esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Multifunctional Unmanned Boat Platform for Aquaculture Automation
## 2 Complete Machine Structure and Working Principle
### Working Principle  
#### 2.2.1 Automatic Cruise Route Generation in Fishing Areas Based on the Monte Carlo Simulation  
In fishery management, optimizing the visit order of feeding points is crucial to improve efficiency and reduce resource waste. Thus, the Monte Carlo simulation, which has been proved to be effective in route optimization [24; 25], is applied for generating the automatic cruise route of the UBP. The basic idea of the Monte Carlo simulation is to generate a large number of random samples and conduct repetitive experiments. By statistically analyzing these samples, it estimates the solution to a given problem. Based on the feeding points in the aquaculture area, this paper designs a cruise path generation algorithm using the Monte Carlo simulation. This algorithm calculates the shortest cruise path in a shorter time to improve feeding efficiency. Figure 7 illustrates the flowchart of the algorithm.
## 4 Methodology
### Integrated Simulation Tool  
A key aspect of the applied methodology is the implementation of a simulation tool integrating all the necessary components. Firstly, measured wind data are sent to the TurbSim 1.21 software (a stochastic, full-field, turbulence simulator) to produce wind profile data in a suitable format for FAST [44]. FAST is a computer-aided engineering (CAE) software provided by the NREL that allows users to simulate the behavior of horizontal axis wind turbines, which can be integrated with MATLAB/Simulink. These wind profiles are one of the most relevant inputs of a FAST simulation model, whose execution produces the outputs of the simulated system (Figure 3 shows the sequence of the stand-alone tools used in this work). The integration of these tools enables the applied methodology, supporting the controllers' development stage and allowing their simulation to generate the necessary data in order to evaluate the controller using performance indexes according to the OC4 simulation model [45; 46].  
Figure 2: Simplified diagram for the reference PI controller structure.
## 4 Implementation of RUL Prediction Based on Proposed Method
### Experimental Environment and Hyperparameter Selection  
The experiments were conducted using the PyTorch 2.3.0 framework to build the deep learning model. The implementation language was Python 3.9, and PyCharm 2025.1.1 was used as the development environment. The software environment was configured with CUDA 12.6. The hardware setup included a Windows 11 operating system, an Intel i5-12500H CPU, and 16 GB of RAM. The selection of hyperparameters and structural parameters plays a critical role in model performance. Additionally, the impact of the input sequence length on prediction accuracy was taken into consideration. The Adam optimizer was employed, and extensive experiments were conducted to optimize the parameter combinations. The optimal configuration was determined based on the RMSE between the predicted and actual values. The final parameter settings are summarized in Table 5.  
To enable a more detailed comparison of the prediction performance across different feature sets and models, the bearing RUL was divided into three stages, referred to as the early stage, middle stage, and late stage.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Cluster Number** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline Feature & P2, P4, P22 & P5 & P9 & P10, P26 & P25, P27 & P28 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Feature clustering results.  
Figure 14: (**a**) Clustering error elbow diagram; (**b**) sensitive feature clustering tree diagram.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Batch size & 16 \\ Learning rate & 0.01 \\ Epoch & 300 \\ Number of multiple attention heads & 2 \\ The number of hidden units of GRU & 128, 64 \\ Window length & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameter setting.
## 4 Conclusions  
The main achievements of the study are as follows.  
1. The optimal percentage of fiber reinforcement of concrete with coconut fibers was determined, which was 1.75%.
2. The enhancement in the concrete properties due to its dispersed reinforcement with coconut fibers was expressed as an augmentation in its SC. The increase in the strength parameters of the obtained FRC samples was for compressive strength--26%, for axial compressive strength--24%, for tensile strength in bending--42%, and for axial tensile strength--43%.
3. The increase in strain characteristics was 46% for deformation under axial compression, 51% for deformation under axial tension, and 16% for the modulus of elasticity.
4. The microstructure of hardened cement paste samples with a percentage of fiber reinforcement of 1.75% was the densest, practically not containing structural defects in the form of voids and cracks. At a dosage of coconut fiber in an amount of 2%, due to their high water absorption, cracking was observed at the phase boundaries, which reduced the anchoring of the fiber in concrete.
5. A significant advantage of the proposed recipe method and the material produced for the economy and ecology was noted, with a general increase in its SC compared to ordinary concrete.
6. Recommendations for future research are to study the effect of coconut fiber on the physical properties of concrete, including the properties of long-term strength, as well as the co-inclusion of coir fiber and other industrial and agricultural waste in composites.
7. The resulting material should be used in individual and cottage housing construction. The high mechanical characteristics of concrete give rise to good predictions for the durability of such buildings.  
Conceptualization, S.A.S., E.M.S., A.N.B. and A.A.S.; methodology, S.A.S., E.M.S., Y.O.O., C.A. and A.C.; software, S.A.S., E.M.S., A.N.B, Y.O.O. and C.A.; validation, A.A.S., S.A.S., E.M.S. and A.N.B.; formal analysis, A.A.S., S.A.S. and E.M.S.; investigation, A.N.B., L.R.M., S.A.S., E.M.S., A.N.B., Y.O.O., C.A. and B.M.; resources, B.M.; data curation, S.A.S., E.M.S. and A.C.; writing--original draft preparation, S.A.S., E.M.S. and A.N.B.; writing--review and editing, S.A.S., E.M.S. and A.N.B.; visualization, S.A.S., E.M.S. and A.N.B.; supervision, L.R.M. and B.M.; project administration, L.R.M. and B.M.; funding acquisition, A.N.B. and B.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The study did not report any data.  
The authors would like to acknowledge the administration of Don State Technical University for their resources and financial support.  
The authors declare no conflict of interest.
## 8 Evaluation of the MRLG Protocol
### Evaluating the Impact of Sink Speed  
Finally, we present performance evaluation results for the MRLG protocol when varying the sink speed in the speed range of 1 to 10 m/s. The purpose of these experiments is to determine whether the routing protocols are able to offer low route convergence times. This requires maintaining delays and packet loss ratios at low values when speed increases; additionally, we aim at obtaining routing overhead values as low as possible.  
Figure 12(a) shows the packet loss rate obtained when we varied the sink speed. The loss rate for the MRLG protocol was quite smaller than that for the CTP protocol, showing a loss rate that was always  
Figure 11: Varying the number of nodes per scenario: (**a**) number of routing/injected packets, (**b**) normalized routing load.  
maintained below 15%. It is also interesting to observe that the loss rate decreased when the sink speed increased, for both protocols. This behavior is due to the fact that the sink was more often near the center of the scenario, where the traffic requires less hops to reach the sink on average, thus experiencing less collision-related losses, which are more prone to occur for higher hop counts.  
Figure 12(b) shows the results relative to end-to-end delay at different sink speeds. Now, we find that the average delay with MRLG was always maintained below 200 ms, whereas for CTP it ranged from 840 to 1350 ms. Such large delay values evidence that MRLG is able to perform adequately, and that high speeds do not represent any performance drawback (delay increases just slightly).  
Finally, Figure 13(a,b) shows the routing overhead and the normalized routing overhead. We now find that the routing overhead was maintained stable for both routing protocols, or was reduced slightly when the sink speed increased. These results evidence that, despite MRLG uses a much higher sink announcement (RREQ) rate to continuously detect local topology changes, it is able to achieve a better use of available resources, reducing the control overhead by up to 200%.  
Figure 12: Varying the sink speed: (**a**) packet loss ratio, (**b**) end-to-end delay.  
The results shown in this section allows us to conclude that, when combined with the IEEE 802.15.4 technology, MRLG is adequate for supporting applications requiring mobile sinks and low-delay data delivery. In fact, compared with a standard tree-based solution, the proposed MRLG protocol is able to drastically reduce routing overhead, which has a very positive impact in terms of packet losses and end-to-end delay.
## 2 HJ-2 A/B Satellites and Sensors
The HJ-2 A/B satellites are China's second civilian remote sensing satellites equipped with a synchronous atmospheric correction instrument, following the Gao Fen Duo Mo satellite. The atmospheric correction instruments on both satellites are polarization sensors, but they operate in different modes. The SMAC instrument on the Gaofen Multi-Mode Satellite employs a split-aperture detection method, with each channel using a dual-pixel imaging mode to perform push-broom observations along the satellite's flight direction. The Polarized Scanning Atmospheric Corrector (PSAC) on the HJ-2 A/B satellites employs a combined split-aperture and split-amplitude simultaneous polarization measurement method to enable the simultaneous detection of all polarization channels. To match the MSC's 800 km swath width, the PSAC performs cross-track scanning at a spatial resolution of 6 km, ensuring compatibility for synchronized observations. Figure 1 illustrates the coordinated detection capability of the PSAC and MSC. In the figure, the green arrow represents the satellite's flight direction, while the blue arrow indicates the along-track push-broom direction of the MSC, which aligns with the satellite's movement. The red arrow denotes the cross-track scanning direction of the PSAC, which is perpendicular to the satellite's flight direction. This demonstrates that the PSAC and MSC can seamlessly perform synchronized observations.
## 3 Results
### Soil GTR Classification
#### 3.1.2.  
Afterberg Limits and Methylene Blue Values  
To look at the absorption properties of the clays and to characterize the clayiness of the materials present in the different soils, the methylene blue values (MBV) and Arterberg limits were determined. The results are given in Table 2.  
The BE42 and Cabale soils have relatively similar plasticity index (PI) values (17 and 20). These soils are classified as high-plasticity clayy silts. For the Morne Coco soil, the plasticity index (PI) of 6 indicates a sandy soil with little clay, i.e., a soil of slight plasticity.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **BE42** & **Cabale** & **Morne Coco** & **MTI** \\ \hline Liquid Limit, wL & 55 & 55 & 32 & ND * \\ Plastic Limit, wP & 38 & 35 & 26 & ND * \\ Plasticity Index, PI & 17 & 20 & 6 & ND * \\ MBV (g/100 g) & 0.53 & 0.31 & 0.09 & 0.02 \\ \hline \hline \end{tabular}  
* Not Determined.  
\end{table}
Table 2: Results of Arterberg limit test and MBV test measurements.  
Figure 5: Granulometric analysis of the 4 studied soils.  
For the fourth, MTI, soil, it is not possible to measure the plasticity index of the soil because the material has very little plasticity (3.3% of the material passing the 80 \(\upmu\)m sieve).  
The results of the methylene blue tests (MBV) indicate low values. These values corroborate the conclusions of the particle size analysis and the determination of the Arterberg limits. The MBV of the MTI and Morne Coco soils (MBV < 0.1) indicates a near absence of clay fines. However, for the BE42 and Cabale soils, the MBV (0.2 \(\leq\) MBV < 1.5) highlights the presence of very small amounts of silty-clay particles.
# Performance Analysis of Internet of Vehicles Mesh Networks Based on Actual Switch Models
## 5 Simulation Results
### Network Performance for Different Caching Capacities  
From the point of view of the node's performance, Figure 10 simulates the variation in network performance with different cache capacities. The initial vehicle and RSU cache capacities are 100 Mb and 500 Mb, respectively. This subsection reduces both by half and observes the results in conjunction with QoS. From the perspective of packet loss and task arrival rate, reducing the cache capacity by half has a minor effect on both metrics at low QoS levels, whereas the impact becomes more pronounced at higher QoS values. This is because, in the case of less network traffic, the lower cache capacity can carry the task packets efficiently. And, under high network traffic conditions, the increase in the total number of packets fills the lower cache queue quickly. Consequently, there is a notable surge in packet loss rate alongside a significant decrease in task arrival rate.  
Regarding the link load rate, reducing the cache capacity by half at the same QoS level reduces this metric. This outcome stems from the decreased storage of packets on the node due to the diminished cache capacity, leading to a corresponding decrease in the number of packets forwarded by the node. Furthermore, the impact of altering cache capacity on the link load rate is more obvious at QoS = 100 Mbps compared to QoS = 20 Mbps, aligning with the rationale provided for the packet loss rate analysis. Analyzed from the perspective of total network traffic and node load rate, under equivalent QoS conditions, the disparity in total network traffic before and after cache capacity adjustment demonstrates a growing trend over time. Moreover, this divergence becomes more conspicuous with higher QoS values. The reason is that halving the cache capacity leads to a large number of packet losses, the total number of packets carried by the network decreases, and the decreasing trend is more obvious with the increase in traffic.  
In addition, the reason for the trend in node load rate with QoS = 20 Mbps is similar to the analysis in Section 5.1. When QoS = 100 Mbps, the total network traffic decreases following a halving of the cache capacity, but the node load rate remains nearly unchanged at this time. This is because when the cache capacity is larger, although there are more packets, they are relatively evenly distributed across the nodes. In contrast, reducing the cache capacity results in vehicle nodes carrying larger packets, so that the overall node load rate of the network does not undergo a significant decrease compared to the previous one.  
Figure 9: Network indicators for different QoS.
# Precoding for RIS-Assisted Multi-User MIMO-DQSM Transmission Systems
###### Abstract  
This paper presents two precoding techniques for a reconfigurable intelligent surface (RIS)-assisted multi-user (MU) multiple-input multiple-output (MIMO) double quadrature spatial modulation (DQSM) downlink transmission system. Instead of being applied at the remote RIS, the phase shift vector is applied at the base station (BS) by using a double precoding stage. Results show that the proposed RIS-MU-MIMO-DQSM system has gains of up to 17 dB in terms of bit error rate (BER) and a reduction in detection complexity of 51% when compared with the conventional MU-MIMO system based on quadrature amplitude modulation (QAM). Compared with a similar system based on amplify and forward (AF) relay-assisted technique, the proposed system has a gain of up to 18 dB in terms of BER under the same conditions and parameters.  
p 1 2023 15 299 1
## 2 Data Characteristics
Pronounced volume scattering also occurs in case of formation of larger ice crystals, due to metamorphosis after thawing and refreezing [2]. SeaWinds data also exhibit strong temporal variability which varies over differing land cover [12]. The standard deviation of noise \(s_{\sigma}\) can be estimated using several months of data as input [12, 18, 29]. Figure 2 demonstrates \(s_{\sigma}\) at Ku-band of un-glaciated terrain in high latitudes. It is much higher than \(s_{\sigma}\) determined for C-band scatterometry such as MetOp ASCAT in those environments [30]. Typical values are \(<\)0.3 dB for ASCAT. It usually exceeds 0.5 dB (mean of 0.57 above 60\({}^{\circ}\)N) for SeaWinds. \(s_{\sigma}\) varies depending on land cover. It is lowest over forest areas and highest over open water. Large parts of the land area above 60\({}^{\circ}\)N are characterized by tundra with many small lakes. These areas are generally classified as barren land or sparsely vegetated from global land cover maps (Table 1) and show greater  
Figure 1: SeaWinds on QuikSCAT timeseries example from autumn 2003 to spring 2004 (Salehard, 66.53\({}^{\circ}\)E, 66.53\({}^{\circ}\)N). Backscatter of all available measurements in dB (green +) compared to daily temperature range (red vertical bars show minimum to maximum in degree Celsius; source: WMO D512 dataset). Blue diamonds represent the difference between average morning and evening backscatter in dB. noise than forested regions. This needs to be accounted for especially when change detection methods which use thresholds are applied. \begin{table}
\begin{tabular}{l l l l} \hline
**GlobCover class** & **Min** & **Max** & **Mean** \\ \hline mixed broadleaved and needleaved forest & 0.45 & 0.97 & 0.50 \\ needle leave deciduous or evergreen forest & 0.45 & 1.17 & 0.49 \\ sparse vegetation (less than 15\%) & 0.46 & 1.66 & 0.59 \\ water & 0.48 & 2.46 & 1.98 \\ permanent snow and ice & 0.48 & 2.20 & 0.87 \\ \hline \end{tabular}
\end{table} 
Table 1: Minimum, maximum and mean of estimated standard deviation of noise \(s_{\sigma}\) for the predominant major land cover classes (source GlobCover, ionia1.esrin.esa.int). Figure 2: Estimated standard deviation of noise in dB (\(s_{\sigma}\)) of SeaWinds on QuikSCAT above 60\({}^{\circ}\)N (excluding Greenland).
# Communication Protocol Design for IoT-Enabled Energy Management in a Smart Microgrid
## 2 System Model
The following assumptions are made about the channel coefficients:  
1. The channels are considered constant within a certain time slot. The channel coefficients in one time slot are considered independent from that in another time slot. 2. All HEMSs know about their local channel state information (CSI) alone and do not have access to the global CSI. 3. The channels are modelled as Rayleigh fading channels where the channel coefficients are considered as complex-valued Gaussian random variables with zero mean and covariance \(M_{i,j}\), where \(i,j\in\mathcal{K}\). The aforementioned assumptions are widely applied in smart grid communication research [44; 49; 50]. It should be noted that the analysis in this paper can also be applied for other channel fading models used in wireless communication research. In addition, the impact of channel estimation errors can be modelled using the approach in [51] and is expected to add a constant SNR penalty in the achievable error performance.
## 5 Conclusions  
This study aimed to illustrate the key factors involved in an SVE remediation treatment. The effectiveness of the SVE system was evaluated as the removal rate of organic pollutants from soil and air matrices. SVE was used for the reduction of short and long-chain hydrocarbons, allowing us to obtain the following main results.  
* Soil texture affects air rate and vapor movement through the ground and, subsequently, the total VOC removal. The higher the soil permeability is, the more effective the SVE system is. The presence of low permeability layers overlying zones of higher permeability reduces the global effectiveness of the remediation system; however, in low permeable sediments, the removal rate reached values of efficiency >12%.
* The SVE remediation system was effective at reducing VOCs in the vadose zone with a total final efficiency equal to 73%. The remediation targets were reached within four working years for all light hydrocarbons, highlighting how SVE is more effective with smaller and lighter organic compounds. Six samples showed concentrations still higher than the remediation target for heavy hydrocarbons and, thus, an extension of the treatment was planned for 24 months more.
* During the first year of SVE operations, over 420 kg of VOCs were extracted, corresponding to approximately 80% of the total mass extracted (540 kg) in four years of treatment.
* To evaluate the SVE performance for completion or transition to another technology, a full rebounding test may be performed.  
**Author Contributions:** Conceptualization, S.D.G., F.T., M.N.; methodology, S.D.G. and F.T.; software, C.L.; validation, M.N. and F.P.; formal analysis, M.N. and F.P.; investigation, S.D.G. and F.T.; resources, F.P.; data curation, C.L. and F.T.; writing--original draft preparation, C.L.; writing--review and editing, S.D.G. and M.N.; visualization, C.L. and F.T.; supervision, F.P. and M.N.; project administration, F.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The authors express their gratitude to the engineers [PERSON] and [PERSON] for the data collection activity.  
**Conflicts of Interest:** The authors declare no conflict of interest.