esa-sceva/satcom-chunk-collection huggingface dataset:
# A Networked Sensor System for the Analysis of Plot-Scale Hydrology
## 3 Results and Discussion
### Hydraulic Properties Estimation  
Figure 8 shows the empirical relationship between the SM, \(\theta\), and the absolute value of the WP, in kPa, \(\mid\psi\mid\) at a depth of 10 cm and 30 cm for the location close to the middle of the hill (i.e., nodes 2282, 2292 and data logger DL2) in site 2. The fitted equations lead to similar results for both equations. At a depth of 10 cm the fitted parameter for the Clapp and Hornberger equation are: \(\psi_{s}\) = 0.658 kPa and b = 4.49; For the van Genuchten equation: n = 1.215 and \(\alpha\) = 1.808. The porosity value \(\theta_{s}\) = 0.31 was calculated from a soil core taken in the same location. For the depth of 30 cm, the parameters for the Clapp and Hornberger equation are \(\psi_{s}\) = 0.521 kPa and b = 5.87; For the van Genuchten equation: n = 1.154 and \(\alpha\) = 3.51. The porosity value \(\theta_{s}\) = 0.42 was calculated from a soil core taken in the same location. Table 1 summarizes these results, including the location close to the lower part of the hill (i.e., Node 2262, 2272 and Data logger DL1). The fitted equations were evaluated using the Nash-Sutcliffe efficiency (NSE) [85].  
Figure 7: Comparison of (**a**) soil temperature, T in Celsius degrees; (**b**) matric water potential (WP), \(\psi\) in kPa; and (**c**) volumetric soil moisture (SM), \(\theta\) in m\({}^{3}\cdot\)m\({}^{-3}\) data collected by a data logger (DL2) and wireless nodes (2282 and 2292) from the ASWP network between 29 July and 23 August 2016. The variable at a depth of 10 cm is shown in dark red for the data logger and light red for the nodes. The variable at a depth of 30 cm is shown in dark blue for the data logger and in light blue for the nodes.
# Automated Rain Detection by Dual-Polarization Sentinel-1 Data
###### Abstract  
Rain Signatures on C-band Synthetic Aperture Radar (SAR) images acquired over ocean are common and can dominate the backscattered signal from the ocean surface. In many cases, the inability to decipher between ocean and rain signatures can disturb the analysis of SAR scenes for maritime applications. This study relies on Sentinel-1 SAR acquisitions in the Interferometric Wide swath mode and high-resolution measurements from ground-based weather radar to document the rain impact on the radar backscattered signal in both co- and cross-polarization channels. The dark and bright rain signatures are found in connection with the timeliness of the rain cells. In particular, the bright patches are demonstrated by the hydrometeors (graupels, hails) in the melting layer. In general, the radar backscatter under rain increases with rain rate for a given sea state and decreases when the sea state strengthens. The rain also has a stronger impact on the radar signal in both polarizations when the incidence angle increases. The complementary sensitivity of the SAR signal of rain in both channels is then used to derive a filter to locate the areas in SAR scenes where the signal is not dominated by rain. The filter optimized to match the rain observed by the ground-based weather radar is more efficient when both polarization channels are considered. Case studies are presented to discuss the advantages and limitations of such a filtering approach.  
SAR; rain signatures; rain rate; sea surface winds Article  
Automated Rain Detection by Dual-Polarization Sentinel-1 Data  
29 June 2021  
4 August 2021  
10 August 2021
# A Gradient-Based Cuckoo Search Algorithm for a Reservoir-Generation Scheduling Problem
## 5 Case Study
### Reservoir Scheduling in Wet Years  
To evaluate the gradient strategy for reservoir scheduling, the optimal scheduling scheme of the cascade hydropower stations were obtained by using GCS and compared with those obtained by IHS, KGSA and CS. The parameter of GCS are \(ns=40,~{}p_{a}^{s}=0.3,~{}p_{a}^{e}=0.1,~{}sl=0.01,~{}u=0,~{}c=1.5\), and the maximum evaluation times is set to 12,000. The initial and end water levels of the reservoirs are set to their normal levels as shown in Table 1. Each month is divided into three periods of approximately 10 days each, resulting in 36 time intervals in a year. Each method is run 100 times, independently. To evaluate the performance of GCS on reservoir scheduling in wet years, the streamflow of Pingshan station in Figure 6 was chosen as the input of the LHGS model. As shown in Equation (15), the water levels of the reservoirs at different time intervals are the optimization parameters. Because the water level at the 36 th time interval is set to the normal water level, the optimization parameters of each reservoir are 35 and the number of reservoirs is 4, meaning that it is a 140-dimensional problem. Equation (1) is the fitness function of LHGS, and the larger power generation means a better scheduling solution.
# Quantum-Based Analytical Techniques on the Tackling of Well Placement Optimization
## 4 Results and Discussion
### Convergence Analysis  
To analyze the performance of different algorithms, the convergence curve and convergence speed is an important tool. So, plots of the mean net present value of algorithms versus iterations number are illustrated in Figure 6. As per the illustration of Figure 6a, QPSO algorithm surpassed all the other algorithms. The QBA provided 2 nd best results in case study 1. The 3 rd and 4 th best net present values are achieved by the DE and PSO algorithm, respectively. However, it should be mentioned that both GA, GSA, BA, and CSA have suffered the problem to converge in local optimum point. Overall, in the first case study, QPSO and QBA have achieved better solutions. As seen from Figure 6b, QBA has exhibited superior performance to provide a better net present value compared to other algorithms in case study 2. After QBA, QPSO and PSO algorithms have gained better results than other methods. However, unlike the previous case study, GA performed the worst with respect average net present value. Furthermore, the CSA, GA, BA, and GSA algorithms again failed to obtain satisfactory NPV in this study.  
Figure 6 suggests that, unlike PSO and BA algorithm, modification in the search expression in PSO and BA with quantum computation helped avoid local optimum in convergence. Furthermore, the quantum computation in the PSO and BA made it more vibrant across both case studies. Hence, in a multimodal problem, QPSO and QBA is more dynamic than PSO and BA and is further validated by the exploration and exploitation graphs shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c} \hline
**Economic Parameter** & **Value** & **Unit** \\ \hline Discount rate & 10\% & - \\ \hline Oil production cost & 72.327 & \$/STB \\ Gas price, \(P_{g}\) & 0.126 & \$/MScf \\ Oil price, \(P_{O}\) & 290.572 & \$/STB \\ CAPEX & \(6.4\times 10^{7}\) & \$ \\ Water production cost & 31.447 & \$/STB \\ Gas price, \(P_{g}\) & 0.126 & \$/MScf \\ \hline \end{tabular}
\end{table}
Table 3: Economic parameters used by authors in well placement optimization.
## 6 Open Challenges, Future Prospects and Research Directions
### FSO Scalability and Reliability for 5G and Beyond Networks
#### 6.1.2 Deployment Strategies  
Effective deployment strategies are essential for the successful scaling of FSO systems. In this regard, site selection and planning are very important. For example, thorough LoS analysis is required to identify optimal locations for FSO transceivers, minimizing the impact of physical obstructions. Also, installing FSO equipment on rooftops, towers, and other elevated structures can enhance coverage and reduce the likelihood of obstructions.  
Additionally, FSO can be effectively used for backhauling small cell networks in dense urban areas, where laying fiber may be impractical or cost prohibitive. High-density deployment of FSO nodes can support the massive device connectivity required for 5G and beyond, providing high-capacity links in congested areas. Also, deploying mobile FSO units on vehicles or drones can provide temporary high-capacity links in emergency situations or during large events, offering flexibility and rapid deployment capabilities.
## 3 Results and Discussion
### Optimal Green Solvent Design for Antioxidant Extraction from Lemon Balm
#### 3.3.2 Analysis of Response Surfaces  
The three-dimensional response surface plots demonstrate the effects of the interaction between two continuous variables (conditions of ChCl:U-UAE) by keeping the third variable constant (at the central level) on the predicted CUPRAC, ABTS, and DPPH of LB extracts (Figure 7).  
Figure 7: Response surfaces and contour plots for CUPRAC (**a–c**), ABTS (**d–f**), and DPPH (**g–i**) of LB extracts obtained by UAE with green DES expressed as a function of ChCl:U and WC (at t = 10 min), ChCl:U and t (at WC = 30%), and WC and t (at ChCl:U = 1:1).  
As can be seen, the shapes of response surfaces for AC of LB extracts determined by different analytical methods were similar. The parabolic shapes of CUPRAC surfaces indicate that the quadratic terms of all independent variables were significant. The ChCl:U ratio displayed negative linear and quadratic effects on AC values, whereas WC and t positively affected the antioxidant properties of LB extracts. Therefore, LB extracts prepared using this DES with various molar ratios of ChCl and U containing the lowest WC and extracted in the shortest time revealed the lowest AC (Figure 7a,b,d,e,g,h). However, the CUPRAC, ABTS, and DPPH values rapidly increased with the increasing extraction time and WC in ChCl:U solvent (Figure 7c,f,i) because these two parameters (t and WC) positively affected the antioxidant potential of all studied samples. These response surfaces demonstrate that the highest AC of the investigated extracts occurred when the highest WC (40%) was added to green solvent ChCl:U (1:1) and the longest time (15 min) was used to extract antioxidants from LB.  
The proposed mathematical models allow for the calculation of the optimum operating parameters of the UAE of total antioxidants from LB. The optimum conditions for DES-UAE were as follows: ChCl:U = 1.01 mol/mol, WC = 40%, and t = 15 min, under which CUPRAC = 493.7 umol TE/g, ABTS = 273.8 umol TE/g, and DPPH = 209.6 umol TE/g were predicted.  
Verification experiments were carried out at the predicted conditions derived from the RSM analysis to evaluate the sufficiency of the proposed mathematical models. Insignificant differences (_p_ > 0.05) between the predicted and experimental (CUPRAC = 488.6 \(\pm\) 15.8 umol TE/g, ABTS = 276.5 \(\pm\) 7.5 umol TE/g, and DPPH = 212.6 \(\pm\) 9.1 umol TE/g) response values confirmed that the proposed models were accurate and adequate for optimizing UAE antioxidants from LB.
# Energy Efficient Multipath Routing Algorithm for Wireless Multimedia Sensor Network
## 4 Energy Efficient GA-Based Multipath Routing
### Multipath Formation Using Genetic Algorithm
#### 4.2.2 Fitness Function  
The fitness function calculates the most energy efficient path. The appropriateness and quality of a particular solution for a given problem should be measured by checking its fitness among the other possible options by employing a fitness function. In this paper, we have used the fitness function illustrated below in Equation (16). The fitness function is based on maximizing the cost value as described in Equation (17) to find an energy-efficient path to the base station. This function considers  
Figure 4: Chromosome representation for multipath.  
the minimum distance to both the sink node and the immediate next connecting node, and the remaining energy of the next hop:  
\[maxf(v_{i}) \tag{16}\]  
where,  
\[f\left(v_{i}\right)=\ \frac{1}{Dist(v_{source},\ v_{i})}+\ \frac{1}{Dist(v_{i},\ BS )}+E_{res}(v_{i}) \tag{17}\]  
where \((v_{i})\) is the fitness value of the next possible node from the current node, \(E_{res}(v_{i})\) is the remaining energy of the node \(v_{i}\), \(Dist(v_{source},\ v_{i})\) moreover, \(Dist(v_{i},\ BS)\), using Equation (15).  
The next hop in the path is selected if it has the maximum fitness value among the possible nodes. Maximum cost value for the fitness function will be obtained if both distances (i.e., \(Dist(v_{source},\ v_{i})\) and \(Dist(v_{i},\ BS)\)) have minimum values, and the next hop has higher residual energy than other possible candidates. In the next step, a chromosome with a fitness value higher than the others will be selected for the next process called crossover. Here, we use Roulette-Wheel [43] selection as this model works better for our problem.
# Rate of Penetration Estimation with Parameter Correction
## 4 ROP Estimation with Models
### Method Limitations  
While the proposed method improves the accuracy of well depth and ROP estimation through the integration of elongation correction and Kalman filtering, several limitations should be acknowledged, particularly under extreme downhole dynamic conditions.  
First, the current drill string model is based on linear stiffness and damping formulations for both axial and torsional responses. This simplification facilitates real-time implementation and computational efficiency but may not capture important nonlinear behaviors. For example, under severe bit-rock interactions, force saturation, intermittent drill string contact with the borehole wall, or stick-slip transitions with discontinuous torque behavior can occur--phenomena that exceed the fidelity of the linear model.  
Second, the dynamic elongation component is constructed assuming moderate levels of vibration, representative of typical operational regimes. In environments characterized by shock loading, resonance, or high-frequency axial-torsional coupling, the estimation accuracy may degrade. Extending the model with nonlinear state estimation techniques, such as the Unscented Kalman Filter or Particle Filter, or employing a high-resolution multi-segment finite element drill string model, would be necessary to handle such scenarios.  
Lastly, the effectiveness of the proposed correction approach assumes that key physical parameters--such as Young's modulus, damping coefficients, and thermal expansion properties--are known or can be accurately approximated. In harsh conditions, such as those encountered in high-pressure, high-temperature (HPHT) wells, these parameters may deviate over time due to material fatigue or sensor drift, introducing model uncertainties that affect the correction and estimation accuracy. Recognizing these limitations, future work can extend the current model to handle more severe dynamic environments by incorporating nonlinear bit-rock interaction laws, multi-segment drill string dynamics, and adaptive estimation techniques that can learn system parameters in real time.
## 3 Results and Discussion
### Characterization of CNCs Properties
#### 3.1.3 Z Potential Versus pH of Dispersed Cellulose Nanocrystals  
Based on the values of their Z potential, the three types of CNCs suspensions showed colloidal stability at pH values ranged between 4 and 10, because negative charges (-COO\({}^{-}\) and -OSO\({}_{3}\)\({}^{-}\)) lead to a strong electrostatic repulsion in a kinetic system.  
As Figure 2 reveals, the independence of the Z potential values versus pH of the CNC\({}_{\text{COOR}}\) were rather constant, and higher at alkaline pH due to presence of both types of negative charges on their surface (residual carboxylates and sulfate half ester). In contrast, Z potential values of CNC\({}_{\text{SO3H}}\) were the lowest and the most constant ones, while those of CNC\({}_{\text{COOH}}\) fluctuated with pH change. Such behavior was expected since the CNC\({}_{\text{COOH}}\) charges deprotonate to CNC\({}_{\text{COO}}\)\({}^{-}\), while ester and sulfate half esters groups are more stable with pH change. Furthermore, at more acidic pH (5-6), Z potential values were found higher than those of the alkaline pH: according to a recent work, this behavior is attributed to the carboxylate ions present in colloidal suspensions [34]. It is worth noting that hydroxyl groups alone are not responsible for negative surface charges, since they are typically protonated under pK\({}_{\text{a}}\) > 12. Such fact-based observation was strongly confirmed in CNCs suspension obtained by HCl, which exhibits a lack of colloidal stability because of the absence of negative charges [35].  
As a result of that, the pH of the CNC\({}_{\text{COOH}}\) and CNC\({}_{\text{COOR}}\) dispersion must be appropriately adjusted before the coating process to guarantee a higher colloidal stability and a subsequent more uniform and stronger adhesion with plastic films.
## 2 Materials and Methods
### Extracts Preparation  
Before the ultrasound-assisted extraction process (UAE), the dried and cut _S. officialis_ herb (EkoHerba, Hajnowka, Poland) was ground in a laboratory grinder and sieved through laboratory sieves (MULTISERW-Morek, Brzeznica, Poland) with a diameter of 0.25 to 0.63 mm. The raw material fraction collected in the receiver (<0.25 mm) was used for extraction. The appropriate amount of plant material was weighed on an analytical balance (RADWAG AS220.R2 PLUS, Radom, Poland) into plastic Falcon test tubes with a capacity of 50 mL, to obtain the concentration of plant material in the extracts equal to 7.5 g/100 mL of solvent, 5.75 g/100 mL of solvent, 4.0 g/100 mL of solvent, and 2.25 g/100 mL of solvent. Aqueous solutions of ethanol with concentrations of 20, 40, and 60% \(v/v\) were used as solvents. The samples were shaken intensively and then extracted in an ultrasonic bath (40 kHz) with a thermostat (FSF-031S, Chemland, Stargard, Poland) at a controlled temperature of 40 \(\pm\) 1 \({}^{\circ}\)C. From each sample, 0.5 mL of extract was taken at the time intervals of 1, 3, 5, 10, 15, and 20 min and stored at +6 \({}^{\circ}\)C until analysis.
###### Abstract  
In regenerative bone tissue medicine, combining artificial bone substitutes with progenitor cells is a prospective approach. Surface modification via cold atmospheric plasma (CAP) enhances biomaterial-cell interactions, which are crucial for successful bone regeneration. Using a rabbit calcural critical-size defect model, we assessed the use of CAP-pretreated beta-tricalcium phosphate (\(\beta\)-TCP), alone or with periodontal ligament stem cells (PDLSCs), for bone regeneration. Histological and histomorphometric analyses at two and four weeks revealed significantly improved bone regeneration and reduced inflammation in the CAP-treated \(\beta\)-TCP with PDLSCs compared to \(\beta\)-TCP alone. Immunohistochemistryal analysis also showed an increase in the bone healing markers, including bone morphogenic proteins 2 and 4, run-related transcription factor 2, collagen-1, and osteonectin, after two and four weeks in the CAP-treated \(\beta\)-TCP implants with PDLSC. This in vivo study demonstrates for the first time the superior bone regenerative capacity of CAP-pretreated \(\beta\)-TCP seeded with PDLSCs, highlighting the therapeutic potential of this combined approach in osteeoregeneration.  
Keywords:bone regeneration; mesenchymal stromal cells; periodontal ligament stem cells; non-thermal atmospheric pressure plasma; tricalcium phosphate +
Footnote †: journal: Applied Sciences
# An Improved Method for Ship Target Detection Based on YOLOv4
## 4 Experiments and Results
### Experimental Environment Configuration and Result  
The training environment is shown in Table 2, and the network training parameters are shown in Table 3.  
The model was trained by feeding the training and validation sets into the network, and all images were trained for a total of 300 epochs. In each epoch, the network received 12 images per batch, and the image size was uniformly adjusted to 800 \(\times\) 800. The loss values on the training and validation sets were calculated for each epoch, and the loss curves of the training process were plotted according to the loss values. As shown in Figure 8, the loss values on both the training and validation sets gradually decreased and leveled off in the first 210 epochs of model training; after 210 epochs, the loss value on the training set decreased from 0.05 to 0.02, but the loss value on the validation set increased slightly, indicating that the model was overfitting. We saved the model with the lowest  
\begin{table}
\begin{tabular}{c c c} \hline
**Platform** & **Configuration Item** & **Configuration Value** \\ \hline \multirow{4}{*}{Hardware Platform} & CPU & Inter Xeon(R) Bronze 3104 CPU @ 1.70 GHz \(\times\) 6 \\  & Memory & 32 GB \\ \cline{1-1}  & GPU & Quadro RTX 8000 \\ \cline{1-1}  & Graphics Memory & 48G \\ \cline{1-1}  & Operating System & Ubuntu 18.04.6 LTS \\ \cline{1-1}  & Platform & Deep Learning Framework & Pytorch \\ \hline \end{tabular}
\end{table}
Table 2: Training environment configuration.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Epoch & 300 \\ Batch size & 12 \\ Max learning rate & 0.01 \\ Min learning rate & 0.0001 \\ Optimizer & adam \\ Learning rate decline mode & cos \\ Input image size & 800 \(\times\) 800 \\ Data enhancement method & mosaic \\ \hline \end{tabular}
\end{table}
Table 3: Training parameter setting.  
loss value on the validation set in the 200-210 epoch, and the loss curves of the model on both the training and validation set leveled off at this time, which proved that the model had converged.  
In order to prove the superiority of our method, we compared it to the highest version of YOLO: YOLOv7 [28]. Table 4 shows the results of the experiment. The mAP of the YOLOv7 improved by 13.43%. The mAP of our method increased by 13.74%. The accuracy of our model reached the same level as the state-of-the-art model with a slight improvement. Additionally, the detection speed did not decrease significantly. There were a large number of small ships in the labeled merchant ships, and the AP of merchant ships of YOLOv7 improved by 11.78%. The AP of merchant ships of our model improved by 12.43%, which demonstrated that our model detects small ships better.
# Application-Specific Integrated Circuit of an Inter-IC Sound Digital Filter for Audio Systems
## 3 Implementation Results and Discussion
### Validation and Testing  
A PCB was designed in order to supply power to the package, connect to the I2S and I2C buses, and test the ASIC in an environment close to its real-world application. The test environment shown in Figure 11 consisted of:  
* I2C master to select the working filter;
* I2S master to provide sound data;
* I2S slave to read the data after filtering;
* Logic analyzer to debug serial protocols.  
With the purpose of capturing the response of the different FIR filters available in the ASIC, a chirp signal was generated and sent through the I2S to the PCB input pins using the I2S master. After capturing the output using the I2S slave, the power spectral density for each filter output mode was obtained with Matlab, as shown in Figure 12.  
Figure 10: ASIC implementation in a DIP48 package.  
Figure 9: The configurable filter integrated circuit: (a) physical design; (b) module mapping.  
The filter bank considered four types of filters: There were two low-pass filters with an equivalent cut-off frequency of 1.894 kHz and 3.875 kHz; these filters were intended for low-/low-mid-frequency filtering. There were two band-pass filters of 1.033 kHz-19.035 kHz and 4.478 kHz-9.474 kHz; these filters were intended for mid-/mid-high-frequency filtering. It can be observed that the true cut-off frequencies had a slight deviation from the theoretical design. Finally, the testing of the adaptive filter was approached by using a spectral density estimation of the recorded data.