esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Based Resource Allocation in E2E Network Slicing with Both Public and Non-Public Slices
## 5 Conclusions  
In this paper, we propose a network slicing resource allocation problem for the scenario where both public and non-public slices coexist. The proposed problem is first formulated as a nonlinear integer programming problem. Then, the IPPO-RF algorithm is proposed to find the resource allocation scheme that makes the operator profits as large as possible. Based on the simulation results, it can be concluded that, firstly, the well-trained RF model has a high accuracy in predicting the feasibility of VNF mapping solutions. Hence, the incorporation of machine learning, particularly the RF algorithm, accelerates the decision-making process of the IPPO algorithm. In addition, the proposed IPPO-RF algorithm outperforms the three benchmark algorithms PPRU, MUR, and MUS by 84%, 46%, and 57%, respectively. It demonstrates that the IPPO-RF algorithm has good performance. Finally, the resource allocation scheme found via the IPPO-RF algorithm not only satisfies all constraints, but also ensures the service priority and privacy of the NPN slices. Therefore, the algorithm based on the combination of deep reinforcement learning and machine learning proposed in this paper provides a worthy idea for solving the problem.  
Conceptualization, Z.P. and X.Y.; methodology, Y.W., N.L. and Z.P.; formal analysis, Y.W. and N.L.; writing--original draft preparation, Y.W.; writing--review and editing, Y.W., N.L., Z.P. and X.Y.; supervision, N.L., Z.P. and X.Y.; Funding acquisition, N.L., Z.P. and X.Y. All authors have read and agreed to the published version of the manuscript.  
This work is partially supported by the Fundamental Research Funds for the Central Universities, 2242022K60001, the National Natural Science Foundation of China under Grants 62071115, and the Research Fund of National Mobile Communications Research Laboratory, Southeast University (No. 2023A03).  
Not applicable.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Success Rate** \\ \hline IPPO-RF & 1.00 \\ \hline PPRU & 0.42 \\ \hline MUR & 0.54 \\ \hline MUS & 0.61 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of success rates of different algorithms.  
Figure 11: Performance comparison of different algorithms.
# A Method for Autonomous Generation of High-Precision Time Scales for Navigation Constellations
## 4 Time-Frequency Driving Algorithm Principle  
### Atomic Frequency Standard Error Model  
Frequency stability is an important indicator of atomic frequency standards, which refers to the frequency or phase changes within a specified time interval. The main causes include deterministic trend items and non-deterministic noise items. The error mathematical model of atomic frequency standards [15] is shown in Equation (14):  
\[x(t)=x_{0}+y_{0}t+\frac{Dt^{2}}{2}+\varepsilon(t) \tag{14}\]  
Figure 2: Genetic Algorithm Parameter Optimization Flowchart.  
where the deterministic trend term includes the initial time error \(x_{0}\), the initial frequency deviation \(y_{0}\), and the linear frequency drift \(D\) caused by aging and temperature changes. \(\varepsilon(t)\) is the random frequency deviation of different types of noise such as stationary noise and non-stationary noise, mainly including: WPM, FPM, WFM, FFM and RWFM noise, and it can be represented by the power law noise spectrum model of the classic oscillator given by [PERSON] [27]:  
\[S_{y}(f)=h_{2}f^{2}+h_{1}f^{1}+h_{0}+h_{-1}f^{-1}+h_{-2}f^{-2} \tag{15}\]  
where \(S_{y}(f)\) represent the power spectral density; \(h_{2}\), \(h_{1}\), \(h_{0}\), \(h_{-1}\) and \(h_{-2}\) represent the noise term coefficients of WPM, Flicker Phase Modulation (FPM), WFM, Flicker Frequency Modulation (FFM) and RWFM, respectively.  
\[S_{y}(f)=\frac{f^{2}}{f_{0}^{2}}S_{\phi}(f)=2\frac{f^{2}}{f_{0}^{2}}L(f) \tag{16}\]  
where \(S_{\phi}(f)\) is the phase noise spectral density, \(f_{0}\) is the center frequency of the frequency source, and \(f\) is the sideband frequency. From Equations (15) and (16), the SSB phase noise spectral density of the frequency standard [28] can be obtained per Equation (17):  
\[L(f)=\frac{f_{0}^{2}}{2}\left(h_{-2}f^{-4}+h_{-1}f^{-3}+h_{0}f^{-2}+h_{1}f^{-1 }+h_{2}\right) \tag{17}\]  
Five kinds of noise parameters can be fitted by non-negative least squares for total Hadamard variance.
# Investigations into Frost Flower Physical Characteristics and the C-Band Scattering Response
## Materials and Methods
### Physical Measurements  
Near-surface (h = 70 cm) meteorological measurements included air temperature and relative humidity (Vaisala HMP45C, temperature accuracy = \(\pm\)0.4 \({}^{\circ}\)C, relative humidity accuracy = \(\pm\)2%) as well as wind speed and direction (Gill WindSonic Anemometer, wind speed accuracy = \(\pm\)2% at 12 m/s, direction accuracy = \(\pm\)3% at 12 m/s). The surface temperature of the seawater and later sea ice was measured remotely using an Apogee SI-111 infrared sensor (calibration uncertainty = 0.02 \({}^{\circ}\)C, measurement repeatability <0.05 \({}^{\circ}\)C). Ice and seawater temperatures were measured vertically at 2-cm intervals (top 32 measurements) and then at 5-cm increments from 65 cm to 190 cm. Then it was measured at 10-cm increments to the bottom of the pool using Type-T 24 AWG thermocouples (special-limits-of-error, specified deviation of 0.1 \({}^{\circ}\)C over 1000\({}^{\circ}\) of wire, Omega Inc., St-Eustache, QC, Canada). The thermocouples were mounted in a polyvinyl chloride pipe suspended into the water from a taught aircraft cable running across the pool (N-S) above the surface. The surface of the sea ice formed in the pool at the level of the fourth thermocouple from the top of our vertical array.  
Frost flowers were carefully sampled from the sea ice using a pre-cleaned plastic spatula and placed into sterilized Whirl-Pak water-tight bags for measuring salinity in the lab. Scrapings of  
Figure 1: The SERF pool located on the University of Manitoba campus. The C-band scatterometer was mounted on the west side of the pool (showed in a stowed protected configuration at the end of the experiment). The LiDAR is located within the protected area beneath the scatterometer. Inset shows the automated meteorological equipment.  
the ice surface were formed for measuring salinity using a smooth stainless steel edge and placed into sterilized Whirl-Pak water-tight bags. Bulk samples of the newly-formed sea ice volume were extracted using a thin Japanese-style rip/crosscut saw and stored in Whirl-Pak bags to melt at room temperature. Physical samples were taken in triplicate (with one exception for the first frost flower samples). The conductivity and temperature of the melt of each sample were measured using a conductivity meter (Thermo Scientific Orion Star A212, Fisher Scientific Company, Ottawa, ON, Canada). The sample salinities were calculated from the conductivity, according to the formulation of Fofonoff [28]. We used Kimwipes(r) (a disposable, high-quality tissue paper) to detect surface wetness by touching the tissue to the surface and observing if any liquid was picked up by the fabric.
# An Analysis of MPTCP Congestion Control
## 7 MPTCP Congestion Control
### Uncoupled CC  
With uncoupled CC, each MPTCP subflow behaves as an independent TCP flow, with its own instance of a CC algorithm running independently. For each subflow, _cvand_ and transmission rate are controlled separately to maximise the use of the path. Uncoupled algorithms are the simplest form of MPTCP CC. Reno [34], NewReno [35], CUBIC [36], and Vegas [37] are among the CC algorithms that can be deployed as uncoupled CC within MPTCP. For example, CUBIC is the current default algorithm for TCP in Linux distributions which is highly scalable and stable when the network bandwidth-delay product is large. Using uncoupled CC makes performance and operation predictable. The uncoupled algorithms are effective at increasing overall MPTCP throughput and are highly responsive to path changes [1; 6; 32].  
However, if \(N\) subflows in an MPTCP connection are competing against a single-path TCP flow at a shared bottleneck, the compound MPTCP throughput will be approximately \(N\) times higher than that of a single TCP flow. Therefore, uncoupled CC cannot achieve Goal 2 [6]. For example, if an MPTCP with 3 subflows transmits through the same bottleneck as a regular TCP, the MPTCP connection will acquire approximately 75 percent of the bottleneck's capacity, effectively starving regular TCP.  
If each MPTCP subflow uses its own instance of a TCP CC, a TCP flow competes fairly over a shared link with a single MPTCP subflow. Let us consider a simple case of a TCP connection and a single MPTCP subflow sharing a link with transmission rate \(R\). Assuming that the two flows have the same MSS and RTT and that no other TCP/UDP/MPTCP connections traverse the shared link, they should achieve similar _cvand_ and throughput.  
The amount of link bandwidth jointly consumed by the two flows is less than \(R\). As such, no loss will occur and both flows will increase their window by one MSS per RTT as a result of the CA algorithm. When the link bandwidth jointly consumed by the two flows exceeds \(R\), packet loss will occur. The flows will then decrease their windows by a factor of two. Because the joint bandwidth use is less than \(R\) again, the two flows will resume increasing their _cvand_ and therefore - throughput. Eventually, loss will again occur and the two connections again decrease their _cvand_. This process keeps going on and the bandwidth realised by the two flows eventually fluctuates along the equal bandwidth share line.  
Figure 5: TCP Furthermore, MPTCP CC examples.
# Experimental Analysis of Pore and Permeability Characteristics of Coal by Low-Field NMR
## 2 Coal Samples and Experiments
### Low-Field NMR Test  
The NMR measurement was carried out using the MesoMR23-060H-I (NIUMAG Co., Ltd., Suzhou, China). The magnetic field intensity, resonance frequency, magnet temperature, probe coil diameter, waiting time, echo time, superposition times, and echo number of the NMR instrument were 0.52 \(\pm\) 0.05 T, 24.68 MHz, 32.00 \(\pm\) 0.02 \({}^{\circ}\)C, 25 mm, 3000 ms, 0.15 ms, 128 times, 10,000, respectively.  
The NMR tests were conducted on the coal samples, which were dried, to different saturation degrees and on centrifugal samples under different centrifugal forces, to understand the NMR signals of various samples under different water contents. The dried coal samples preparation was as follows: coal samples were placed in a drying oven and dried for more than 48 h at 70 \({}^{\circ}\)C. The saturated coal samples preparation was as follows: the dried coal samples were evacuated, saturated in deionized water for 24 h under the negative pressure, and then saturated for about 100 h under 0.1 MPa.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample ID** & **Mining Area** & **Sample Direction** & **Coal Rank** & \(R_{\alpha,\max}\) **(\%)** & \(M_{\alpha}\) **(\%)** & \(A_{\delta}\) **(\%)** & \(V_{\Delta t}\) **(\%)** & \(FC_{\delta}\) **(\%)** \\ \hline CZ-1 & Jincheng & bedding & anthracite & 2.69 & 3.53 & 9.72 & 7.23 & 83.75 \\ CZ-2 & Jincheng & bedding & anthracite & 2.87 & 2.58 & 9.90 & 7.72 & 83.14 \\ CZ-3 & Jincheng & vertical & anthracite & 2.79 & 2.56 & 9.87 & 7.74 & 83.15 \\ CZ-4 & Jincheng & bedding & anthracite & 3.10 & 3.46 & 11.45 & 7.32 & 82.07 \\ WY-1 & Luan & bedding & bitumite & 1.94 & 1.03 & 8.91 & 13.13 & 79.13 \\ WY-2 & Luan & vertical & bitumite & 2.05 & 1.00 & 8.92 & 13.20 & 79.06 \\ YJL-1 & Yulin & bedding & sub-bitumite & 0.66 & 6.17 & 3.77 & 29.51 & 67.83 \\ YJL-2 & Yulin & vertical & sub-bitumite & 0.69 & 6.18 & 3.78 & 29.51 & 67.83 \\ \hline \hline \end{tabular}  
* \(R_{\alpha,\max}\), max vitrinite reflectance; \(M_{\alpha,\min}\), moisture (as air-dried basis); \(A_{\delta}\), ash (as dry basis); \(V_{\Delta t}\), volatile (as dry, ash-free basis); \(FC_{\delta}\), fixed carbon (as dry basis); CZ, Chengzhuang coal mine; WY, Wuyang coal mine; YJL, Yujialiang coal mine.  
\end{table}
Table 1: Coal sample information, proximate analysis, and vitrinite reflectance.  
Figure 1: Coal samples used in the NMR experiment.  
The centrifugal coal samples preparation was as follows: saturated coal samples were placed in the high-speed refrigeration then centrifuged at different rotational speeds. We recorded the coal samples' weight before and after each centrifugation.
## 3 Results and Discussions
### The Co-Registration of Temporarily-Coherent Points  
It is expected that the co-registration offset derived from the TCPs is superior to that derived from the evenly-distributed window. Figure 4a exhibits that the difference between the interferometric phases of the resampled TCP from the conventional and TCP polynomials (Table 2). It is observed that the phase difference can be up to more than \(2\pi\). Since the conventional polynomials were estimated from the windows over the whole image, which is dominated by the water, the offsets from the pixels on the water can be very unreliable and degrade the co-registration results. By using the TCP polynomials, an improvement in coherence can be achieved for all the TCPs (see Figure 4b), with an average value of about 0.12.  
Figure 7: **(a)** The deformation rates of the identified TCPs for the Donghai Bridge; **(b)** An enlarged view of the deformation rates of the pier-stayed part of the bridge; **(c)** An enlarged view of the deformation rates of the island. Triangles represent the reference point; **(d)** An enlarged view of the deformation rates of the cable-stayed part of the bridge; **(e)** A field picture of the cable-stayed part of the bridge. The figure is plotted in the WGS 84 coordinate system.  
It should be highlighted that the displacement signal would not be removed or reduced by the co-registration using only coherent points along the bridge. Indeed, the pixel offset tracking method has always been used to monitor the ground deformation associated with geophysical phenomena, such as earthquakes [40]. However, this method can generally reach an accuracy of 1/30 pixel, equaling to about 30 cm LOS deformations when PALSAR FBD images are used. This level of precision is insufficient to detect the bridge's deformation of several centimeters. Therefore, the TCP co-registration would not affect the follow-on deformation estimation.
## 5 Influence of Relative Density on the Maximum Damping Ratio  
The maximum damping ratio (\(\lambda_{\rm{max}}\)) of granular materials at various relative densities (\(\rm{D_{r}}\)) is presented in Figure 9. Figure 9 shows that \(\lambda_{\rm{max}}\) inversely correlates with \(\rm{D_{r}}\), decreasing as \(\rm{D_{r}}\) increases, a trend that is opposite to that observed for the maximum dynamic shear modulus.  
Figure 9 also reveals that the attenuation rate of \(\lambda_{\rm{max}}\) progressively diminishes with increasing \(\rm{D_{r}}\). This is likely due to tighter particle contacts at higher \(\rm{D_{r}}\), resulting in smaller relative displacements under external forces, thereby leading to a reduced damping ratio.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Specimen Number**} & \multicolumn{3}{c}{**Maximum Dynamic Shear Modulus**} \\ \cline{2-4}  & \(e\) & \(f\) & \(R^{2}\) \\ \hline S1 & 25.653 & 69.060 & 0.961 \\ S2 & 22.076 & 82.776 & 0.986 \\ S3 & 21.066 & 88.061 & 0.975 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fitting results of Equation (4).  
Figure 8: \(G_{\rm{dmax}}\)–\(D_{r}\) fitted relationship curves.  
Figure 9: \(\lambda_{\rm{max}}\) vs. \(D_{r}\) fitted relationship curves.  
Analysis and fitting of these 12 datasets using fitting software suggest that D\({}_{r}\) and \(\lambda_{\text{max}}\) exhibit a power law relationship, expressed by this Equation:  
\[\lambda_{\text{max}}=mD_{r}^{n} \tag{5}\]  
where \(m\) and \(n\) are parameters.  
Fitting these 12 datasets with Equation (5) and summarizing the results in Table 3, Table 3 demonstrates a high congruence between the fitting curves and the experimental data, with coefficients of determination exceeding 0.93. The maximum discrepancy between the fitted and experimental values of \(\lambda_{\text{max}}\) was under 5%. Hence, this study posits that the relationship between relative density \(D_{r}\) and \(\lambda_{\text{max}}\) can be effectively described by Equation (5).
# Resource Optimization for 3D Video SoftCast with Joint Texture/Depth Power Allocation
## 3 Minimum Distortion Optimization
### Problem Formulation
\[g_{i}^{*}=\sqrt{\frac{\sigma_{n}^{2}\cdot\rho_{i}}{\lambda_{i}}}=\sqrt{\frac {\sigma_{n}^{2}\cdot N\cdot\text{SNR}_{chk}}{\sqrt{\lambda_{i}}\sum_{i=1}^{K} \sqrt{\lambda_{i}}}}=\lambda_{i}^{-1/4}\sqrt{\frac{P}{(h\kappa w)\sum_{i=1}^{ K}\sqrt{\lambda_{i}}}} \tag{18}\]  
From (12), the total power budget \(P\) is inversely proportional to the reconstruction distortion. Increasing the power budget increases \(\rho_{i}^{*}\) and therefore decreases the distortion. Nevertheless, the bandwidth resource \(K\) in (17) is the summation term upper bound. Its discrete nature complicates the effort to attain the optimal bandwidth \(K^{*}\) in closed form.
## 5 WST Applications
### Fire Detection  
Current surveillance systems use a camera, an infrared sensor system and a satellite system. These systems cannot support real-time surveillance, monitoring and automatic alarm. A wireless sensor network can detect and forecast forest fire more promptly than the traditional satellite-based detection approach. WSN based fire surveillance systems was designed and implemented. WSN measure temperature and humidity, and detect smoke [68, 69].
## 2 System Module
### The Placement of Fault Indicator Based on LoRa and NB-IoT  
Currently, power companies often install a fault indicator every fixed distance, which lacks scientific and effective methods. The installation method seems simple and effective, and the higher the number of installations, the more accurate the fault localization. However, in fact, the method wastes a large number of manpower and material resources, and the actual effect of fault localization is not obviously improved with the increasing number of installations. Therefore, in the proposed method, the placement of the acquisition unit and collection unit is crucial and needs to be optimized.  
Figure 3: The working principle of fault indicator based on LoRa and NB-IoT.  
Figure 2: The layout of the collection unit of the proposed fault indicator.  
The acquisition unit of the fault indicator includes a current acquisition function and a wireless communication function of which the current acquisition function is mainly considered. By adding the number of acquisition units of fault indicators in the distribution network, the location interval of fault section will be smaller and the inspection time will be less, so the outage time will reduce and users of distribution networks will benefit from it. However, it will increase the economic cost. Meanwhile, the more acquisition units are configured, the more collection units need to be configured. Some communication constraints are imposed on the optimization problem due to the need for reliable communications between acquisition units and collection units. Although a collection unit can communicate with acquisition unit within a radius of 10 km (the further the signal is transmitted, the less reliable it is) [24], increasing the acquisition unit number will lead to an increase in the amount of data that the collection unit needs to transmit. However, the communication performance of NB-IoT is certain. Therefore, in order to ameliorate communication reliability, we need to add the number of collection units, which is less economical. Therefore, we need to consider a balance to reasonably configure the number and location of acquisition units and collection units of fault indicators on the line to ensure power-supply reliability and communication reliability together.
# Communication Network Standards for Smart Grid Infrastructures
## 3 Discussion  
Sustainable energy infrastructure design must be based on interoperable, standardized technologies, which ensure the quality of services provided, energy, information, security, and privacy [41]. A key condition is overcoming competitive monopoly efforts for the prevalence of business solutions or standards and, respectively, the full consensus in the establishment, development, and support of existing energy standards. Coordinated efforts should be made to address the gaps and problems identified in existing standards and develop new ones, which will ensure the further adoption of smart grid technologies [7].  
An important fact that confuses energy infrastructure designers when it comes to communications is the multiple choices in existing standards and rules that can determine the form, timing, order, control, and correction of errors during information transmission [79]. The fact that the established standards overlap themselves creates serious confusion which lead to functional gaps.  
A significant process which is currently at an initial level but is estimated to contribute greatly to the further development of intelligent energy infrastructure in terms of communications is the process of their certification based on an international ISO standard. This standard will provide a set of policies, guidelines, and documented procedures to ensure that no key elements required by an intelligent network to be successful are omitted. This process will consider evaluation criteria and provide documentation of communication standards mapping, thus providing the essential framework for further developments.