esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions  
In this work, we provide a novel idea of a hybrid reconfigurable intelligent surface decision-making algorithm to eliminate the signal power loss and limited coverage area of the conventional T/R RIS and the high delay of BS-BS handover. HRIS-DMA uses the exact user location to provide the user with maximum power coverage. The numerical results confirm the superiority of the proposed HRIS-DMA algorithm over existing conventional approaches. Using the HRIS-DMA saves up to 70% of signal power, potentially providing 100% coverage and 45% less delay. These results provide the potential and effective use of HRIS-DMA for the new wireless generation of 6G and its diverse range of applications.  
In this work, we manged to achieve reduced power loss, which is achieved through dynamic adjustment and optimal signal reflection; lower latency, due to real-time adaptability and efficient signal paths; enhanced coverage area, through intelligent beamforming and reflection; a lower failed signal rate, ensured by strong and stable signal connections; and improved system efficiency, resulting from optimised energy utilisation and reduced power loss.  
The application of the proposed HRIS-DMA approach in 6G networks offers several advantages. In urban environments with high building density, RISs can be strategically placed on building facades to direct signals around obstacles, ensuring continuous and reliable connectivity. In indoor settings such as large commercial complexes, RISs can enhance coverage by reflecting signals into areas that are typically hard to reach, such as basements or isolated rooms. Additionally, in vehicular networks, RISs can dynamically adjust to the movement of vehicles, providing stable and high-quality communication links. By leveraging the exact user location data, our approach ensures that the benefits of RIS technology are fully realised, leading to improved network performance and user experience in various 6G deployment scenarios.  
Conceptualisation; methodology; software; validation; formal analysis; investigation; resources; data curation; writing--original draft preparation; writing--review and editing; and visualisation, A.K.A.; supervision and review, H.S.A.-R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Derived data supporting the findings of this study are available from the corresponding author upon request.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
5G & Fifth Generation; \\
6G & Sixth Generation; \\ AI & Artificial Intelligence; \\ AP & Access Point; \\ BS & Base Station; \\ EE & Energy Efficiency; \\ ES & Energy Splitting; \\ FSR & Failed Signal Rate \\ HRIS-DMA & Hybrid Reconfigurable Intelligent Surface-- Decision-Making Algorithm; \\ IOS & Intelligent Omni Surface; \\ IoT & Internet of Things; \\ LoS & Line of Sight; \\ MIMO & Multiple-Input Multiple-Output; \\ MISO & Multiple-Input Single-Output; \\ MS & Mode Switching; \\ NLoS & No Line of Sight; \\ PIN & Positive-Intrinsic-Negative; \\ \end{tabular}
## 5 Conclusions  
This paper presented a new control strategy to regulate voltage and share reactive power from distributed generators in a microgrid when EVs are connected and disconnected at different nodes and times. The control strategy considered using fixed and variable virtual impedances created in the microgrid (MG) when loads change or when an EV is connected or disconnected. Results were obtained for a system with three generators, which with the control strategy distribute active power regardless of where the vehicles are connected in the microgrid. It was possible to deliver reactive power according to the place where the load variation is presented. Thus, for a few moments, the reactive power supplied by the distributed generators is increased and, in another period, it is decreased to regulate the voltage in the microgrid. Therefore, this control strategy, based on variable virtual impedance related to the connection distance of EVs, works properly to control the voltage in the different nodes of the microgrid. The control strategy manages to effectively regulate the frequency close to 50 Hz, and the active and reactive power are also maintained to balance the network. This strategy is useful for the new microgrid to adapt and share active and reactive power regardless of the distance where EVs are connected. As only unidirectional vehicles were considered in this research, in future work, bidirectional vehicles should be tested to provide a power backup to the node where the EV is connected.  
Conceptualization and methodology: E.M. and J.E.C.-B.; formal analysis, investigation, writing, review, and editing: E.M., J.E.C.-B., and F.E.H. This research received no external funding. The authors thank COLCIENCIAS and the National Doctorate program. The authors also thank Universidad Nacional de Colombia, Sede Manizales and Sede Medellin.  
Figure 14: Frequency response by considering different load variations.
## 3 Results and Discussion  
### Moisture Contents and Air-Drying Behavior  
The fresh moisture contents of _F. antarctica_ and _R. patagonica_ were 90.65 \(\pm\) 3.7% and 91.5 \(\pm\) 1%, respectively. The moisture content of the tested species as a function of drying time (Xr = X/X0, fixing a relative moisture of Xr = 0.01) for different temperatures is presented in Figure 2. Significantly less dehydration time was required at 70 \({}^{\circ}\)C for both species (Table 1). Between mushrooms, a remarkable difference in drying time was detected, as _Fistulina antarctica_ required almost twice the drying time compared with _R. patagonica_ (Table 1). The coralloid shape of _R. patagonica_, with a cylindrical axis of 0.5 cm diameter, on average, can explain the difference found with _F. antarctica_.  
\begin{table}
\begin{tabular}{c c c} \hline
**50 \({}^{\circ}\)C** & **60 \({}^{\circ}\)C** & **70 \({}^{\circ}\)C** \\ \hline _F. antarctica_ & 9.75 \(\pm\) 1.06 \({}^{\text{b}}\) & 7.37 \(\pm\) 0.53 \({}^{\text{b}}\) & 3.62 \(\pm\) 0.17 \({}^{\text{a}}\) \\ _R. patagonica_ & 3.87 \(\pm\) 0.18 \({}^{\text{b}}\) & 2.37 \(\pm\) 0.70 \({}^{\text{b}}\) & 1.62 \(\pm\) 0.77 \({}^{\text{a}}\) \\ \hline \end{tabular}
\end{table}
Table 1: Drying times at different air-drying temperatures. Different letters indicate significant differences between temperatures for each species (\(p<0.05\)).  
The moisture of the fresh fructifications was similar to those reported for other edible mushrooms, for example, a moisture content for _Fistulina__hepatic_ of 87.51% [32], for _Flammulina__veloptives_ of 90.7% [33], for _Lactarius__delicisous__of_ 90.0% [12], for _Lentin__eudos_ of 88.4% [34], for _Lepista__nuda_ of 93.8% [35], for _Pleutrotus__eudos__eudos_ of 91.5% [36], for _Ramaria__area_ of 88.5% [33], and for _R.__brotris_ of 89.8% [35]. Beyond the morphology of the fruitings, the water contents of the edible mushrooms were very similar.  
The shorter drying time (3.62 h and 1.62 h at 70 \({}^{\circ}\)C for _F.__antarctica_ and _R.__patgonica_, respectively) is an advantage in comparison with other edible mushrooms that are used for global consumption and production. Since there are no previous reports of drying of species of the genus _Fistulina_ and _Ramaria_, our results are compared to those for other edible mushrooms. In the case of _Hericium__ein__eucas_, the drying times at 40 \({}^{\circ}\)C and 70 \({}^{\circ}\)C were 12 and 7 h, respectively [7]. In the case of _Agaricus__bisporus_, the drying times were 8.6 and 2.6 h at 50 \({}^{\circ}\)C and 75 \({}^{\circ}\)C, respectively [37]; for _Lentin__eudodes_, this time was 7.5 h at 60 \({}^{\circ}\)C [22] and drying time was 8, 7, and 6 h at 50 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, and 70 \({}^{\circ}\)C, respectively, for _Pleutrotus__eudos_[38].
# Detection of On-Chip Generated Weak Microwave Radiation Using Superconducting Normal-Metal SET
## 2 Experimental Section
### Coupling Method and Device Layout  
To deliver the signal from the generator to the detector, a two-wire coplanar transmission stripline was designed and implemented bearing in mind that the source is put apart from the detector (_cf._, [19]) and, in this way, a possible contribution of phonon mediated signals is reduced. Figure 4 shows schematically the layout of the fabricated chips including the source, the detector and the coupling element. The line was designed to have a relatively high characteristic impedance \(Z_{0}\approx 250\)\(\Omega\) as compared to a standard value of 50 \(\Omega\), which was carried out with the purpose of increasing the amplitude \(V_{1}\sim V_{\mathrm{C}}\approx I_{\mathrm{C}}Z_{0}\). The line consists of two Al thin film microstrips, 50 um apart, each with a width of 1 um, thickness \(\approx 80\) nm and with a length \(d_{\mathrm{SD}}\approx 600\) um which is comparable to the typical wavelength of the microwave signal emitted by the source (\(\lambda\sim 1\) mm). To avoid signal reflection, the line was terminated by a matched load consisting of two on-chip resistors \(R_{\mathrm{S}}^{\prime}\approx 120\)\(\Omega\) and an interdigital capacitor \(C_{\mathrm{S}}\approx 0.1\) pF. Voltage is measured via two other leads, each equipped with an on-chip resistor \(R_{\mathrm{S}}^{\prime\prime}=4R_{\mathrm{S}}^{\prime}\approx 480\)\(\Omega\).  
For comparison, we had also realized other ways of device coupling, first, in the form of a direct capacitive coupling using a 100 um long Al thin film microstrip line connected to the Josephson source on one side and coupled capacitively to the floating gate on the other side. The length of this coupling line is intentionally chosen to be shorter in order to avoid possible resonances and reduce the losses. Furthermore, an extra source which was not coupled electromagnetically was placed on each chip at the same distance from the detector, to evaluate the possible contribution of substrate-mediated phonons to the detector signal.  
Figure 4: Equivalent diagram of the complete circuit consisting of the source, the detector and the transmission line coupling. (**Lower left inset**): SEM picture of the detector circuit; (**Upper left inset**): Close-up SEM picture of the detector showing the NISIN transistor structure; (**Upper right inset**): SEM picture of the SQUID (microwave source).
# Design of Positioning Mechanism Fit Clearances Based on On-Orbit Re-Orientation Accuracy
### Design of Fit Clearance  
By substituting the relative position relationships of the positioning points into Equation (6) and Equations (10) and (11), the simplified expressions of these equations can be obtained. In addition, because the index requirements of the re-orientation angles around three axes were not more than 12\", the above equations can be expressed by the following matrix:  
\[\left[\begin{array}{ccc}\frac{234}{1541052}&\frac{885}{1541052}&\frac{651}{ 1541052}\\ \frac{822}{1541052}&\frac{184}{1541052}&\frac{1006}{1541052}\\ \frac{1541052}{2396.6}&\frac{1}{2012}&0\end{array}\right]\left[\begin{array} []{c}\Delta A\\ \Delta B\\ \Delta C\end{array}\right]=\left[\begin{array}{c}\frac{\pi}{54000}\\ \frac{\pi}{54000}\\ \frac{\pi}{54000}\end{array}\right]. \tag{12}\]  
When the maximum rotation angles around three axes were all 12\", the fit clearances that were obtained from Equation (12) were \(\Delta A=0.062\) mm, \(\Delta B=0.065\) mm, \(\Delta C=0.027\) mm. Within this range, considering the operating and manufacturing factors, the design values were calculated as \(\Delta A=0.02\) mm, \(\Delta B=0.06\) mm, and \(\Delta C=0.02\) mm. By substituting \(\Delta A\), \(\Delta B\), and \(\Delta C\) into Equation (6) and Equations (10) and (11), we obtained \(\left|\theta_{\Delta}\right|_{\max}=9.5\"\), \(\left|\theta_{y}\right|_{\max}=6.4\"\), and \(\left|\theta_{z}\right|_{\max}=7.9\"\).
## 2 Materials and Methods
### Data Sources and Processing of MODIS Products
#### 2.2.2 Processing of MODIS Products  
The three types of data needed in this study were from MODIS products, namely, NDVI, MNDWI, and NPP. NDVI and MNDWI were used for the construction of the ecospatial network of the YRB, while NPP was used for indirect validation of the NBP of vegetation nodes obtained from the Biome-BGC model simulations. The NDVI of the YRB was extracted from the spatial distribution dataset of NDVI at 1 km resolution for the growing season in China produced by the RESDC (Resource and Environmental Science and Data Center), which was verified to accurately reflect the cover status of surface vegetation. We clipped this data to obtain the 2018 NDVI of vegetation in the YRB.  
The NPP was obtained from our processing of the MOD17 product from MODIS, which can be obtained from the open database of the National Aeronautics and Space Administration (NASA) [35]. This product provides annual NPP information at a 500 m pixel resolution. After we selected the row number where this study area was located, the obtained original product was mosaicked, reprojected, and data formatted by MRT (MODIS Reprojection Tool). After that, the invalid values (32,761 to 32,767) in the image of this product were set to null values. In addition, a coefficient conversion was performed on the remaining raster values. Multiplying the raster values by 0.1 produced the NPPunits required for this study in g-C m\({}^{-2}\cdot\)year\({}^{-1}\). Finally, the images were clipped using the extract by mask tool to obtain the 2018 annual NPP for the YRB required for this study.  
The extraction of MNDWI for the YRB was from the MODIS MODIS MOD09 product, which includes atmospherically corrected surface reflectance data from the MODIS L1B product. Similar to the MOD17 product used for extracting the NPP, this product is also derived from NASA. We used the MRT (MODIS Reprojection Tool) to mosaic, reproject, and format the original product to extract surface reflectance data at a resolution of 500 m. This product has seven bands (\(B1\) to \(B7\)). We obtained the MNDWI results by band math using the method proposed by [PERSON] for calculating MNDWI [36]. The final MNDWI for 2018 in the YRB required for this study was obtained by clipping. The band calculation formula for MNDWI is  
\[\text{MNDWI}=\frac{(B_{4}-B_{6})}{(B_{4}+B_{6})}, \tag{1}\]  
where \(B_{4}\) and \(B_{6}\) represent the reflectance of the B4 and B6 bands of the MODIS product [37].  
A flowchart is provided in Figure 2 representing the abovementioned processing of the MODIS product, the information extracted, and the subsequent usage.
## 5 Conclusions  
This study focused on the performance of highway subgrades over their whole life-cycle during the operation period. It selected 20 test sites across 4 typical highways to carry out distress surveys for up to 3 years. Moreover, the assessment and prediction of subgrade performances were carried out. The following conclusions were obtained:  
1. The development trend of subgrade distress is affected by internal factors. External factors, such as rainfall, directly cause the formation of subgrade distress. Therefore, the relationships  
Figure 10: Life-cycle decision-making framework for preventive maintenance.  
Figure 9: Subgrade performance curve of the life-cycle and an optimal time for preventive maintenance.  
between the internal and external factors are important when analyzing the evolution mechanism of subgrade distress.
2. The results of the SPI calculations show that the prediction accuracy of the SPI prediction model based on PSO-LSSVM is better than the prediction accuracy of the other two models, which are based on Grid-SVM and GA-SVM. In addition, the PSO-LSSVM model accurately predicts both the gently decreasing type and rapidly decreasing type of SPI.
3. The prediction accuracy of provincial highways is higher than that of the national highway. This is mainly due to the large traffic volume on the national highway and the frequent occurrence of overloaded trucks, which result in redundant damage to the subgrade, thus increasing the difficulty of SPI prediction.
4. According to the SPI, the subgrade is divided into four levels. Corresponding maintenance and treatment measures are taken for different levels. Preventive maintenance is applied before the SPI drops below 70.
5. The establishment of a subgrade performance assessment-prediction-maintenance-management architecture framework provides a clear method for road maintenance managers to reasonably lay out the subgrade life-cycle assessment decision system.  
This paper was accomplished based on collaborative work of the authors. Conceptualization, Q.L.; Data curation, K.Z., Z.C. and Z.T.; Funding acquisition, Y.W.; Investigation, Q.L., K.Z., Z.C. and Z.T.; Project administration, Y.W.; Software, Q.L.; Writing--original draft, Q.L.; Writing--review & editing, Y.W. and K.Z. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the National Key R & D Program of China (2016 YFC0802500) and the Traffic Science and Technology Project of Guangdong Province (4 February 2014).  
The authors declare no conflict of interest.
## 3 Results
### Correlations Between In Situ SM, GPP, and Satellite Information
Figure 9: Relationship between daily in situ SM at different depths, MOD11A1 (\(n\) = 287) and MYD11A1 (\(n\) = 274). Table 2 shows the correlation between in situ SM and MODIS LST for the same day and the average up to 4 days after from SM data for both stations. The results suggest that increases in SM have an impact on LST for the days following the pulses (also shown in Section 3.2.2). R2 values were similar at different depths, with no correlation in the Ti Tree station at a 60 cm depth. Regarding the in situ SM/LST relationship, the low correlation in the deepest horizons can be explained by the absence of a hardpan in this station, which does not favor the increase in SM and the extraction of water by plants at those depths. With the reflectance data, the association between daily GPP and spectral indices in ASM is shown in Figure 10. The strong correlation present is consistent with previous studies analyzing the EVI at coarser temporal scales in the study region (e.g., [11; 58]). Ref. [59] reported relationships between the LSWI from MODIS and the GPP derived from eddy covariance over grassland of Northern China. Ref. [60] found correlations between the EVI and LSWI from MODIS in semi-arid areas of South Africa. We observed a slightly stronger correlation (\(p<0.005\)) with the EVI than LSWI (see also Figure 6). Finally, Figure 11 shows the relationship between MODIS LST and in situ daily GPP in ASM. Unlike spectral indices, lower correlations (\(p<0.005\)) were observed. This can be explained by the delayed response of GPP in relation to changes in LST related to wet pulses; i.e., GPP is a result of the previous use of water by vegetation. In the Ti Tree station, a low negative relationship was also observed with Terra LST with no correlation with Aqua LST (Figure 7). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**SM Depth** & **R2 (MOD11A1) ASM** & **R2 (MOD11A1) ASM** & **R2 (MOD11A1) Ti Tree** & **R2 (MYD11A1) Ti Tree** \\ \hline SSM & 0.50 (LST 3-day average) & 0.46 (LST 2-day average) & 0.53 (LST 4-day average) & 0.46 (LST 3-day average) \\ \hline
10 cm & 0.55 (LST 4-day average) & 0.48 (LST 4-day average) & 0.49 (LST 4-day average) & 0.45 (LST 4-day average) \\ \hline
60 cm & 0.49 (LST 4-day average) & 0.50 (LST 4-day average) & 0.12 (LST 4-day average) & 0.00 (LST 4-day average) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Highest R2 values (\(p<0.005\)) for the relationship between in situ SM and MODIS LST for the same day and the average up to 4 days after, from SM data in the ASM and Ti Tree stations. Figure 10: Relationship between daily in situ GPP (gC/m2), LSWI (**left**), and EVI (**right**) values from the AHI in ASM (n = 145). Note that 2018–2019 was not included, as there was no evident growing season.
# Efficient On-Off Keying Underwater Acoustic Communication for Seafloor Observation Networks
## 2 System Model
### Channel Model and Capacity  
The received signal at the \(i\)-th subchannel is expressed as  
\[y_{i}=H_{i}u_{i}+n_{i} \tag{1}\]  
where the channel fading coefficient \(H_{i}\) and the additive white noise \(n_{i}\) both are zero-mean circularly-symmetric complex Gaussian random variables with the variance of \(2\sigma^{2}\) and \(N_{0}\), respectively. The probability density function (PDF) of the magnitude of \(H_{i}\), i.e., \(|H_{i}|\), is thus Rayleigh distributed [20; 21]. Without loss of generality, it is assumed \(2\sigma^{2}=1\) in the following discussion. The transmitted symbol \(u_{i}\) is taken from a binary OOK constellation. The conditional probability \(P\left(Y_{i}|u_{i}\right)\) of the amplitude of the received signal, \(Y_{i}=|y_{i}|\), is given as [22]  
\[\left\{\begin{array}{l}p\left(Y_{i}|u_{i}=0\right)=\frac{2Y_{i}}{N_{0}}{\rm exp }\left(-\frac{Y_{i}^{2}}{N_{0}}\right)\\ p\left(Y_{i}|u_{i}=1\right)=\frac{2Y_{i}}{N_{0}+E_{\rm c}}{\rm exp}\left(-\frac {Y_{i}^{2}}{N_{0}+E_{\rm c}}\right)\end{array}\right. \tag{2}\]  
where \(E_{\rm c}\) is the symbol energy. The channel capacity is computed as [21]  
\[\begin{split} C&=E\left[{\rm log}_{2}\left(\frac{p\left(Y_ {i}|u_{i}\right)}{p\left(Y_{i}\right)}\right)\right]\\ &=\frac{1}{2}\int_{0}^{\infty}\left[p\left(Y_{i}|0\right){\rm log}_{2} \left(\frac{p\left(Y_{i}|0\right)}{p\left(Y_{i}\right)}\right)\right]{\rm d}Y _{i}+\frac{1}{2}\int_{0}^{\infty}\left[p\left(Y_{i}|1\right){\rm log}_{2} \left(\frac{p\left(Y_{i}|1\right)}{p\left(Y_{i}\right)}\right)\right]{\rm d}Y _{i}\end{split} \tag{3}\]  
where \(P(u_{i}=0)=P(u_{i}=1)=0.5\) is assumed and \(p(Y_{i})=\frac{1}{2}[p(Y_{i}|1)+p(Y_{i}|0)]\).  
The capacity curves for four noncoherent modulations are compared in Figure 2, where \(E_{\rm b}/N_{0}\) is the SNR per bit. From the figure, two observations are made. First, the OOK can achieve a much higher spectral-efficiency than MFSK. Second, with the increase of spectral-efficiency, the \(E_{\rm b}/N_{0}\) threshold required by MFSK increases sharply. When the spectral-efficiency reaches the theoretically maximum value of \(0.5\) bit/s/Hz, the required \(E_{\rm b}/N_{0}\) threshold is infinite. By comparison, the OOK only requires \(E_{\rm b}/N_{0}=9.6\) dB for \(0.5\) bit/s/Hz.  
Figure 1: Block diagram of the proposed noncoherent UAC (underwater acoustic communication) scheme.
# IEC-61108-7 SBAS Standard for Shipborne Receivers: Preliminary Testing Validation Activities +
## 2 Setup Design and Realization
TC 006 is designed to confirm the receiver's ability to track the SBAS signal at varying power levels, while TC-007 verifies the receiver's capacity to track and process SBAS PRN codes from 120 to 158. The receiver's behavior in the presence of interference is scrutinized in TC-008 to TC-010, considering different types of interference, including pulsed, continuous wave, and wideband interference. In these cases, the receiver should consistently deliver accurate position results (95% horizontal error below 10 m) without failing to report a position in less than 5% of the time. Velocity information accuracy is assessed under different speed and course conditions in TC-011 to TC-013, with the pass/fail criteria based on a 95% acceptance rate for horizontal velocity error. The same TCs are used to evaluate the course error, with the pass/fail criterion again based on a 95% acceptance rate. TC-014 checks whether the receiver correctly provides the navigational and integrity status flag under three different signal and monitoring conditions. TC-015 aims to verify the receiver's correct use of SBAS messages through a variety of different SBAS signal messages. Finally, TC-016 examines the correct switching between SBASs corresponding to the same (or different) SBAS service providers. The following subsections describe in detail some example tests, for which the results are presented in Section 3.