esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
Nine results include an intelligent information sharing platform construction of disaster (C2), marine climate professional and technical personnel training (C3), accelerating the development of ship power clean energy (C4), construction of marine natural barrier (C5), promote the intelligent marine pollution control method (C7), promote the development of ship intelligent (C8), standardization of ship operation (C10), promote the development of financial derivatives (C13), and raise the public awareness of marine conservation (C19). These methods have a weak impact on risk prevention of the Marine Silk Road, but they are more likely to be affected by other factors and change. Therefore, appropriate attention and control should be paid to the actual management to help improve the prevention effect. According to the order of centrality, the factors are C17, C18, C12, C11, C19, C9, C6, C7, C16, C13, C8, C2, C11, C15, C14, C10, C3, C5, and C4 from the largest to the smallest. According to Formula (9), as shown in Table 7, the reach-ability matrix M composed of 0 and 1 is constructed. 1 indicates a strong relationship between the two factors, and 0 indicates no or weak relationship between the two factors. The reachable and antecedent sets of factors of the first-level decomposition structure are shown in Table 8.  
It can be seen from Table 7 that the reachable set and the common set intersect with factors C12, C17 and C18, so elements C12, C17, and C18 constitute the first-level influencing factors. The rows and columns of the influencing factors C12, C17 and C18 in the matrix M are deleted to obtain a higher-level decomposition matrix, and the above process is repeated. The factor set Nq (q = 1, 2..., 5): first-level node N1 = {C3, C4, C5, C10, C13,}; Level 2 node N2 = {C2, C7, C19}; Level 3 node N3 = {C1, C6, C8, C11}; N4 = {C9, C14, C15, C16}; Level 5 node N5 = {C12, C17, C18}. Finally, we can build a hierarchical path from the ISM model for indicators to interact with each other, as shown in Figure 3. The orange arrows represent the same level of interaction. The green arrows show the effect on the upper layers, and the blue arrows show the effect on the cross layers. This provides a complete guide for the intelligent prevention of Marine Silk Road risks. Figure 3: Interpretive structural modeling (ISM) model structure diagram of influencing factors. According to the ISM model analysis of influencing factors, C12, C17, and C18 are the key to establishing the risk prevention system of the Marine Silk Road. To sum up, the methods for establishing an intelligent risk prevention system for the Marine Silk Road are very complex, involving the interactions among nature, transportation, economy, society and the international community. The influence mode, influence mechanism, and effect degree of different methods are different, thus forming a systematic integration framework for risk wisdom prevention of the Marine Silk Road.
# Blind Recognition of Forward Error Correction Codes Based on Recurrent Neural Network
## The Recognition Process  
The section presents the schematic diagram of the digital communication system based on FEC coding recognition, as shown in Figure 1. It includes source coding, channel coding, modulator, demodulator, data storage and abundant channel simulation modules which aims to put the simulated channel propagation model into use of simulated code data.  
Generally, source coding technique needs to compress data in order to transmit information more quickly, while channel coding technique is on the contrary. So as to assure the reliability of transmission, redundant codes, called supervised codes, will be added to the information codes to form a certain constraint relationship. We assume that bits of information from transmission is encoded to obtain the code words of code length \(N(N>k)\) through channel coding at the transmitter end and then mapped to the symbol vector through BPSK modulation. In the model, the channel adopts additive  
Figure 1: General block diagram of the blind recognition.  
white Gaussian noise channel (AWGN) and the Gaussian noise samples are distributed to \(n\sim N(0,\sigma^{2}),\sigma\in R\). The SNR is defined as the bit error rate (BER) and bit energy noise ratio (Eb/N0).  
The received codeword is defined as \(r\), which is also called the receiving codeword. Because the symbol vector is disturbed by noise through AWGN channel, then is defined as:  
\[r=e+v \tag{1}\]  
where \(e\) is the error pattern. In general, BPSK demodulation is performed before the types of forward error correction codes are identified. On the basis of the Gaussian distribution of channel noise, the logarithmic likelihood ratio (LLR) of the transmitted information is showed as:  
\[LLR(r)=\ln\frac{P(v=0|r)}{P(v=1|r)}=\frac{2}{\sigma^{2}} \tag{2}\]  
\(\sigma\) is the variance of the channel noise and \(r\) is the code words of reception and the recognition model of FEC code is used to identify the information bit vector sent by \(r\). In this work, we focus on the recognition of types of FEC codes.
## 5 Conclusions  
In terms of mechanical behaviour, the stabilisation of mine tailings from copper extraction using alkali-activated FA is an effective procedure which can be considered for low-strength applications (<20 MPa), like embankments or road bases. This procedure has the advantage of avoiding thermic treatment to increase the amorphisation level, while requiring only 25%-30% FA, i.e., 70%-75% of the paste will be tailings. This is significant since it represents an alternative to traditional landfilling, and it also reduces cement consumption. Considering that the above-mentioned applications require high volumes of material, this solution represents a suitable alternative to conventional binders in terms of mechanical properties, environmental friendliness, and cost-effectiveness. A UCS of 15 MPa was obtained after a curing period of 24 h at 85 \({}^{\circ}\)C using a 15 molal SH activator (P2C). However, the P2B paste was chosen for further analyses, since its strength/cost compromise was considered to be more effective. The results showed that strength increase is related with the formation of N-A-S-H gel, although sodium sulphate carbonate is also developed, suggesting that the total sodium input could be optimized without strength loss. The solubility of all the metals analysed in the P2B paste (78%MT/22%FA) was below the threshold for non-hazardous waste. However, additional environmental studies are required to validate this technique, especially considering the SO\({}_{3}\) contents in the tailings.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multirow{2}{*}{_LTT1_ **(mg/kg)**} & \multicolumn{4}{c}{_LTT2_ **(mg/kg)**} \\ \cline{3-6}  & & **3 d** & **7 d** & **14 d** & **28 d** \\ \hline Ca & 6.58 & 2.67 & 2.27 & 1.77 & 1.67 \\ Fe & 4.08 & 1.33 & 2.21 & 3.83 & 8.46 \\ K & 16.74 & 20.01 & 22.52 & 21.85 & 24.80 \\ Mg & 2.37 & 2.09 & 1.72 & 1.75 & 1.13 \\ Na & 2254.9 & 1891.2 & 1882.2 & 1634.5 & 1274.5 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Solubility of PB2 species not included in 2003/33/EC.  
**Author Contributions:** Conceptualization, N.C. and M.O.; methodology, N.C. and J.C.; validation, A.F.-J., N.C.C. and A.P.; formal analysis, N.C. and A.F.-J.; investigation, J.C.; resources, N.C., A.F.-J. and M.O.; data curation, J.C. and N.C.C.; writing--original draft preparation, N.C.; writing--review and editing, A.F.-J. and A.P.; supervision, N.C. and M.O.; project administration, N.C.; funding acquisition, N.C and M.O. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Portuguese funding through the Foundation for Science and Technology--FCT/MCTES (PIDDAC), within the framework of the R&D Project \"JUSTREST--Development of Alkali Binders for Geotechnical Applications Made Exclusively from Industrial Waste\", reference PTDC/ECM-GEO/0637/2014.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 5 Experimental Results
### Evaluation on an Embedded System
#### 5.5.1 Feature Extraction on an Embedded System  
The measurement of \(t_{extraction}\) has been individually obtained. The value of \(t_{extraction}\) can be added to the timing of the sound classifiers to obtain the \(t_{exec}\) when acquiring the audio data directly from a microphone (Equation (1)). Table 13 summarizes the timing for the different types of audio features running on the embedded system. The computational effort to extract Set 3 requires a lot of time, becoming around 20 times slower than on the laptop (Table 7).
## 5 Discussion and Future Opportunities
### The Interpretability of the Model  
While machine learning models can be highly accurate in terms of landslide susceptibility assessment, their black-box nature makes it difficult for non-users to understand how decisions are reached. This lack of transparency can make it hard to explain the decision process and outcomes, which may reduce trust in the results [129]. In landslide susceptibility assessment, comprehending the impact of evaluation factors on landslide susceptibility is paramount. However, machine learning models often automatically select features or struggle to explain the importance of features, complicating the comprehension of how the model predicts outcomes based on input features. Moreover, machine learning models frequently struggle to capture the uncertainty of assessment results, which is crucial for quantifying uncertainty in landslide susceptibility assessment [136]. In the future, developing interpretable machine learning models to enhance understandability and explanation will pose a significant challenge, such as utilizing models with stronger interpretability or devising novel methods to explain the model's decision process. Additionally, augmenting the analysis of the importance of input features (evaluation factors) and conducting an uncertainty analysis of the model to elucidate prediction results is also a pivotal research direction. The interpretability of machine learning models in landslide susceptibility assessment presents a significant challenge, yet this can be addressed through the design of interpretable models, bolstering feature importance analysis, uncertainty estimation, and other methodologies. These endeavors will bolster an evaluator's trust in the assessment results and deepen their understanding of the factors influencing landslide susceptibility.
## 4 Discussion  
The advantage of the water drop method in this study is its simplicity. The response time could be characterized by just dropping liquid on the sensor, and no additional procedures or changes in the experimental setup are required. The temperature sensors within the wetted area could be measured simultaneously. However, the present methods had limitations in measuring the response time of conventional bulky thermal probes, such as a Pt-100 probe. A drop of water could not cover the entire probe surface simultaneously. Additionally, the high heat capacity of conventional probes also prevents water droplets from generating sufficient temperature changes. For these thermal probes, immersing them in a thermostat would be the best way to measure the response time.  
The measured response would be affected by the thickness and thermal conductivity of the PI layer since the water was dropped to the PI layer for the electrical insulation of RTDs. A thick layer of low thermal conductivity material reduces the heat transfer rate, which results in a slow response of thermal probes. The difference in response time depending on the presence of the PI layer, however, would be negligible because the PI substrate was very thin (2.5 \(\upmu\)m) compared to the thickness of the polyurethane rubber underneath it (10 mm). The response time of the thin-film RTD array sensor was sufficiently fast to evaluate HIFU devices that continuously apply thermal energy over a few seconds [6, 17, 18].  
The most common method for measuring the thermal time constant is to apply adequate power to RTDs, keep it on until they reach thermal stability, and then turn off the power, called the \"self-heating\" method. In this case, the thermal time constant is the time it takes for the temperature of RTDs to decrease to the temperature representing a 63.2% difference. However, this method may not always be the best for all applications. For the applications we are targeting, such as using an RTD for the temperature measurement of a medium, it is best to measure the thermal time constant by using a step change of temperature, as we demonstrated in this work, rather than the self-heating method.  
Conceptualization, I.D. and S.D.L.; methodology, I.D., D.J.J. and M.S.G.B.L.; writing--original draft preparation, I.D. and M.S.G.B.L.; writing--review and editing, M.S.G.B.L., D.J.J., S.D.L. and I.D. All authors have read and agreed to the published version of the manuscript. This research was supported by the Development of Measurement Standards and Technology for Biomaterials and Medical Convergence, funded by the Korea Research Institute of Standards and Science (KRISS--2022--GP2022-0006), the Korea Medical Device Development Program fund by the Korean Government through the Ministry of Science and ICT (the Ministry of Trade, Industry, and Energy, the Ministry of Health and Welfare, Republic of Korea, and the Ministry of Food and Drug Safety) under Project 202014X59-01 and Project KMDP_PR_20200901_0265, and the Commercialization Promotion Agency for R&D Outcomes (COMPA) funded by the Ministry of Science and ICT (MSIT) (2023-22040301-00). Not applicable.
## Appendix A Algorithm Parameters  
This appendix provides the key algorithm parameters used in our experiments. The values for each parameter were chosen based on empirical tuning and literature suggestions.  
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Description** & **Justification** \\ \hline \hline \multirow{2}{*}{Discount Factor (\(\gamma\)/gamma)} & \multirow{2}{*}{0.95} & Determines the importance of future rewards. & A common value used in reinforcement learning for traffic control systems. \\ \hline \hline \multirow{2}{*}{Learning Rate} & \multirow{2}{*}{0.0003} & Step size for updating the weights in the optimization process. & Chosen to ensure stable convergence during training. \\ \hline \hline \multirow{2}{*}{Batch Size} & \multirow{2}{*}{64} & Number of experiences used for each gradient update. & Empirically selected to balance learning speed and memory usage. \\ \hline \hline \multirow{2}{*}{Experience Replay Size} & \multirow{2}{*}{100,000} & Number of experiences stored in the replay buffer for training. & Large buffer size improves training stability and efficiency in large-scale systems. \\ \hline \hline \multirow{2}{*}{Reward Scaling} & \multirow{2}{*}{Varies} & Scaling factor applied to individual reward components for balancing objectives (waiting time, throughput, fairness). & Tuned to ensure a balanced trade-off between competing objectives. \\ \hline \hline \end{tabular}  
These parameters were tuned on a validation set using a grid search method. The values presented are optimal for the simulation environment described in Section 4.1, ensuring convergence and performance improvements in various traffic conditions.
## 2 Experimental Methods
### Experimental Plan  
The \"Technical Specification for Highway Tunnel Construction\" (JTG 3370.1-2018 [18]) in China defines tunnels with temperatures exceeding 28 \({}^{\circ}\)C as high-temperature tunnels. To compare with normal temperature (25 \({}^{\circ}\)C), four temperatures--25 \({}^{\circ}\)C, 55 \({}^{\circ}\)C, 85 \({}^{\circ}\)C, and 95 \({}^{\circ}\)C--were set for this study. According to references [19; 20; 21; 22], sandstone reaches saturation after 3 h of soaking, and temperature increase enhances the interaction between water and the mineral structure, strengthening the water absorption effect of the rock. Therefore, three soaking durations were set: unsaturated (0.5 h, 1 h) and saturated (3 h). First, an electric constant temperature box was used to heat the sandstone at a rate of 4 \({}^{\circ}\)C/min to 105 \({}^{\circ}\)C, where it was held for 24 h to remove the original moisture inside the specimen. Then, the temperature was lowered at a rate of 0.5 \({}^{\circ}\)C/min to reduce thermal shock and ensure that only the thermal gradient would affect thermal cracking. Next, the sandstone was subjected to a water bath at different temperatures and times using a warm water tank. After heating the sandstone specimens to the target temperature and time, they were removed and immediately insulated using polyethylene film and a homemade asbestos insulation shell. (The polyethylene film used is a common commercial polyethylene film. The asbestos insulation shell is made through a specific processing procedure, where nano-sized silica aerogel particles are fully integrated into ceramic fiberglass cotton. The customized aerogel felt is bonded to the polyethylene shell using adhesive). Finally, the combined effects of temperature, water, and time on the damage and failure of sandstone were investigated using uniaxial compression and acoustic emission systems. The specific experimental scheme is shown in Table 1. For example, H-25-0.5  
Figure 1: Mineral composition of red sandstone.  
Figure 2: Some sandstone samples.  
represents a sandstone specimen at 25 \({}^{\circ}\)C with a soaking time of 0.5 h. The calculation formula for the water content of the sandstone specimen is:  
\[w_{c}=\frac{m_{1}-m_{0}}{m_{0}}\times 100\% \tag{1}\]  
In the formula: \(w_{c}\) represents the moisture content, \(m_{1}\) is the mass of the sandstone after immersion, and \(m_{0}\) is the mass of the dry sandstone before immersion, and the unit is g.
## 4 Results and Discussion
### Performance Comparison between the Monthly and Annual Averages  
The weather in Korea 30 years ago was less unusual than it is now, with spring from March to May, summer from June to August, autumn from September to November, and winter from December to March. However, Korean weather patterns have now compressed the period of spring from April to May and autumn from October to November, while the summer season (June to September) and winter season (December to March) has become longer. As shown in Figure 8, the greatest to least impact of the meteorological factors on solar power was SR > sunlight > WS > temperature > CC > humidity [51]. Thus, while five of the meteorological factors had a substantial impact on solar power generation, humidity does not significantly affect the month-to-month solar power generation forecast. Table 5 shows the results of the proposed LSTM method for a multivariate model combining the six considered meteorological factors (with the RMSE < 1.5). Among the 22 multivariate models, model 16 was the best at accurately predicting solar power regardless of the season. The input parameters of model 16 are SP, SR, sunlight, and WS. Model 1 has the advantage of a short computation time because it has only a few input parameters; however, since the model learns with previous SP data values, it cannot accurately predict solar power. As shown in Table 5, humidity and WS do not significantly affect the solar power forecasts individually; however, models 19, 21, and 22 have smaller RMSE values owing to the correlation between humidity and wind speed. The results in Table 5 indicate that the models cannot accurately predict SP given the seasonal weather conditions in Korea, but model 16 is better for long-term solar power forecasting. The  
Figure 7: Solar power forecasting using the proposed method. models with more checkmarks in the columns of Table 5 are better for medium-term solar power forecasting, depending on the regional and climatic conditions.
# Recent Developments on Biomineralization for Erosion Control
## 3 Multi-Investigations of Biomineralization Erosion Control in Ocean Engineering
### Local Scour Protection Around Monopile
(2020) evaluated the erosion protection effectiveness of polyvinyl alcohol (PVA) coupling MICP technology for bridge pier local scour through scale model flume experiments [128]. A 3D-printed pier model (12\(\times\)3\(\times\)16 cm) was embedded in PVA-MICP-treated Ottawa sand stratum and subjected to 20 h continuous flow simulation at 0.15 m/s (70% of untreated sand's critical velocity). The results demonstrated  
Figure 4: Local scour of MICP protection around monopile: (**a**) CaCO\({}_{3}\) distribution along depth, (**b**) SEM of sand without MICP treatment, (**c**) relationship between the maximum scour depth (CaCO\({}_{3}\) content at surface layer), (**d**) erosion resistance mechanism of MICP protection [126]. Figure 3: Local scour of MICP protection around monopile: (**a**) visual observation, (**b**) scour depth evolution under different protection method, (**c**) terrain scanning after scour, (**d**) scour profile at cross section, (**e**) sediment transport rate at different location [126]. that untreated sand developed a 25 mm horseshoe-shaped scour hole stabilizing within 2 h, while PVA coupling MICP-treated seabed maintained minimal erosion (<0.3 mm) with structural integrity. Microscopic characterization (SEM/XRD) revealed that PVA viscosity modulation facilitated dense distribution of 2-3 \(\upmu\)m vaterite-type CaCO\({}_{3}\) crystals at particle contacts, significantly enhancing interlayer cementation strength. Erosion function apparatus (EFA) quantification showed modified sand exhibited 500-fold increased critical shear stress (94.4 Pa vs. 0.18 Pa for untreated sand), sustaining a low erosion rate (0.1 mm/h) under extreme 6 m/s flow velocity. Structure from Motion (SfM) topographic reconstruction confirmed over 95% reduction in sediment loss from treated zones, demonstrating that PVA's delayed infiltration effectively controlled cementation layer thickness (~5 cm) through single-surface application without deep grouting. MICP and PVA coupling MICP effectively mitigate local scour around the monopile through biomineralization and polymer-enhanced crystal adhesion. Their success hinges on controlled reagent, optimized treatment cycles, and geometric design to balance erosion resistance and edge scour.