, powdery clay, powdery fine sand, strongly weathered mudstone, and moderately weathered mudstone from shallow to deep in order. To avoid the influence of soil body dimensions on the heat exchange of the energy pile, the length and width of the soil body are not less than 20 times the diameter of the energy pile, and the height of the soil body is not less than 2 times the length of the energy pile.  
The grid dissection of the model adopts a user-controlled grid, the maximum and minimum cell sizes of the soil region around the pile are 7760 and 564, the maximum and  
Figure 3: Energy pile geometry model, meshing and boundary condition.  
minimum cell sizes of the energy pile region are 4940 and 212, the maximum and minimum cell sizes of the heat exchanger pipe edge are 2820 and 28.2, respectively, the final number of grids in the model area is 207,407, the minimum cell quality is 0.1271, and the model grid dissection results are shown in Figure 3.  
When the seepage rate of groundwater in the soil body is less than 0.5 m/a, the effect of seepage on heat exchange can be ignored, so the two layers of fine powdered sand are regarded as a seepage layer, and the other strata are regarded as a water barrier without groundwater seepage.  
This numerical model considers the pile concrete and the surrounding soil as linear elastic materials. The material model of pile concrete is set as the Bresler-Pister criterion and the material model of surrounding soil is set as the Mohr-Coulomb criterion. Since both the pile deformation due to the heat transfer temperature difference and the structural load of the energy pile do not reach the yield strength of the material, the yield damage of the material is not considered.
# GNSS Performance Modelling and Augmentation for Urban Air Mobility +
## 3 GNSS Performance Characterization  
This section presents error models for GNSS, and in particular, analyses errors dominant in the urban environment viz. Non-Line of Sight (NLOS) and multipath errors. Secondly, the analysis is used to generate a guidance strategy that maximizes GNSS performance.
## 2 Secure Network Encryption Protocol
### Node to Node Key Agreement Sub-Protocol  
The node to node key agreement sub-protocol, shown in Figure 4, is being used when a node wants to communicate with another node and they do not have any shared secret key. With the help of the base station (here, the trusted third party \(S\)), two nodes can establish a shard secret key, \(SK_{AB}\). Note that with \(S\), \(A\) and \(B\) share secret \(\chi_{AS}\) and \(\chi_{BS}\), respectively.  
The following security properties are expected to be fulfilled by this protocol:  
* Due to the use of \(N_{A}\) and \(N_{B}\), two properties are guaranteed: (i) strong key freshness to both \(A\) and \(B\); and (ii) authentication of \(S\) to \(A\) (at the end of the third message) and \(S\) to \(B\) (at the end of the fourth message).
* The confidentiality of \(SK_{AB}\) through encryption.
* The data authenticity and integrity of \(SK_{AB}\) through keyed MAC, which confirms that it was generated by the base station.
* Defending the base station against the denial-of-service attack through the MAC in the second message; thus, the base station confirmed that the request has come from a legitimate node.  
Figure 4: Node to node key agreement sub-protocol.  
Figure 3: Counter exchange sub-protocol.  
* Saving energy of the nodes by engaging base station to perform the major tasks, e.g., key generation and distribution.
## 3 Results and Discussion
### Photocatalytic Performance Results

Table 3: The textural properties of all samples. electron-hole recombination and prolonging electron lifetime [13; 21]. Consequently, this enhances the overall photocatalytic activity of the material. \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Concentration** & \multirow{2}{*}{\(\mathbf{k\ (\text{min}^{-1})}\)} & \multirow{2}{*}{\(\mathbf{\text{R}^{2}}\)} \\  & **[ppm]** & & \\ \hline TiO\({}_{2}\)/zeolite & 10 & 0.0436 & 0.963 \\ Pure synthesized TiO\({}_{2}\) & 10 & 0.0286 & 0.917 \\ TiO\({}_{2}\)/zeolite & 20 & 0.0207 & 0.994 \\ Pure synthesized TiO\({}_{2}\) & 20 & 0.0139 & 0.994 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Kinetic parameters in the photocatalytic experiments. Figure 7: Photodegradation of methyl orange using pure TiO\({}_{2}\) and TiO\({}_{2}\)/zeolite material at concentrations of (**a**) 10 ppm and (**b**) 20 ppm.
## 4 Conclusions  
The present study showed that FDM can be successfully utilized to process relatively cheap PLA/WSP liposomes filaments and achieve acceptable thermal and mechanical properties. The experimental results showed: the thermal and mechanical properties of PLA/untreated WSP decreased due to the poor compatibility between WSP and PLA, which was dramatically improved by NaOH/KH550 treatment; the crystalline, thermal gravity, and thermal stability of untreated biocomposite was improved from 1.46%, 60. 3 \({}^{\circ}\)C, and 239.87 \({}^{\circ}\)C to 2.84%, 61.3 \({}^{\circ}\)C, and 276.37 \({}^{\circ}\)C; the tensile, flexural, and compressive strength mounted to 52.5 MPa, 51.7 MPa, and 276.6 MPa, respectively. The tensile strength was further improved to 56.2 MPa by adding more WSP into PLA matrix. SEM morphology revealed that the number and size of micro voids, and leaks between layers were reduced with an increase of the KH550 concentration and ratio of WSP. Finally, the porous scaffolds with controllable porosity (40-75%) and pore size (50-200 \(\upmu\)m) were manufactured. 10.93-104.07 MPa of the compression modulus showed that the scaffold has the potential to be used as trabecular bone implants applied in tissue engineering. The utilization of FDM technology employing walnut shells in the process not only cut down the cost of PLA filament by about 15%, but also led to several benefits, such as making good use of wastes, enhancing the degradation of PLA, and customizing complex components with interconnected pores and suitable porosity.  
Conceptualization, X.S. and W.H.; methodology, S.Y.; software, G.H.; validation, X.S. and W.H.; formal analysis, X.S.; investigation, X.S. and T.Y.; data curation, X.S.; writing--original draft preparation, X.S.; writing--review and editing, W.H. and S.Y.; visualization, G.H.; supervision, W.H.; project administration, X.S. and W.H.; funding acquisition, X.S.  
This research was funded by \"(1) the National Nature Science Foundation of China, grant number 51705094\", \"the Nature Science Foundations of Guangxi province, grant numbers 2017 JJB150072 and 2018 JJJA160218\".  
The authors gratefully acknowledge financial support from: the National Nature Science Foundation of China (51705094), the Nature Science Foundations of Guangxi province (2017 JJB150072 and 2018 JJA160218).  
The authors declare no conflict of interest.