esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Model Implementation and Validation
### Estimation of the Transit Navigation Window  
The reanalysis and ARIMA-model-forecasted SICs serve as the basis for determining the navigational window for transit. SIC values that fall below 0.15 or 0.25 are considered to mark the threshold of ice-free conditions, as suggested in [31]. Hence, an 80% navigational window for transit is defined as the number of days per year in which the SIC values along 80% of the route surpass the threshold of ice-free. For any given date, if the SICs in six of the eight sub-regions fall below the threshold, that date is deemed to be included within the navigational window.  
Pursuant to this principle, we proceed to estimate the 80% transit navigation window from the year 2022 to 2025. Figure 22 portrays the reanalysis and forecast transit navigation windows in conjunction. The navigation window that results from the use of a higher ice-free threshold is wider than the estimate obtained with a lower threshold, though the trends of both navigation windows are consistent. The navigation windows undergo cyclical  
Figure 21: October, November, and December mean SICs from ERA5, CMIP5, and the ARIMA model in sub-region no. 2.  
Figure 20: The RMSEs of SICs between CMIP5 and ERA5 from January 2006 to September 2020 in sub-region No. 2.  
fluctuations from wide to narrow from the year 1991 to 2025. Prior to 2013, the navigation window would experience contraction approximately every seven years, in 1999, 2007, and 2013. Since then, the navigation window has remained periodically vibrational, yet the narrowest windows are now noticeably broader than in the past. In 2021, the navigation window commenced in early August and terminated in mid-September (when using a high SIC threshold), implying a relatively narrow window. By the year 2022, the commencement of the window was postponed by approximately two weeks, but the window was extended until October. Since 2023, the navigation window will broaden, remaining from mid-July to the end of September. The navigation window will continue to expand slightly until 2025.  
To validate the estimated navigation window using SICs from the statistical interpolation, we also utilized the SICs from the kriging interpolation to assess the navigation windows. As demonstrated in Figure 23, the navigation windows obtained from both kriging and statistical interpolations are in agreement for the period from 2016 to 2021. This convergence attests to the suitability and veracity of the proposed methodology for estimating the transit navigation window.
# An Integrated Safety Monitoring and Pre-Warning System for Fishing Vessels
Accidents involving fishing vessels have the potential to seriously harm crew members, boats, and marine ecosystems, leading to financial losses, social issues, environmental effects, and human casualties. Therefore, reviewing current procedures and identifying new ways to lower the number of incidents involving fishing vessels is essential for ensuring sustained maritime safety and transportation. Although numerous safety monitoring systems have been proposed, several persistent limitations hinder their effectiveness in real-world fishing operations. These include high deployment and maintenance costs due to reliance on satellite communication, limited responsiveness in dynamic maritime environments, and a lack of intelligent decision-making capabilities for real-time risk assessment. Furthermore, many existing systems are not well suited for integration across vessels of different sizes and operational scales, which restricts their applicability in developing regions or small-scale fisheries.
## 4 Computational Simulations and Experimental Results
### Hardware Experimental Testing
#### 4.3.4 Comparative Analysis between NLOS and LOS Communication  
Based on the experimental results of NLOS and LOS communication scenarios, we can evaluate and discuss the comprehensive communication capabilities of the ultraviolet communication system.  
Error rate trend: In NLOS communication, when obstacles obstruct the path, the BER increases with the elevation angle at the receiver. Particularly beyond 20-25 degrees, there is a significant rise in the BER. This is  
System stability: Our field tests revealed that in NLOS communication environments, due to the scattering characteristics of ultraviolet light, communication requirements can be met over a larger angle range, facilitating easier communication link establishment. For LOS communication, higher angle pointing requirements are necessary, and communication link establishment requires precise alignment between the transmitter and receiver.
## 4 Experimental Validation
### Experimental Setup  
The setup consisted of three main parts: the GFM inverter, the connection filter and impedance, and a controlled voltage source capable of emulating the grid. A photo of the setup with the real components is shown in Figure 13a, and the main diagram in Figure 13b.  
The GFM IBR consists of two inverters connected in a back-to-back topology. One of them emulates a battery, with the sole purpose of generating a stiff 320 Vdc bus. The second inverter operates as a three-phase inverter used to test different SVG strategies. Both inverters are commercial INF-50 power inverters from Dutt Electronics. The grid was emulated using a 320-AMX bidirectional three-phase power supply from Pacific Power, which is capable of generating sinusoidal perturbations in order to obtain the \(\Delta P/\Delta\omega_{g}\) response of the GFM inverter. The inverter under test and the grid were coupled using an LC filter with damping resistors in series with the capacitor. An additional RL branch was used to emulate the grid impedance. All passive elements and sensing devices were off-the-shelf components.  
An OP4512 from OPAL-RT, which includes a Quad-Core 3.7 GHz CPU and a Kintex-7 FPGA, was used as the control hardware. Control algorithms, signal acquisition, and grid voltage management were centralized in this control device.  
The main parameters of the setup are summarized in Table 2.  
Figure 13: Experimental setup. (**a**) Laboratory setup and (**b**) main scheme of the laboratory setup.
###### Abstract  
In recent years, with the rapid increase in renewable energy sources (RESs), a Virtual Power Plant (VPP) concept has been developed to integrate many small-scale RES, energy storage systems (ESSs), and customers into a unified agent in the electricity market. Optimal coordination among resources within the VPP will overcome their disadvantages and enable them to participate in both energy and balancing markets. This study considers a VPP as an active agent in reserve provision with an upward reserve capacity contract pre-signed in the balancing capacity (BC) market. Based on the BC contract's requirements and the forecasted data of RESs and demand, a two-stage stochastic optimization model is presented to determine the VPP's optimal scheduling in the day-ahead (DA) and balancing energy (BE) markets. The probability of reserve activation in the BE market is considered in this model. The ESS's reserve provision model is proposed so as not to affect its schedule in the DA market. The proposed optimal scheduling model is applied to a test VPP system; then, the effects of the BC contract and the probability of reserve activation on the VPP's trading schedule are analyzed. The results show that the proposed model has practical significance.  
balancing capacity market; balancing energy market; day-ahead market; energy storage system; renewable energy source; stochastic programming; virtual power plant Article
## 3 Dynamic Performance Analysis  
### SHPB Experiment  
We used the SHPB test device (as shown in Figure 4) at the State Key Laboratory of Response and Disaster Prevention and Control in Deep Coal Mines to conduct impact compression tests on six different sandstone ring specimens with inner diameters of 0 mm, 5 mm, 10 mm, 15 mm, 20 mm, and 25 mm following ten dry-wet cycles. In the test, the impact pressure drove the bullet to impact the impact rod to produce a dynamic load. In the pre-test, the impact pressure gradually increased, and we found that the pressure could cause the sample to crack and maintain a good crushing shape according to the selection pressure of the test. Following the pre-test, 0.3 MPa was selected as the test impact pressure.  
Figure 4: SHPB test device.  
Figure 3: Variation in the mass, volume, and density of the sandstone ring with inner diameter.  
The test equipment shown in Figure 4 comprises a nitrogen tank, a transmitter, a strain gauge, and an oscilloscope. Among the equipment, the material comprising the pressure bar was high-strength alloy steel; its diameter was 50 mm; the length of the incident bar was 2 m; the length of the transmission bar was 1.5 m; the wave velocity of the pressure bar was 5380 m/s; the density was 7636 kg/m\({}^{3}\); and the Poisson's ratio was 0.28.  
The SHPB test data of the sandstone ring specimens with different inner diameters following dry-wet cycles are shown in Table 2.
## 3 Experimental Results
### Simultaneous Nanotexturing with Femtosecond Laser Trimming
If the trimmed surface were flat like a laser-polished surface, no Moire patterns or no two-dimensional angulations could be seen. If the trimmed diamond coating had randomly distributed roughness, the two-dimensional profile analyzed by WLI would have an irregular convex-concave pattern. The measured two-dimensional profile in Figure 7a has a regular distribution with the spatial period. In particular, regularly modulated nanotextures in the X-axis are aligned in the Y-axis. Figure 7b shows this local surface profile in the X-axis or in the lateral direction of the punch surface, which was analyzed by the DEAP algorithm. Although this profile gradually deviates from the center line, the nanotextures are formed on the trimmed punch surface with a skewed angle in the axial direction. Figure 5: SEM and LM images on the trimmed CVD-diamond coating with different magnifications. (**a**) Overall trimmed surface, (**b**) measurement of the trimmed edge width by LM, and (**c**) the unidirectional formation of nanotextures on the trimmed side surfaces of the diamond coatings. Figure 6: Comparison of the SEM images of the nanotextures formed onto the trimmed diamond coatings among the three diamond-coated punches with different depths (d) of cut. (**a**) d = 1.8 \(\upmu\)m, (b) d = 2.4 \(\upmu\)m, and (**c**) d = 3.6 \(\upmu\)m.  
The measured spatial period of the nanotextures (\(\Lambda_{\text{punch}}\)) is 900 nm, and their average height reaches 300 nm.
# Performance Analysis of Undifferenced NRTK Considering Time-Varying Characteristics of Atmosphere
## 2 Positioning Model of BDS Undifferenced NRTK
### Random Walk Model of Atmospheric Parameters  
The atmospheric parameters in Equation (26) are generally described by the random walk process and can be expressed as:  
\[\left\{\begin{aligned} \
abla\Delta\tau(i)-\
abla\Delta\tau(i+1) \text{=}W_{\tau},\ W_{\tau}\sim N(0,\Delta t\cdot q_{\tau}^{2})\\ \Delta I^{s}_{r}(i)-\Delta I^{s}_{r}(i+1)\text{=}W_{I}\,\ W_{I}\sim N (0,\Delta t\cdot q_{I^{s}_{r}}^{2})\end{aligned}\right. \tag{31}\]  
In Equation (31), \(q_{\tau}\) represents the tropospheric power spectral density (TPSD); \(q_{I^{s}_{r}}\) represents the ionospheric power spectral density (IPSD); and \(\Delta t\) represents the time interval of atmospheric variations. PSD describes the variations speed of atmosphere, and also determines the effect of atmospheric parameter constraints in the positioning model; positioning performance can be optimized by adjusting the PSD. The random walk model is very important for fixing ambiguity in positioning, and it has a significant impact on the fixing ambiguity in the reference station network [30]. Any imprecise random models will lead to a decrease in the ambiguity fixing success rate [31]. The virtual observation equation of random walk of atmospheric parameters can be expressed as [32; 33; 34]:  
\[\
u_{\tau}=\
abla\Delta\tau(i)-\
abla\Delta\tau(i+1),\rho_{\tau}=\frac{{\sigma _{0}}^{2}}{{q_{\tau}}^{2}\cdot\Delta t} \tag{32}\]  
\[\
u_{I^{s}_{r}}=\Delta I^{s}_{r}(i)-\Delta I^{s}_{r}(i+1),\rho_{I^{s}_{r}}= \frac{{\sigma_{0}}^{2}}{{q_{\tau}}^{2}\cdot\Delta t} \tag{33}\]  
In Equations (32) and (33), \(p_{\tau}\) represents the weight of tropospheric virtual observation equation; and \(p_{I^{s}_{r}}\) represents the weight of the ionospheric virtual equation; the corresponding normal equation can be shown as:  
\[\begin{bmatrix}\rho_{\tau}&-\rho_{\tau}&0&0\\ -\rho_{\tau}&\rho_{\tau}&0&0\\ 0&0&\rho_{I^{s}_{r}}&-\rho_{I^{s}_{r}}\\ 0&0&-\rho_{I^{s}_{r}}&\rho_{I^{s}_{r}}\end{bmatrix}\begin{bmatrix}\
abla \Delta\tau(i)\\ \Delta I^{s}_{r}(i+1)\\ \Delta I^{s}_{r}(i+1)\end{bmatrix}=\begin{bmatrix}0\\ 0\\ 0\\ 0\end{bmatrix} \tag{34}\]  
Finally, the normal equation of random walk of atmospheric parameters is superimposed with the normal equation of observation:  
\[NX=W \tag{35}\]  
By using the float solution and covariance matrix of ambiguity solution, the optimal ambiguity can be obtained through LAMBDA search [35], and the integer ambiguity can be solved.
# On Developing Techniques for Sharing Satellite Spectrum with Indoor Small Cells in 5G +
## 1 Introduction  
### Background  
Spectrum sharing is considered as one of the most effective techniques to address the enormous capacity and high data rate demands of next-generation mobile networks due to the unavailability of sufficient licensed spectrums for a mobile network operator (MNO). Spectrum sharing can be performed either between homogeneous systems or heterogeneous systems. For heterogeneous systems, spectrum sharing between space-satellite systems (SPSs) and terrestrial-mobile systems (TMSs) is considered as a crucial and viable option, particularly for the future fifth-generation (5G) mobile system. With this consideration, many countries such as Japan have allocated 5G spectra to four mobile network operators including 3.7 GHz, 4.5 GHz, and 28 GHz [1]. However, the 3.4 GHz to 4.2 GHz spectrum has already been allocated to the fixed-satellite service (FSS) system in the downlink [2], resulting in sharing the 5G cellular in the 3.7 GHz band with the FSS.  
In general, two major types of spectrum sharing between SPSs and TMSs are well recognized, namely integrated satellite-mobile systems (iSMSs) and hybrid satellite-mobile systems (hSMSs). IniSMSs, the terrestrial component, termed as the complementary ground component (CGC) in Europe and ancillary terrestrial component (ATC) in North America, is complementary and works as part of the system in the same frequency as that of the satellite component. In hSMSs, satellite and terrestrial components are interconnected even though they operate independently from one another. Since almost 80% of data is generated in indoor environments [3, 4], small cells are considered as effective candidates to serve indoor users due to their small coverage. Hence, by exploiting the high external wall penetration loss of a building [5], particularly in urban areas, the same satellite spectrum can be shared with in-building small cells of a mobile system. It has to be noted that by enforcing a certain maximum received interference level at the indoor user equipment (UE), the same satellite spectrum can be exploited further for reuse in small cell base stations (SBSs) more than once within a building. Hence, along with serving a high indoor data traffic demand with the shared satellite spectrum, both the system-level spectral efficiency and energy efficiency of a mobile system can be improved, which necessitate developing techniques to share the spectrum of a satellite system with the in-building small cells of a mobile system.
## 3 Results and Discussion
### Photocatalytic Reaction  
#### 3.2.1 Adsorption Experiments and Catalyst Dosage  
The loading of the catalyst is a key parameter in catalytic applications [38; 39]. First, blank adsorption experiments at different pH values in the dark (without exposure to visible light) revealed that the change of initial ethiofencarb concentrations after 12 h of mixing with SnIn\({}_{4}\)S\({}_{8}\) was less than 6% (Supplementary Materials Figure S1). Thus, the adsorption of ethiofencarb on SnIn\({}_{4}\)S\({}_{8}\) is insignificant and can be neglected.  
Next, different dosages of the SnIn\({}_{4}\)S\({}_{8}\) photocatalyst (from 0.1 to 1.0 g L\({}^{-1}\)) were employed to study the photodegradation rate of ethiofencarb. The results of these experiments are presented in Figure 5. In the absence of the catalyst, ethiofencarb degradation was negligible under visible-light irradiation for 24 h, contrary to the presence of SnIn\({}_{4}\)S\({}_{8}\) photocatalyst. Increasing the SnIn\({}_{4}\)S\({}_{8}\) dosage resulted in an increase in the photocatalytic degradation rate until the optimal dosage was reached at 0.5 g L\({}^{-1}\). The increase in the reaction rate can be explained by the increase in the total surface area/available catalytic active sites for ethiofencarb degradation upon increasing the dosage of the photocatalyst. After the optimal dosage, the increase of SnIn\({}_{4}\)S\({}_{8}\) concentration may lead to decrease the intensity of available incident visible light as a result of more light scattering and less light penetration. This in turn can restrain the positive effect arising from increasing the photocatalyst dosage, and, thus, reduce the overall rate [40]. The optimal catalytic dosage (0.5 g L\({}^{-1}\) of SnIn\({}_{4}\)S\({}_{8}\)) was, therefore, used in all subsequent experiments.
## 3 Results and Discussion
### Homology Modeling and Overall Structure
The RMSF analysis also shows differences between the enzymes. Glt3 and Glt20 have higher flexibility in \(\beta\)1-\(\alpha\)1, as well as in \(\beta\)5-\(\alpha\)5, in comparison to Glt1, whereas Glt1 and Glt3 have higher flexibility in \(\beta\)6-\(\alpha\)6 than Glt20. It is worth noticing that no correlation between sequence similarity and loop fluctuations was found. For example, \(\beta\)1-\(\alpha\)1 shows higher flexibility in Glt3 and Glt120. However, the sequence identity is higher 62.5% between Glt1 and Glt3, while 25% between Glt1 and Glt20. A similar situation occurs for loops \(\beta\)5-\(\alpha\)5 and \(\beta\)6-\(\alpha\)6. Comparison of the modeled structures before molecular dynamic (MD) simulation and the average structure obtained after MD suggests that interactions with surrounding loops can affect the different fluctuations observed in \(\beta\)1-\(\alpha\)1, \(\beta\)5-\(\alpha\)5, and \(\beta\)6-\(\alpha\)6. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Protein** & **AA** & **Z-Score** \\ \hline \(Rm\)Bgt17A (4 WTS) & 264 & \(-\)8.92 \\ Glt1 & 284 & \(-\)8.22 \\ Glt3 & 284 & \(-\)8.48 \\ Glt20 & 286 & \(-\)8.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Z-scores of the modeled enzyme structures, calculated by ProSA. The Z-score of the crystallographic structure of \(Rm\)Bgt17A used as the main template for building the hybrid models is included for comparison. Figure 2: Modeled overall structures of the models obtained for Glt1, Glt3 and Glt20. The (\(\alpha\)/\(\beta\))s classical TIM-barrel fold is represented as a ribbon diagram. A front view of the TIM-barrel is shown to the left and a side view to the right. The catalytic amino acids are shown in the lateral views using CPK representation, \(\alpha\) helixes are in purple, \(3_{10}\) helixes in blue, \(\beta\)-strands in yellow, coil in white and turns in cyan. Notice that A4 helix is present in Glt20. Figure 3: Schematic diagrams of super-secondary structures. The variations in loops associated with the catalytic cleft are indicated with red arrows. The potential proton donor amino acid is represented with a red spot between \(\beta\)4 strand and the A4 alpha-helix, while the potential nucleophile is a blue spot in the end of the \(\beta\)7 strand. Predicted \(\beta\)-strands are shown in orange, \(\alpha\)-helices in light blue and 310-helices in darker blue.
## 4 Conclusions  
This is the first study investigating the impact of co-inoculation with yeast and LAB on the fermentation of ripe and immature coffee fruits. Among the different treatments, combined inoculations with _Pichia fermentatus_ YC5.2 and _Pedicococcus acidilactici_ LPBC161 in ripe coffee fruits showed interesting features. It was possible to reach increased coffee pulp-sugar consumption and production of metabolites (lactic acid, ethanol, and ethyl acetate), evidencing a positive synergic interaction between these two microbial groups. On the other hand, when using immature coffee fruits, only _Pichia fermentatus_ was able to improve metabolite formation during fermentation and impact volatile composition of resulting coffee beans. This may be due to the high nutritional requirement of LAB species and poor adaptability in immature coffee pulp. Howsoever, since immature coffee beans usually have a low quality because the formation of flavored precursors is incomplete, yeast metabolism has great potential to add flavor quality to these beans.  
Chemical analysis revealed a more complex volatile composition of fermented coffee beans from combined treatment in relation to pure inoculations and spontaneous process. The major compounds impacted were benzeneacetaldehyde, 1-hexanol, benzylalcohol, 2-heptanol, and phenylethyl alcohol, which are reported as important aroma-impacting compounds. Thus, this study shows the great potential of combined inoculation for the formation of desirable aroma compounds and production of specialty coffees. Further studies are still required to investigate the mechanisms of synergism between yeast and LAB and influence on sensory properties of coffee products.  
[PERSON] and [PERSON] designed the experiments. [PERSON] conducted the physicochemical characterization of fermenting coffee pulp. [PERSON] and [PERSON] performed the experiments and wrote the manuscript. [PERSON], [PERSON], and [PERSON], reviewed and edited the manuscript. All authors read and approved the final manuscript.  
This work was supported by the Brazilian National Council for Scientific and Technological Development (CNPq) (project number 429560/2018-4).  
The authors declare no conflict of interest.
# A Graph-Cut-Based Approach to Community Detection in Networks
## 3 Methods
### The Betweenness Centrality  
To apply MINIMUM-CUT to a graph, source and sink vertices \(s\) and \(t\) should be provided as well as the capacity \(c\) for each edge. For community detection, the source and sink vertices should be chosen so that the two vertices do not belong to the same community, because the two vertices are cut by MINIMUM-CUT. The _betweenness centrality_ in graph theory is used to select two vertices in our proposed method. In particular, the twovertices of highest betweenness centrality values are chosen as the source and sink vertices in applying MINIMUM-CUT.  
In graph theory, betweenness centrality is a measure of the centrality of a vertex in a graph [63]. Roughly speaking, the betweenness centrality of a vertex represents the degree of how many pairs of nodes are connected via the shortest paths passing through the vertex. The betweenness centrality \(c_{B}(v)\) of a vertex \(v\in V\) of a graph \(G=(V,E)\) based on shortest paths is defined as the following  
\[c_{B}(v)=\sum_{s,t\in V}\frac{\sigma(s,t|v)}{\sigma(s,t)}, \tag{1}\]  
where \(\sigma(s,t)\) is the number of shortest paths from \(s\in V\) to \(t\in V\), and \(\sigma(s,t|v)\) is the number of those paths passing through a vertex \(v\) other than \(s\) and \(t\)[64,65]. If \(s=t\), we let \(\sigma(s,t)=1\), and if \(v\in\{s,t\}\), we let \(\sigma(s,t|v)=0\). On unweighted graphs, computing betweenness centrality takes \(O(|V||E|)\) times using [PERSON] algorithm [64].  
As briefly mentioned before, the betweenness centrality values for all the vertices are computed to select the source and sink vertices \(s\) and \(t\), which are provided as inputs to MINIMUM-CUT. Those two vertices of the highest values are chosen because it is expected that many shortest paths connecting vertices of different communities pass through these two nodes.