esa-sceva/satcom-chunk-collection huggingface dataset:
## Nomenclature  
\begin{tabular}{l l} \(C\) & Concentration, mg/L \\ \(C_{e}\) & Adsorbate concentration in the inlet stream, mg/L \\ \(D_{ax}\) & Axial diffusion coefficient, cm\({}^{2}\)/min \\ \(D_{S}\) & Surface diffusion coefficient, cm\({}^{2}\)/min \\ \(F\) & Volumetric flow of the effluent, mL/min \\ \(k_{L}\) & Langmuir constant, L/mg \\ \(k_{S}\) & Intra-particular mass transfer coefficient, min\({}^{-1}\) \\ \(K_{L}\) & Langmuir constant, dimensionless \\ \(K\)s & Intra-particular mass transfer coefficient, dimensionless \\ \(J_{ij}\) & Sensitivity coefficient, dimensionless \\ \(L\) & Bed size, cm \\ \(U\) & Interstitial velocity, cm/min \\ \(\overline{u}\) & Auxiliary random sample of a uniform distribution \\ \(Q\) & Amount adsorbed by the solid phase, mg/g \\ \(\overline{q}\) & Auxiliary probability density function \\ \(q_{max}\) & Maximum amount of adsorption, mg/g \\ \(q_{S}\) & Equilibrium amount/concentration on the surface, mg/g \\ \(q_{r}\) & Reference quantity, mg/g \\ \(Q\) & Amount adsorbed by the solid phase, dimensionless \\ \(Q^{\bullet}\) & Equilibrium amount/concentration on the surface, dimensionless \\ \(m_{a}\) & Mass of adsorbent, g \\ \(i\bar{n}_{S}\) & Surface diffusion flow, mg/(min.cm\({}^{2}\)) \\ \(\bar{n}_{F}\) & Diffusion flow in the film, mg/(min.cm\({}^{2}\)) \\ \(P\) & Parameter vector \\ \(P^{\bullet}\) & Vector of candidate parameters \\ \(P^{(i)}\) & Vector of parameters based on the current state of the chain \\ \(Pe\) & Peclet number, dimensionless \\ \(r_{p}\) & Radius of the adsorbent particle, cm \\ \(t\) & Time, min \\ \(t_{S}\) & Saturation time, min \\ \(t_{b}\) & Breakthrough time, min \\ \(t_{ref}\) & Reference time, min \\ \(Y\) & Measurement vector \\ \(z\) & Spatial coordinate, cm \\ w & Step size \\ Greek Letters & A & Probability of acceptance, dimensionless \\ \(\varepsilon_{B}\) & Bed porosity, dimensionless \\ \(\varepsilon\) & Disturbance in the parameter, dimensionless \\ \(\rho_{B}\) & Bed density, g/L \\ \(\pi\) & Probability distribution function \\ \(\tau\) & Time, dimensionless \\ \(\theta\) & Concentration, dimensionless \\ \(\eta\) & Spatial coordinate, dimensionless \\ Subscripts \\ \(i\) & Experimental point index \\ \(j\) & Parameter index \\ \(P_{j}\) & Reduced-sensitivity coefficient index \\ \(N_{par}\) & Number of parameters to be estimated \\ \end{tabular}
# Altitude Optimization and Task Allocation of UAV-Assisted MEC Communication System
## 5 Conclusions  
In this paper, we investigated a UAV-assisted MEC uplink maritime communication system in which the UAV is employed as an MEC server to provide computing services to the user. In addition, the user can offload tasks to BS for computation. Our objective is to minimize the total delay of the system by optimizing the flying altitude of UAV and the task allocation ratio. The numerical results verified the effectiveness of the theoretical analyses and reveal that the optimal flying altitude of the UAV is determined by the horizontal distance between the UAV and the user. We also showed the relationship between the optimal task allocation ratio and system parameters such as the transmit power, the bandwidth, and the CPU frequency.  
Writing--original draft preparation, methodology and software, S.H.; writing--review and editing, J.Z.; project administration and funding acquisition, Y.W. All authors have read and agreed to the published version of the manuscript.  
This researchwas funded by the National Natural Science Foundation of China, grant number U1805262, 61901117, 92067201, and 62071247. This research was also funded by the Special Funds of the Central Government Guiding Local Science and Technology Development (2021I.3010), by Key provincial scientific and technological innovation projects (2021G02006), by Natural Science Foundation of Fujian Province, China.(Grant No.: 2022J01169, 2020J01157, 2018J01569), by Jiangsu Provincial Key Research and Development Program under Grant BE2020084-4.  
Not applicable.  
Data Availability StatementData available on request.  
The authors declare no conflict of interest.  
Figure 9: Minimum system delay, optimal flying altitude, and optimal task allocation ratio versus the CPU frequency allocated for computation on the BS. (**a**) Minimum system delay, (**b**) optimal flying altitude and optimal task allocation ratio.
## 3 Results and Discussion
### Individual and Simultaneous Determination of DA and UA
The analytical performances were compared to those in previous works (Table 1). Obviously, the sensing parameters of the proposed sensor are comparable to, or even better than, previously reported modified electrodes [7; 46; 47; 48; 49; 50; 51; 52; 53; 54]. Figure 8: Linear scanning voltammetry (LSVs) at the MnO\({}_{2}\)NFs/NrGO/GCE in 0.1 M PBS (pH 3.93) containing 1 uM UA and various DA concentrations from 0.4 uM to 10 uM (**A**) and from 10 uM to 100 uM (**C**); Linear plots of the \(i_{\text{pa}}(\text{DA})\) versus the DA concentrations varying from 0.4 uM to 10 uM (**B**) and from 10 uM to 100 uM (**D**). Figure 10: **(A)** LSVs on the MnO\({}_{2}\)NFs/NrGO/CCE in 0.1 M PBS(pH3.93) containing various concentrations of DA and UA ranging from 0.2 uM to 6.0 uM **(A)** and from 6.0 uM to 100 uM **(C)**; **(B)** Plots of the anodic peak currents as the function of DA and UA concentrations in the range of 0.2 uM–6.0 uM (\(n=3\)); **(D)** Plots of the anodic peak currents as the function of DA and UA concentrations in the range of 6.0 uM–100 uM (\(n=3\)). Figure 9: **(A)** LSVs at the MnO\({}_{2}\)NFs/NrGO/CCE in 0.1 M phosphate buffered solution (PBS) (pH 3.93) containing 1 uM DA and various UA concentrations from 0.4 uM to 6.0 uM **(A)** and from 6.0 uM to 100 uM **(C)** (\(n=3\)); Linear plots of the \(i_{\text{pa}}\)(UA) versus the UA concentrations varying from 0.4 uM to 6.0 uM **(B)** and from 6.0 uM to 100 uM **(D)** (\(n=3\)).
# On the Benefits of Listen before Talk Scheme for NB-Fi Networks
## 7 Numerical Results
### Results for a Circle with Radius of 0.4 km
#### 7.1.3 Throughput  
Figure 6 shows the dependencies of throughput on load for six operating modes. We see that at low loads, the throughput plots for Aloha and CSMA modes almost coincide, which corresponds to the PLR results because almost all the packets are transmitted successfully through the channel and the throughput is equal to the load. At high loads, the difference in throughput is explained by the difference in PLR. A sensor in the Aloha mode loses most of its packets because of frequent collisions and consequently has the lowest throughput, while in the CSMA modes, listening to the channel makes transmissions more effective and results in a lower PLR and higher throughput. We model that sensors have a retry limit equal to 7, which means that the sensor discards its frame after making such a number of retries. However, increasing the retry limit does not change the PLR because the sensors very rarely reach it: with a much greater probability, they either transmit their frames or their frames are preempted by new frames. Similar to [12], among all the considered CSMA modes, the persistent CSMA achieves the lowest throughput, while  
the other kinds of CSMA achieve a comparable throughput. However, at very high loads (above 100 packets per second) and high data rates (3200 bps and 25,600 bps), we see that p-persistent CSMA with high \(p\) achieves the best performance, because at such loads, most packets are lost due to the buffer overflow, and delaying the frame transmission increases the PLR and decreases the throughput. Thus, we see that the buffering model provides results different from the classic results of CSMA.  
As a strategy to obtain the highest throughput, we suggest using the data rate of 3200 bps and non-persistent CSMA with frequency hopping for loads up to 200 packets/s. For higher loads, we recommend using p-persistent CSMA with properly configured \(p\).
## 3 Results and Discussion  
### PDMSB Synthesis  
Although anionic ROP of DMSB monomer in THF at low temperatures (-48 \({}^{\circ}\)C to -60 \({}^{\circ}\)C) (Figure 1) leads to a turbidity after approximately 10 to 15 min after addition of _n_-BuLi as initiator independently of the intended molar mass, high molar masses up to 60 kg mol\({}^{-1}\) could be obtained with low polydispersity values (PDI \(<\) 1.15). The results are summarized in Table 1.  
From Table 1, it becomes clear that molar masses obtained via conventional SEC with PS standards were slightly lower compared to the absolute molar mass values obtained by using SEC-MALLS. An exemplary monomodal, narrowly distributed SEC-MALLS trace for PDMSB with an average molar mass of approximately 32 kg mol\({}^{-1}\) is given in Figure 2. The anionic ROP of DMSB is quite fast, and living chain ends can be thought to be very reactive. However, after a longer reaction time (>1 h) under applied conditions, PDMSB samples showed a significant shoulder in SEC traces shifted to higher molar masses, which can be assumed to be caused by an undesired coupling process of living chain ends with the silane moieties.  
\begin{table}
\begin{tabular}{c c c c c} \hline \(\mathbf{M_{n}^{a}(g mol^{-1})}\) & \(\mathbf{M_{w}^{a}(g mol^{-1})}\) & \(\mathbf{PDI^{a}(g mol^{-1})}\) & \(\mathbf{M_{n}^{b}(g mol^{-1})}\) & \(\mathbf{M_{w}^{b}(g mol^{-1})}\) & \(\mathbf{PDI^{b}}\) \\ \hline
2 250 & 2 590 & 1.15 & 4 110 & 4 650 & 1.13 \\
5 200 & 5 620 & 1.08 & 7 220 & 8 190 & 1.13 \\
8 210 & 8 920 & 1.09 & 9 920 & 10 480 & 1.06 \\
28 680 & 32 900 & 1.15 & 31 450 & 32 610 & 1.04 \\
52 250 & 59 760 & 1.14 & 57 590 & 59 700 & 1.04 \\ \hline \end{tabular}  
* Molecular weight and PDI obtained by SEC measurements (PS standards, THF); \({}^{\mathrm{b}}\) molecular weight and PDI obtained by SEC-MALLS measurements; the refractive index increment dn/dc could be determinated to 0.0966 (THF, 30\({}^{\circ}\)C). THF: tetrahydrofuran; PDI: polydispersity index.  
\end{table}
Table 1: Size exclusion chromatography (SEC) with polystyrene (PS) standards _vs._ multi-angle laser light scattering size exclusion chromatography (SEC-MALLS) results of investigated PDMSB homopolymers.  
Figure 1: Anionic ring opening polymerization (ROP) of 1,1-dimethylsilacyclobutane (DMSB).
# Efficient Conjunctive Keywords Search over Encrypted E-Mail Data in Public Key Setting
## 6 Conclusions  
In this paper, we propose a secure and efficient SPE-CKS scheme for realizing multi-keywords search over the encrypted e-mail data. The rigorous theoretical analysis shows that our scheme has better performance on the time and space complexities than the previous SPE-CKS schemes. Furthermore, an experiment over a real world e-mail dataset illustrates that our scheme is practical in the mobile cloud setting. Considering that advanced search functions, for example, disjunctive,  
Figure 3: Impact of \(n\) on the space cost of \(pk\)**(a)**; \(sk\)**(b)**; index (c)** and trapdoor (d). (\(D=1000\), \(m=5\) and \(n=\{5;10;15;20;25\}\)).  
Boolean and fuzzy keywords search, are very useful for the encrypted e-mail system, we will construct a secure and efficient SPE scheme supporting various advanced search functions in the future work.  
Conceptualization, Y.Z. and Y.L.; Data curation, Y.Z. and Y.L.; Formal analysis, Y.Z. and Y.L.; Funding acquisition, Y.L.; Methodology, Y.Z.; Software, Y.Z. and Y.W.; Validation, Y.Z., Y.L. and Y.W.; Writing--original draft, Y.Z. and Y.L.; Writing--review & editing, Y.Z., Y.L. and Y.W.  
We gratefully acknowledge the support of the National Natural Science Foundation of China under Grant Nos. 61402393 and 61601396, and Nanhu Scholars Program for Young Scholars of XYNU.  
The authors declare that they have no conflict of interest.
# Two Hop Adaptive Vector Based Quality Forwarding for Void Hole Avoidance in Underwater WSNs
## 5 Performance Evaluation
### Energy Tax  
Energy tax tends to decrease with the increase in node number in all four schemes. This is due to the fact that the increase in node density increases the number of successful transmissions. In low node density regions, packet drop due to the unavailability of forwarding nodes results in more energy consumption. As in Figure 9, the AHH-VBF scheme takes more energy, while the node number is less. In the AHH-VBF scheme, information of the next hop forwarding node is not considered while transmitting the packet. Therefore, it leads to packet transmission towards the void region, and unnecessary energy tax is consumed due to transmission failure. It can be seen that the 2 hop-AHH-VBF scheme consumes less energy than the AHH-VBF scheme because the unsuccessful transmissions are avoided here. While node number increases beyond 300, the difference of energy consumption reduces. QF-2 hop-AHH-VBF performs better in terms of PDR; hence, it takes more energy consumption due to successful transmissions towards the destination. Moreover, the selection of the quality forwarder in QF-2 hop-AHH-VBF is based on the two-hop information and the residual energy of forwarding nodes. Optimized selection of the qualified node for the forwarding process minimizes network overhead due to control packets. Hence, initially, the energy consumption of the QF-AHH-VBF scheme is high because of high PDR when compared with other routing schemes. Gradually, the energy tax difference is observed as the node number increases beyond 400. Similarly, QF-AHH-VBF outperforms WDFAD-DBR because the constrained pipeline radius avoids packet duplication, and qualified node selection based on the composite function provides the optimal path towards ATD. WDFAD-DBR decides the path on the basis of the weighting depth difference, which does not completely avoid packet duplication. Thus, WDFAD-DBR has unnecessary energy consumption.  
Figure 8: Packet delivery ratio comparison.
# Network Diffusion Algorithms and Simulators in IoT and Space IoT: A Systematic Review
## 3 Survey Comparison  
The following questions were used to compare various surveys (Table 1 shows the detailed comparison):  
* Q1: Does this survey include network simulators?
* Q2: Does this survey include diffusion algorithms?
* Q3: Does this survey include categories of diffusion algorithms?
* Q4: Does this survey include network topologies?
* Q5: Are the algorithms, simulators, and topologies covered until 2024?
* Q6: Does this survey evaluate the performance of algorithms, simulators, or topologies using specific metrics?  
Table 1 provides a comparison of the surveys in terms of these six questions. It highlights the inclusion of network simulators, diffusion algorithms, algorithm categories, network topologies, and coverage until 2024, as well as performance evaluation using specific metrics.  
However, none of the previous surveys provided a complete answer to all six questions. As shown in Table 1, each survey either lacks coverage in one or more aspects or does not extend its analysis up to 2024, leaving gaps in providing a comprehensive overview of diffusion algorithms and simulators.
# SINDVis: User-Centered Dynamic Interactive Visualization System for Space Information Networks
## 6 Conclusions
### Limitations  
As we know, although we designed the SINDVis, and displayed and analyzed the results through a typical SIN, we think that there were the following insufficiencies in our study.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Architecture** & **Service Type** & **Interactivity** & **Scope** \\ \hline STK & C/S & Heavy-weight & Weak & Commercial software \\ SaVi & C/S & Light-weight & Weak & Research software \\ SINDVis & B/S & Light-weight & Strong & Comprehensive software \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation platform contrast analysis in the entity domain.  
1. In order to illustrate the problem and simplify the operation, we controlled a certain small range of SIN (26 nodes, 58 edges) in the case description. However, the real network is much larger and more complex, and how to achieve a clearer display based on this system is not mentioned in this article.
2. This study was implemented only to determine the underlying functionality and achieve a preliminary view display. Therefore, the integrity of the system was weakened during validation and the SINDVis system is not a complete system that can be used by users at its current stage.
3. System development is a continuous iterative process, and we only constructed the SINDVis system based on the users' initial requirements. At present, there is no mature product can consider the users' real-time operation and use requirements. Whether the SINDVis can better achieve the different functions of users' needs to be researched and demonstrated at a later stage. Meanwhile, we also improve and optimize the system in the future.
# Federated Optimization of \(\ell_{0}\)-norm Regularized Sparse Learning
## 4 The FedIter-HT Algorithm
### Statistical Analysis for M-Estimators  
Due to the good property of the FedIter-HT, we also study its constrained M-estimators derived from more concrete learning formulations. Although we focus on the sparse linear regression and sparse logistic regression in this paper, our method can be used to analyze other statistical learning problems as well.  
**Sparse Linear Regression** can be formulated as follows:  
\[\min_{x\in\mathbb{R}^{d}}f(x) =\frac{1}{N}\sum_{i=1}^{N}\frac{1}{B}\|Y^{(i)}-Z^{(i)}x\|_{2}^{2},\] subject to \[\|x\|_{0}\leq\tau,\]  
where \(Z^{(i)}\in\mathbb{R}^{B\times d}\) is a design matrix associated with client \(i\). For each row of matrix \(Z^{(i)}\), we further assume that they are independently drawn from a sub-Gaussian distribution with parameter \(\beta^{(i)}\), \(Y^{(i)}=Z^{(i)}x^{*}+\epsilon^{(i)}\) denotes the response vector, and \(\epsilon^{(i)}\in\mathbb{R}^{B}\) is a noise vector following Normal distribution \(N(0,\sigma^{2}I)\), \(x^{*}\in\mathbb{R}^{d}\) with \(\|x^{*}\|_{0}=\tau^{*}\) is the underlying sparse regression coefficient vector.  
Corollary 5: _If all the conditions in Theorem 2 hold, with \(B\geq C_{1}\tau\log(d)\max_{i}\{(\beta^{(i)})^{2}\}\) and a sufficiently large number of communication rounds \(T\), we have_  
\[E[\|x_{T}-x^{*}\|^{2}]\leq O(\frac{(2N\tau+\tau^{*})\sigma^{2}\mathcal{B}^{2 }(\sum_{i=1}^{N}\beta^{(i)})^{2}\log(d)}{NB})\]  
_with a probability of at least \((1-\exp(-C_{5}NB))\), where \(C_{5}\) is a universal constant._  
Proof: Let \(Z=[Z^{(1)};\ldots;Z^{(N)}]\in\mathbb{R}^{NB\times d}\) be the overall design matrix of the linear regression problem, and each row of \(Z\) can be treated as drawn IID from a sub-Gaussian distribution with parameter \(\sum_{i=1}^{N}\beta^{(i)}\). \(\epsilon=[\epsilon^{(1)};\ldots;\epsilon^{(N)}]\in\mathbb{R}^{NB\times 1}\) is the random Gaussian noise. Then Lemma C.1 in [45] immediately implies that \(f_{i}\) is restricted \(\rho_{s}\)-strongly convex and restricted \(l_{s}\)-strongly smooth with \(\rho_{s}=\frac{4}{5}\) and \(l_{s}=\frac{6}{5}\), respectively, with a probability of at least \((1-\exp(-C_{2}B))\) if the total sample size \(B\geq C_{1}\tau\log(d)\max_{i}\{(\beta^{(i)})^{2}\}\), where \(C_{1}\) and \(C_{2}\) are universal constants. Furthermore, we know that \(\|\
abla f(x^{*})\|_{\infty}=\|\frac{Z^{\ell}_{\infty}}{NB}\|_{\infty}\leq C_{ 3}\sigma\sum_{i=1}^{N}\beta^{(i)}\sqrt{\frac{\log(d)}{NB}}\), with a probability of at least \((1-\exp(-C_{4}NB))\), where \(C_{3},C_{4}\) are universal constants. Gathering everything together yields the following bound with a high probability.
# The Protective Effect of Polyunsaturated Fatty Acids against Dry Eye Disease: A Literature Review
## 3 PUFAS Supplementation and DED  
### Experimental Studies  
An in vitro study by [PERSON] et al. [47] reported that EPA and DHA supplementation showed anti-inflammatory effects on cultured human meibomian gland epithelial cells by inhibiting cyclooxygenase-2 gene expression, decreasing production of pro-inflammatorycytokines, such as TNF-\(\alpha\), IL-6, and interferon (IFN)-\(\gamma\), and facilitating the production of anti-inflammatory molecules, such as resolvin D1 [47]. A study using a rat model revealed that supplementation with \(\omega 3\) (EPA and DHA) and \(\omega 6\) (GLA) elevated the concentrations of the PUFAs in phospholipids in both the lacimal gland and the plasma, suggesting that the PUFAs intake may help modulate inflammation in the lacimal gland and preventing DED [48]. In a pharmacologically induced rat DED model, the intake of EPA, DHA, and GLA for 28 days resulted in the incorporation of the PUFAs into the phospholipids in the extraorbital lacimal gland, and inhibited the elevation of intraglandular concentrations of both PGE1 and PGE2 [49]. The combined oral supplementation also prevented damage of corneal epithelial cells, reduction in mucin production and the overexpression of MHC II molecules in the conjunctiv epithelial cells [49]. [PERSON] et al. [50] also showed that oral supplementation with ALA increased the lacimal peroxidase activity and improved lacimal secretionin a rat DED model [50].
## 4 Back Analysis of Slope Parameters
### Back Analysis Results  
The displacement monitoring values on 6 June 2021 and 7 April 2022 were taken as input data for the back analysis of the rock parameters of the slopes in the first stage of water storage and the second stage of water storage, respectively. The back analysis results of the two water impoundment stages are shown in Tables 2 and 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Rock Groups**} & \multicolumn{1}{c}{**Deformation**} & \multicolumn{1}{c}{**Poisson’s**} & \multicolumn{1}{c}{**Cohesion**} & \multicolumn{1}{c}{**Internal Friction**} \\ \hline
**Class** & **Subclass** & **Modulus (GPa)** & **Ratio** & **(MPa)** & **Angle (\({}^{\circ}\))** \\ \hline III & III1 & 11.0–13.0 & 0.22–0.28 & 1.4–1.6 & 48.2–52.2 \\ III2 & III2 & 6.3–9.8 & 0.26–0.32 & 1.1–1.3 & 40.4–45.4 \\ IV & IV1 & 4.5–7.5 & 0.28–0.34 & 0.8–1.0 & 38.4–42.4 \\ IV2 & IV2 & 4.1–5.9 & 0.30–0.36 & 0.4–0.8 & 32.9–40.9 \\ V & V1 & 1.6–3.4 & 0.29–0.39 & 0.1–0.7 & 23.0–39.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reference intervals for back analysis of mechanical parameters.  
Figure 3: Comparison of nonlinear results and simulation results of displacements.  
Comparing the back analysis results of the parameters of the two stages, the back analysis results of the second impoundment stage will be smaller than the back analysis results of the first impoundment stage, which indicates that the elevated reservoir level will lead to the decrease of the mechanical parameters of the slope rock, presumably because the water enters the interior of the slope and thus deteriorates the mechanical parameters of the rock, which in turn leads to the displacement of the slope.
## 4 Methodology
### Unmanned Ariel Vehicles (UAVs)  
The data collection process is executed with the help of unmanned aerial vehicles, which collect data from the sensors deployed at the fog site. The path of the UAVs is pre-configured. A fleet of UAVs flies through this pre-defined path to collect the data. On cycle completion, UAVs return to the charging station to recharge their batteries for the next trip.