esa-sceva/satcom-chunk-collection huggingface dataset:
# Broadband Power Line Communication for Integration of Energy Sensors within a Smart City Ecosystem
## 6 Experimental Simulation Results  
This section describes the achieved experimental results of the simulations. The first part of the simulations considers LV lines, where the throughput trend is monitored, based on the change in PSD, cable type, and bandwidth. Therefore, the target values of throughput were not set for these simulations, but only the verification of the expected trend was considered. These results made it possible to design a simulation of a real topology and real BPL devices, which were experimentally verified in practice by measurements. The second part of the simulations considers MV lines with experimental simulation setup using G.hn implementation, where the input parameters were taken from the IEEE P1901 compliant BPL modems.
# GPS Multipath Analysis Using Fresnel Zones
## 3 Identification of Diffracted Satellite Signals Using Fresnel Zones and OAEMs  
Obstruction Adaptive Elevation Masks can be derived from georeferenced models of the antenna environment, such as point clouds from terrestrial laser scanners (TLS) [25]. After determining the azimuth and elevation of all points and allocating them to an azimuthal grid, the OAEM is obtained by selecting the highest elevation angle in each grid cell. From the viewpoint of the antenna, the OAEM separates obstructed areas from open sky and can be used to identify satellite signals that are subject to NLOS reception or signal diffraction, leading to a substantial improvement in the positioning accuracy. However, in this algorithm, only the direct line-of-sight (LOS) directions of the satellite signals are analyzed and the Fresnel zones along the signal path (cf. Figure 8) are not taken into account.  
For this purpose, in Section 3.1, the integration of Fresnel zones into the OAEM algorithm is presented. Based on the data of a field test described in Section 3.2, the algorithm is assessed in Section 3.3.
# Study on the Particle Surface Fractal Characteristics of
## 4 Experimental Results and Analysis  
We put the glass slides under the microscope after sampling, then chose the center region of the center line on the slide as the subject of observation and analysis. The size, area, perimeter and other characteristics of the particle in the observation region were disclosed by the test analysis. The binary images of the samples are shown in Figure 4.  
The fractal dimensions of the projection of a single particle range from 1.4392 to 1.5492, and the surface fractal dimension ranges from 2.4392 to 2.5492 for the sulfide ore particles examined in the experiment, as shown in Figure 6 and Table 4. According to the calculation results, the correlation coefficients of the three fractal dimension curves of the sulfide ore particles were all above 0.94, which shows that the differences between the fractal properties of the three different types of particle are substantial.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Slope \(K\)** & \begin{tabular}{c} **Fractal** \\ **Dimension \(D_{\text{L}}\)** \\ \end{tabular} & \
\begin{tabular}{c} **Fractal** \\ **Dimension \(D_{\text{s}}\)** \\ \end{tabular} &
\begin{tabular}{c} **Correlation** \\ **Coefficient \(R^{2}\)** \\ \end{tabular} \\ \hline A & 0.7196 & 1.4392 & 2.4392 & 0.9672 \\ B & 0.7264 & 1.4528 & 2.4528 & 0.9439 \\ C & 0.7746 & 1.5492 & 2.5492 & 0.9568 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical results of sulfide ores’ fractal dimension.  
Figure 5: _lgL-lgA_ curves of three sulfide ore microparticles. (**a**) Fractal curve of 60–100 mesh sulfide ore microparticles. (**b**) Fractal curve of 100–140 mesh sulfide ore microparticles. (**c**) Fractal curve of microparticles of sulfide ore larger than 200 mesh.  
The complexity of the projection profile of sulfide ones can be characterized by the various particle projection profile fractal dimensions. The greater the fractal dimension, the more irregular the shape of the particles. The projection profile fractal dimension of the particles with size greater than 200 mesh is the largest, and it reached a value of 1.5492, as shown in Figure 6 above. This indicates that the projection profile curve of the sulfide ore particles is far off the circular path. The surface fractal dimension of particles larger than 200 mesh is up to 2.5492, and it is the largest fractal dimension in the three groups. This indicates that the group of particles has a lot of multi-level fine structures on it, and it has a large degree of self-similarity. At the same time, the particles have concave convex surfaces.
# Augmented Lagrangian-Based Reinforcement Learning for Network Slicing in IIoT
## 3 Proposed Augmented Lagrangian-Based Reinforcement Learning
### Preliminary of Augmented Lagrangian Method  
The augmented Lagrangian method not only replaces the constrained optimization problem with an unconstrained problem but also introduces a penalty term to accelerate convergence [30]. Given an objective function \(f(\mathbf{x})\) to maximize with parameter \(\mathbf{x}\) and the constraint functions \(c_{i}(\mathbf{x})>0\), this optimization problem can be solved by its dual problem as follows:  
\[\min_{\mathbf{\lambda}>0}\max_{\mathbf{x}}\mathbf{L}(\mathbf{x},\mathbf{\lambda},\mu) \triangleq f(\mathbf{x})+\sum_{i}\lambda_{i}c_{i}(\mathbf{x})+\frac{\mu}{2} \sum_{i}||c_{i}(\mathbf{x})||^{2}, \tag{14}\]  
where \(\mathbf{\lambda}\) denotes the Lagrange multiplier vector for the constraints, and \(\mu\) denotes the parameter for the penalty term.  
Then, the typical solving process alternatively optimizes \(\mathbf{\lambda}\) and \(\mathbf{x}\) during iterations. Generally at \(t\)-th iteration, \(\lambda_{i}^{(t)}\) is updated according to the rule as  
\[\lambda_{i}^{(t)}=\lambda_{i}^{(t)}+\mu^{(t)}c_{i}(\mathbf{x}^{(t)}). \tag{15}\]  
Additionally, when the constraint is not satisfied, \(\mu\) is enlarged with a scalar.
## 5 Map of Telluric Field Response to a Geomagnetic Disturbance over Northern Russia
For the average resistivity of the crust in the Arctic region of \(\rho\)-3-50 Ohm-m, the strong skin-effect condition remains valid for periods under consideration. The impedance approach may cause doubt only for long-period disturbances with time scales 5-20 min above those of high-resistivity regions, such as the Kola Peninsula with \(\rho\)-10\({}^{4}\)-10\({}^{5}\) Ohm-m. In this specific region, the full wave solution of the electromagnetic disturbance structure in the crust may be needed. Should the PSP changes during strong substorms be taken into account when maintaining the cathodic potential to protect against electrocorrosion? This question may be answered using the constructed map. Different analytical and numerical methods have been elaborated to calculate the induction in a grounded conductor [34; 35; 36; 37; 38]. Those methods demand prior information on the telluric field intensity, polarization, and spatial structure. According to calculations in [35], currents in the south of Finland for an electric field of 1.0 V/km should cause a GIC of \(\sim\)50 A in the pipeline and up to 25 A in its grounding, which, in the case of insulation failure, may well lead to a noticeable enhancement of corrosion. A very crude estimate using the results of the measured PSP in oil pipelines was carried out in [39]. They showed that the telluric field calculated in the approximation of uniform conductivity is linearly proportional to the measured PSP. The relation found shows that the potential created by telluric fields can exceed the maintained PSP-2 V at \(E>0.5\) V/km. According to the constructed map (Figure 10), applying this result to the Yamal region (the area of oil/gas productive Mesozoic sediments), a geomagnetic disturbance of \(\sim\)500 nT with a time scale of \(\sim\)100 s is required to cause a disturbance of PSP menacing the cathodic protection.
# Sulfonation Treatment of Polyether-Ether-Ketone for Dental Implant Uses
## 2 Materials and Method
### Surface Characterization  
The surfaces of untreated PEEK, which served as the control, and sulfonated PEEK were analyzed using the following criteria:  
1. All of the analyses of the sulphonated PEEK's' chemical composition were carried out at room temperature and using attenuated total reflection-Fourier transform infrared (ATR-FTIR). (IRAffinity-1, ATR-FTIR Shimadzu, Shimadzu Scientific Instruments, Kyoto, Japan) was used to obtain the transmittance spectrum recorded from 4000 to 600 cm\({}^{1}\) in order to validate the presence of new sulfonated groups with PEEK polymer chains after the treatments.  
2. Scanning electron microscopy (SEM) (SEM Test Speed Vega 111, Electron Optics Instruments, LLC., West Orange, NJ, USA) was utilized in order to evaluate the surface morphology. For the purpose of elemental analysis, a scanning electron microscope with energy dispersive X-ray (EDX) analysis was carried out.  
3. Conduction of a test of wettability. The wettability of the samples was measured using a contact angle goniometer made by Creating Nano Technologies Inc. in Taiwan, China (model number Cam110). In order to assess the surface hydrophilicity of sulphonated PEEK at room temperature, the static contact angle measurement was utilized to quantify the water contact angle of the material. The water contact angle on a PEEK surface that had not been treated in any way served as the control for this experiment.  
4. The atomic force microscopy, also known as the AFM. In the tapping mode, a contact AFM (BenYuan CSPM-5500, Being Nano-Instruments Ltd., Beijing, China) was utilized to produce a 3D topographical picture, as well as the surface area ratio (Sdr) for the selected sulphonated PEEK and the average surface roughness (Sa) in nanometers. Additionally, the average surface roughness was measured in nanometers [17].  
**Scheme 1.** Sulfonation of the PEEK and neutralization of the sulfonated PEEK by rinsing in acetone, immersion in a solution of sodium hydroxide (NaOH), and hydrothermal treatment at 120 C.
## 2 Signal Model and Problem Statement  
This section first presents the signal model for GNSS receivers with an antenna array and analyzes the anti-jamming effect on the carrier phase. Then, the shortcomings of existing phase compensation techniques are briefly introduced.
# A3C-R: A QoS-Oriented Energy-Saving Routing Algorithm for Software-Defined Networks
## 6 Conclusions  
The experimental results show that, compared with the ECMP algorithm, I-DQN algorithm, and DDPG-EEFS algorithm, the A3C-R algorithm reduces the delay by approximately 9.42%, increases the throughput by approximately 7.04%, reduces the packet loss rate by approximately 9.51%, and increases the energy saving percentage by approximately 10.78%. The A3C-R algorithm in this paper only considers the QoS and energy-saving optimization of the SDN data plane. With the continuous complexity of network devices and traffic, the performance and energy-saving optimization of the SDN control plane still has important research significance.  
In the future, we will further investigate the joint optimization of both the SDN control and data planes to achieve a more comprehensive balance between QoS and energy efficiency. Furthermore, incorporating real-world deployment and validation in large-scale SDN environments will be a crucial step to assess the practicality and scalability of our approach.  
Conceptualization by [PERSON], [PERSON] and [PERSON]; methodology by [PERSON], [PERSON] and [PERSON]; software by [PERSON] and [PERSON]; validation by [PERSON], [PERSON], [PERSON] and [PERSON]; formal analysis by [PERSON] and [PERSON]; investigation by [PERSON] and [PERSON]; data curation by [PERSON], [PERSON] and H.L.; writing--original draft preparation by [PERSON] and [PERSON]; writing--review and editing by [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Guangdong Province Key Field Special Project \"Research on Key Technologies of Zero-Trust Business Data Security Monitoring for Industry-Specific Networks\" No.: 2021 ZDZX1098, China University Industry-University-Research Innovation Fund \"Zero-Trust API Gateway System for Industry Private Networks\" No.: 2021 FNB3001, \"Research on Key Technologies for Security Monitoring of Private Network Big Data under Elastic Cloud Platform\" No.: 2022 IT020, Shenzhen Science and Technology Innovation Commission Stable Support Plan \"Research and Application of Key Technologies for 5G Network Protection Based on Active Defense\" No.: 20231128083944001.  
The data presented in this study are available on request from the corresponding author due to privacy.  
The authors declare no conflicts of interest.
## 3 Proof of security of BB84 against collective attacks  
### Parity strings for the code and the key  
We recall that bitstrings of length \(n\) are identified with elements of \(\mathbf{F}_{2}^{n}\). Vector addition thus corresponds to component-wise sum modulo 2 and thus to the eXclusive-OR of the corresponding bitstrings. We denote \(\mathbf{a}\cdot\mathbf{b}\) the scalar product (modulo 2) of the two strings \(\mathbf{a}\) and \(\mathbf{b}\) of the same length, e.g., for \(n\)-bit strings, \(\mathbf{a}\cdot\mathbf{b}=\sum_{i=1}^{n}a_{i}b_{i}=a_{1}b_{1}+\ldots+a_{n}b_{n}\). Let \(\{v_{1},\ldots,v_{n}\}\) be a basis of \(\mathbf{F}_{2}^{n}\). For any \(r^{\prime}\) let \(V_{r^{\prime}}\) denote the span of \(\{v_{1},\ldots,v_{r^{\prime}}\}\) and \(V_{r^{\prime}}^{c}\) the span of \(\{v_{r^{\prime}+1},\ldots,v_{n}\}\); it is clear that \(V_{r^{\prime}}+V_{r^{\prime}}^{c}=\mathbf{F}_{2}^{n}\); moreover, if we let \(v,w\in V_{r^{\prime}}\) and \(v^{\prime},w^{\prime}\in V_{r^{\prime}}^{c}\) then  
\[v+v^{\prime}=w+w^{\prime}\quad\Longrightarrow\quad v=w\;\;\mbox{and}\;\;v^{ \prime}=w^{\prime}. \tag{27}\]  
This property is normally summarized by saying that \(\mathbf{F}_{2}^{n}\) is the _direct sum_ of \(V_{r^{\prime}}\) and \(V_{r^{\prime}}^{c}\), i.e., \(V_{r^{\prime}}+V_{r^{\prime}}^{c}=\mathbf{F}_{2}^{n}\) and \(V_{r^{\prime}}\cap V_{r^{\prime}}^{c}=\{0\}\).  
The vectors \(v_{1},\ldots,v_{r}\) are used as the rows of \(P_{C}\), the parity check matrix for the error correcting code which yields the syndrome \(\boldsymbol{\xi}=\mathbf{x}P_{C}^{\mathrm{T}}\); the vectors \(v_{r+1},\ldots,v_{r+m}\) are used as the rows of a privacy amplification matrix \(P_{K}\) such that if \(\mathbf{x}\) is the string sent by [PERSON], then the \(m\)-bit key is \(\mathbf{x}P_{K}^{\mathrm{T}}\). Let  
\[d_{r,m}\triangleq\min_{r\leq r^{\prime}<r+m}d_{H}(v_{r^{\prime}+1},V_{r^{ \prime}})=\min_{r\leq r^{\prime}<r+m}d_{r^{\prime},1}. \tag{28}\]  
This parameter on which security depends relates in terms of Hamming distance the parity strings used to generate the key \(\mathbf{k}\) to the parity strings used to generate the error correction information \(\boldsymbol{\xi}\). A large value of \(d_{r,m}\) will be shown to imply little information for [PERSON] on the key \(\mathbf{k}\), given she knows \(\boldsymbol{\xi}\) (Theorem 8).
## 2 Conventional SOM, Its Selected Generalizations, and Related Work
### Growing SOMs (GSOMs) and Growing Grid Networks (GGNs)  
GSOMs and GGNs are methods of improving the learning speed of the network and reducing the risk of poor \"unfolding\" its structure in the initial phase of the map formation. It is achieved by \"starting the map with very few units and increasing that number progressively until the map reaches its final size\" (a quotation from [7]). In GSOMs--according to [7]--\"when the number of units increases, the locations of new units are interpolated from the locations of the old units\"--see also the illustration in Figure 9. In turn, in GGNs--according to [8]--\"by inserting complete rows or columns of units the grid may adapt its height/width ratio to the given pattern distribution\". Figure 2 (Initialization-, WTM-algorithm-, and GSOM- or GGN-blocks) presents extensions of the conventional-SOM learning algorithm for the cases of GSOMs and GGNs. These extensions consist in modification of the Initialization block and adding the GSOM block or GGN block, respectively. As far as the initialization is concerned, the initial dimensions of the map are set to \(m_{1}\times m_{2}=2\times 2\). The GSOM block introduces--at the end of each epoch--a mechanism that doubles the dimensions of the map by replacing each old neuron with four new ones as shown in Figure 9. In turn, the GGN block activates mechanism that inserts new columns or rows of neurons into the map.  
Figure 10 presents several stages of the GSOM's evolution for the more complex data set of Figure 6a (similar results are generated by GGN). In general, there are two stages in the map formation. In the first one the map \"unfolds\" itself to place the neurons in the desired order (see Figure 10a-g). In the second one the collection of neurons asymptotically approaches the distribution of data (see Figure 10h,i). Figure 11 shows the U-matrix (with final values of \(m_{1}=32\) and \(m_{2}=32\)) for that data set. It is clear that GSOM is much more efficient than the conventional SOM in the initial phase of the map formation. However, the final results of both approaches are comparable (compare Figure 11 with Figure 8), i.e., the images of the input data in terms of detecting data concentration are  
Figure 8: Gray-level U-matrix map for the visualization of the synthetic data set of Figure 6a (U-matrix is calculated for the neuron map of Figure 6i).  
Figure 9: Increasing the Growing SOM (GSOM)’s structure: (**a**) before. (**b**) during, and (**c**) after performing a single step of the operation.
## 4 Brillouin Optical Correlation Domain Analysis: BOCDA
### Overview of Performance Improvement Schemes for BOCDA
#### 4.3.5 Time Division Polarization Diversity Scheme to Reduce Polarization-Induced Noise  
When the sensing fiber is an ordinary single mode fiber, the state of the polarization (SOP) can easily be changed under the lightwave propagation along the fiber, which creates a rather large fluctuation in the BOCDA output. To reduce the difficulty, a scheme named the \"time-division polarization diversity scheme\" is proposed [57]. In the scheme, the probe lightwave polarization is switched between the orthogonal SOPs, such as the two orthogonal linear polarizations. The two BOCDA outputs that correspond to the two SOPs are measured, and these are then squared and added together. This calculated value is stabilized against the SOP fluctuation. The stabilization is guaranteed by the optical fiber's \"unitary\" nature, in which the two orthogonal SOPs are always orthogonal with each other, even when the shapes of the SOP are changed by the perturbation in the fiber.  
3.6 Double Lock-In and Beat Lock-In Scheme to Enhance Spatial Resolution and Reduce Reflection-Induced Noise  
In the basic BOCDA system, only the pump lightwave is modulated in its intensity for the lock-in detection to enhance the S/N ratio. In the system, there are several fiber connection points or splicing points, which have the possibility of making the back reflection. These reflections act as noise, which cannot be eliminated by the lock-in detection, because the reflected waves are also modulated by the IM for the lock-in detection. To overcome the difficulty, a \"double lock-in and beat lock-in scheme\" [58] has been proposed. In these schemes, the probe lightwave is additionally modulated for the additional lock-in detection. In the double lock-in scheme, two synchronous detections are done in tandem, with the two intensity modulation frequencies. In the beat lock-in scheme, only one synchronous detection is done with the beat frequency between the two IM frequencies. The influence of non-desired back reflection and Rayleigh backscattering is dramatically reduced. An example of the scheme is shown in the next section. This scheme is also effective when we enhance the spatial resolution by applying a large FM amplitude.
## 6 Application
### Customized Microbiota as Donors  
Once a substance or gene is known to have a positive effect on a disease, then the substance-modified microbiota from donors can be used in FMT as a transplantation solution. For example, a ketogenic diet (KD) is known to be useful in the treatment of refractory epilepsy, but the mechanisms underlying its neuroprotective effects remain unclear. By transplanting the most abundant microbiota from the KD-diet mice into antibiotic-treated mice, [PERSON] et al. revealed a potential mechanism by which the gut microbiota modulates the host's metabolism and susceptibility to seizures [20]. Similarly, phlorizin (PHZ), a phytonutrient in apples, can promote good body health. [PERSON] et al. performed FMT by transplanting the feces of PHZ-fed mice to the high-fat-diet (HFD)-fed mice, confirming that feeding HFD mice the gut contents of the PHZ-modulated mice attenuates HFD-induced metabolic disorders [55]. Similar studies also used genetically engineered mice or known disease-tolerant races as bacteria donors [56; 57]. Another method is the application of a mixture of several bacteria from a donor. These can be core microbiota found through meta-analysis in previous studies, and their function can be re-validated by FMT [2; 58].
## 3 Experimental Results
### Degradation of Axial Rigidity  
The axial stiffness of the experiments can be calculated as follows [8]:  
\[K_{i}=N_{i}/\Delta_{i} \tag{1}\]  
In which, \(N_{i}\) and \(\Delta_{i}\) denote the peak load and corresponding displacement under every loading, respectively, \(i\) denotes the step of loading, and \(K_{i}\) denotes the axial stiffness. The initial axial  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Specimen No.**} & \multicolumn{2}{c}{**Crack Load**} & \multicolumn{2}{c}{**Yield Load**} & \multicolumn{2}{c}{**Ultimate Load**} & \multicolumn{2}{c}{**Failure Load**} \\ \cline{2-10}  & \(N\)/MN & \(\Delta cr\)/mm & \(N\)/MN & \(\Delta y\)/mm & \(N\)/MN & \(\Delta p\)/mm & \(N\)/MN & \(\Delta u\)/mm \\ \hline HGW-1 & 3.6 & 0.463 & — & — & 20.46 & 3.535 & 20.46 & 3.535 \\ HGW-2 & 5.4 & 0.697 & — & — & 23.20 & 3.617 & 21.91 & 3.960 \\ HGW-3 & — & — & 21.63 & 2.927 & 26.14 & 7.503 & 19.42 & 20.743 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristic values.  
Figure 6: Load-displacementcurves. (**a**) HGW-1 and HGW-2; (**b**) HGW-3.  
stiffnesses \(K_{1}\) of the three specimens, utilizing Equation (1), were 14,005 kN/mm, 14,980 kN/mm, and 15,372 kN/mm, respectively.  
The calculated formula for the initial axial stiffness is defined as follows [8]:  
\[K_{cal}=E_{\mathrm{s}}A_{\mathrm{s}}+E_{\mathrm{c}}A_{\mathrm{c}} \tag{2}\]  
In which \(E_{\mathrm{s}}\) and \(E_{\mathrm{c}}\) denote the elastic modulus of the steel plate and concrete, respectively, and \(A_{\mathrm{s}}\) and \(A_{\mathrm{c}}\) denote the cross-sectional area of the steel plate and concrete, respectively. The calculated values of the three specimens are 13,511 kN/mm, 14,021 kN/mm, and 14,352 kN/mm, respectively, which are lower than the test values of 3.66%, 5.49%, and 7.11%, mainly because the concrete inhibition effect is not considered in the calculation of the elastic modulus.  
Figure 7 shows the stiffness degradation curves of the axial stiffness versus the axial displacement. For specimens HGW-1 and HGW-2, the corresponding stiffness of the failure load, declined by 58.8% and 62.9%, respectively, when compared to the initial stiffness. For specimen HGW-3, the stiffness of the yield load declined by 51.9%, the stiffness of the ultimate load decreased by 77.3%, and the stiffness of the failure load declined by 99.4%. Therefore, it is revealed that the stiffness degradation of specimen HGW-3 is faster than specimens HGW-1 and HGW-2 at every stage, and is sufficient during the whole progress.