esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Adsorption Behaviours of the Biosorbents
#### 3.2.2 Effect of pH  
The solution pH is the most essential component in the adsorption process. It can be influenced by adsorbate species dissociation and adsorbent surface chemistry. The influence of pH on the adsorption of Fe(III) onto N-PPF and C-PPF was studied at pH 2, 4, 7, and 12. The two adsorbents showed similar characteristics of adsorption behaviour. The highest percentages of Fe(III) removal for C-PPF and N-PPF were observed at pH 2, at 92.00% and 91.20%, respectively (Figure 5). Because the adsorbent is a fibre with a negative surface charge, the efficiency of Fe(III) removal decreased as the pH of the solution increased. This could be due to hydrogen bonding between Fe(III) and the surface of the adsorbent. However, pH 4 was chosen for subsequent experiments because the removal efficiency of Fe(III) at this pH was comparable to that at pH 2. At a pH greater than 4, however, Fe\({}^{3+}\) ions reacted with OH\({}^{-}\) and formed the sediment Fe(OH)\({}_{3}\), resulting in decreased adsorption of Fe\({}^{3+}\). Therefore, pH values of 2-4 are the most suitable for adsorption as there is no conflict with OH\({}^{-}\), yet Fe\({}^{3+}\) ions have sufficient opportunity to completely bind to the active site of adsorbents [4; 10; 28].
# Performance of Dense Wireless Networks in 5G and beyond Using Stochastic Geometry
## 2 System Model
### Array Gain Model  
Each AP equipped with the array of antennas is positioned at height \(h\) from the ground at \((0,0)\) planar coordinates as in Figure 1. The array gain for the UcylA in far-field \(G(\phi,\theta)\) depends on the elevation angle (\(\theta\)) and azimuth (\(\phi\)), which in turn depends on the number of antennas partitioning between \(N_{v}\) and \(N_{c}\). The beamforming for the cylindrical array is conveniently decomposed into the design of two compound arrays, and thus the array gain \(G(\phi,\theta)=G_{c}(\phi)G_{v}(\theta)\) is separable into the UCA gain \(G_{c}(\phi)\) and vertical ULA gain \(G_{v}(\theta)\)[38; 39]. The approximation holds true in UcylA when using separable weightings [40; 41]. The beamforming used here is the conventional method, which is optimum for uniform interference, and the array gains for half-wavelength inter-element spacing either for UCA and ULA are [41; 42]:  
\[G_{c}(\phi) = J_{0}\left(\frac{N_{c}}{2}\sqrt{(\cos\phi-\cos\phi_{o})^{2}+( \sin\phi-\sin\phi_{o})^{2}}\right), \tag{1}\] \[G_{v}(\theta) = \frac{\sin[\pi(\sin\theta-\sin\theta_{o})N_{v}/2]}{N_{v}\sin[\pi (\sin\theta-\sin\theta_{o})/2]}, \tag{2}\]  
where \((\phi_{o},\theta_{o})\) denotes the pointing azimuth and elevation pair to the intended UE, and for UCA the gain approximation (1) is obtained using the zero-order Bessel function of the first kind \(J_{0}(.)\), which can be shown to be accurate for \(N_{c}\geq 16\). Note that array gains are normalized for convenience to have \(G(\phi_{o},\theta_{o})=1\). The beam widths along the two angles, \(\Delta\phi\) and \(\Delta\theta\), are inversely proportional to \(N_{c}\) and \(N_{v}\), respectively. The elevation beam width is further distorted by the effective array aperture, which makes the beam width scale with the cosine of the tilt angle (stretching effect): \(\Delta\theta/\cos(\theta-\theta_{o})\).
## 6 Simulation Results and Discussion
### Average Number of Jobs (Shipments) Completed  
We have also compared the average number of shipments completed during a period of one month, on a weekly basis (5 weeks in total). We note that our model finds that, on average, about 20% are completed compared to the actual data (see Figure 13). Apart from the randomness introduced, our model fails to consider the fact that during a trip (regardless of the vehicle being used), a shipping plan may include more shipping orders than the ones actually scheduled by our model. This is a fact that affects the resource utilization as well.  
From the simulation results we obtained, we made some conclusions regarding our model:  
Figure 12: Comparing the actual and simulated human resource (drivers) utilization values.  
Figure 13: Comparing the actual and simulated average shipment completion values.  
1. The modeling methodology described in Section 3 managed to model a real system based on real data, with quite good accuracy (the divergences were natural, as explained in this section).
2. The hierarchical modeling approach allows the addition of extra details that can help us approach the model with even higher accuracy.
3. Apart from the statistics presented in this section, the model is able to provide statistics for every single entity of the system (worker or vehicle). For example, we could study the utilization of each single driver during a period of time and analyze his efficiency (based on the number of shipping tasks he has completed). This is one of the main strengths offered by our modeling approach: each entity can be separately studied, if necessary.
4. The results can be obtained with numerous different scenarios, for example, reducing or increasing the number of human and non-human resources, without changes to our modeling approach. Thus, our modeling approach can be used as an aid for optimization.
5. The hierarchical modeling approach described in Section 3 can be used to model numerous different real life models.
###### Abstract  
We present a Geographical Information System (GIS)-based framework implementing a Mamdani fuzzy rule-based system to partition in an unsupervised mode an urban system in urban green areas. The proposed framework is characterized by high usability and flexibility. The study area is partitioned into homogeneous regions regarding the characteristics of public green areas and relations with the residents and buildings. The urban system is initially partitioned into microzones, given the smallest areas in which a census of the urban system is taken in terms of resident population, type and number of buildings and properties, and industrial and service activities. During a pre-processing phase, the values of specific indicators defined by a domain expert, which characterize the type of urban green area and the relationship with the residents and buildings, are calculated for each microzone. Subsequently, the fuzzy rule-based system component is executed to classify each microzone based on the fuzzy rule set constructed by the domain expert. Spatially adjoining microzones belonging to the same class are dissolved to form homogeneous areas called urban green contexts. The membership degrees of the microzones to the fuzzy set of their class are used to evaluate the reliability of the classification of the urban green context. We test our framework on the municipality of Pozzuoli, Italy, comparing the results with the ones obtained in a supervised manner by the expert appropriately partitioning and classifying the urban study area based on his knowledge of it.  
GIS framework; Mamdani fuzzy rule based; fuzzy partition; fuzzy sets; UGC; UGA class
## 4 Simulation Design of NR-ODBR-DCSK System Based on USRP
### USRP-Based NR-ODBR-DCSK Transmitter  
The design idea of the NR-ODBR-DCSK communication system in this section is to organize the chaotic signals generated by MATLAB simulation into the form of text, and then write the text signals into the USRP device through the LabVIEW program. The USRP device processes the text signals into the digital baseband signals, and then upconverts the signals into IF through digital up-conversion and transmits the signals into the real channel through the operations of digital-to-analog conversion, filtering and amplification. Then, the signal is transmitted to the real channel through digital-to-analog conversion, filtering and amplification, etc. The following Figure 8 shows the schematic diagram of the NR-ODBR-DCSK system transmitter through USRP:  
Figure 8: LabVIEW front panel for NR-ODBR-DCSK transmitter.  
Figure 7: Schematic of USRP-based NR-ODBR-DCSK transmitter side.  
Enter the name of the user's USRP device in the Device Name dialog box on the front panel. By marking the name of the user's device, the transmission of the RF signal is realized, and parameters such as the IQ sampling rate, carrier frequency, and gain are set at the same time. I and Q represent the in-phase and quadrature components of the signal data, respectively, which are divided into two paths, and the operation of the digital upconverter in the USRP is controlled by controlling the IQ rate, which in turn converts the sampling rate of the baseband IQ signal into a digital IF signal. The IQ rate is used to control the operation of the digital upconverter in the USRP, which in turn controls the sampling rate of the quadrature baseband data and upconverts the baseband IQ signal into a digital IF signal. In the front panel, the text signal data are imported into the text path, the data information in the text file is read, the text signal data are converted into a double-precision two-dimensional array, the data in the array are stored in a subset of the array, and then each signal is indexed, and the time domain waveform of the signal is displayed on the waveform graph. The channel parameters are set as follows, the transmit gain is 15.6 dBm, and its reference range is 0-35 dBm, the receive gain is 50 dBm, and its reference range is 0-99 dBm, the SNR is 20 dB, and the effective path loss is 24.4 dB. The following Figure 8 shows a diagram of the LabVIEW front panel, which sets the parameters of the transmitter as shown below.  
The display waveform of an oscilloscope connected to the transmitting signal is shown in Figure 9.  
In this section of the transmitter side design, the signal data are continuously written to the USRP device, so the USRP device transmits the RF signal uninterruptedly, and after stopping the running program, it releases the memory occupied by the transmitter side program while running.
## 5 Experiments and Discussion
### Comparative Verification Analysis  
To further show the effectiveness of the proposed model, a group of experiments was designed to compare the longitudinal critical distance between lane-changing vehicles and the front vehicles in the platoon. Three experiments were conducted, which were based on the safety potential field (SPF) model [16], the safety distance model of the desired time headway (DTH), and the proposed IPF. In experiments, the velocity of the ego vehicle was set to 30 m/s and the velocity of the front vehicle in the target lane was 20 m/s. The two vehicles adopted one of four acceleration strategies in the lane-changing process: the two vehicles drive at a constant speed, the ego vehicle accelerates and the front vehicle drives at a constant speed, the ego vehicle accelerates and the front vehicle decelerates, and the ego vehicle decelerates and the front vehicle accelerates. Under each strategy, the critical distances of SPF and IPF under different combinations of motion states were calculated using Equations (1) and (2), respectively, and were compared with the critical distances when the time headway was 1.4 s. The critical distances obtained using IPF was an interval with upper and lower bounds. Therefore, this study chose the optimal distance between the platoon vehicles, that is, the position where the minimum value of the IPF field of the two vehicles coincided, for comparison with the critical distance of the other two models. On this basis, the interval of the IPF critical distance was also marked. The results are shown in Figure 9.  
the maximum distance that vehicles need to meet and the lower bound is the minimum distance. As can be seen from Figure 9, the DTH safety model could not provide feedback on the difference in the critical distance under different acceleration strategies, and the IPF proposed in this study could distinguish the critical distances of vehicle lane changing under different motion states of vehicles. When the ego vehicle and the front vehicle moved at the same velocity, the critical distances based on the IPF were consistent with the results of the DTH, which were in line with the safety requirements. Meanwhile, it was found that the SPF broken line was between the critical distance interval of IPF and it was larger than the optimal distance in the IPF. This was also consistent with the fact that the optimal distance between platoon vehicles was less than that between non-platoon vehicles. In other words, the IPF realized the virtual link between the platoon vehicles. However, when the vehicle motion state was affected by the acceleration parameters, significant differences occurred. It was found that the optimal critical distance of the IPF was less than the SPF and the DTH when the ego vehicle accelerated and the front vehicle decelerated; when the ego vehicle decelerated and the front vehicle accelerated, the optimal critical distance of the IPF was greater than the DTH but still less than the SPF. This was consistent with reality and verified the rationality of the model.
## 7 Conclusions and Further Remarks
\begin{tabular}{l l} APEX & Airborne Prism Experiment \\ BRDF & Bi-directional Reflectance Distribution Function \\ Cal & Calibration Samples \\ Cal/Val & Calibration and Validation \\ CEOS & Committee on Earth Observation Satellites \\ CV & Coefficient of Variation \\ DN & Digital Numbers \\ ETM+ & Enhanced Thematic Mapper Plus \\ EO-1 & Earth Observing-1 Mission \\ Eva & Evaluation Samples \\ FASat-C & Air Force Satellite-C \\ FWHM & Full Width at Half Maximum \\ GaoFen-1 & Chinese High Resolution Imaging Satellite-1 \\ GOE & Space Operations Group \\ GSD & Ground Sampling Distance \\ HDF & Hierarchical Data Format \\ HSI & Hyperspectral Imager \\ L8-OLI & Landsat-8 Operational Land Imager \\ LISA & Local Indicators of Spatial Association \\ LSpec & LED-Based Spectral Surface Monitoring Calibration Site \\ MAPE & Mean Absolute Percentage Error \\ m.a.s.l. & meters above sea level \\ MBE & Mean Bias Error \\ \end{tabular}
## 2 Materials and Methods
### Data Collection for the Design of the Drying Stage  
Drying must reach a concentration of approximately 85% to 95% in dry biomass solids to avoid decomposition. This can be achieved by drying in the sun, in a drum dryer, spray drying, freeze-drying, or any other technique that allows the removal of moisture in the biomass [11; 29]. In this stage, two drying techniques were considered, spray drying and the drum dryer, since they are the two techniques with a large-scale application for drying a great variety of microalgae, Case E1 and E2, respectively [34; 35]. In this stage, it was possible to concentrate the biomass up to 992.74 g/L and with a final humidity of approximately 5% (\(w/w\)), for both cases. The outlet temperature was set at 40 \({}^{\circ}\)C since it has been reported in the literature that if the temperature is higher it could cause changes in the composition of the biomass [41]. The energy consumption was set at 1.16 kWh/kg evaporated water for the Spray Dryer and 0.89 kWh/kg evaporated water for the Drum Dryer [41].  
After collecting the information associated with the operation in each stage of the process (cultivation of _Chlorella_ in a photobioreactor, flocculation for the primary harvest, centrifugation and filtering for the secondary harvest, and the drying of the biomass with two technologies), we proceeded to perform the simulation of the technologies individually. The technologies were then combined to form the different production scenarios. Figure 1 summarizes the technologies included in each stage of the process for the formation of the scenarios and Table 2 shows the cases considered in each scenario analyzed.  
\begin{table}
\begin{tabular}{c c c} \hline
**Technology** & **Energy Consumption** & **Energy Consumption** \\ **(Case)** & **Considered (kWh/m\({}^{3}\) Feed)** & **Reference (kWh/m\({}^{3}\) Feed)** \\ \hline Centrifugation (D1) & 8.70 & 3.3 [39], 3.6–10.8 [40], 0.7–1.30 [41] \\ Vacuum filter (D2) & 5.80 & 0.1–5.9 [40], 1.22–5.9 [41] \\ Belt filter press (D3) & 0.88 & 0.054–0.384 [39], 0.16–0.88 [40] \\ Pressure filter (D4) & 0.90 & 0.88 [40], 0.5–0.9 [41] \\ \hline \end{tabular}
\end{table}
Table 1: Energy requirement in second stage of harvest.
# Estimation of Inter-System Biases between BDS-3/GPS/Galileo and Its Application in RTK Positioning
## 2 RTK Positioning Models
### Lcm Rtk  
Assuming that two receivers, rover station \(r\) and base station \(b\), are tracking the same satellites \(s_{*}=1_{*},2_{*},\cdots,m_{*}\), where \(m\) is the number of the common-view satellites of a single GNSS \(*\) (assume the \(1_{*}\) as the pivot satellites), the intra-system DD observation equation of multi-system, multi-frequency can be given as:  
\[\begin{split}\phi^{1_{\text{CS}}_{\text{FD},f}}_{r,b,f}& =\rho^{1_{\text{CS}}_{\text{FD}}}_{rb}+\lambda_{f}\pi^{1_{\text{CS}}_{ \text{FD}}}_{r,b,f}+\varepsilon^{1_{\text{CS}}_{\text{FD}}}_{r,b,f}\\ \phi^{1_{\text{s}},s_{\text{r}}}_{r,f}&=\rho^{1_{ \text{s}},s_{\text{s}}}_{rb}+\lambda_{f}\pi^{1_{\text{s}},s_{\text{r}}}_{r,b,f}+ \varepsilon^{1_{\text{s}},s_{\text{r}}}_{r,b,f}\\ P^{1_{\text{FD}}}_{r,b,f}&=\rho^{1_{\text{CS}}}_{rb}+ \varepsilon^{1_{\text{CS}}_{\text{FD}}}_{r,b,f}\\ P^{1_{\text{s}},s_{\text{r}}}_{r,b,f}&=\rho^{1_{ \text{s}},s_{\text{s}}}_{rb}+\varepsilon^{1_{\text{s}},s_{\text{r}}}_{r,b,f} \end{split} \tag{3}\]  
where \([.]^{1_{\text{s}},s_{\text{r}}}_{r,b,f}\) denotes the DD procedure (\(\cdot\) denotes different elements in Equation (3)) and \(s_{*}\) is the satellites, except the pivot satellite \(1*\). The delays in the receivers and satellites are eliminated completely in the process of DD. Most of the atmospheric delays can be eliminated, so we ignore the influence of the atmospheric delay where there are relatively short baselines in this article. However, the impact of the ionosphere on the positioning with a relatively long baseline cannot be ignored. In this case, ionospheric delays should be estimated [29; 30].  
In the LCM RTK function model, every GNSS needs a pivot for the difference inside the GNSS. Thus, when more GNSSs are used, more pivot satellites are needed.
# Research on a Novel Nanosecond Marx Generator and Its Efficiency Analysis
## 3 Energy Efficiency Analysis of the Proposed Nanosecond Marx Generator
### Efficiency Analysis  
To analyze the energy efficiency of the circuit more intuitively, the charging efficiency \(\eta_{\rm c}\) and discharge efficiency \(\eta\) of the circuit can be calculated by Equation (16).  
\[\eta_{\rm c}\ =\ \frac{P_{\rm c}}{P_{\rm in}}\times 100\%,\ \eta\ =\frac{P_{\rm o}}{P_{\rm in}} \times 100\% \tag{16}\]  
\[P_{\rm c}\ =\ \frac{1}{2}fNCu_{\rm c}^{2} \tag{17}\]  
\[P_{\rm o}=\ f\int_{0}^{T}U_{\rm o}^{2}/R_{\rm L}dt \tag{18}\]  
\[P_{\rm in}=\ fV_{\rm dc}\int_{0}^{T}idt \tag{19}\]  
where \(P_{\rm c}\) is the power of the storage capacitors expressed by Equation (17), \(P_{\rm o}\) is the output power of the generator, \(P_{\rm in}\) is the input power as shown by Equations (18) and (19).  
Because the output waveform of the generator can be regarded as a zero-order Gaussian waveform, and the normalized zero-order Gaussian function can be expressed by Equation (20) [17; 18]. Then, Equation (21) can be obtained from Equations (18) and (20).  
\[f_{0}(t)=\ U_{\rm e}\exp\left(-\alpha_{0}t^{2}\right),\ \alpha_{0}=\frac{4\ln 2 }{T_{\rm e}^{2}} \tag{20}\]  
\[P_{\rm o}=\ f_{\rm e}\int_{-\infty}^{\infty}\left[\frac{U_{\rm e}^{2}}{R_{\rm L }}\exp\left(-2\cdot\frac{4\ln(2)}{T_{\rm e}^{2}}t^{2}\right)\right]dt\ =\frac{\sqrt{\pi}}{2\sqrt{2\ln(2)}} \frac{T_{\rm e}f_{\rm e}U_{\rm e}^{2}}{R_{\rm L}} \tag{21}\]  
where \(U_{\rm e}\) is the amplitude of the output voltage and \(T_{\rm e}\) is the full-width at half-maximum (FWHM) of the output voltage. \(F_{\rm e}\) represents the repeatable frequency of the circuit and \(R_{\rm L}\) is the load resistance.
# Reversible Data Hiding in Encrypted Color Halftone Images with High Capacity
## 4 Experimental Results and Discussions
### Security  
The original images and the watermark are encrypted by the stream cipher scheme. As shown in Figure 7, the encrypted images are completely meaningless and it is difficult to infer their original images from them. To assess the security quantitatively, we used correlation coefficient \(r\) (Equation (17)) and peak signal-to-noise ratio (PSNR), which is widely used in evaluating the quality of digital images. The PSNR between cover image \(\mathbf{I}\) and original halftone image \(\mathbf{O}\) is defined via the mean square error (MSE) by  
\[PSNR(\mathbf{O},\mathbf{I})=10\cdot\log_{10}\frac{M_{\mathbf{O}}^{2}}{MSE(\mathbf{O},\mathbf{I})} \ (\text{dB}) \tag{19}\]  
where \(M_{\mathbf{O}}\) is the maximum possible pixel value of \(\mathbf{O}\). It is 1 since the pixels of halftone images are represented using 1 bit per sample. The calculation results are consistent with the presentation of Figure 7.  
Figure 7: _Cont._
## 3 Current Data
### Synthetic Aperture Radar  
Since the loss of ENVISAT in 2012, all currently available SAR satellites are commercially operated. All options (RADARSAT-2[71], COSMO-SkyMed[72] and TerraSAR-X/TanDEM-X[73]) have a range of imaging modes and capabilities. As was true historically, these satellites do not operate a program of regular long-term observations unless they are tasked and paid to do so. However, since the loss of ENVISAT, the European Commission (EC) and ESA are coordinating current SAR satellites to fill the gap required to support EC-funded Copernicus (formerly GMES) environmental information services. This has resulted in SAR acquisitions over large parts of the Arctic and Antarctic on a regular basis. These data are used for the generation of Copernicus information products, which are freely available online, but the original input SAR data products are not available under the terms of the data license (see [[http://www.myocean.eu/web/24-catalogue.php](http://www.myocean.eu/web/24-catalogue.php)]([http://www.myocean.eu/web/24-catalogue.php](http://www.myocean.eu/web/24-catalogue.php))). Individual users will likely pay for access to these data if they wish to license their use.