esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Fiber Surface Charge Estimation via Determination of PZC  
Point-of-zero-charge (PZC) analysis reflects the surface charge of the raw and treated samples and the differences between them. The results in Figure 8 revealed that the samples had a low PZC value (\(\sim\)5.0-5.5). The PZC values for the raw (5.5), modified Fenton (5.0), and alkali-modified Fenton (5.5) were estimated. Flax fiber generally carries a negative surface charge in water because it contains ionizable acidic substances like lignins. As expected, oxidation of flax fibers resulted in a reduction of fiber surface charge, according to the shift in PZC values [39; 40]. This phenomenon can be related to enhancement of the fiber's surface area and increased exposure of oxygen-rich groups, which leads to a more negative surface charge versus the untreated flax fibers that possess a more intact fibril structure. However, the slight positive shift in the surface charge in the F-T15-ALK sample can be attributed to the presence of adsorbed sodium ions on the surface of the fibers.  
Figure 8: PZC of raw and treated flax fiber samples, via the \(\Delta\)pH of the starting and final solutions after 48 h. The reader is referred to Table 2 for definitions of the sample ID codes used in this figure.  
Figure 7: Solid-state \({}^{13}\)C spectra of fiber samples: raw flax fiber before treatment, modified-Fenton-treated sample (F-T15), and modified Fenton and alkali-treated sample (F-T15-ALK).
## 4 Experimental Results and Analysis
### Assessment of Multi-Rx and Multi-Tx LEO-LEO Occultation Constellation Performance  
#### 4.3.1 Orbital Parameter Setup for Multi-Satellite Experimental Constellations  
The analysis of impacts of orbital parameters on the LEO-LEO occultation performance showed that NPO constellations with the same Rx and Tx inclinations are good for observing the entire Earth with a great number of occultation events and global coverage. NEO constellations with supplementary Rx and Tx inclinations are good for observing middle and low latitude zones with a high number of occultation events and long revisiting times. Therefore, the observation efficiency of LEO-LEO occultation constellations was investigated by using these two types of orbits.  
In this study, the two-orbit constellations are composed of one Rx orbit and one Tx orbit and have certain numbers of Rx and Tx satellites running in these prescribed orbits. Table 3 summarizes all cases using a concise notation. The types of constellations are denoted Rxi98-Txi98, Rxi140-Txi40, or something similar, with 2 Rx/2 Tx, 6 Rx/6 Tx, and 12 Rx/12 Tx specifying the numbers of satellites in these orbits. For example, Rxi98-Txi98 2 Rx/2 Tx denotes one such constellation in the first line of Table 3, where the choice of M0 for each of the two Tx/2 Rx satellites is also shown. Likewise, the multiple-orbit constellations are composed of certain numbers of Rxi98-Txi98 two-orbit constellations, e.g., Rxi98-Txi98 2 Rx/2 Txorbit denotes two such orbit pairs (distinguished by their respective RAANs) as shown in the seventh line of Table 3.  
For the specific orbital parameters, the orbital heights of the Rx and Tx satellites are fixed at 500 km and 600 km, respectively, AP is fixed at 80\({}^{\circ}\), and e is fixed at 0.0001. Different LEO-LEO occultation constellations were designed by setting different values of the I, M0, and RAAN parameters. Six two-orbit and three multiple-orbit constellations were designed and evaluated, and their main orbital parameters are shown in Table 3.  
In order to quantitatively analyze the observation efficiency of the multi-Rx and multi-Tx occultation constellations, the orbital parameters of the nine constellations shown in Table 3 were used to carry out a numerical experiment, in which 10-day occultation events were simulated, and the 1-day, 3-day, and 9-day global coverage fractions of occultation events were calculated.
## 3 Results and Discussions  
### Morphology of Fillers  
In order to enhance the thermal conductivity of the SSPCM, a heat transfer network constructed by fillers is required. This demands a good dispersion of fillers in the SSPCM. EG particles have a worm-like structure with nano-platelets adhered to each other (Figure 1a), with size of hundreds of microns. Due to the high surface energy of MWCNTs, they tend to entangle together (Figure 1c), thus strong shearing is required to disentangle or exfoliate them and realize good dispersion. When fillers are melt-blended with low viscosity SSPCM, the shear force is relatively small, so it is difficult to overcome the interaction between fillers just by melt blending, even at high mixing speeds, and the fillers cannot be dispersed evenly in the SSPCM matrix. Therefore, the pretreatment of fillers is necessary to weaken their interaction and lessen the aggregation.  
Figure 1 shows the morphologies of two fillers and their hybrid after pretreatment by shear and sonication. As shown in Figure 1a, expandable graphite was treated at 900 \({}^{\circ}\)C for 30 s to obtain expanded graphite (EG) with a worm-like structure, and its volume was expanded hundreds of times. After high-speed shear and sonication, the multi-layer in the worm-like structure was exfoliated to form EGP, as show in Figure 1b. In Figure 1c, primary MWCNT was severely aggregated. After high-speed shear and sonication, MWCNTs became even and loose, as shown in Figure 1d. Figure 1e-j are micrographs of hybrid fillers with different EGP/MWCNT ratios. MWCNTs were adhered on the surface of EGPs. This special bridging structure would benefit the heat transfer between EGPs and MWCNTs and decrease the interface thermal resistance. However, with the increase of MWCNT content in the EGP\({}_{\mathrm{x}}\)-MWCNT\({}_{\mathrm{10-x}}\) hybrid filler, more MWCNTs were aggregated, rather than adhered on EGPs. For example, in EGP\({}_{\mathrm{8}}\)-MWCNT\({}_{\mathrm{2}}\), we can observe more MWCNTs on EGPs than that in EGP\({}_{\mathrm{3}}\)-MWCNT\({}_{\mathrm{7}}\).
## Appendix A Optimization Method
### Electrode Filtering Characteristic  
CPW electrodes acts as a low-pass filter on the RF data given to the electrode. Based on RF response obtained in the simulation, filter coefficients are generated for the CPW electrodes, and the spectrum is calculated. Figure 12a shows the power spectrum of the filter. For comparison, the power spectrum of an ideal filter with bandwidth 159 GHz is also shown in the figure. Generated bit stream was passed through the designed electrode,and the output stream is shown in Figure 14b. The effects of filtering are clearly visible on the bit stream in the figure.
# A Comprehensive Survey on Enabling Techniques in Secure and Resilient Smart Grids
## 4 Security and Resilient Architecture
### Power System State Estimation (PSSE)
#### 4.2.1 Observability Analysis  
Observability analysis is an important pre-process before the measurement data enter the state estimation stage. The observability analysis helps to identify the minimum number of sensors that are required to accurately estimate the state variables in the power system.  
For SSE, the observability analysis is usually based on topological or numerical analysis models, such as checking the rank of the measurement Jacobian matrix, to determine whether the system is observable or not, which is a binary outcome [49; 50].  
For DSE, different from SSE observability's binary outcome, it usually can be classified into strong or weak observable systems. The linear DSE observability is determined by the observability matrix's full rank state; and, for non-linear DSE observability, the system can analyse the local observability and perform a linearisation analysis [51]. There are two feasible approaches for observability analysis: (1) use small signal approximation, which uses the first order linear approximations to analyse the system observability, and (2) use Lie derivation to build an observability matrix [49].  
For DSSE, because of lacking real-time measurements, the observability analysis highly relies on pseudo measurements. The real-time measurements' improvement, optimal placement, and resiliency against cyber-physical attacks are the study focus recently [52].
# New Semi-Prime Factorization and Application in Large RSA Key Attacks
## 4 Proposed Method for Factoring Semi-Primes
### Algorithm of Our Proposed Semi-Prime Factorization  
Cryptographic algorithms make use of standard integer factorization algorithms found in the literature, such as [PERSON]'s factoring algorithm, [PERSON]'s elliptic curve factorization algorithm, and others with different kinds of number sieves [58,59]. For our algorithm to factor semi-primes, we capitalize on the properties of semi-prime representations as the sum of three squares, which is supported by well-proven theorems [60]. Based on the theoretical background postulated in this work, our algorithm consists of four key steps, as given below:  
Step 1. Square the semi-prime to be factored: \(N^{2}\).  
Step 2. Find sums of three squares such that:  
\[\left(N^{2}-x^{2}\right)=prime\equiv 1\ mod\ 4=\beta^{2}+a^{2}\]  
// Increment \(x\) until a prime is found congruent to 1 mod 4 [61]  
{x+  
\(+x^{2}\)  
\(N^{2}-x^{2}\) }  
Do primality test, 1 mod 4 test, sum of two squares]  
Step 3. Save \(\{x,\ a,\ b\}\) such that \((N,x,a,b),\beta:\ \beta\in\{x,a,b\}\)  
Test \(\beta\) to find \(\alpha\) such that \(a^{2}=N^{2}-\beta^{2}\)  
Step 3.1 Test \(\{x,\ a,\ b\}\) to find \(\{\alpha,\beta\}\), \(x^{2},a^{2}\), \(b^{2};N^{2}-x^{2}\), \(N^{2}-a^{2}\), \(N^{2}-b^{2}\)  
Step 3.2 Test for perfect square // use a square root function)  
Step 4. Calculate and output the semi-prime factors \(c_{1}\) and \(c_{2}\) such that \(N=c_{1}c_{2}\)  
Step 4.1 \(c_{1}=\gcd(\alpha,\beta)\) // use gcd function  
Step 4.2 \(c_{2}=\frac{N}{c_{1}}\)
# An Optical and SAR Based Fusion Approach for Mapping Surface Water Dynamics over Mainland China
## 4 Results
### Classification Accuracy
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & \multicolumn{3}{c}{**Land**} & \multicolumn{3}{c}{**Water**} & \multicolumn{3}{c}{**Samples**} \\ \cline{2-9}  & **OA** & **UA** & **PA** & **UA** & **PA** & **Water** & **Land** & **Total** \\ \hline Seasonal Strata & 87\% & 88\% & 90\% & 86\% & 83\% & 1785 & 2568 & 4353 \\ \hline All data & 94\% & 94\% & 97\% & 94\% & 89\% & 5082 & 8631 & 13713 \\ \hline \end{tabular}
\end{table} 
Table 2: The summarized overall classification accuracy, displayed as a heat map, across all sites and strata (cf. all data) and for the seasonal strata separately. OA = Overall accuracy, UA = User’s accuracy, PA = Producer’s accuracy.
# New Hybrid Techniques for Business Recommender Systems
## 2 Related Work
### Hybrid Recommenders  
Forming hybrid recommenders [40, 41] is an active field of research since combinations of different approaches can often help to combine the strengths and/or avoid the weaknesses of the combined approaches. For instance, content-based filtering can be combined with collaborative filtering, e.g., to mitigate the so-called cold-start problems associated with collaborative filtering, i.e., problems with recommending newly introduced items or serving new users: new items can be recommended immediately by content-based techniques as long as they have a meaningful description that can be matched against user profiles. Besides cold start problems, hybridisation can be used, e.g., to augment similarity in collaborative filtering with the reasons behind user preferences and thus give it a stronger CBR flavour [42]. Another motivation for using hybridisation is to improve the quality of recommendations. For example, [PERSON] et al. [43] combine CBR recommendations with multi-agent systems to improve the accuracy of recommendations. Further possible complementary strengths and weaknesses of knowledge-based and knowledge-weak recommenders are discussed in [44].  
In order to effectively combine the strengths of individual recommendation techniques, [PERSON] [40] has proposed seven different hybrid strategies: weighted, mixed, switching, feature combination, cascade, feature augmentation and meta-level. These strategies are still being successfully applied to address various problems in recommender systems. For instance, [PERSON] et al. [45] have used the cascade strategy to improve the _novelty_ and _diversity_ of recommendations; [PERSON] et al. [46] have applied the switching strategy to address the problem of _long tail recommendations_; [PERSON] et al. [47] have combined algorithms in a cascading fashion to improve the _personalization_ of recommendations; and [PERSON] et al. [48] have implemented a meta-level hybrid recommender to explore metrics such as _coherence_ and _diversity_ in music recommendations.  
In the context of the problem of this research, we explore hybridisation to improve the _ranking accuracy_ of the recommendations by first evaluating different recommender techniques individually and then identifying an appropriate strategy to combine them.  
Overall, there is a rather large number of suggestions for enriching recommenders with contextual knowledge. However, as outlined in Section 1.2, we see a gap in exploring which of these suggestions is best suited to support scenarios of business consultancy. We furthermore see a need to gain a deeper understanding of the (complementary) strengths and weaknesses of the mentioned approaches that will lead to successful hybridisation strategies.
## 4 Experimental Results and Analysis
### Error Analysis and Performance of AOD Fusion
According to the comparisons of monthly averaged AOD among the ground-based AERONET AOD measurements, original and merged satellite-based AOD observations centered on two AERONET stations in Beijing (listed in Supplementary Table S5), the GL-BME fusion algorithm significantly increases the valid AOD observation days over Beijing in these months. Moreover, the ground-measured PM\({}_{10}\) concentrations in Beijing are adopted to supplementarily explain the reasonability of these recovered relatively high AOD values in the merged AOD dataset by comparing the variation trends of the monthly averaged satellite-based AODs and PM\({}_{10}\) throughout the year (seen in Supplementary Figure S1). It is demonstrated that the variation trends of monthly averaged merged AOD and monthly averaged PM\({}_{10}\) concentrations present a generally good consistency in Beijing during the experimental period. At the same time, the monthly averaged original AHI AOD demonstrates an obvious underestimation of the severity of atmospheric pollution in these months. Therefore, it is proven that the merged AOD after the GL-BME fusion could better characterize the real atmospheric pollution situation than the original AHI AOD product.
## 2 Materials and Methods
### Fujita\(-\)Takahashi Model  
The commonly used typhoon field models in the world are Fujita\(-\)Takahashi model and Holland model. For the parameterized wind field, the pressure formula is usually substituted into the cascade wind equation to obtain the wind speed field. In this paper, Fujita\(-\)Takahashi model was selected.  
Formula of pressure field:  
\[P(r)=\left\{\begin{array}{c}P_{\infty}-\frac{P_{\infty}-P_{c}}{\sqrt{1+2(r/R) ^{2}}}0\leq r\leq 2R\\ P_{\infty}-\frac{P_{\infty}-P_{c}}{1+r/R}\quad r>2R\end{array}\right. \tag{6}\]  
in which \(P_{\infty}\) is the peripheral air pressure, \(P_{c}\) is the pressure of the typhoon center, \(P_{n}\) is the peripheral pressure of typhoon, and \(R\) is the maximum wind speed radius.  
Formula of the gradient wind:  
\[\left\{\begin{array}{c}V_{g}=\left[\frac{\ell_{s}^{2}r^{2}}{4}+\frac{2r^{2}} {\rho_{a}R^{2}}(P_{n}-P_{c})\Big{(}1+2\frac{r^{2}}{R^{2}}\Big{)}^{-\frac{3}{2} }\right]^{\frac{1}{2}}-\frac{f_{r}}{2}\ 0<r\leq 2R\\ V_{g}=\left[\frac{\ell_{s}^{2}r^{2}}{4}+\frac{r}{\rho_{a}}(P_{n}-P_{c})\frac{ 1}{(1+r/R)^{2}R}\right]^{\frac{1}{2}}-\frac{f_{r}}{2}\ 2R<r<\infty\end{array}\right. \tag{7}\]  
where \(P(r)\) is the pressure value of the sea surface \(r\) from the typhoon center; \(B\) is typhoon shape parameter; \(V_{g}\) (\(r\)) is the tangential wind speed with distance \(r\) from the typhoon center; \(\rho_{a}\) is the air density; \(f\) is Coriolis parameter.  
Composite wind velocity:  
\[\overset{\rightarrow}{W}_{m}=exp\bigg{(}-\frac{\pi}{4}\frac{|r-R|}{R}\bigg{)} \bigg{(}v_{x}\overset{\rightarrow}{i}+v_{y}\overset{\rightarrow}{j}\bigg{)} \tag{8}\]
# Optimized Architecture for Efficient OFDMA Network Design
## 3 Analytical Model for OFDMA Networks
### Scheduling Policies
#### 3.3.1 Conventional Scheduling Policies
Consequently, to ensure fairness in throughput among active users, not in failure, \(x_{k}(n_{0}\),...,\(n_{K})\) must satisfy the following condition: \[m_{k}x_{k}(n_{0},...,n_{K})\ =\ C\ \text{ for }\ k\ \
eq\ 0\] (7) where \(C\) is a constant such that \(\sum\
olimits_{k=1}^{K}n_{k}x_{k}(n_{0},\ldots,n_{k})=1\), so: \[C=\frac{1}{\sum\
olimits_{k=1}^{K}\frac{n_{k}}{m_{k}}}\] (8)By substituting these proportions into the generic expression (3), the average number of bits per slot \(\overline{m}(n)\), when there are n active users, becomes \[\overline{m}(n)\!=\!\sum\
olimits_{(n_{0},\ldots,n_{K})}^{(n_{0},\ldots,n_{K})} =(0,\ldots,0)\,\frac{(n|\!-\!|n_{0})n!\prod_{k=0}^{K}\frac{p_{k}^{n_{k}}}{n_{k}!} }{\sum_{k=1}^{K}\frac{n_{k}}{m_{k}}}\] (9) \[ \begin{array}{c}n_{0}+\ldots+n_{K}=n\\ n_{0}\
eq n\end{array} \]
3. Opportunistic Scheduling: In the case of opportunistic scheduling, all resources are allocated to the users with the highest transmission rate, i.e., those with the best channel conditions, represented by the highest MCS. Assume that the MCS levels are ordered from lowest to highest: \(m_{0}<\ldots<m_{K}\). Consider a system with n active users. Let \(\alpha_{i}(n)\) represent the probability that at least one active user is using MCSi and no other user is using a higher MCSi for \(j>i\). This can be interpreted as the probability that the scheduler allocates all resources to users operating with MCSi. The average number of bits per slot when there are n active users is expressed as \[\overline{m}=\sum_{i=1}^{k}\alpha_{i}(n)m_{i}\] (10) To calculate \(\alpha_{i}(n)\), first define \(p\leq i(n)\), the probability that none of the users are using an MCS higher than MCSi: \[p_{\leq i}(n)=\left(1-\sum_{j=i+1}^{k}p_{j}\right)^{n}\] (11) Next, calculate \(p_{=i}(n)\), the probability that at least one user is using MCSi given that no other user is using a higher MCS: \[p_{=i}(n)=1-\left(1-\frac{p_{i}}{\sum_{j=0}^{k}p_{j}}\right)^{n}\] (12) Thus, \(\alpha_{i}(n)\), the probability that the scheduler allocates all resources to users operating with MCSi, is given by \[\alpha_{i}(n)=p_{=i}(n)p_{\leq i}(n)\] (13)
4. Performance Parameters: There are three key performance parameters that can be derived from the model. These are: \(\bullet\) Average resource utilization (\(U\)) during a TDD frame, \(\bullet\) Average number of active users (\(Q\)), and \(\bullet\) Average instantaneous throughput (\(X\)) of active users during an ON period. To calculate these parameters, the stationary probabilities of the CTMC \(\pi(n)\) are required. \(\pi(n)\) represents the probability that n mobile stations are active at any given moment.
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 3 System Architecture for Autonomous Vehicles
This processed information is fused and passed to the decision and planning stage. 2. Decision and Planning: Utilising the data gathered in the perception process, this stage decides, plans and controls the motion and behaviour of the AV. This stage is analogous to the brain and makes decision such as path planning, action prediction, obstacle avoidance, etc. The decision is made based on the current as well as past information available including real-time map information, traffic details and patterns, information by the user, etc. There may be a data log module that records errors and information for future reference. 3. Control: The control module receives information from the decision and planning module and performs functions/actions related to physical control of the AV such as steering, braking, accelerating etc. 4. Chassis: The final stage includes the interface with the mechanical components mounted on the chassis such as the accelerator pedal motor, brake pedal motor, steering wheel motor and gear motor. All these components are signalled to and controlled by the control module. After discussing the overall communication and sensor architecture of an AV, we discuss the design, functionality and utilisation of some main sensors.
# Biodegradation of Sulfamethoxazole in Milkfish (_Chanos chanos_) Pond Sediments
## 2 Materials and Methods
### Sediment Sampling and Sampling Site  
In July 2016, sediment samples were collected from a farm-scale milkfish pond at the Mariculture Research Center, Fisheries Research Institute, Tainan, Taiwan. The latitude and longitude of the sampling site is 23\({}^{\circ}\)07\({}^{\prime}\)20.3\({}^{\prime}\) N 120\({}^{\circ}\)04\({}^{\prime}\)47.8\({}^{\prime}\) E. Samples were collected randomly, in triplicate, from an area of about 1 m\({}^{2}\). The physicochemical parameters of the mixture of milkfish pond sediment samples were pH (8.5 \(\pm\) 0.3), salinity (32 \(\pm\) 2 %), total nitrogen (811 mg kg\({}^{-1}\)), total phosphate (196 mg kg\({}^{-1}\)), organic matter (0.97%), sand (59.4%), silt (25.1%), and clay (15.5%). The three target sulfonamides, SMX, SMZ, and SDM were not detected in the sediment samples of milkfish ponds. The processes of adaptation was conducted by adding 2 mg L\({}^{-1}\) to 500 g of sediment and incubation under 25 \({}^{\circ}\)C in the dark for 3 months.
## 3 Results
### Particle Size Distribution  
To quantify the degree of particle breakage, samples from before and after testing were initially evaluated with a laser diffraction particle size analyser (Malvern Mastersizer 3000) to determine the particle size distribution (PSD). It was found that the small sample size (approximately 5 g) required for the laser diffraction analysis was very sensitive to small quantities of dust and soot in the post-blast test samples, and therefore, a sieve analysis was conducted using larger samples (>100 g) to rule out any sampling errors. The results of the PSD analysis are presented in Figure 5, which shows that there is a larger degree of particle breakage in the quartz-based sand than the carbonate sand. This is further apparent when comparing the \(D_{10}\), \(D_{50}\), \(C_{c}\), and \(B_{t}\) factors (summarised in Table 3), which are the sizes above which 10% and 50% of the measured diameters fall, and the total breakage factor defined by [PERSON] [18], respectively. The total breakage factor (\(B_{t}\)) represents the change in the integral of the particle size distribution curve and measures the degree of overall particle breakage in the sample:  
\[B_{t}=\int_{0}^{1}(b_{po}-b_{pl})df \tag{2}\]  
in which \(b_{po}\) = the original values of \(b_{p}\), and \(b_{pl}\) = the values of \(b_{p}\) after loading. \(b_{p}\) is the potential for breakage of a particle of a given size, \(D\), and may be represented by  
\[b_{p}=\log_{10}\left[\frac{D\text{ in mm}}{0.074\text{ mm}}\right]\text{ for }D\geq 0.074\text{ mm}\quad\quad\&\quad b_{p}=0\text{ for }D<0.074\text{ mm} \tag{3}\]  
The quartz sand samples consistently show a higher degree of breakage than the carbonate sand; however, the overall change is still small (e.g., a 3.3% reduction in the median particle size and breakage factor of 7.4% for quartz sand). Figure 5 and Table 3 show that there is no substantial increase in particle breakage for the carbonate sand; in fact, the results show a lower degree of breakage than that in the quartz sand contrary to previous findings [15]. The lack of substantial difference in breakage of the carbonate sand would therefore predict that the loading follows the trend outlined in Figure 3 and Equation (1).  
Figure 4: Pressure time history for five bars in a single test (other traces excluded for clarity).