esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low-Order Series Approximation of Thin-Bed PP-Wave Reflections
## Appendix A  
In Equation (1), \(\mathbf{M}\) is a 4 \(\times\) 4 matrix with 4 column vectors, \(\mathbf{M}\equiv[\mathbf{m}_{1}\ \mathbf{m}_{2}\ \mathbf{m}_{3}\ \mathbf{m}_{4}]\), defined as:  
\[\mathbf{m}_{1}=-\mathbf{A}\left[\begin{array}{c}\sin\theta\\ -\cos\theta\\ -j\rho_{1}\omega v_{P1}\cos 2\delta_{1}\\ j\rho_{1}\omega\frac{v_{P1}^{2}}{v_{P1}}\sin 2\theta\end{array}\right] \tag{1}\]  
\[\mathbf{m}_{2}=-\mathbf{A}\left[\begin{array}{c}\cos\delta_{1}\\ \sin\delta_{1}\\ j\rho_{1}\omega v_{S1}\sin 2\delta_{1}\\ j\rho_{1}\omega v_{S1}\cos 2\delta_{1}\end{array}\right] \tag{2}\]  
\[\mathbf{m}_{3}=\left[\begin{array}{c}\sin\theta_{3}\\ \cos\theta_{3}\\ -j\rho_{3}\omega v_{P3}\cos 2\delta_{3}\\ -j\rho_{3}\omega\frac{v_{P1}^{2}}{v_{P3}}\sin 2\theta_{3}\end{array}\right] \tag{3}\]\[\mathbf{m}_{4}=\left[\begin{array}{c}\cos\delta_{3}\\ -\sin\delta_{3}\\ j\rho_{3}\omega v_{53}\sin 2\delta_{3}\\ -j\rho_{3}\omega v_{53}\cos 2\delta_{3}\end{array}\right] \tag{10}\]  
\(\mathbf{n}\) is a \(4\times 1\) vector defined as:  
\[\mathbf{n}=-\mathbf{A}\left[\begin{array}{c}-\sin\theta\\ -\cos\theta\\ j\rho_{1}\omega v_{P1}\cos 2\delta_{1}\\ j\rho_{1}\omega\frac{v_{53}^{2}}{v_{P1}}\sin 2\theta\end{array}\right] \tag{11}\]  
\(\mathbf{A}\) is a \(4\times 4\) matrix with elements \(a_{\mathrm{ij}}\) defined as:  
\[a_{11}=a_{44}=2\sin^{2}\delta_{2}\cos P+\cos 2\delta_{2}\cos Q \tag{12}\]  
\[a_{12}=a_{34}=-j(\tan\theta_{2}\cos 2\delta_{2}\sin P-\sin 2\delta_{2}\sin Q) \tag{13}\]  
\[a_{13}=a_{24}=\frac{j\sin\theta_{2}}{\rho_{2}\omega v_{P2}}(\cos P-\cos Q) \tag{14}\]  
\[a_{14}=\frac{1}{\rho_{2}\omega v_{52}}(\tan\theta_{2}\sin\delta_{2}\sin P+ \cos\delta_{2}\sin Q) \tag{15}\]  
\[a_{21}=a_{43}=-j\bigg{(}\frac{v_{52}\cos\theta_{2}}{v_{P2}\cos\delta_{2}}\sin 2 \delta_{2}\sin P-\tan\delta_{2}\cos 2\delta_{2}\sin Q\bigg{)} \tag{16}\]  
\[a_{22}=a_{33}=\cos 2\delta_{2}\cos P+2\sin^{2}\delta_{2}\cos Q \tag{17}\]  
\[a_{23}=\frac{1}{\rho_{2}\omega v_{P2}}(\cos\theta_{2}\sin P+\tan\delta_{2}\sin \theta_{2}\sin Q) \tag{18}\]  
\[a_{31}=a_{42}=2j\rho_{2}\omega v_{52}\sin\delta_{2}\cos 2\delta_{2}(\cos Q- \cos P) \tag{19}\]  
\[a_{32}=-\rho_{2}\omega\bigg{(}\frac{v_{P2}\cos^{2}2\delta_{2}}{\cos\theta_{2} }\sin P+4v_{52}\cos\delta_{2}\sin^{2}\delta_{2}\sin Q\bigg{)} \tag{20}\]  
\[a_{41}=-\rho_{2}\omega v_{52}\bigg{(}\frac{4v_{52}\sin^{2}\delta_{2}\cos \theta_{2}}{v_{P2}}\sin P+\frac{\cos^{2}2\delta_{2}}{\cos\delta_{2}}\sin Q \bigg{)} \tag{21}\]  
\[P=\frac{\omega h}{v_{P2}}\cos\theta_{2} \tag{22}\]  
\[Q=\frac{\omega h}{v_{52}}\cos\delta_{2} \tag{23}\]  
where \(v_{P}\) and \(v_{5}\) are P-wave and S-wave velocities, respectively, \(\rho\) is density, \(\delta\) is S-wave transmitted or reflected angle, and their subscripts 1, 2, 3 refer to three layers of the thin bed respectively; \(\theta\) is P-wave incidence angle, \(\theta_{2}\) and \(\theta_{3}\) are P-wave transmitted or reflected angle in the target thin bed and its floor respectively; \(h\) is the thickness of the middle layer, \(\omega\) is angular frequency, and \(j\) is the imaginary symbol.
## 4 Emergency Maneuver Strategies and Blind 60 Spot Management
### Current Technologies and Algorithms for Emergency Response and Blind Spot Management
#### 4.2.1 Emergency Response Technologies  
Emergencies often require rapid replanning of the vehicle's trajectory. [PERSON] et al. [85] proposed a model predictive control (MPC) approach for real-time trajectory optimization in the presence of dynamic obstacles, enabling quick and smooth evasive maneuvers.  
Ensuring system reliability is crucial for handling emergencies. [PERSON] et al. [86] discussed the importance of redundancy in critical systems and proposed a fail-safe operational architecture for autonomous driving that can maintain safe operation even in the event of component failures.
## 7 Future Prospects  
The study of graphyne is still in its nascent stages, and substantial efforts are needed to realize its full potential in practical applications. A primary challenge lies in the controlled and scalable synthesis of graphyne with high structural purity and reproducibility. Although \(\gamma\)-graphyne has been synthesized via various methods, including mechanochemical, ultrasonic, and coupling reactions, these techniques are often hampered by low yields, complex procedures, or prohibitive costs. Future research should prioritize the development of streamlined, low-temperature, and catalyst-efficient methods to produce large-area graphyne films with minimal defects.  
From a computational perspective, integrating high-accuracy simulations, such as density functional perturbation theory, time-dependent DFT, and machine-learning-enhanced models, can enhance predictions of band structures, phonon behavior, and defect tolerance across different graphyne allotropes. Moreover, collaborative studies that combine theoretical predictions with experimental validation are crucial for understanding transport properties, such as carrier mobility and thermoelectric efficiency, particularly in doped or heterostructure systems.  
Integrating graphyne into devices presents another significant challenge. Embedding graphyne into flexible substrates, hybrid electrodes, or membranes necessitates addressing issues related to interfacial stability, mechanical anchoring, and long-term environmental durability. Emerging application areas, including electrocatalysis, wearable electronics, energy harvesters, and desalination technologies, hold great promise for innovation, provided that material synthesis can meet the performance demands of these fields.  
Lastly, the development of functional graphyne-based heterostructures, composites with transition metals, and defect-engineered configurations can unlock properties that are unattainable with graphene alone. Long-term success in this domain will hinge on interdisciplinary collaboration among chemists, physicists, and engineers to surmount existing obstacles and translate graphyne's unique attributes into viable technologies.
## 3 CC-VC-UKF
### Obtain a Reference Time Series
#### 4.1.1 K-Means Clustering  
Although a certain periodicity is found in the load-day characteristic of the load, only the changing trend is periodic. The load difference between different days is still relatively large due to the influence of various factors. Therefore, to obtain the reference sequence, the historical data should be clustered and divided into six categories. The errors caused by the clustering can be ignored when the experimental verification data have been divided into six categories.  
K-means algorithm clustering is performed on historical data. The K-means algorithm flow is shown in Algorithms 1:  
```
Input:  Historical time series Output:  Classification result  Select the initial \(k\) samples as the initial cluster center \(a_{1},a_{2},\ldots,a_{k}\). For each sample \(x_{i}\) in the dataset, its distance is calculated to \(k\) cluster centers and classified into the class corresponding to the cluster center with the smallest distance. For each category\(a_{j}\), its cluster center \(a_{j}=\frac{1}{|c_{i}|}\sum x\) is recalculated.  Repeat steps 2 and 3 for 10 iterations.
```  
**Algorithms 1** K-Means Algorithm Flow  
In the K-means algorithm, if the value of \(K\) is extremely small, that is, the number of classifications is extremely small, then each category will have more data, and the load characteristics of different days cannot be distinguished effectively, thereby affecting the filtering results. If the value of \(K\) is extremely high, that is, the number of classifications is extremely high, then each category will have extremely less data, thereby magnifying the influence caused by the accidental measurement error; it will also be detrimental to the filtering result. Therefore, the value principle of \(K\) should be as large as possible while satisfying the accuracy requirements. The value method of \(K\) is shown in Algorithms 2:  
```
Input:  TTU history sequence Output:  Value of k The TTU sequence of a certain day is used to filter the FTU sequence of the day to obtain the filtered sequence FTU1 and calculate the difference \(e\) between the mean of FTU1 and the mean of FTU. A number \(x\) is added to the data in the TTU sequence to obtain TTU2 and use TTU2 to filter the FTU to obtain the filtered sequence FTU2; then, the mean value of FTU2 and the mean difference \(e^{\prime}\) of FTU are calculated. \(x\) is continuously adjusted until \(e^{\prime}=e\), and \(x\) is written down at this time. The TTU sequences of all days are averaged, and the difference between the maximum value and the minimum value is used to obtain the range \(R\) of the TTU sequence. The number of categories \(k\) that can be obtained by \(K=R/x\).
```  
**Algorithms 2** Value Process of \(K\)
## 2 Development of Key Mechanisms
### Hydraulic Enclosed Core-Cutting Mechanism  
Existing research predominantly employs mechanical pressure core-cutting methods, which are complex and inefficient. In response to the core extraction requirements in loose and fractured strata, this paper designs a hydraulic enclosed core-cutting mechanism that automatically cuts the core by controlling the flushing fluid pump volume. This mechanism simplifies the process while achieving effective core cutting. The enclosed core-cutting mechanism mainly consists of a flushing fluid flow control valve and an enclosed core claw. The flushing fluid flow control valve, as shown in Figure 1, operates based on the pressure difference across the valve stem and the action of the valve stem's reset spring, which regulates the up-and-down movement of the valve stem. The pressure on the control valve core can be adjusted by changing the size of the mud pump flow, thereby altering the pressure difference between the upper and lower interfaces of the valve core.  
During normal drilling operations, the pressure differential across the valve stem of the flushing fluid flow control valve is maintained by the valve stem's reset spring, which keeps the valve stem in its upper position, allowing the flushing fluid to pass smoothly through the flow control valve (Figure 1a). When cutting the core, the pump rate is increased, which raises the pressure differential across the valve stem of the flushing fluid flow control valve. Under the influence of this increased pressure differential, the valve stem moves downward, gradually closing off the flushing fluid outlet located below the valve body of the flow control valve (Figure 1b). This creates a back pressure within the entire drill pipe above the flushing fluid flow control valve. Under the hydraulic action of the flushing fluid, the entire valve body of the flushing fluid flow control valve moves downward, transferring pressure through the drive shaft to the sleeve and subsequently to the core barrel. The downward movement of the core barrel compresses the enclosed core claw, completing the core cutting process [15; 16]. In the coring process, forces are transmitted through the spring, and since the loose formation itself has a low hardness, it ensures that under the propulsion of the core barrel, the core claws undergo uniform displacement to complete the coring process.
## 3 Results and Discussion
### Adsorption Tests in Batch-Mode: Aerogels (A) vs. Cryogels (C)  
Different amounts of aerogel (150, 200 and 300 mg for A1, A2 and A3 experiments, respectively) were used to test the removal efficiency of CV from 50 mL of an aqueous solution. The apparatus used for these experiments is illustrated in Figure 1. CV concentration values were recorded using a UV-Vis spectrophotometer and their values are plotted in Figure 3.  
Figure 3 shows that the increase in the quantity of adsorbent used, produced a larger dye adsorption, up to 50% of CV removal. This result is due to the availability of a larger number of active sites that can promote more physical-chemical interactions with CV. Indeed, the increased amount of -OH groups could lead to increased electrostatic interactions between crystal violet and agarose. The three curves showed an asymptote at about 6 h of testing; this result could indicate saturation of the active sites useful for adsorption.  
Cryogels were also used for adsorption tests in batch-mode, testing the same dye concentration and quantity of solid material of the previous case. The obtained values, over time, are reported in Figure 4.  
The trend described for the aerogels was also observed for cryogels: the increase in adsorbent amount led to a higher CV removal efficiency, at up to 34% of dye removal. Also in this case, a pseudo-asymptotic behavior was observed around 6 h.  
However, the asymptotic values of cryogels were larger than those observed for the corresponding aerogels; cryogels possessed a lower number of active sites useful for adsorption and this result can also be correlated with the lower specific surface area of these samples.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Aerogel** & **Cryogel** \\ \hline Porosity [\%] & \(91\pm 4\) & \(71\pm 3\) \\ BET specific surface area & \(154\pm 12\) & \(11\pm 6\) \\ [m\({}^{2}\)/g] & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Porosity and surface area values of 3% _w/w_ agarose cryo/aerogels.  
However, the concentration of CV did not decrease down to 2.5 ppm for A3, and down to 3.3 ppm for C3. Summarizing, the aerogels showed a better adsorbent performance when compared with the samples obtained by freeze-drying; but the obtained performances were not satisfactory.  
Figure 4: Adsorption kinetics of cryogel samples analyzed in the experiments of Set C (Cryogels) performing a batch adsorption (T = 25 \({}^{\circ}\)C, rpm = 200, \(\lambda\) = 591) and using three different amounts of cryogels: 150 mg (black line), 200 mg (red line) and 300 mg (blue line).  
Figure 3: Adsorption kinetics of aerogel samples analyzed in the experiments of Set A (Aerogels) performing a batch adsorption (T = 25 \({}^{\circ}\)C, rpm = 200, \(\lambda\) = 591) and using three different amounts of aerogels: 150 mg (blue line), 200 mg (black line) and 300 mg (red line).
# Prediction of Soil Compaction Parameters Using Machine Learning Models
###### Abstract  
Maximum Dry Density (MDD) and Optimum Moisture Content (OMC) are two important parameters of soil filling, which affect the soil stability and bearing capacity, and thus the reliability and durability of facilities such as highways and bridges. Therefore, it is important to make reasonable predictions of OMC and MDD. Four machine learning algorithms, namely, Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), and Extreme Gradient Boosting Tree (XGBoost), are adopted in this paper to establish MDD and OMC prediction models. After training and testing, the best models of the four algorithms are compared. The results show that, as an ensemble learning algorithm, XGBoost is the best model for predicting MDD and OMC, with an \(R^{2}\) of 0.9234 for OMC, and an \(R^{2}\) of 0.9098 for MDD. Finally, the feature importance analysis concludes that the plastic limit (PL) and the liquid limit (LL) are the two features that affect OMC and MDD the most. The prediction of soil compaction parameters using machine learning models, especially ensemble learning, can significantly reduce the amount of laboratory work and improve the efficiency of optimizing design for soil resource utilization in engineering construction.  
machine learning; ensemble learning; maximum dry density; optimal moisture content; XGBoost
# Microplastics in Water Resources: Threats and Challenges
## 4 Methods for Separation and Extraction of MPs from Environmental Matrices
### Flotation  
Another method practiced for separating MPs from environmental samples is flotation of froth. Generally, materials are classified as hydrophilic or hydrophobic [94]. Most artificial polymer materials have hydrophobic surfaces, so adding a surfactant to the solution causes its molecules to accumulate on the surfaces of MPs [95]. This phenomenon is utilized in the froth flotation process. With appropriate aeration, gas bubbles lift MPs particles that are physically attached to the surfactant. The lower the density of the plastic and the higher the surface-to-mass ratio, the more efficient the process. Meanwhile, other hydrophilic substances present in the system sink to the bottom [96]. Froth flotation shows promise for samples containing large amounts of hydrophilic substances, such as various matrices containing sand. However, scientists are cautious about making statements regarding the efficiency of this process, often defining it as relatively low. In their studies, [PERSON] et al. [97] estimated the overall efficiency of the flotation process in separating microplastics was approximately 55%. This value was highly dependent on the type and density of the polymer. The size and shape of MPs particles are also significant for the overall efficiency of this process. Surface properties can even vary for the same type of plastic, as different additives used during production can alter these properties. Additionally, the aging process changes the surface properties and density of the plastic, which in turn affects the flotation process [98]. A drawback of froth flotation is the duration of the process (the longer it is run, the greater the recovery of MPs) [99]. Moreover, the separating fluid must be replenished continuously [100]. Froth flotation also does not guarantee the separation of all types of MPs and can essentially only be used as one of the sample preparation processes. Large fragments of high-density plastics (e.g., PVC) are difficult to separate using this method. In a broader context, one of the key advantages of flotation of froth is its continuous nature. This process also allows for the permanent removal of MPs. Furthermore, the relatively low technological complexity and operational costs of froth flotation could argue for its wider application. This process does not require complex equipment or advanced infrastructure, which could make it an accessible means of removing microplastics, for example, in wastewater treatment plants [101].
# Three-Dimensional Localization of Underwater Nodes Using Airborne Visible Light Beams
## 4 The 3D-LUNA Localization Framework
### Estimated Position with Few Received Transmissions
#### 4.4.2 Two Received Transmission  
If a node receives two transmissions, the resultant contours will overlap, and the points of overlapping indicate the possible location of the underwater node, as depicted in Figure 5.  
To simplify the position calculation, we again consider the 2D scenario. Essentially, we consider every pair for received VLC transmissions, discretize the corresponding contours by depth, and determine the intersection points of the pair of circles at each depth. The centroid of all intersection points, i.e., at various depth values, is considered by 3D-LUNA as the possible location of the underwater node. In this case, for each height \(z_{i}\), we have two circles which can be defined as:  
\[r_{1}(z_{i})^{2} =(x-x_{1})^{2}  
\(z\)-levels, the centroid \((x_{c},y_{c},z_{c})\) is computed. Let \(N_{c}\) be the total number of intersection points collected:  
\[x_{c}=\frac{1}{N_{c}}\sum_{i=0}^{N_{c}}x_{i} \tag{29}\]  
\[y_{c}=\frac{1}{N_{c}}\sum_{i=0}^{N_{c}}y_{i} \tag{30}\]  
\[z_{c}=\frac{1}{N_{c}}\sum_{i=0}^{N_{c}}z_{i} \tag{31}\]
# Pilot-Scale Fenton-like System for Wastewater Treatment Using Iron Mud Carbon Catalyst
## 2 Materials and Methods
### Pilot-Scale Device and Performance Testing  
To save cost, the system with better COD removal performance was explored using sequential batch experiments. In order to explore the potential application of FSC/Fe(VI)/H\({}_{2}\)O\({}_{2}\) in engineering, this study developed a pilot-scale device with a wastewater treatment capacity of 600 L/d. The sewage indicators of the sewage treatment plant are shown in Table 1. The COD of the influent water is 70 mg\(\cdot\)L\({}^{-1}\). Figure 1a,b are the schematic diagram and physical picture of the pilot-scale device. This device is equipped with three catalyst layers to ensure the full utilization of the catalyst and the full occurrence of the oxidation reaction. Glass beads are laid below the catalysts, which can provide a stable supporting structure for the catalysts and prevent them from aggregating or sinking due to gravity or air flow impact, ensuring that the catalysts are evenly distributed in the reaction area and maintaining a good reaction state. Four peristaltic pumps are set up in the device, including an acid-base pump, a hydrogen peroxide pump, a ferrate potassium pump, and a reflux pump. They operate in the mode of bottom-in and top-out, with some reflux. This ensures that the wastewater fully contacts with the catalysts. To monitor the pH of the reaction system in real time, an online pH control system is configured in the pilot-scale device. In addition, COD online monitoring equipment and an online controller are configured to monitor the COD concentration, as well as control the switches and flow rates of the pumps in real time. Specifically, the device operated for a total of 65 days. The flow rates of the acid-base pump, hydrogen peroxide pump, ferrate potassium pump, and reflux pump were 10 mL/min, 0.38 mL/min, 20.59 mL/min, and 1044 mL/min, respectively. Hydrogen peroxide is 1.5 mmol\(\cdot\)L\({}^{-1}\) and ferrate potassium is 8 mmol\(\cdot\)L\({}^{-1}\). The acid-base pump regulates the pH at 6 \(\pm\) 0.5. The dosing pump uses a four-way device to uniformly add the reagents to the reactor. The reflux device is equipped with two pumps, and the reflux ratio of each pump is 1:2, with a total reflux ratio of 1:4.  
Figure 1: (**a**) Pilot plant schematic diagram and (**b**) actual plant diagram.
# Testing and Analysis of MOSFET-Based Absorber Integrated Antenna for 5G/WiMAX/WLAN Applications
## 2 Double Material Substrate (DMS) Cylindrical Surrounding Patch Antenna (CSPA) Design and Analysis
Now that G and D values have been input into Equation (1), K has been determined to be equal to 0.843 and 0.848, corresponding to radiation efficiencies of 84.3% and 84.8% for the lower and upper bands, respectively. It has been noted that the computed value of K for the antenna design satisfies the maximum performance criteria of \(0\leq\text{K}\leq 1\). Additionally, the DMS CSPA antenna displayed a radiation efficiency of 81% in both bands prior to changing its dimension. From the gain vs frequency plot presented in Figure 5, it has been observed that there is an increase in gain value as frequency increases from the lowest frequency (2 GHz) of the  
Figure 4: Far-field 3D-radiation pattern at 2.5 GHz. (**a**) Directivity value of 3.35 dBi and (**b**) gain value of 2.83 dBi. Figure 3: Far-field 3D radiation pattern at 5.3 GHz. (**a**) Directivity value of 6.15 dBi and (**b**) gain value of 5.2 dBi. lower band to the highest frequency (6.1 GHz) of the high band. In essence, this antenna has been excited by the microwave port for testing and experimentation purposes. One of the main benefits of the suggested design is the reasonably small size that results from the combination of FR-4 and polyimide substrate [34; 36]. This antenna design, whose front view is depicted in Figure 6, addresses the issue of device fitting and dimension accuracy during manufacture. This advancement will be helpful in the 5G frequency band, where mobile devices now require a smaller antenna to transmit and receive information.