esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Let \(F:D\subseteq\mathbb{C}^{n}\rightarrow\mathbb{C}^{n}\) be Gateaux-differentiable and \(D\) be an open set. Let also \(x_{0}\in D\) be a point at which \(F^{\prime}(x)\) is continuous and positive definite. Suppose that \(F^{\prime}(x)=H(x)+S(x)\), where \(H(x)=\frac{1}{2}(F^{\prime}(x)+F^{\prime}(x)^{*})\) and \(S(x)=\frac{1}{2}(F^{\prime}(x)-F^{\prime}(x)^{*})\) are the Hermitian and Skew-Hermitian parts of the Jacobian matrix \(F^{\prime}(x)\), respectively. Many problems can be formulated like the equation  
\[F(x)=0, \tag{1}\]  
using mathematical modelling [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. The solution \(x_{*}\) of Equation (1) can rarely be found in explicit form. This is why most solution methods of Equation (1) are usually iterative. In particular, Hermitian and Skew-Hermitian Splitting (HSS) methods have been shown to be very efficient in solving large sparse non-Hermitian positive definite systems of linear equations [11; 12; 17; 19; 22].  
We study the semilocal convergence of the multi-step modified Newton-HSS (MMN-HSS) method defined by  
\[\begin{array}{l}x_{k}^{(0)}=x_{k},\\ x_{k}^{(i)}=x_{k}^{(i-1)}-\left(I-T(\alpha;x)_{k}^{(i)}\right)F^{\prime}(x_{k })^{-1}F(x_{k}^{(i-1)}),\ \ 1\leq i\leq m,\\ x_{k+1}=x_{k}^{(m)},\ i=1,2,\ldots m,\ \ k=0,1,\ldots,\end{array} \tag{2}\]where \(x_{0}\in D\) is an initial point, \(T(\alpha;x)=(\alpha I+S(x))^{-1}(\alpha I-H(x))(\alpha I+H(x))^{-1}(\alpha I-H(x))\), \(l_{k}^{(i)}\) is a sequence of positive integers, and \(\alpha\) and \(tol\) are positive constants  
\[\|F(x_{k})\|\leq tol\|F(x_{0})\|\]  
and  
\[\|F(x_{k})+F^{\prime}(x_{k})d_{k,l_{k}}\|\leq\eta_{k}\|F(x_{k})\|,\ \ \eta_{k}\in[0,1),\ \ \eta_{k}\leq\eta\leq 1.\]  
The local and semilocal convergence analysis of method (2) was given in [19] using Lipschitz continuity conditions on \(F\). Later, we extended the local convergence of method (2) using generalized Lipschitz continuity conditions [8].  
In the present study, we show that the results in [19] can be extended as the ones for MN-HSS in [8]. Using generalized Lipschitz-type conditions, we present a new semilocal convergence analysis with advantages (A):  
1. [label=()]
2. Larger radius of convergence,
3. More precise error estimates on \(\|x_{k}-x_{*}\|\),
4. The new results can be used in cases where the old ones in [19] cannot be used to solve Equation (1).  
The advantages (A) are obtained under the same computational cost as in [19]. Hence, the applicability of the MMN-HSS method is extended.  
The rest of the paper is structured as follows: Section 2 contains the semilocal convergence analysis of the MMN-HSS method. Section 3 contains the numerical examples.
## Notations  
\begin{tabular}{l l} Symbol & Description \\ \(L\) & Total channel response \\ \(\tau_{ij}\) & Path loss between node \(i\) and node \(j\) \\ \(Y\) & Shadowing fading \\ \(X\) & Multi-path fading \\ \(C_{1}\) & Constant offset of path loss \\ \(d\) & Distance between transmitter and receiver \\ \(f\) & Center frequency \\ \(P_{i}\) & Transmitted power from \(i\) node \\ \(x\) & Packet from the source node \\ \(\Psi(*)\) & Incomplete gamma function \\ \(\Gamma(*)\) & Complete gamma function \\ \(B\) & Bandwidth \\ \(N_{0}\) & Thermal noise power spectral density \\ \(\mu\) & SNR threshold \\ \(\rho\) & Outage probability \\ \(w\) & Weight vector \\ \(R\) & Spectral efficiency \\ \(\kappa\) & Packet size \\ \(R_{b}\) & Bit rate \\ \(V_{D}\) & Source voltage \\ \(I_{0}\) & Source current \\ \(f_{cor}\) & Corner frequency \\ \(C\) & Parasitic capacitance \\ \(L_{c}\) & Minimum channel length \\ \(\delta\) & Correcting factor \\ \(v\) & Power amplifier efficiency \\ \(\omega\) & Drain efficiency \\ \(\tau\) & Peak to average ratio \\ \(P_{EL}\) & Basic power consumption to run a sensor board \\ \(P_{AMP}\) & Amplifier power consumption \\ \(P_{TX}\) & Total transmitted RF power consumption \\ \(P_{RX}\) & Total received RF power consumption \\ \(P_{BAS}\) & Baseband power consumption \\ \(P_{MIX}\) & Mixer power consumption \\ \(P_{SYN}\) & Synthesizer power consumption \\ \(P_{FIL}\) & Filter power consumption \\ \(P_{DAC}\) & Digital analog converter power consumption \\ \(P_{LNA}\) & Low noise amplifier power consumption \\ \(P_{IFA}\) & Intermediate frequency amplifier power consumption \\ \(P_{ADC}\) & Analog digital power consumption \\ \(P_{SYN}\) & Synthesizer power consumption \\ \end{tabular}
## 2 Forward Model for O\({}_{2}\)_A_-Band Night Glow Limb Radiation in MLT
### O\({}_{2}\) A-Band Night Glow Radiation Model  
In the lower atmosphere (\(\leq\)70 km), the detection accuracy of the space-borne instrument is seriously affected due to the self-absorption effect of the ground state oxygen molecules on the target airglow. While in the MLT (80-120 km), the O\({}_{2}\)\(A\)-band optical thickness decreases significantly, making it possible to measure the radiation from space. O\({}_{2}\)\(A\)-band night glow band ranges 759-775 nm, with the central wavelength being 762 nm. It is produced in the process of excited state \(O_{2}\left(b^{1}\Sigma_{g}^{+}\right)\) transition to the ground state \(O_{2}\left(X^{3}\Sigma_{g}^{-}\right)\). Most of the generation of the excited state \(O_{2}\left(b^{1}\Sigma_{g}^{+}\right)\) is based on the Bath two-step generation mechanism [7].  
\[O+O+M\to{O_{2}}^{*}+M\] (R1) \[{O_{2}}^{*}+O_{2}\to O_{2}\left(b^{1}\Sigma_{g}^{+},\
u=0\right)+O_{2}\] (R2)  
Part of the excited state \(O_{2}\left(b^{1}\Sigma_{g}^{+},\
u=0\right)\) from the above reactions produce O\({}_{2}\)\(A\)-band night glow, and the other part is quenched by the collision of the ground state atmospheric molecules in the atmosphere. Figure 1 shows a typical volume emission rate (VER) vertical profile of O\({}_{2}\)\(A\)-band night glow. As shown in Figure 1, \(A\)-band night glow VER has a maximum \(3\times 10^{3}\)\(photons/s/cm^{3}\) near 94 km. Because the lifetime of the molecule \(O_{2}\left(b^{1}\Sigma_{g}^{+},\
u=0\right)\) is about 12 s, it is generally considered that the molecule is in local thermodynamic equilibrium, so the dynamic temperature of the atmosphere can be obtained from the rotational band structure of emission [8].  
For O\({}_{2}\)\(A\)-band airglow, the rotational band under thermodynamic equilibrium obeys Boltzmann distribution at rotational temperature. Based on the rotational band structure, the number of radiated photons is distributed on the narrow emission line within the band, represented by \(\eta_{rot}\) below. The normalized fine spectral structure of \(A\)-band airglow is shown in Figure 2.  
Figure 1: The simulated VER of the O\({}_{2}\)\(A\)-band nightglow at 30\({}^{\circ}\) N and 80\({}^{\circ}\) E and local solar time at 22:00.
## 4 Numerical Analysis Results
### Effects of Blasting Vibration on the Tunnel Face and Ground Surface  
Through numerical analysis, this study aimed to predict the effects of blasting vibration caused by NATM extending blasting on a TBM and TBM backup device and estimate an appropriate powder factor based on rock type and length of the pilot tunnel (separation distance between the blasting point and TBM). Furthermore, the vibrations transmitted to the tunnel face and ground surface were measured to ensure that no damage to the ground structure occurred and that no casualties occurred. Figure 7 presents the vibration velocities transmitted to the tunnel face and ground surface. The vibration velocities transmitted to the tunnel face and ground surface are lower than the regulated permissible vibration values (Seoul subway, house, and apartment vibration regulation values) in all cases, except for the case of hard rock with a powder factor of 2.0 kg or more. Consequently, it is determined that blasting vibration will not significantly affect the ground surface or superstructures.  
Figure 6: Analysis results for vibration velocity according to rock type and powder factor (measurement points C and D): (**a**) TBM (hard rock), (**b**) TBM (soft rock), (**c**) TBM (weathered rock), (**d**) ground above TBM (hard rock), (**e**) ground above TBM (soft rock), and (**f**) ground above TBM (weathered rock).  
Figure 7: Cont.  
Figure 7: Analysis results for vibration velocity according to type of rock and powder factor (measurement points E and F): (**a**) tunnel face (hard rock), (**b**) tunnel face (soft rock), (**c**) tunnel face (weathered rock), (**d**) ground surface (hard rock), (**e**) ground surface (soft rock), (**f**) ground surface (weathered rock).
## 2 Study Area and Datasets
### Ground Measurements  
Over the study area, there are 17 automatic soil moisture observation sites that are equipped with the DZN1 sensors [48], installed and maintained by Jiangxi meteorological bureau. The DZN1 sensors are based on the Frequency Domain Reflectometry (FDR) technique, which derives soil moisture content on the basis of changes in the frequency of signals due to the dielectric properties of the soil [49]. All of the sites are located in the local dominant cropland, including paddy field, dryland, nursery, and orchard. At each site, volumetric soil moisture measurements are taken up to a depth of 80 cm, every 10 cm, with an hourly time interval. Among the 17 sites, two of them were screened out in suspicion of data exception. The remaining sites were also checked carefully for implausible measurements and outliers, discarding three records at last. To match the passing time and penetration depth of Sentinel-1 satellites, soil moisture records of the 15 sites measured in 10:00 a.m. at the depth of 0-10 cm were used. The spatial distribution of the mean surface soil moisture of these sites is shown in Figure 1b, and the descriptive statistics of each site is provided in Table 2. In addition, daily temperature and precipitation measurements collected at site 9 (Nanchang) were obtained from the China Meteorological Data Sharing Service System [50].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **Month** & **Day** & **Adays** & **Sensor** \\ \hline \multirow{8}{*}{2015} & April & 18 & / & \\  & May & 24 & 36 & \\  & June & 05, 29 & 12, 24 & \\  & July & 11, 23 & 12, 12 & \\  & August & 16 & 24 & A \\  & September & 09, 21 & 24, 12 & \\  & October & 03, 15, 27 & 12, 12, 12 & \\  & November & 20 & 24 & \\  & December & 02, 14, 26 & 12, 12, 12 & \\ \hline \multirow{8}{*}{2016} & January & 07, 19, 31 & 12, 12, 12 & \\  & February & 24 & 24 & \\  & March & 07, 19, 31 & 12, 12, 12 & \\  & April & 12, 24 & 12, 12 & \\  & May & 06, 18, 30 & 12, 12, 12 & A \\  & June & 11 & 12 & \\  & July & 05, 17 & 24, 12 & \\  & August & 10, 22 & 24, 12 & \\  & September & 15, 27 & 24, 12 & \\  & October & 03, 09, 15, 21, 27 & 6, 6, 6, 6, 6 & B, A, B, A, B \\  & November & 02, 08, 14, 26 & 6, 6, 6, 12 & A, B, A, A \\  & December & 02, 08, 14, 20, 26 & 6, 6, 6, 6, 6 & B, A, B, A, B \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sentinel-1A/1B acquisitions used in this study.
## 3 Results  
A total of 488 images were initially included in this study. After excluding 123 radiographs, 365 images were analyzed in this study. There was adequate inter- and intraobserver concurrence in image fractal dimension assessment due to the high two-way random consistency ICC (>0.75, \(p\) value < 0.001). A total of 54.8% of the individuals (200) were male, and 45.2% (165) were female. The sex and age ranges of all participants divided according to the existence of bruxism are demonstrated in Table 1.  
Figure 3: ROIs after the (**a**) clear outside, (**b**) Gaussian blur, (**c**) subtracting, and (**d**) make binary functions. Figure 2: Logarithm of N(r) versus logarithm of r diagram. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Age Range**} & \multicolumn{2}{c}{**Male**} & \multicolumn{2}{c}{**Female**} & \multirow{2}{*}{**Total**} \\ \cline{2-2} \cline{4-5}  & **B 1** & & **NB 2** & & **NB** \\ \hline
50–59 & 26 & 44 & 25 & 39 & 134 \\
60–69 & 39 & 38 & 25 & 21 & 123 \\
70–79 & 12 & 17 & 22 & 13 & 64 \\ \(>\)80 & 13 & 11 & 11 & 9 & 44 \\ Total & 90 & 110 & 83 & 82 & 365 \\ \hline \end{tabular}  
1 Bruner. 2 Non-bruzer.  
\end{table} 
Table 1: Demographic information for study participants. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ROI** & **Group** & **Number** & **Mean** & **SD 1** & \(p\) **Value** \\ \hline Right condyle & B 2 & 173 & 1.41 & 0.12 & \\  & NB 3 & 192 & 1.41 & 0.11 & 0.760 \\ \hline Left condyle & B & 173 & 1.42 & 0.12 & \\  & NB & 192 & 1.42 & 0.12 & \\ \hline Right gonial region & B & 173 & 1.40 & 0.14 & \\  & NB & 192 & 1.39 & 0.13 & \\ \hline Left gonial region & B & 173 & 1.40 & 0.14 & \\  & NB & 192 & 1.41 & 0.13 & 0.190 \\ \hline Right alveolar region & B & 173 & 1.38 & 0.14 & \\  & NB & 192 & 1.38 & 0.16 & 0.806 \\ \hline Left alveolar region & B & 173 & 1.36 & 0.15 & \\  & NB & 192 & 1.37 & 0.14 & 0.541 \\ \hline \end{tabular}  
1 Standard deviation, 2 bruxer, 3 non-bruzer.  
\end{table} 
Table 2: FD values of different ROIs in the study groups. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ROI** & **Sex** & **Number** & **Mean** & **SD 1** & \(p\) **Value** \\ \hline Right condyle & Male & 90 & 1.42 & 0.12 & \\  & Female & 83 & 1.40 & 0.13 & 0.322 \\ \cline{2-6}  & Total & 173 & 1.41 & 0.12 & \\ \hline Left condyle & Male & 90 & 1.40 & 0.12 & \\  & Female & 83 & 1.43 & 0.11 & 0.258 \\ \cline{2-6}  & Total & 173 & 1.42 & 0.12 & \\ \hline \end{tabular}
\end{table} 
Table 3: FD values of different ROIs for the two sexes in the bruxer group. Moreover, the Kruskal-Wallis test showed a significant difference between ROIs, and the Mann-Whitney test, as a post hoc test, demonstrated that the FD values were significantly different between age ranges for the right condyle (\(p\) value = 0.03), right gonial region (\(p\) value = 0.001), left gonial region (\(p\) value = 0.002), and left alveolar region (\(p\) value \(<\) 0.001). Further information about the FD values in different age ranges after performing the Kruskal-Wallis test is mentioned in Table 5.
# Research on Wind Speed Inversion Method for X-Band Networked SAR Satellite
## 5 Discussion  
The SAR data is simulated according to the imaging principle of the SAR system, which can make the information of the SAR data more complete. However, in the process of generating simulated data, it is impossible to consider all the mechanisms and factors that affect SAR data. There are some differences between simulated SAR data and actual SAR data. Therefore, this article used XMOD2 to simulate the networked SAR satellite data under different conditions. The SAR backscatter coefficient simulated by XMOD2 was closer to the actual SAR backscatter coefficient. In order to understand the effect of noise on wind speed accuracy, noise was introduced into the simulation data. The experiment was to study a single pixel. When adding noise to the simulation data, a fixed noise was added to the backscatter coefficient of a single pixel.  
The wind speed inversion method based on the difference of the incident angle has been successfully applied to a single spaceborne SAR data. However, this approach has two disadvantages. The first point is that the method has high requirements for SAR data, and it is necessary to ensure that changes in sea conditions on the sea surface are small. The second point is to ensure that the angle of incidence is different, so the resolution of wind speed inversion is low (\(25\times 25\) km). This paper applied this method to X-band networked SAR satellite data. The observation of the same sea surface by multiple satellites at the same time can ensure that the wind field information of each point in the SAR data is basically consistent, which lifts the limitation of the method on sea conditions. Because each satellite operates at a different angle of incidence, the resolution of the wind speed inversion is also unlimited.  
The study found that in the wind speed inversion process of networked SAR satellite simulation data, relatively accurate wind speed information can be obtained without referring to wind direction information, as shown in the experimental results of the first screening method. In the case of low noise, the introduction of external wind direction information improves the accuracy of wind speed, as shown by the experimental results of the second screening method, but with the increase of noise, the introduction of wind direction information will reduce the accuracy of wind speed extraction.  
The experiments in this paper used simulated X-band networking SAR satellite data, so the method has good applicability. However, when the method is applied to X-band actual SAR satellite data, the accuracy of wind speed extraction is related to the applicability of GMF to SAR data.
# Dynamic Optimization of Data Packet-based Communication for PLC Visual Monitoring
## 3 Experimental Studies and Discussions
### Initialization and Optimization of Communication Packet Size  
It has been proven in Section 2 that the optimized packet size of packing \(P^{*}\) successive sub-networks can be identified from a candidate set by taking into account the real-time packet loss or retransmission rate \(\delta\) and the human response time limit \(T^{\mathbb{R}}\). In the experimental studies, the maximum human response time \(T^{\mathbb{R}}\) is set to be 200 ms as a benchmark, rather than a precise measurement [13; 29]. Furthermore, a larger \(P^{*}\) can be achieved by tightening the upper bound of the length of sub-networks in the present monitoring view. The size of the communication packet should be reduced if \(\delta\) increases. Otherwise, the size of the communication packet could be made larger. Basically, the following steps can be considered to initialize and optimize the communication packet size.  
(1) Collect the initial parameters of the monitoring system and calculate \(P^{*}\) with the use of the optimization models in Section 2.3.  
(2) Monitor the communication performance, especially the actual real-time packet loss or retransmission rate \(\delta\).  
(3) Adjust the tightening upper bound of the length of sub-networks and restart the packet size optimization process, when the packet loss or retransmission rate is changed outside a pre-specified range, e.g., \(\pm 10\%\).
## 2 Materials and Methods
The values measured on the scan data obtained using the intraoral scanner with the 3D inspection software program (Geomagic Control X version 2018.0.0; 3D Systems, Rock Hill, SC, USA) and those measured on the additively manufactured dental casts were individually subtracted from the corresponding values obtained from the reference dendiform models, and the absolute values of these deviations were used for the analyses. Group IOS was defined as the absolute values of the deviations of the distances between the scan data obtained with the intraoral scanner and the corresponding values directly measured on the reference dendiform. Group PPIA was defined as the absolute values of the deviations measured as differences in the distances between the additively manufactured dental casts immediately after post-polymerization and the corresponding values directly measured on the reference dendiform model; in group PP1D it was measured 1 day after post-polymerization, in group PP7D after 7 days, and in group PP30D after 30 days. The mean values of 64 measurements in the arch pair were selected as representative values of overall measurements. The Shapiro-Wilk test was conducted to evaluate the assumption of normal distribution of the obtained values. Subsequently, a one-way repeated-measures analysis of variance (RMANOVA), followed by post hoc Bonferroni test, were applied for the analysis of dimensional stability among the groups IOS, PPIA, PP1D, PP7D, and PP30D. The significance level was set to \(\alpha\) = 0.05.
# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 5 Position Related Aspects
### Placement and Trajectory Design
Finally, the optimization problem of proper dynamic placement of UAVs as BSs in downlink scenarios was studied in Reference [130]. It was considered that the UAVs should make decisions about their placement in each time-slot, depending on the unknown density of ground users in an ML manner. Moreover, a constraint regarding the minimum UAV-recall-frequency or otherwise the maximum life-time, indicating the energy efficiency of mobile UAVs networks, should be also satisfied. In each time-slot, the UAV-recall-frequency was considered static and the results showed that the optimal UAV placement is achieved when the transmit power becomes equal to the onboard circuit power. In addition, the optimal hovering altitude that minimizes transmit power is proportional to the coverage radius, whereas the slope depends on the propagation environment and tends to increase in areas with high-rise buildings. These results also underlined that limiting on-board circuit power prolongs the life-time of mobile UAV networks. Next, the multiple time-slot scenario was studied,where unstable and non-ergodic time-varying density of ground users served with fixed data rate exists. For this scenario, the optimization problem was more complex and required a multi-stage decision process leading to an integer nonlinear programming coupled with an inherent integer linear programming. Since this problem was NP-hard, a sequential-Markov-greedy-decision (SMGD) method was proposed in order to achieve near-minimal UAV-recall-frequency in polynomial time. According to the results, a large number of sample sets are indispensable for effective pattern formation and reduction of UAV-recall-frequency in large areas with high-rise buildings and low ground user density whereas the SMGD becomes more complex as the number of UAVs increases. Table 6 includes the relevant works on ML-based resource allocation and network planning techniques for UAV-enabled communication networks.
# A Finite Regime Analysis of Information Set Decoding Algorithms
## 3 A Finite Regime Analysis of Information Set Decoding Techniques  
In the following, we report an analysis of the best known variants of Information Set Decoding (ISDs) and their execution on a classical computer, namely the ones proposed by [PERSON] [10], [PERSON] and [PERSON] [11], [PERSON] [12], [PERSON] [13], [PERSON] and [PERSON] [14], [PERSON], [PERSON] and [PERSON] [15], and [PERSON], [PERSON], [PERSON] and [PERSON] [16]. For the sake of clarity, we describe the Information Set Decoding (ISD) algorithms in their syndrome decoding formulation, highlighting for the first variant amenable to dual-use, i.e., [PERSON] and [PERSON]'s, how to adapt the technique to the Codeword Finding Problem (CFP). For all these algorithms, we provide finite-regime time complexities and space complexities, with the aim to analyze the actual computational effort and memory resources needed to solve both the Syndrome Decoding Problem (SDP) and the Codeword Finding Problem (CFP) on instances with cryptographically sized parameters. We also report lower bounds on the complexities of the execution of [PERSON], [PERSON] and [PERSON]'s and [PERSON]'s variants of the Information Set Decoding (ISD) on a quantum computer, allowing an evaluation of the corresponding computational efforts.  
We provide the exact formulas for the time complexity of ISD variants as a function of the code length \(n\), the code dimension \(k\) and the number of errors \(t\). We note that the ISD algorithms having the best asymptotic time complexity are also characterized by an exponential space complexity, which may significantly hinder their efficiency or make their implementation unpractical. In particular, we also analyze the computational cost of such algorithms with a logarithmic memory access cost criterion. Indeed, the logarithmic access cost criterion is the one which fits better scenarios where the spatial complexity of an algorithm is more than polynomial in its input size, therefore resulting in a non-negligible cost for the memory accesses.  
In the reported formulas, we employ the \(\mathcal{O}\)-notation simply to remove the need to specify the computing architecture- or implementation-dependant constants.