esa-sceva/satcom-chunk-collection huggingface dataset:
# Fabrication of Hydrophobic Coatings Using Sugarcane Bagsase Waste Ash as Silica Source
###### Abstract  
Eco-friendly hydrophobic coatings were fabricated on tiles via the drop-casting process. Sugarcane bagasse waste ash (SBA) was used as a silica source and dimethyldiethoxysilane (DMDEOS) was used as a surface functionalizing agent. The elemental composition of SBA was measured using X-ray fluorescence (XRF) and energy-dispersive spectroscopy (EDS) techniques. The surface morphology of SBA was analyzed through the field-emission scanning electron microscopy (FESEM) technique. The surface wettability of SBA coated tiles was evaluated by determining the static water contact angle (WCA). XRF studies showed that the impurities were removed, and the silica content was enriched by the acid treatment. SBA coated tiles showed good hydrophobicity with a WCA of 135\({}^{\circ}\). The high hydrophobicity of the coated tiles may be attributed to the increase of surface roughness by SBA. Moreover, the SBA coating was successfully tested on various substrates such as tiles, brick, glass, and cotton cloth. SBA coated glass substrate was more durable compared to other substrates at normal room temperature.  
hydrophobicity; self-cleaning; coating; water contact angle; tiles +
Footnote †: journal: Applied Physics Letters
# Optimal Control Method of Semi-Active Suspension System and Processor-in-the-Loop Verification
## 3 Results
### Experimental Test Results  
The ride comfort and road holding characteristics of SASS with MR damper were investigated in the experimental test setup as shown in Figure 8. Three different half-sine bumps of 25 mm, 15 mm, and 10 mm amplitudes were used as road input in the system. The system response to the road disturbance was examined by observing three suspension system parameters. These parameters are sprung mass displacement, sprung mass acceleration, and unsprung mass displacement. Although unsprung mass displacement is not meaningful on its own, it plays a significant role in achieving suspension deflection.  
Figure 22: Test results for the medium bump (**a**) passive system, (**b**) SASS.
# Wavelength Selection for Satellite Quantum Key Distribution
## 5 Results and Discussion
### Component Size Optimization  
One wavelength from each region is selected to represent its corresponding transmission window, and the performance of each is compared in a range of turbulent strengths. Additionally, the two highest-performing wavelengths across the commonly used sections of the NIR and SWIR windows are also analysed. \(w_{0}\) is then varied between 0.1 m and 1.2 m to identify the ideal component sizes for each \(\lambda\). Figure 4 shows the secret key rate in (a) Tucson and (b) Waterford, for a range of \(D_{r}\) and \(w_{0}\) values. In the case of the downlink channel, the transmitter sits on-board the satellite, which places size limitations on \(w_{0}\). For a given \(w_{0}\), the best \(\lambda\) and \(D_{r}\) were chosen. Similarly, \(\lambda\) and \(w_{0}\) can be chosen to achieve high secret key rates for a ground receiver. The optimum \(\lambda,D_{r},w_{0}\) combinations for each wavelength region and the maximum secret key rates achieved are presented in Table 1. SWIR  
Figure 3: Three-dimensional surface plots representing the secret key rate, \(R\), in (**a,b**) Tucson and (**c,d**) Waterford, for a set of receiver diameters, \(D_{r}\), and wavelengths, \(\lambda\), when the initial beam waist, \(w_{0}\), is 10 cm (left) and 35 cm (right). The simulation parameters are listed in the text.  
and MWIR produce the highest secret key rates in the Tucson and Waterford scenarios. However, it is important to note the large \(w_{0}\) and \(D_{r}\) required to generate these rates.
## 5 Results  
### Experimental Validation of the Calculated Results  
In order to verify the 3D analytical method, the TLs obtained by experiment, 3D analytical method and FEM are shown in Figure 8. The FEM model and boundary conditions are shown in Appendix B. Generally, the calculated results are in good agreement with the measured results and the accuracy of the analytical method is validated to a certain degree. However, the frequencies of TL peaks from FEM agree less with the experimental results than those obtained by the 3D analytical method in this paper. The inaccuracy of the measured TLs below 200 Hz shown in the Case 1-1 of Figure 8 should be attributed to the insufficient energy of the sound source in this frequency range. As a result, the TLs below 200 Hz are not presented in the other cases of Figure 8. The discrepancies between the experiment and the 3D analytical method at higher frequencies may be caused by the following reasons. First, the analytical method regards the REC as rigid, while the prototypes under test are made of plastic with certain elasticity. Second, the damping in the air is ignored with the analytical method. Third, the ignorance of the \(B_{+}J_{0}(k_{\mathrm{z}}\alpha)\), \(B_{-}J_{0}(-k_{\mathrm{z}}\alpha)\) in the Equation (9) and the \(X\) in Equation (14) also causes errors.
# Performance Prediction and Optimization of Multi-Mirror Combined Solar Dish Collector
## 4 Discussion
The present optical performance study indicates that the optical efficiency of the combined solar dish system with less than 4 mrad optical error will have a higher intercept factor than that of a paraboloidal dish system with 5 mrad optical error under the same design. This agrees with [PERSON]'s results. As the processing of the paraboloidal mirror is more complicated than that of the spherical mirror, the optical error of the spherical mirror is often less than that of the paraboloidal mirror; for example, the optical error of a spherical mirror for solar energy application is about 1-2 mrad [35], but that for a parabolic mirror is about 3-4 mrad [36]. The present study indicates that the performance of a combined solar system with a spherical mirror is better than that of a paraboloidal solar dish system if the same processing is applied or the same cost of manufacturing is applied. The main virtue of the present method is the high computation speed, enabling the development of an optimization method. For simplicity, the net thermal efficiency optimization model of the system assumes that the operating temperature of the receiver is fixed; at this point, the heat loss rate per unit area of glass or window area can be regarded as a constant. In this paper, q/DNI is the energy loss coefficient of the system, i.e., the ratio of the heat loss of the receiver to the direct solar energy received by the system. When q/DNI is 18.177 and 168.25, it represents energy loss rates of \(1.67\times 10^{3}\) W /m\({}^{2}\) and \(1.63\times 10^{4}\) W /m\({}^{2}\), respectively, produced by two typical receivers under the conditions with and without a glass window. This may be the first available method for optimization of a combined solar dish system. The present method cannot calculate the solar flux of the receiver surface. As the geometrical ratio of the solar dish system is often over 1000, the maximum power of the receiver surface is often very high, and it is easy to exceed the limit of the receiver. Therefore, the calculation of the solar flux of the receiver surface is a necessary step of the design, and the present method cannot replace the ray tracing method for flux calculation.
## 2 Materials and Methods
### Data  
Carbon dioxide fluxes data [3] were retrieved from the AmeriFlux network ([[https://ameriflux.lbl.gov/sites/siteinfo/US-Ha1](https://ameriflux.lbl.gov/sites/siteinfo/US-Ha1)]([https://ameriflux.lbl.gov/sites/siteinfo/US-Ha1](https://ameriflux.lbl.gov/sites/siteinfo/US-Ha1))). It is a dataset (\"US-Ha1 28-years\") with hourly NEE values calculated with the EC methodology, and other meteorological variables like PAR, \(T_{air}\) and \(T_{soil}\) at different heights and depths or VPD, among others. It started in August 1991 and continues to December 2019. In this work, NEE values outside the range (\(-60\); 40) micromode m\({}^{-2}\) s\({}^{-1}\) were labeled as outliers and discarded.  
The R package ReddyProc 1.2.1 [36] was used to filter low turbulence periods, to gap-fill the data, and to partition NEE into \(R_{coo}\) and GPP. It requires some meteorological variables as input data, besides the fluxes, which are Rg, \(T_{air}\), \(T_{soil}\), and VPD or rH. As in the dataset are many possibilities to choose for each variable, the ones with less missing data were used, which were: above canopy \(T_{air}\) (TA_PI_F_1\(1\)1) and rH (RH_1\(1\)1), \(T_{soil}\) measured at the lowest depth (TS_PI_1), and PAR (PFFD_IN_PI_F_1\(1\)1) divided by 0.47 as Rg [43; 44]. Carbon flux (FC) and carbon storage (SC) were summed to complete NEE.  
To filter low turbulence periods, the software calculated yearly \(u_{*}\) thresholds following [35] using the 50 percentile criterion and it discarded the points with \(u_{*}\) below that bound. Then, it created a Look-Up Table with the remaining data points, as described in [19], to fill the missing NEE values. NT methodology was selected over DT or others to partition the flux because, up to date, there are more publications to compare the results.  
After processing all the data, weekly, monthly, yearly, and multi-yearly NEE, \(R_{co}\), and GPP budgets were calculated to describe inter-annual and intra-annual variability, as well as their financial extrapolations. An online platform displaying daily US-Ha1 data (previous day) and cumulative values can be found here: www.pemcarbon.com/ecas/. We note that extrapolation of US-Ha1 flux tower data to larger areas is purely for illustration. Upscaling the US-Ha1 results to surrounding areas requires additional analysis including remotely sensed data and scale-aware models and are not addressed in this study.
## 2 Methods/Experimental Approach  
In this article, key technologies in uplink scheduling and the design of an uplink link adaptation scheme for NB-IoT systems are focused on. The remainder of the article is organized as follows: First, in Section 3, a comprehensive literature survey that concisely and critically discusses the different existing channel coding and MCS selection approaches found in literature is provided. Section 4, then presents and discusses the proposed algorithm as well as the modelling. This section is then followed by a detailed description of the methods used for validation of the proposed approach in Section 5. In this section, a comprehensive table of key simulation parameters is provided, and the different design considerations and assumptions are presented and justified. A step-by-step description of the research design using a simulation script written in the R2019b version of MATLAB and its LTE toolbox, is presented in Section 5.2 before the obtained results are presented in Section 5.3 and critically analyzed and discussed. In this section various simulation results using MATLAB are presented to demonstrate the effectiveness of the proposed scheme. The existing approaches are compared under the same setting to show the strength of the proposed EEACC scheme compared to the traditional repetition-based as well as the NBLA schemes. Finally, conclusions are drawn in Section 6 based on the research objectives and a recommendation for future work is formulated.
# Building Realistic Mobility Models for Mobile Ad Hoc Networks
## 6 Real Shipping Movement Model
### Results and Discussion
#### 6.8.4 Normalized Routing Load on Random Waypoint Shipping Model
When comparing the protocols, DSR shows a significantly lower routing load in all cases where the base stations are randomly placed and moving, but when the base stations are fixed, AODV gives better routing performance for the middle radio ranges (10 km, 15 km, and 20 km), whereas DSR generates less routing overhead for the extremes of 10 km and 30 km.
## 5 Experiments
### Result and Discussion
#### 5.2.2 Prediction Performance  
We compare the proposed algorithm with the following competing baselines. All methods use the four types of features listed in Table 1.  
* Decision tree used for career choice prediction.
* SVM: A kind of generalized linear classifier that classifies data based on supervised learning.
* Random forest used for career choice prediction.
* Logistic regression used for career choice prediction.
* XGBOOST [25] is a boosting-tree-based method.
* ACCBOLR is a new method we propose based on logistic regression, incorporating prototypical cluster center generation, and a novel regularization item.
* ACCBOX represents the application of our proposed method based on XGBOOST, incorporating prototypical cluster center generation and a novel regularization item.  
As shown in Table 2, adding the regularization item and prototype examples improves the predictive accuracy of logistic regression and XGBOOST models. By using our method, the accuracy of ACCBOX increases from 0.604 to 0.638, the Micro-F1 score rises from 0.622 to 0.647, Micro-Precision increases from 0.617 to 0.629, and Micro-Recall rises from 0.627 to 0.666. The accuracy of ACCBOLR increases from 0.605 to 0.623, the Micro-F1 score rises from 0.607 to 0.624, Micro-Precision increases from 0.601 to 0.626, and Micro-Recall rises from 0.615 to 0.623. In contrast, the performance of other models is much worse. The accuracy of SVM is 0.606, the value of Micro-F1 is 0.621, Micro-Precision is 0.610, and Micro-Recall is 0.632. The accuracy of the random forest method is 0.623, the value of Micro-F1 is 0.633, Micro-Precision is 0.620, and Micro-Recall is 0.647. The performance of the  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Feature**} & \multicolumn{3}{c}{**Metrics**} \\  & **R** & **M** & **B** & **F** & **Acc** & **Micro-F1** & **Precision** & **Recall** \\ \hline
1 & & & & 0.462 & 0.484 & 0.481 & 0.487 \\
2 & ✓ & & & 0.493 & 0.511 & 0.494 & 0.530 \\
3 & & ✓ & & 0.555 & 0.567 & 0.557 & 0.547 \\
4 & & & ✓ & 0.486 & 0.506 & 0.488 & 0.5  
decision tree is the worst: its accuracy is 0.532, the Micro-F1 score is 0.562, Micro-Precision is 0.541, and Micro-Recall is 0.584.  
As displayed in Table 3, we perform a further ablation study to demonstrate the contribution of each design in ACCBOX. The second row represents the XGBOOST method trained with reducing the model capacity. In the third row, only the novel regularization approach is added in XGBOOST. It is clear that the novel regularization term makes a good contribution to regularizing the learner.  
The experimental results prove that our approach outperforms the state-of-the-art counterparts. It is clear that by using the prototypical cluster center generation approach and the novel regularization item contribute to the performance of the model.
# A Portable Optical Sensor for Microplastic Detection: Development and Calibration
## 2 Materials and Methods
Initial testing focused on PLA due to its prominence in the relevant literature. The sensor quantifies the degree of light absorption or transmission by the sample, utilizing a process known as \"absorbance measurement\". This parameter helps differentiate microplastic particles from organic matter. We map the detected intensity \(I_{detected}\) on a scale of 0 to 3.3 volts, with 3.3 volts indicating sensor saturation. We express the relationship between the incident intensity (\(I_{0}\)) and the transmitted intensity (\(I_{t}\)) through the sample as follows:  
\[I_{detected}=3.3\cdot\frac{I_{t}}{I_{0}} \tag{2}\]  
Equation (2) is the detected intensity equation. This equation determines the detected light intensity (\(I_{Detected}\)) as a scaled factor of the transmitted light intensity (\(I_{t}\)) relative to the initial intensity (\(I_{0}\)). Because \(I_{detected}\) corresponds to the voltage output proportional to the sample's optical properties, this equation reflects the linear scaling of the photodiode output to the maximum measurable voltage (3.3 V). Samples that show lower transmitted intensities yield proportionally lower output voltages, which facilitates the differentiation between microplastic particles and other materials. The first step in the process is to use gloves and a clean work area to avoid contamination, followed by collecting the sample (PLA) and measuring its attenuation. Subsequent to the measurement of attenuation, the samples are examined under a microscope to identify the microplastics and discard the non-plastic debris. Moreover, we used digital image processing (DIP) to classify the microplastics based on their shapes. Despite the fact that this article focuses on LED-based detection, we did not perform a detailed microscopic analysis, but preliminary tests based on morphological characteristics will be explained in the next article [28]. And we discussed it in the \"Microscopy and Image Processing Results\" section. Finally, we send the samples for spectroscopic analysis to determine their composition. This method ensures that the samples are relevant and reliable, providing accurate data for evaluating microplastic contamination [29,30]. In the future, \"Phase 2\" research could focus on sampling the environment and identifying sites with significant microplastic accumulation. The selection of an appropriate sampling method will be decided according to the needs of the study, e.g., net sampling for water column analysis, bulk sampling for volumetric concentration, continuous plankton recorder for plankton-associated microplastics, or direct in situ filtration for immediate processing.
# A New Subject-Sensitive Hashing Algorithm Based on MultiRes-RCF for Blockchains of HRRS Images
## 2 Preliminaries  
### Blockchain  
A blockchain [13] is a chain structure with data blocks as the basic unit. Blockchains were initially used in the field of digital currency and later expanded to many industries such as the Internet of Things [14; 15], supply chain management [16; 17], medical care [18], education [19] and e-commerce [20]. Essentially, a blockchain is a decentralized database: instead of just one master database storing the blockchain's data, multiple copies of the same blockchain data are distributed across a peer-to-peer network. In a blockchain, data are stored on the chain in the form of blocks. Each block is connected in chronological order and is mainly composed of a block header containing metadata and a block body containing the specific block data stored in this block [21]. The implementation process of a blockchain integrates various technologies such as cryptography and distributed systems to build a distributed environment, in which transactions can be performed safely without mutual trust and solves the problems of data storage security that may be caused by central institutions. With the in-depth development of blockchain technology, it has also been initially developed and applied in remote sensing [7; 8]. The immutability guarantees the integrity of the on-chain HRRS image to a large extent.  
However, there are still many deficiencies in the authentication technology of a HRRS images' blockchain. The outstanding problem is that the blockchain is too sensitive to changes in the content of HRRS images. For example, after the format conversion of the HRRS image on the chain, although the image has changed a lot at the binary level, its effective content has not changed significantly. In fact, the users of HRRS images are more concerned about whether the effective content of the HRRS images has been tampered with, rather than whether the carrier of the content has changed. The root of the above problem is that the blockchain uses a cryptographic hash function (SHA-1 or SHA-256) to generate the digital digest of the HRRS image, which can be solved by subject-sensitive hashing.
## 6 Simulation Results
### Experimental Results
#### 6.6.4 Effect of Buffer Size  
Figures 8-10 present the performance of RCPED with different data buffer size at a number of malicious ratio.  
Figure 8 shows the result of a malicious ratio at 10%. It can be seen that the utilization of a larger buffer does not possess a very distinct advantage. The lines of performance that indicate the transmission cost wind around each other in many cases. On the other hand, as the nodes number in the network arises, the transmission costs of RCPED with different buffer sizes reveal descending tendencies. However, minor fluctuations remain.  
Figure 9 illustrates the result when the malicious ratio rises to 20%, the advantage possessed by the adoption of larger buffer size becomes a bit clearer. A larger buffer size means less energy consumed for data transmission in most cases. Transmission costs with different buffer sizes still tend to decrease, but fluctuations appear less violent.  
As demonstrated in Figure 10, when the malicious ratio reaches 30%, the adoption of a larger buffer size shows a clear advantage. However, minor fluctuations in transmission cost lines of different buffer sizes show no signs of going away. The fluctuations in Figures 8-10 is probably due to that in the simulation setup, and there are many parameters (such as nodes locations) that are completely random as efforts trying to imitate the realistic scenario. Nonetheless, the overall tendency in each scenario is quite clear, despite these fluctuations.  
In conclusion, RCPED is generally in possession of superior performance given a larger buffer size, but this advantage is not entirely clear if the malicious ratio is low. This advantage becomes increasingly distinct as the malicious ratio of the network arises.  
Figure 8: RCPED performance with different buffer size.  
Figure 10: RCPED performance with different buffer size.  
Figure 9: RCPED performance with different buffer size.
# Relay Selection Scheme for Multi-Hop Transmission of MU-MIMO System
## 1 Introduction
There are amplify-and-forward (AF) relaying scheme and decode-and-forward (DF) relaying scheme in the typical multi-hop transmission schemes [11]. In the AF relaying scheme, the relay receives a signal from the BS, and then amplifies the received signal, and transmits the amplified signal to the UE. Therefore, the signal processing in the relay is simple, but the noise is also amplified when the signal is amplified. In the DF relaying scheme, the relay receives a signal from the BS, and then the relay performs a decoding and detection. Then, the relay re-modulates the signal and forwards it to the UE. Therefore, the signal processing in the relay is complicated, but the noise is not amplified because the signal is not amplified [12]. The proposed relay selection scheme adapts  
BS and relays, which have the greatest influence on error propagation. Therefore, the proposed relay selection scheme can be applied to the actual system more easily than the BHM scheme because it only uses the channel information between the BS and relays. This paper is organized as follows. Section 2 shows the system model of the proposed relay selection scheme. Section 3 explains the conventional relay selection schemes. Section 4 describes the proposed relay selection scheme. Section 5 shows the simulation results by comparing the conventional and proposed relay selection scheme. Finally, Section 6 presents our conclusion.