esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The goal of the present study was to assess the efficiency of natural and modified zeolite sediments to stabilize heavy metals from contaminated mining soil. Collected soil samples were found to be contaminated with Cd and Pb, and characteristic variations were found among the collected and modified zeolite sediments. Generally, sediments with nanoparticle size showed a higher CEC value, while a gradual decline was noted in the pH of nanosized sediments. Sediments with nanoparticle size recorded higher potential in stabilizing heavy metals against microparticle-sized sediments. Among all sediments, LDH-N, NZ-N, and HCl-N showed better performance in stabilizing Cd and Pb from contaminated mining soil. LDH-N possessed additional NO3\({}^{-}\) surface functional groups, a crystalline composition of feldspar, and the highest surface area (37.195 m\({}^{2}\) g\({}^{-1}\)). Therefore, LDH-N showed higher removal efficiency for Cd (>98%) and Pb (>95%), followed by NZ-N and HCL-N. A pH of 7 was found to be the optimum pH for Cd and Pb removal and sorption. Kinetics models revealed that both the first-order and the pseudo-second-order models fitted well to the experimental data. Electrostatic interaction, ion exchange, and surface complexation were found to be the principal mechanisms for heavy metal sorption. The incubation study revealed an excellent efficiency of the tested sediments in removing Cd and Pb, with LDH removing 100% Cd even after 8 weeks of incubation, while 100% Pb was removed at the 16 th week of incubation. HCl-N and NZ-N reduced extractable Cd by 83.70% and 80.54% and extractable Pb fraction by 81.42% and 77.98%, respectively, whereas LDH-N showed superior performance in reducing extractable fraction of Cd and Pb by 89.26% and 86.19%, respectively. The outcomes of this research indicate that the physical and chemical modifications of NZ could be an efficient methodology to synthesize natural zeolites in the nanoparticle size with enhanced performance in mitigating heavy metal toxicity. In particular, the modified LDH-N, NZ-N, and HCl-N zeolite sediments could be used to fix heavy metal contaminants from mining and other contaminated soils.  
Conceptualization, S.S.A., A.G.A. and H.M.I.; methodology, S.S.A., A.G.A. and H.M.I.; software, S.S.A., A.G.A. and H.M.I; validation, A.G.A. and H.M.I.; formal analysis, S.S.A.; investigation, S.S.A.; resources, A.G.A.; data curation, S.S.A.; writing--original draft preparation, S.S.A. and A.G.A.; writing--review and editing, S.S.A., A.G.A. and H.M.I.; visualization, A.G.A. and H.M.I.; supervision, A.G.A. and H.M.I.; project administration, A.G.A.; funding acquisition, S.S.A. and A.G.A. All authors have read and agreed to the published version of the manuscript.  
Researchers Supporting Project Number RSPD2024R825, King Saud University, Riyadh, Saudi Arabia.  
Not applicable.  
The data presented in this study are available from the corresponding author on a reasonable request.  
We thank King Saud University, Riyadh, Saudi Arabia, for Researchers Supporting Project Number RSPD2024R825.  
The authors declare no conflicts of interest.
# Asymmetric Dual-Band Tracking Technique for Optimal Joint Processing of B1I and B1C Signals
## 4 The ASYM-DBT Method and Thermal Noise Performance Analysis
### The ASYM-DBT Method  
Figure 4 depicts the tracking channel architecture of ASYM-DBT for the B1-ADS signal, which consists of two groups of different types of correlators and one processing unit. Two groups of correlators on the left and right are used to correlate the received signal with locally generated BII and BIC signals, respectively. Their outputs are then fed into the processing unit in the middle. The processing unit is responsible for combining the outputs of the two groups of correlators coherently or non-coherently, forming the discriminator outputs of the code, subcarrier, and carrier, and filtering them. Closed loops for code, subcarrier, and carrier tracking are finally formed by feeding their corrected NCO frequencies back to the correlators. Three kinds of measurements--code phase \(\hat{\tau}\), subcarrier phase \(\hat{\theta}_{s}\), and carrier phase \(\hat{\theta}_{c}\)--can be extracted from the corresponding loops. As can be seen in the next section, code tracking errors are on the order of decimeters, subcarrier tracking error is on the order of centimeters. Therefore, a natural idea is to combine the subcarrier phase measurement (which is high-precision but ambiguous) with the code measurement (which is just the opposite) to form a high-precision and unambiguous measurement given the subcarrier wavelength of 42.9 m, as shown below [17]:  
\[\hat{\tau}_{c}=\hat{\theta}_{s}+round(\frac{\hat{\tau}-\hat{\theta}_{s}}{ \lambda})\times\lambda \tag{13}\]  
where \(\hat{\tau}_{c}\) represents the combined pseudorange measurement and \(\lambda\) represents the subcarrier wavelength.  
The following describes the outputs of correlators and ASYM-DBT algorithms in the processing unit in detail.
## 2 Materials and Methods
### Convolutional Neural Network  
The classification neural network was designed and trained from scratch, following a convolutional network scheme with an architecture inspired by the LeNet family of neural models [34], known to achieve very good results during image classification tasks while maintaining a small network size. Our architecture design sought short execution times so they would have a minimum impact on the overall process time.  
Our network (Figure 7) consisted of a 64 \(\times\) 64 pixel input layer, which was connected to several stages of convolutional layers that were alternated with pooling layers. The output of the last convolutional processing stage was connected to a fully connected layer, which was directly connected with the nine outputs or categories of our system. These outputs were regularized following softmax distribution so that the output vector would resemble a probability distribution of belonging to each class (i.e., the sum of the nine outputs must always equal 1).  
Figure 6: Categorized images using the Patch Classifier interface, with (**a**) drill; (**b**) countersink; (**c**) rivet; (**d**) protruding rivet; (**e**) temporary fastener 1; (**f**) temporary fastener 2; (**g**) hexagonal; (**h**) screw; and (**i**) background categories.  
Figure 7: Architecture of the developed neural network.  
To improve the training results of the network and to avoid overfitting, the L2 regularization and dropout techniques were followed.  
L2 regularization consists of a mathematical model, added in the training phase, that reduces overfitting by penalizing the appearance of high values in the weights of the neural network [21]. In our network, we applied a factor of 0.001 to convolutional layers.  
Dropout is a technique based on omitting a certain percentage of the connections between layers during training to force the neural network to develop redundant connections by increasing robustness and reliability in the final classification [35; 36]. In our network, we applied a factor of 10% on both the first and last layers.  
Our dataset of 126,000 images was randomized and structured following a five-fold cross-validation scheme (Figure 8), where the set was split into five equal parts so that one of the portions would be used during each iteration to validate, and the remaining four would be used to train. Therefore, the training/test ratio was 80:20. This way, the category distribution influence on the training data could be analyzed.  
The network was trained for 150 epochs using the Adadelta [37] optimizer and the categorical cross entropy loss function. Furthermore, class balancing weights were applied during training to reduce the differences between the number of images in each category.
## 5 Conclusions and Future Outlooks
This activity would be particularly useful in those countries with a limited access and funding to support on-purchase facilities, and where local personnel lacks advanced space-borne remote sensing skills as it was recently demonstrated by the EDUU ([[https://www.eduu.unibo.it/](https://www.eduu.unibo.it/)]([https://www.eduu.unibo.it/](https://www.eduu.unibo.it/)), last accessed on 16 January 2022) and EAMENA ([[https://eamena.org](https://eamena.org)]([https://eamena.org](https://eamena.org)); last accessed on 16 January 2022) projects. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs14041009/s1](https://www.mdpi.com/article/10.3390/rs14041009/s1)]([https://www.mdpi.com/article/10.3390/rs14041009/s1](https://www.mdpi.com/article/10.3390/rs14041009/s1)), Figure S1: The GERD area and Figures S2-S4: selection of VHR imagery visualized through the Google Earth time-lapse function over the GERD area. Conceptualization, F.Z. and D.T. ; methodology, F.Z. and D.T. ; software, D.T. ; validation, F.Z. ; formal analysis, F.Z. and D.T. ; investigation, F.Z. ; resources, D.T. ; data curation, D.T. ; writing--original draft preparation, F.Z. and D.T. ; writing--review and editing, F.Z. and D.T. ; visualization, F.Z. and D.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. We have also used the data into derived products. Sentinel-2A/B scenes were sourced as open data from the Copernicus Open Access Hub. Google Earth images (2021 Maxar Technologies) were sourced and analyzed from Google Earth Pro. This paper builds upon previous work on the impact of dams on Near Eastern heritage initiated by [PERSON] at the University of Bologna and then continued with [PERSON]. [PERSON] (Politecnico di Milano) provided much insightful \"food for thought\". To both of them, the authors wish to express their gratitude. The authors declare no conflict of interest. References
# Simple Ultrasonic-Based Localization System for Mobile Robots
## 4 Evaluation and Discussion
Figure 11: Standard deviations of measurements of distances from T2. The standard deviation of coordinates x, y, and z is expressed in the row \"\(\pm\)\". The nature of the measurement confirmed the assumption that the smallest standard deviations would be observed in the z-axis. The highest standard deviation was found at points A4 and E4, which is precisely as expected. The standard deviation at these points is  
13.42 mm and 13.79 mm, respectively. Regarding the relevant range (green) from the intersections of all three radii, the standard deviation ranges from 1.83 mm to 3.28 mm. The trend of computed values is depicted in Figure 14.  
Figure 12: Standard deviations of measurements of distances from T3. Figure 13: Arithmetic averages of coordinates and their standard deviations measured at points A0 to E4. Similarly, to the y-axis, it was anticipated that the worst results would come from points outside the valid range for the x-axis. For the selected point C1, located within the valid range, a cloud of the occurrence of computed coordinate values from all measured values is displayed. Since the measured distance values are discrete, this is also reflected in the computed values, which exhibit some rasterization shown in Figure 17.  
Figure 16: Standard deviations of the y-coordinate. Figure 5.0 Cloud of calculated points from measurements displayed in the x and y axes for point C1 (top view). The graph depicts the variance of measured values as well as their arithmetic averages. For better visualization and to ensure that values do not create clusters located outside of the other values due to transformations, the occurrence of points is displayed in different colors depending on the original coordinate system. Additionally, the overall arithmetic average (blue) and the arithmetic averages of the calculated values from transformations are shown. Compared to system [34], which is similar to the presented design, the system exhibits similar standard deviations in individual axes (9.45 mm in the x-axis, 11.28 in the y-axis, 7.01 in the z-axis). As indicated, for example, in [35], the use of BLE for indoor positioning includes methods based on signal strength fingerprinting, which significantly increases localization accuracy in conditions where direct line-of-sight to the beacon is limited. The system can be quite accurate for certain applications but may not meet the requirements of applications that demand high precision. The maximum error is approximately 1.3 m, and the minimum positioning error ranges from approximately 6 cm to over 85 cm depending on the specific position and the function used.
## 3 Results and Discussion  
### Effect of Gasification Temperature on Combustible Gas Composition  
The effects of gasification temperature on gas composition and calorific value, gas yield, carbon conversion rate, and gasification efficiency are shown in Figure 2. Gasification temperature is a significant factor affecting volatile form and combustible gas composition [27; 28; 29; 30]. As shown in Figure 2, with an increase in gasification temperature, the concentrations of CO and H\({}_{2}\) gradually increased in the gas produced, while the concentration of CO\({}_{2}\) significantly decreased and that of CH\({}_{4}\) slightly increased. The calorific value, gas yield, carbon conversion rate, and gasification efficiency increased gradually. This is because, in the biomass, cellulose, hemicellulose, and lignin tightly combine into a single organic material, similar to the glass fiber tissue in polymerized resin. During the gasification of the biomass, cellulose, hemicellulose, and lignin oxidised and decomposed, and the increase in the pyrolysis' reaction speed and the gasification's reforming reaction speed at a high temperature were the main reasons for the gas component change, which was shown as follows. First, the temperature accelerated the speed of the biomass's volatilization to split and the speed of the secondary cracking of the tar. It also improved the production of small molecule gases, such as H\({}_{2}\), CO, and CH\({}_{4}\)[31; 32]. Second, according to [PERSON]'s principle, the endothermic boudouard reaction and the water gas reaction were strengthened under a high temperature. More coke and CO\({}_{2}\) were converted to generate CO and H\({}_{2}\), and the CO\({}_{2}\) concentration was further reduced. Third, the increasing temperature also promoted the reaction between H\({}_{2}\) and CO\({}_{2}\) to generate CO and H\({}_{2}\)O, which caused an increase of CO concentration and a slight increase of H\({}_{2}\) concentration [33]. During the gasification process, cellulose, hemicellulose, and lignin oxidised and decomposed, and the increase of temperature and the quantity of the gasification reaction releases more free radicals, which is bound to affect the migration process of the alkali metal in rice straw.
# Smart Irrigation Systems from Cyber-Physical Perspective: State of Art and Future Directions
## 1 Introduction
### Comparison to Other Survey Articles  
In the literature review, we conducted a comprehensive system research review based on the Cochrane research method [20; 21]. The Cochrane reporting methodology was adopted to mitigate bias risks during the literature review process. This approach enables the construction of a 3D problem space, facilitating a robust evaluation of the reviewed papers. The following research question guided our study: \"What are the latest technologies used in current irrigation systems, including physical sensors and applications?\" We selected papers published between 2018 and 2023, using the keywords \"smart farm\" and \"smart agriculture\" to focus on agriculture systems. We then narrowed the scope with the keywords \"IoT\" and \"Internet of Things\". Further, we refined it with \"irrigation\" and \"water\", filtering out 92 papers that focused on irrigation systems or included a chapter discussing irrigation systems. Of these, 17 papers were classified as surveys. Table 1 summarizes the comparison results between our work with the other 17 survey works.  
Some research papers narrow their focus to specific technologies when evaluating irrigation systems. For instance, [PERSON] et al. [3] reviewed LoRa applications without covering other technologies, such as NB-IoT applications in smart irrigation systems. [PERSON] et al. [6] concentrated on artificial intelligence (AI) in smart farming. [PERSON] et al. [15] and [PERSON] et al. [10] reviewed machine learning in smart farming. [PERSON] et al. [7] focused on applications related to energy-saving technologies, and [PERSON] [12] discussed IoT applications used in smart agriculture. Likewise, [PERSON] et al. [8] used the Jinsha River Basin's data, including temperature, humidity, and sunlight, as an input for their experiment lab setup to evaluate technologies such as RFID recognition.  
While specific research papers delve into advanced technologies within smart irrigation systems, they often overlook essential factors such as overall cost, which is vital for a comprehensive evaluation. For instance, [PERSON] et al. [4] divided the IoT system into four components, physical structure, data acquisition, processing, and analytics, to review the latest technologies and trends. Likewise, [PERSON] et al. [5] divided the IoT system into components (the physical layer, network layer, decision layer, and application layer) and then identified four factors that could heavily affect smart farming performance, which are soil erosion, chemical pesticides, climate change, and water usage. Neither of them consider cost-effective factors in their survey.
## 2 Research Methodology
### Numerical Simulation Details  
For the simulation of the modified compression test, represented by a hot forging experiment, an FE computational model based on dedicated numerical simulation software, namely QFormUK 11.0.2.13 was also developed as it can enable deeper understanding of the fracture process and also can be validated by the experimentally determined strain fields. In this model, the geometric representation of the experimental setup was adopted, as can be seen in Figure 4a, including the tool and workpiece. The finite element mesh was generated automatically and its size was adapted to the shape of the tool and sample models, as can be seen in Figure 6. The number of nodes was31,309 for tool part 1 (the upper part) and 34,585 for tool part 2 (the lower part). The mesh of the workpiece was remeshed during the simulation and the number of nodes varied between 6083 and 16,053 elements. As the automatic remeshing feature was enabled, it was not necessary to carry out a mesh sensitivity study. Thus, after determining an appropriate initial number of elements, the remeshing procedure was employed in order to produce a sufficiently fine mesh in regions which require a finer mesh, such as the regions with high stress concentration.  
Figure 5: Different sample positions in relation to the DIC cameras during deformation of cuboidal samples.  
Figure 6: Finite element mesh distribution on the tools and sample models before (**a**) and after (**b**) modified compression test simulation.  
The material models were adapted from QForm database (elastic-plastic material for the deformable workpiece) regarding the mechanical properties through stress-strains curves at different temperatures (20-250 \({}^{\circ}\)C) and strain rates (0.01-500 s\({}^{-1}\)). Furthermore, the Cockcroft-Latham and Rice and Tracey damage models were tested and friction was modeled by Levanov friction law with friction value of 0.5 and Levanov coefficient of 1.25. Moreover, thermal parameters such as thermal conductivity (154 W/(m\(\cdot\)K))and specific heat (880 J/(kg\(\cdot\)K)) were taken into account as the simulation is essentially thermomechanical. For these properties, the data were also obtained from the QForm database. Other boundary conditions, such as tool movement velocity were taken into account based on laboratory tests. The total simulation time was 27.678 s and the numerical timestep was variable between 235 and 500 ms. Simulations were carried out for the two different materials with two different damage criteria and afterwards simulations for a backward extrusion model were also carried out.
# A Mathematical-Based Model for Estimating the Path Duration of the DSDV Routing Protocol in MANETs
## 3 Mathematical Model
### PDF of the Path Duration  
The path duration from a source to a destination strictly depends on the time durations of all links established between intermediate nodes that form the complete transmission path. As shown in Figure 8, to calculate the duration of the full path from source to destination, the shortest durations among all the links will be taken, considering that an interruption of any link can lead to losing the connection [18]. Thus, the full path duration can be represented as follows (Equation (21)).  
\[t_{\mathrm{path}}=Min(t_{1},\ t_{2},\ t_{3},..,\ t_{n-1}) \tag{21}\]  
Figure 7: Number of hops representing the expected distance between any two nodes.  
By applying the [PERSON]'s theorem [20], the PDF of \(T_{path}\) can be represented by Equation (22)  
\[f_{T_{path}}\left(t_{\text{path}}\right)=E_{h}\cdot f_{T}(t)\cdot C_{T_{path}}^{E_ {h}-1} \tag{22}\]  
where (\(C_{T_{path}}\)) is the complementary CDF of (\(T_{path}\)) represented as follows (Equation (23)).  
\[C_{T_{path}}=1-F_{T_{path}} \tag{23}\]  
By substituting Equation (22) in Equation (21), we get the following equation (Equation (24)).  
\[f_{T_{path}}\left(t_{\text{path}}\right)=h\cdot f_{T}(t)\cdot\left(1-\int_{0}^{ t_{\text{path}}}f_{T}(t)\right)^{h-1} \tag{24}\]  
Finally, the expected path duration can be estimated using Equation (25).  
\[E_{T_{path}}\left(t_{\text{path}}\right)=\int_{0}^{a}t_{\text{path}}\cdot f_{ T_{path}}\left(t_{\text{path}}\right)\text{dt}_{\text{path}} \tag{25}\]
# Experimental Analysis of Ultrasonic Multiple Scattering Attenuation through the Air with Fine Dust
## 5 Experimental Results
### Multiply Scattered Acoustic Waves  
Figure 5 presents examples of the acoustic wave responses measured by the experimental setup described in Section 4. The total number of ensembles to obtain the ensemble-averaged response presented in Figure 5a is 10 (\(M=10\)), and Figure 5b represents one of the 10 ensembles. The single-realization response (Figure 5b) exhibits noise-like incoherent wave components, as well as a coherent wave pulse that is the dominant component. The incoherent wave components might have been set up by the standard particles distributed in the air of the test chamber. In the ensemble-averaged response (Figure 5a), only the coherent pulse is significant. The incoherent wave components observed in the single-realization response (Figure 5b) are suppressed and negligible in the ensemble-averaged response (Figure 5a).  
The time series of the ensemble-averaged response was transformed in the frequency domain, as illustrated in Figure 6. The center frequency is 4.8 MHz while the half-power bandwidth (_HPB_) ranges from 4.6 to 5.1 MHz. The attenuation of coherent waves over a 5 mm travel distance is clearly presented in the magnitude spectrum. The attenuation triggered by geometric spreading was compensated in the frequency domain. Then, using Equation (9), the scattering attenuation is calculated by considering the slope of the linear relationship between the travel distance \(x_{i}\) and the logarithm of the coherent wave's magnitude, \(\ln[x_{i}A(x_{i},\ f)]\). An example of the linear regression procedure is presented in Figure 7. Based on the coefficient of determination (R\({}^{2}\)), the range that exhibits a strong positive relationship over 0.7 is between 4.4 and 5.2 MHz, which correlates well with the half-power bandwidth of the measured signals.  
Figure 5: Examples of acoustic wave responses: (**a**) ensemble-averaged response \(\langle s(x_{1},t)\rangle\) and (**b**) single-realization response \(s(x_{1},t)\).  
Figure 6: Acoustic wave responses \(\langle S(x_{i},f)\rangle\) in the frequency domain.