esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Incorporating Waste Limestone Powder into Solid Waste Cemented Paste Backfill Material
## 4 Discussion
### Microstructure Analysis  
From the data presented in Table 1, the existence of SiO\({}_{2}\) was assumed to be about 36.87%, which was involved in the hydration reaction with Ca(OH)\({}_{2}\) in the cement. The particles as crystal nuclei were mostly encapsulated in C-H-S gel and embedded in the hydration reaction product as crystalline nuclei. The C-H-S gel with stone powder had higher strength than pure C-H-S gel, but the swelling volume of the hydration reaction decreased compared to the 100% cement group by the same amount of cementitious material.  
In the SEM images of group A (Figure 6), the proportion of C-H-S gel was positively correlated with the amount of cement, and the proportion of C-H-S in 100% cement group A1 (Figure 6a) was the largest. In addition, crystals of AFt, a small amount of unreacted Ca(OH)\({}_{2}\) crystals, and a certain number of exposed tailing particles were found. The structures of A2, A3, and A4 (Figure 6b-d) were similar. SEM images showed almost invisible AFt, completely reacted Ca(OH)\({}_{2}\) particles, and stone powder existed in the form of crystal nuclei. Among the three, A4 had the highest replacement amount of stone powder; its structure was denser and had reduced the porosity, indicating that the microstructure of group A can be optimized with a certain amount of limestone powder.  
In group B, the SEM images (Figure 7) revealed a significant increase of AFt than those of group A, approaching towards the formation of C-H-S. Ca(OH)\({}_{2}\) almost completely reacted, and a part of the tailing particles was exposed. Limestone powder particles mostly existed among the hydration reaction structures, but some of them were exposed outside. The strength of B4 (Figure 7b) was increased by replacing a certain amount of limestone powder. As a result, the structures of B4 were improved than that of B3 (Figure 7a) with a decrease in large pores, indicating that a certain amount of limestone powder could optimize the microstructure of group B.
## 2 Datasets and Method
### Description of Datasets
#### 2.1.1 DALES Datasets  
The DALES dataset is a large-scale aerial LiDAR dataset for point cloud segmentation. The objective of the DALES datasets is to help advance the field of deep learning within aerial LiDAR. The LiDAR data were collected using a Riegl Q1560 dual-channel ALS system over the City of Surrey in British Columbia, Canada. The LiDAR datasets have an average of 12 million points spanning a 10 km\({}^{2}\) area and contain 40 tiles (area of each tile is 500 \(\times\) 500 m with a resolution of 50 pts/m\({}^{2}\) (ppm) and multiple return level of 7) of dense and labeled data with eight categories, namely, ground, vegetation, car, truck, pole, power line, fence, and building. The average ground sample distance (GSD) is approximately 0.15 m. The mean error was determined to be \(\pm\)8.5 cm at 95% confidence level for the vertical accuracy [16; 17; 18].  
The DALES datasets were used to train the PointNet++ model and test for the performance of the model. Figure 2 shows locations of 40 tiles of DALES datasets. Figure 3 shows 29 training and 11 test datasets that were fed to PointNet++. Figure 4 depicts the LiDAR data and color-labeled land cover classes. Figure 5 is a 3D perspective view of the LiDAR data.  
Figure 1: Multiple returns of LiDAR: (a) property of the multiple returns; (b) two returns (i.e., last return is the second return) on the building and superstructure boundaries.
## 4 Comparison of Prediction Accuracy
### Discussion
This comparative analysis reveals a consistent predictive hierarchy: the PPW mechanism emerges as the most reliable predictor for anisotropic rock strength, closely trailed by the Pariseau formulation, while the SPW framework exhibits the weakest correlation with experimental measurements. The pronounced MAE disparities between these criteria quantitatively validate the necessity of incorporating multi-scale failure interactions in geomechanical modeling, particularly for complex layered systems where stress redistribution mechanisms operate across multiple bedding orientations. The SPW criterion's reliance on idealized bedding plane homogeneity overlooks matrix-pore fluid interactions and localized stress concentrations at bedding interfaces, leading to overpredictions in hybrid failure regimes (Figure 3a). While the PPW model introduces parameter \(\eta\) to account for patchy weakness distribution, its formulation assumes uniform defect spacing, neglecting spatial heterogeneity in natural laminations. [PERSON]'s criterion, though continuous, lacks explicit representation of bedding plane friction-cohesion dynamics, limiting its capacity to resolve multi-scale failure interactions. All models inadequately address strain rate effects, which may alter anisotropy thresholds under dynamic loading conditions encountered in drilling or seismic events. While the PPW achieves the lowest aggregate errors, its operational complexity (five parameters) limits field utility compared to Pariseau's fewer variables. The SPW remains viable for quick-look analyses in weakly anisotropic formations (CV < 0.2) but requires empirical correction factors (-0.85) for hybrid failures. Engineers must prioritize model selection based on lithology, stress regime, and acceptable error margins--e.g., the PPW for fault-proximal stability assessments vs. Pariseau for salt cavern design.
## 3 Case Study  
### Project Overview  
The 8006 working face of the Wuyang Coal Mine, located in Changzhi, Shanxi Province, serves as a pivotal example (illustrated in Figure 7) [33]. This working face measures 280 m in width and 1280 m in advance length. It exploits the No. 3 coal seam of the Shanxi Formation, which has an average thickness of 5.5 m and is situated at an average depth of 620 m, yielding approximately 2.233 million tons of coal resources. The mining method employed is the longwall top-coal caving technique, and roof management is achieved through the natural span falling method. Several high-value and immovable structures are present on the ground surface above this working face, including eight 550 kV high-voltage towers, a gas station, and an oil station. To protect these structures from potential damage due to ground surface subsidence, grouting into overburden separation was implemented at the working face. The mining operations spanned from May 2021 to November 2022, while the grouting period extended from June 2021 to December 2022. During this time, approximately 650,000 tons of dry fly ash were injected into the overburden, achieving an actual injection-extraction ratio of 33%.  
Figure 6: Diagram of numerical simulation method and steps for grouting into overburden separation.  
Figure 8 illustrates the layout diagram of surface subsidence monitoring stations at the 8006 working face of the Wuyang Coal Mine.
### Main Contributions  
The NCS is an emerging area of the control system. It is worth reviewing the state-of-the-art developments in NCS. Although many review papers have been published in this area, to the best of the authors' knowledge, these focused only on a certain dimension of the NCS, and a comprehensive approach was lacking. In this paper, a comprehensive review on the evolution of NCSs in last the 30 years is discussed. The evolution of NCS is broadly divided in three phases as: (i) prior to 2000, (ii) 2001-2010, and (iii) 2011 onwards. The reason for this division is to understand the initial, the intermediate, and the latest developments related to NCS. The main focus of this discussion would be on the advancementof different control techniques during these phases. Based on the extensive review, the different types of NCSs and their related challenges are described. At the end, this review paper highlights the following innovations in the field of NCS: new novel methods for selecting the best sampling time in the NCS, new jitter compensation techniques, developing the theory and practice for control systems operating in a distributed and asynchronous packet-based environment, stability analysis of networked control systems in the presence of network-induced delays and packet dropouts (due to propagation delay and also due to the network congestion), and developing of advanced networked control methods that provide the desired performance in the presence of uncertainties and adversaries. This review paper also highlights some important practical applications that are implemented using the NCSs. Lastly, the important future research areas for NCSs are discussed.  
The remaining sections are as follows: Section 2 reviews the development of NCS prior to 2000 AD; The development of NCS from 2001 AD to 2010 AD is discussed in Section 3; The advancement of NCS in recent phase after 2011 AD is discussed in Section 4; Section 5 describes about different topologies of NCSs; Section 6 presents different types of NCSs and their related challenges; Section 7 discusses some of the vital practical applications of the NCSs; Section 8 is the conclusion, along with the observations and potential future developments.
# Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks
## 2 Seismic Wave Velocity
Field measurements of seismic velocity can be performed using surface surveys such as refraction surveys or surface wave analysis. These approaches mitigate the problem of soil disturbance since no samples are required. However, they are less repeatable and have larger uncertainty in measure values. The frequency of the seismic wave is in the 10 Hz to several 100 Hz ranges. It should be noted that field-measured seismic velocities are usually lower than laboratory-measured velocities.
## 3 Results and Discussion
### Entrapment Efficiency and Drug Loading Calculation  
Calculation of EE (%) and DL (%) of the nanosponges indicates drug entrapment and drug loading in NS (Table 1). EE (%) was found to be in the range of 18.67 \(\pm\) 1.23%-82.32 \(\pm\) 1.63%, whereas DL (%) of ITB ranged from 1.16 \(\pm\) 0.55% to 20.18 \(\pm\) 0.13%. Results of particle analysis and EE and DL calculation revealed that an increase in the concentration of EC increases the PS, EE, and DL to a certain level (162.5 mg), and thereafter further increase in the EC proportion increases the size but decreases the EE and DL. This could be due to rheological behavior of polymer [44; 45]. An increase in viscosity due to an increase in EC leads to a decrease in the diffusion of ITB from the organic phase to the aqueous phases of the stabilizer (PVA) [16].  
Therefore, IBNS4 NSs could be selected as the optimized nanocarriers for further characterization. The particle analysis and EE and DL calculation showed IBNS4 composed of ITB (50 mg), EC (162.5 mg), and PVA (50 mg) has 640.9 nm, 0.35, \(-\)30.2 mV, regarding size, PDI, and \(\zeta\)p, respectively [23; 39].
# Wireless Channel Models for Over-the-Sea Communication: A Comparative Study
## 4 Channel Models for Over-the-Sea Wave Propagation
### High-Capacity and Long Range Microwave Over-the-Sea Link Propagation Using the Evaporation Duct  
The paper presented in [16] is the first ever study of the propagation of a high-capacity radio-link over-ocean evaporation duct from the Australian mainland to the Great Barrier Reef (GBR). GBR is the largest marine park of the world. Monitoring GBR is a great challenge since it involves a complex ecological system. Real-time monitoring of different environmental parameters is highly necessary.  
Using ad hoc sensor networks to perform this monitoring task can be helpful for the researchers. The major problem of this kind of ad hoc system is relaying on gathered information of the sensor nodes over the sea surface, to the mainland, from GBR. To overcome this problem, authors in [16] proposed to use the evaporation duct by appropriately selecting the antenna height and operating frequency for an extended range. A 78 km link that operates at a frequency of 10.6 GHz with a data rate of 10 Mb/s was used to verify the technique.  
The refractive index depending on atmospheric pressure, temperature, and humidity is calculated by the following equation:  
\[N=\ \frac{A_{N}}{T}+\frac{B_{N}e}{T^{2}} \tag{29}\]  
where \(A_{N}\) = 77.6, and \(B_{N}\) = 3.73 \(\times\) 10\({}^{5}\), \(T\) is the temperature in Kelvin, \(p\) is atmospheric pressure in Pascal, and \(e\) is humidity. The path loss is calculated using the following equation:  
\[Pr_{(dB)}=Pt_{dB}+Gt_{dB}-pathloss_{(dB)}+Gr_{dB} \tag{30}\]  
where \(Pt\) is the transmitted power, \(Gt\) and \(Gr\) are the antenna gains of transmitting and receiving antennas, respectively, and \(pathloss\) is the attenuation between the transmitter and receiver antennas.  
For the purpose of studying the feasibility of using the evaporation duct to transmit signals from the mainland to the GBR, a computer simulation was undertaken using the Advanced Refractive Effects Prediction System (AREPS) program [42]. The propagation path loss is calculated using this software. Link budget calculation is done for the test link sent from the mainland to GBR. Parameters used in the link budget calculation are:  
* Path loss = 141 dB
* Transmitted power = 27 dBm
* Antenna gain of the transmitter and receiver = 40 dB
* Bandwidth = 14 MHz  
The path loss used in this calculation is based on 10.6 GHz frequency, with antennas located 7 m above the mean sea level separated by a 78 km range.  
This work [16] is the first real-time implementation of wireless radio link propagation over the sea surface. Authors were successful in sending a video captured in GBR back to the mainland using their model and calculations. The received signal in the receiving antenna; however, experiences a 34 dB lower signal level than expected. Authors did not mention the reason behind this in the paper. Additionally, an effort to find out the fade margin and PDP could have made the work presented in [16] more reliable.
## 4 Methodology
### Causal Configuration Analysis Using fsQCA
#### Truth Table Construction  
After calibration, the fsQCA method constructs a \"truth table\" that systematically analyzes all possible combinations of conditions and their relationship to the outcome. Each row in the truth table represents a unique combination of conditions, with \"1\" indicating the presence of a condition and \"0\" indicating its absence. The truth table also includes measures of consistency and coverage for each configuration. Consistency, in the context of sufficiency, assesses the degree to which a configuration is consistently associated with the outcome. Coverage, on the other hand, measures the extent to which a specific configuration accounts for the occurrence of the outcome. Analyzing the truth table, it is possible to identify the combinations of conditions, or \"configurations\", that are sufficient for producing the outcome. These configurations reveal complex causal relationships and provide insights into the combinations of conditions (variables) that lead to the outcome of interest--in this study, a high propensity for business failure.
# Ammonia Gas Adsorption in Fixed Bed and Fluidized Bed Using Bentonite Particles
## 3 Results and Discussion
### Aluminum Pillared Bentonite Characterization
#### 3.2.2 Nitrogen Adsorption-Desorption  
Figure 5 represents the nitrogen adsorption-desorption isotherms of the sodium bentonite and aluminum pillared bentonite, which resemble an isotherm type IV from the Brunauer-Emmett-Teller (BET) classification. The BET specific surface area value of the raw bentonite is 77.23 m\({}^{2}\)/g, and after the pillaring process, the specific surface area increases to the value of 169.051 m\({}^{2}\)/g. The Brunauer-Deming-Deming-Teller (BDDT) classification methodology categorizes the nitrogen adsorption-desorption isotherms for the bentonite studied into the H4 hysteresis from the International Union of Pure and Applied Chemistry (IUPAC), typical for porous materials which contain mesoporous materials [45; 46; 47].  
The pore size of sodium bentonite and aluminum pillared bentonite was determined by Barrett-Joyner-Halenda (BJH) method (Figure 6).
## 1 Introduction  
Ultrafast laser technology is rapidly developing under the impulse of exciting perspectives of applications. In particular, time-resolved spectroscopy of physical systems is driving progresses in ultrashort light pulse generation, aiming at understanding and controlling nuclear and electronic dynamics on an extremely short time scale, down to the attosecond domain.  
In the 1990s, mode-locking-based solid-state laser systems were introduced, soon becoming the dominant solution for ultrafast applications, by enabling ultra-short pulse duration on the few-femtosecond scale [1]. The interest in investigating the ultrafast light-matter interaction regime under strong-field perturbations boosted the power scaling of solid-state sources. In this framework, the Chirped Pulse Amplification (CPA) [2] scheme was proposed, which enabled single pulse energy enhancement up to three orders of magnitude, thus attaining the mJ-level.  
Since solid-state sources provide pulses centered at a well-defined wavelength, typically in the visible part of the spectrum, a great effort has been dedicated to the development of tunable sources. In this sense, Optical Parametric Amplification (OPA) provides a practicable strategy for intense few-cycle laser pulse generation, even in those spectral regions that are not supported by amplified solid-state laser sources [3].  
In this paper, the attention is focused on describing current progress in high-energy few-cycle parametric sources tunable in the IR spectral window beyond 800 nm. The structure of the paper is presented below. In Section 2, a brief overview of the operating principles of parametric amplification is provided. Section 3 is devoted to the description of the most advanced OPA implementations for IR pulse generation. Before illustrating in detail practical implementations, a preliminary discussion on the main technological and operational issues underlying the development of these ultrafast optical sources is provided. Finally, applications to the study of strong-field phenomena are reported in Section 4.
## 2 Materials and Methods
### CFD Simulation
#### 2.4.5 Discrete Model and Mesh Validation
Considering the rotor with three dual-coherent blades, the highest value of the Courant number at blade interfaces was obtained for a rotor angle of about \(200^{\circ}\), where the torque coefficient was the highest. Table 3 summarizes the average and maximum values of \(y+\) and average values of the Courant number obtained within the blade walls (\(Co\_BW\)), the blade domains (\(Co\_BD\)), the rotor domain (\(Co\_RD\)), and the flow domain (\(Co\_FD\)) obtained for the rotor angle of \(200^{\circ}\), depending on the rotor angular increment and corresponding time step size. It can be noticed that the average values of \(y+\) were about 0.1, whereas the maximum resulted values of \(y+\) were around 0.5, which was \(y+<\)1 for all of the analyzed cases. In turn, the average Courant number obtained for rotor domain \(Co\_RD\) indicated that only angular increments of \(0.2^{\circ}\) and \(0.5^{\circ}\) could be considered in further analysis. In order to save computational time, the angular increment of \(0.5^{\circ}\) was accepted for final analysis. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(dx\) & \(dt\) & \(y+\_ave\) & \(y+\_max\) & \(Co\_BW\) & \(Co\_BD\) & \(Co\_RD\) & \(Co\_FD\) \\ \hline
0.2\({}^{\circ}\) & 7.75702 \(\times\) 10\({}^{-4}\) & 0.105 & 0.52 & 0.20 & 2.79 & 5.83 & 0.19 \\
0.5\({}^{\circ}\) & 1.93925 \(\times\) 10\({}^{-3}\) & 0.103 & 0.51 & 0.56 & 5.78 & 14.57 & 0.48 \\
1\({}^{\circ}\) & 3.87851\(\times\) 10\({}^{-3}\) & 0.102 & 0.51 & 3.56 & 13.96 & 29.00 & 0.95 \\
2\({}^{\circ}\) & 7.75702 \(\times\) 10\({}^{-3}\) & 0.100 & 0.51 & 2.23 & 27.92 & 58.29 & 1.90 \\
5\({}^{\circ}\) & 1.93925 \(\times\) 10\({}^{-3}\) & 0.096 & 0.49 & 5.10 & 69.81 & 145.75 & 1.81 \\
10\({}^{\circ}\) & 3.87851\(\times\) 10\({}^{-3}\) & 0.002 & 0.47 & 10.2 & 139.62 & 291.51 & 9.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Resulted \(y+\) and Courant number values for rotor angle of \(200^{\circ}\). Figure 7: Torque coefficient \(C_{n}\) depending on mesh quality. Considering \(d\alpha=0.5^{\circ}\), the values of \(y+\) and the Courant number determined as average values for the whole second rotation are presented in Table 4.
# Reinforcement Learning for Energy Optimization with 5G Communications in Vehicular Social Networks
## 1 Introduction
Centralized Q-learning aims to maximize the system's energy efficiency, while distributed Q-learning aims to maximize the vehicle's achievable data rate. As the energy efficiency of the system and the SINR of a vehicle are in a trade-off relationship, it is important to identify a point that optimizes both objectives. Our algorithm ensures vehicular capacity by considering outage probability as a QoS constraint while maximizing system energy efficiency. The rest of this paper is organized as follows. In Section 2, we introduce related work. In Section 3, we formulate the system model and the problem definition. The joint mode selection and power-control algorithm using multi-Q-learning is introduced in Section 4. A performance evaluation and discussion of our algorithm are presented in Section 5. We present the conclusions of this study in Section 6.
# PM2.5 Concentration Prediction Based on CNN-BiLSTM and Attention Mechanism
## 3 Experiment
### Results and Discussion
#### 3.3.1 Short-Term Forecast with Multi-Source Data
It can be seen that when the epoch is almost 20, the loss value of the CNN-BiLSTM-Attention model is already lower than the other two models. To further compare the prediction effects of the proposed method and other comparative model methods, Figure 11 shows the predicted values of each model and the actual observed PM2.5 value of the test set. The \(x\)-axis in the figure represents the time stamp, and the \(y\)-axis represents the PM2.5 concentration. On the left is the color bar, and the blue line represents the actual observation value. Similar to Figure 9, our proposed model with the red star-shaped line is still closest to the real data and is better than the other two deep learning models, represented by a red dashed line and a brown dashed line, respectively. Figure 10: Comparisons of convergence speed in three deep learning models. Figure 9: Comparisons of the observed and predicted values of PM2.5 concentration values. The quantitative performance of the proposed model and those used as comparison models are summarized in Table 5. For the proposed model, the prediction results that are denoted in bold are significantly improved compared to the other six models, achieving the best indicator values (MAE: 2.366 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\), RMSE: 3.095 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\), \(R^{2}\): 0.960). The main reason is that this model can not only handle the characteristics of non-linear relationships, but the attention mechanism can mine better feature information. Therefore, compared with other comparison models, the proposed model has a competitive advantage when predicting PM2.5 concentrations.