esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Prediction of Future SIUS and Flows  
Empirical evidence from major developed countries that have completed industrialization and urbanization shows that SIUS, particularly per capita SIUS, follows an S-shaped growth trajectory. The logistic curve, being the most widely adopted model for such sigmoidal growth patterns [23; 37; 38], is employed in this study to estimate future per capita SIUS across downstream sectors (\(S_{p}(t)\)) (Equation (8)). To fit this model using historical stock data, it is common to first externalize the value of \(K\) to reflect expectations of future socio-economic levels of material metabolism. The setting of \(K\) in this study is based on [PERSON] et al. (2023) and [PERSON] et al. (2020) (Table S4) [14; 39]. Finally, by fitting historical trends and considering the population development trends, the SIUS are predicted.  
\[S_{p}(t)=\frac{K}{1+m\times e^{-a(t-t_{0})}} \tag{8}\]  
\[S(t)=\sum_{p}S_{p}(t)\times P(t) \tag{9}\]where \(p\) represents the downstream sectors; \(m\) and \(a\) are the parameters of the logistic curve fitted based on historical values; and \(P(t)\) represents the population in year \(t\), million persons.  
The total recyclable scrap steel (TS) is mainly divided into three categories: Home Scrap (HS), Process Scrap (PS) and Depreciated Scrap (DS). HS is the scrap produced in the production of steel, whereas PS refers to the scrap produced at each stage of steel product manufacturing and processing. DS refers to the scrap steel that exceeds its service life in steel products, which can be obtained from the stock-driven model (see Text S3). The calculation of scrap resources is as follows:  
\[HS(t)=\varepsilon F_{in}(t) \tag{10}\]  
\[PS(t)=\rho SP(t) \tag{11}\]  
\[DS(t)=\gamma_{p}F_{out}(t)=\gamma_{p}\sum_{t^{\prime}\leq t}Fin(t^{\prime}) \times\lambda(t),(t^{\prime}\leq t) \tag{12}\]  
\[TS(t)=HS(t)+NW(t)+DS(t) \tag{13}\]  
where \(F_{in}(t)\) represents the total steel inflow from the steel system of each downstream sector \(p\) in \(t\) year, Mt; \(\alpha\) represents the recovery rate of \(HS\), which is about 5% [40]; \(SP(t)\) represents the steel production, Mt; \(\beta\) represents the recovery ratio of \(PS\), approximately 6% [41]; \(F_{out}(t)\) represents the outflow from the steel system after the end of the service life of steel products, Mt; and \(\gamma\) indicates the recovery ratio of \(DS\) from the downstream sector \(p\)[41]. See Table S4.
## 2 Dataset and Methodology
### Dataset and Criteria
#### 2.3.5 Distance to Road  
Accessibility to roads is crucial for the construction, maintenance, and operation of PV farms. Sites located close to roads are preferred because they reduce transportation costs and logistical challenges associated with the delivery of materials and equipment. Additionally, proximity to roads ensures ease of access for technicians and maintenance teams, contributing to the long-term sustainability of the PV farms. The distance to roads is typically divided into several categories, with closer distances receiving higher suitability scores for AHP. Land within 500 m of a road may be classified as highly suitable, while areas 5 km or more away from roads may be less suitable due to the increased cost and difficulty of transportation [7; 17; 22; 50]. In this study, OpenStreetMap road data were downloaded and analyzed with respect to highways and surrounding roads [51]. Areas within 1 km of a road were considered the most suitable for PV farms, as they allow for easier access and transportation of materials and maintenance teams. In contrast, areas located 6 km away from roads were considered the least suitable due to the increased logistical challenges and transportation costs associated with these locations. This classification helps in optimizing the efficiency and cost-effectiveness of solar farm construction and operation by prioritizing sites with better road accessibility.
## 3 Hydrological Applications
### 3.3 Impervious surface cover  
Surfaces that impede the natural infiltration of water and enhance surface runoff are classified as impervious surfaces. Associated with urbanization and construction of pavements, roads and buildings, impervious surfaces play an important role in surface runoff and the transport of contaminants. Remote sensing has been used as an effective technique to map impervious surfaces using spectral characteristics of surfaces ([PERSON] 2004b, [PERSON] and [PERSON], 2007). [PERSON] (1995) and [PERSON] et al. (1998) showed that the relation between the fractional vegetation cover (FVC) and fractional impervious surface area (FIS) for developed areas as  
\[\text{FIS}=1-\text{FVC} \tag{3}\]  
In surface runoff estimation, impervious surfaces are classified as hydraulically connected and those that are not. The hydraulically connected surfaces such as parking lots and roads are connected to the drainage system where runoff from such surfaces leads to the drainage network. Those surfaces such as roof-tops are classified as hydraulically not connected. Rain water from roof tops can fall into the pervious surface area such as grass hence termed as disconnected. The resulting storm runoff from such surfaces can be lower than the hydraulically connected impervious surface areas.  
Figure 7 shows the 2004 impervious surface area map of south Florida as estimated from MODIS data using equation (3).  
Figure 6: Percent vegetation cover for Econlockhatchee River sub-basin (Econ sub-basin) in Florida (a) 1984 (b) 2000.
# Effect of Optical Aberrations on Laser Transmission Performance in Maritime Atmosphere Turbulence
## 2 Numerical Simulation Method  
Figure 1 illustrates the specific steps and logic of the numerical simulation algorithm employed in this paper as follows:  
According to Figure 1, the flowchart of this simulation algorithm mainly includes the following modules: the system initialization module, the laser emission light field setup module, the simulation environment setup module, the transmission equation solving module, the light field characteristic quantity calculation module, and the simulation results output module. Through the coordinated operation of these modules, the far-field spotlight intensity information and the characteristic physical parameters of the laser during its transmission in marine atmospheric turbulence can be obtained.
## 7 Conclusions  
Based on the CTS system, a novel LPSA algorithm has been developed for fast pulse compression. Compared to the SAW filter for physical pulse compression, digital pulse compression can avoid the difficult dispersion characteristic match between the expander chirp signal and the SAW filters, as well as the large attenuation and limited operational bandwidth of the SAW devices. The novel LPSA algorithm can realize fast pulse compression in a much simpler way compared to the classical digital pulse compression method. The computation is far less consequent compared to the time-domain pulse compression method when dealing with an input signal with a sparse spectrum and small dynamic range. The phase approximation of the extracted sampling points will degrade the system performance, i.e., the amplitude accuracy and the dynamic range, which will influence the sensitivity of the system. To obtain a precise approximation of the orthogonal sampling points, the sampling rate required in the LPSA algorithm will be much higher than that in the classical digital pulse compression method. The factors that mainly influence the amplitude accuracy focus on the number of the sampling points, the sampling rate, the approximate factor p and the conversion loss of the nonideal mixer. In general, the proposed LPSA method can realize fast pulse compression for sparse spectrum measurements based on the CTS system at the expense of higher sampling rates and smaller dynamic range. This novel LPSA algorithm could be applied not only to CTS for fast pulse compression, but also to fast pulse compression in radar systems, especially for sparse signals with small dynamic range.  
Conceptualization, Q.Z.; methodology, Q.Z.; validation, Q.Z., L.T. and B.G.; formal analysis, Q.Z. and L.T.; resources, Q.Z., L.T. and B.G.; writing-original draft preparation, Q.Z. and L.T.; writing-review and editing, Q.Z. and L.T.; funding acquisition, L.T. and B.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by The Pre-research Project of Civil Aerospace Technology of China (D040109).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank [PERSON] for the supply of the arbitrary waveform generator (7082c) in the experiment.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Application of Modern Research Methods for the Physicochemical Characterization of Ion Exchangers
## 1 Introduction
### Microporous, Macroporous and Hypercross-Linked as Well as Polyhydrophilic Ion Exchangers  
The polymerization of only two monomers leads to the ion exchangers of the so-called gel (microporous) type, while the addition of an appropriate inert compound to the mixture of co-monomers leads to the ion exchangers of the porous (macroporous) type (Figure 2).  
Macroporous ion exchangers, also known as the second generation polymer network, have a sponge-like structure that provides greater stress tolerance and resistance to fouling and physical breakdown (osmotic shocks, changes of pH, polarity), oxidizing reactions and swelling.  
The third generation of polymer network is known as the hypercross-linked polystyrene (HCPs) ion exchangers [18; 19; 20]. The most common are Purolite (Hypersol-Macronet, MN-series) and Dow Chemical (Optipore) as well as Lanxess (Lewatit VP OC 1163 or S 7768). They are characterized by the large specific surface areas 600-2000 m\({}^{2}\)/g, permanent small pores and large micropore volumes. The sorption properties from the unfunctionalized hypercross-linked polystyrene with the high surface area above 2100 m\({}^{2}\)/g are mostly based on hydrophobic and \(\pi\)-\(\pi\) interactions [21]. However, their functionalization could be achieved by the post-polymerization chemical modifications or introducing a functional group containing monomers.  
Ion exchangers with interconnected pores with a large content of HIPE (high internal phase emulsion) are known as polyhydrophilic polymers. They have a porosity of up to 90%, a low density of 0.1-0.2 g/cm\({}^{3}\), a pore size in the wide range of 5-100 \(\upmu\)m and a specific surface of up to over 500 m\({}^{2}\)/g. Therefore, they can be used in 3D cell culture tissue engineering, peptide synthesis, production of hydrogen carriers as well as modern catalysts and reagents designing [22].  
Figure 1: Synthesis of ST-DVB ion exchanger.
# Software-Defined Networking: Categories, Analysis, and Future Directions
## 7 SDN Programming Languages
These languages help network administrators to implement access control, and to develop and test network applications on the basis of low-level constructs as well as high-level abstractions. However, there is still a wide scope for researchers to offer new abstractions and also contribute to the advance of NBI standardization. The future programming languages may include functionalities of load balancing to avoid congestion on specific resources. Researchers may be interested in using language constructs to scale-up the resources in an elastic way. Moreover, future programming languages should incorporate NFV to manage the virtualized functions and OpenFlow updates to take advantage of the new features offered by OFP. Network forensics are quite useful to collect information on network devices to verify the evidence of crimes. However, this important area is also lacking in research with regard to SDN programming languages. Finally, the current programming languages still do not provide an open interface to allow new modules to be developed and incorporated into the language, which is also an interesting future research area. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Studies** & **Programming Paradigm** & **Description** & **Strengths** & **Weaknesses** \\ \hline \multirow{2}{*}{Frenetic [183; 184]} & Declarative (functional) & Useful for the categorization and accretion of network traffic & Facilitates modular reasoning & Lack in flow matching and monitoring services \\ \hline \multirow{3}{*}{NetCore [187]} & Declarative (functional) & It is the successor of Frenetic and carries an enhanced policy management library & Effective handling of controller and switch interaction & Does not support flow matching and monitoring services \\ \hline \multirow{3}{*}{Nettle [188]} & Declarative (functional, reactive) & Low-level programming language that deals with streams and not with events & Supports dynamic policies, traffic engineering, and load balancing & Does not consider event-driven approach \\ \hline \multirow{2}{*}{Procera [189]} & Declarative (functional, reactive) & Helps in portraying reactive and temporal behaviors & Good for reactive applications and protocols & Does not support basic flow matching and monitoring \\ \hline \multirow{2}{*}{FML [191]} & Declarative (data flow, reactive) & High-level language based on non-recursive Datalog [151] & Provides efficient and flexible policies & Limited QoS and monitoring services \\ \hline \multirow{3}{*}{Flog [193]} & Declarative (logic), event-driven & Event-driven programming that adopted ideas from FML and Frenetic & Supports basic flow matching and monitoring capabilities & Limited security and traffic engineering capabilities \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of SDN Programming Languages.
# Diamonds for Life: Developments in Sensors for Biomolecules
## 4 Diamond-Based Electrodes
### Histamine Detection  
The electrochemical response of a polycrystalline, BDD thin-film electrode to histamine in aqueous media at a pH of 7.2 was investigated with cyclic voltammetry and hydrodynamic voltammetry [47]. The cyclic voltammograms of histamine were well defined, peaking at 1.40 V, having a detection limit of 1 \(\upmu\)M and a linear response range for histamine concentrations between 0.5 and 100 \(\upmu\)M.
# A Novel Perceptual Hash Algorithm for Multispectral Image Authentication
## 4 Experiments and Discussion
### Perceptual Hash Values Generation  
There are several parameters in the perceptual hash algorithm, and we describe the parameter settings used in our experimental results in the following. Referring to the existing research [17; 18], we set the damping factor \(\lambda\) as 0.5 and the maximum number of iterations as 100 during the band-clustering. The clustering process need not set the clustering number. During the grid division process, the size of non-overlapping grids is 128 \(\times\) 128 pixels.  
We opted for the Landsat TM image as an example to validate robustness performances and tamper sensitivity. Figures 6 and 7 present several bands of the typical TM images to show the comparison between the bands of the same area, in which the content of the same geographical location in each band is quite different. The details of the mutual information between the bands of the above two images are given in Tables 1 and 2.  
The results of band clustering for each TM image are the same as below: \(IC_{1}=\{I^{1},I^{2},I^{3}\}\), \(IC_{3}=\{I^{6}\}\), \(IC_{4}=\{I^{5},I^{7}\}\). Thus, \(\text{In}R_{1}\), \(\text{In}R_{2}\) and \(\text{In}R_{3}\) would be divided into a band group and be generated as the perceptual hash value. Similarly, \(\text{In}R_{5}\) and \(\text{In}R_{7}\) would be generated as the perceptual hash value as a group.
# Regression of Likelihood Probability for Time-Varying MIMO Systems with One-Bit ADCs
## 4 Proposed Solution
### Regularization  
In this subsection, the effective regression technique is discussed under the slow-fading channel. The linear regression in (19) provides a reasonable solution for approximating the true probability. However, under the assumption of a slow-fading channel, the probability changes slowly. Regularization techniques such as ridge and LASSO regressions are effective in preventing abrupt changes. In this study, LASSO regression is employed because it not only prevents sudden changes but also eliminates irrelevant parameters. This elimination improves memory utilization by reducing the number of weights and by simplifying the computation in (22). To achieve this, LASSO regression is formulated as follows:  
\[\mathbf{\Theta}_{d}^{\star}=\underset{\mathbf{\Theta}_{d}}{\text{argmin}}\sum_{n\in \mathcal{N}_{d}}\sum_{r\in\mathcal{R}}\sum_{k\in\mathcal{K}}\|p_{r,k}[n]-\hat{ p}_{r,k}[n](\mathbf{\Theta}_{d})\|^{2}+\lambda\|\mathbf{\Theta}_{d}\|, \tag{23}\]  
where \(\lambda\) is a tuning value that controls the trade-off between fit and sparsity in the regression coefficient estimates. In particular, LASSO regression exploits the \(L_{1}\) norm, which encourages certain parameters to be exactly zero, promoting sparsity in the solution. This allows significant parameters to be identified and extracted using a regularization technique. Although solving the LASSO regression is more complicated than solving the linear regression, the proposed method utilizes the pre-computed optimal parameters. By eliminating insignificant parameters, this approach helps efficiently calculate the proposed likelihood probability in (22) during transmission.
## 3 Results and Discussion
### Discussion of Applicability of Digital Image Photogrammetry  
This section discusses the applicability of digital image photogrammetry (using digital cameras and cellphone cameras) for studying the displacement measurement of MSE walls in restricted and controlled areas. The discussion on applicability is presented by analyzing the error rate. The error rate is defined as the percentage of each digital image photogrammetry error with respect to the value assuming the reliability of the measurement results of the traditional monitoring method to be 1.  
First, the error rate was evaluated through field experiment results conducted for about 7 months using the traditional monitoring method and digital camera-based digital image photogrammetry. The error rate of the digital camera was found to be the largest when the elapsed time was 174 days. In particular, as shown in Figure 14a, the maximum error rate was 655% in STA.80_4, and the error rates of STA.85_4 and STA.85_5 were 433% and 516%, respectively.  
Through a field experiment conducted for 4 months, the digital image photogrammetry error rate using a digital camera and a cellphone camera was evaluated and compared to the traditional monitoring method. As shown in Figure 14b, the error rate of the digital camera was the largest at 405% in STA.80_5, when the elapsed time was 120 days. The error of the digital camera occurred at a specific elapsed time and location, and it showed a similar error occurrence trend to the results of the experiment conducted for 7 months. Figure 14c shows the maximum error rate of the cellphone camera, and the maximum error rate was 367%, which was similar to that of the digital camera. However, the error rate of the cellphone camera showed a very irregular error occurrence trend regardless of the experimental period and location.  
If the error rate trend is similar, it is easy to identify the error occurrence variable and reduce it. However, the cause of the error is very complicated if judging the error trend is  
Figure 13: Difference in displacement between total station and digital camera and cellphone camera results: (**a**) total station–digital camera; (**b**) total station–cellphone camera.  
difficult, such as the error trend of the cellphone camera. In this study, the measurement error of the cellphone camera was analyzed as a result of the combined effects of low pixels and image distortion on the location's information. Therefore, it was found to be very unsuitable for evaluating the applicability of MSE wall displacement monitoring using cellphone camera-based digital image photogrammetry. In other words, it was evaluated that optical research is necessary to solve this.
## 3 Results and Discussion
### Box-Behnken Experiment Design and Significance Test
#### 3.1.2 Significance Test  
The standard quadratic regression equation (Equation (1)) was analyzed for variance, and the results are shown in Table 7. The model was tested for significance using ANOVA. The significance level was set at 0.05, i.e., when the \(p\)-value was less than 0.05, the indicator was considered significant; when the \(p\)-value was greater than 0.05, the indicator was considered insignificant. Table 7 shows that the \(p\)-value of the quadratic regression model for compressive strength is less than 0.05, and the multivariate correlation coefficient \(\text{R}^{2}\) is 0.85. This indicates that the regression equation approximates the true surface well, and the model can accurately predict the compressive strength of the manufactured sand concrete.  
As shown in Table 7, the squares for SP, PFA, and SF were 6.12, 3.12, and 0.50, respectively, which shows a significant effect on the compressive strength of the single factors: SP has the greatest effect on the compressive strength of the concrete, PFA has the second greatest effect, and SF has the least effect. Similarly for the interaction, SP and PFA had the most significant effect on the compressive strength of the manufactured sand concrete, and the interaction between SP and SF and PFA and SF had the same effect on the compressive strength.
## 3 Governing Equations  
Based on the wave propagation theory of a continuum in the axisymmetric condition, the governing equation for the \(j\)th disturbed sub-layer proposed by [PERSON] and [PERSON] [34] is adopted:  
\[(\lambda_{j}^{s}+2C_{j}^{s})\frac{\partial^{2}}{\partial z^{2}}u_{j}^{s}(r,z,t)+ C_{j}^{s}(\frac{1}{\beta}\frac{\partial}{\partial x}+\frac{\partial^{2}}{ \partial z^{2}})u_{j}^{s}(r,z,t)+c_{j}^{s}\frac{\partial}{\partial z}\Big{[} \Big{(}\frac{\partial^{2}}{\partial z^{2}}+\frac{1}{r}\frac{\partial}{\partial x }+\frac{\partial^{2}}{\partial z^{2}}\Big{)}u_{j}^{s}(r,z,t)\Big{]}=\rho_{j}^{s} \frac{\partial^{2}}{\partial z^{2}}u_{j}^{s}(r,z,t) \tag{2}\]  
where \(u_{j}^{s}(r,z,t)\) is the longitudinal displacement of the \(j\)th disturbed sub-layer.  
The longitudinal shear stress at the interface (i.e., \(r=r_{1}\)) between the first annular sub-layer and the pile shaft is expressed by  
\[\tau_{1}^{s}(r_{1},z,t)=G_{1}^{s}\frac{\partial u_{1}^{s}(r_{1},z,t)}{\partial r }+c_{1}^{s}\frac{\partial^{2}u_{1}^{s}(r_{1},z,t)}{\partial t\partial r}. \tag{3}\]  
According to the Rayleigh-Love rod model theory [31], the governing equation for the longitudinal vibration of a large-diameter pile shaft can be written as  
\[E^{\mathrm{P}}A^{\mathrm{P}}\frac{\partial^{2}u^{\mathrm{P}}(z,t)}{\partial z ^{2}}-m^{\mathrm{P}}\bigg{(}\frac{\partial^{2}u^{\mathrm{P}}(z,t)}{\partial t ^{2}}+(v^{\mathrm{P}}r_{1})^{2}\frac{\partial^{4}u^{\mathrm{P}}(z,t)}{ \partial z^{2}\partial t^{2}}\bigg{)}-2\pi r_{1}f^{s}(z,t)=0 \tag{4}\]  
where \(f^{s}(z,t)=\tau_{1}^{s}(r,z,t)\big{|}_{r=r_{1}}\), \(m^{\mathrm{P}}=\rho^{\mathrm{P}}A^{\mathrm{P}}\), \(A^{\mathrm{P}}=\pi r_{1}^{2}\).
# Routing Schemes in FANETs: A Survey
## 5 Routing Schemes in FANETs
### Proactive  
This section presents three proactive routing schemes for FANETs.