esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Radio Frequency Identification for URLLC in Industrial IoT Networks
## 6 Conclusions  
In this paper, we first proposed an RFI-aided URLLCs system with massive MIMO that aims to improve transmission efficiency and access robustness with low cost and computational complexity. To obtain a remarkable machine-learning-based model, we involved SSL with meta pseudo labels. We established a novel one-dimensional RFIResNet for smarts devices' physical-layer access. We experimentally validated the performance of RFISSL on the basis of practical LTE datasets, and results showed that identification error converged to \(10^{-5}\), with samples equal to 4500. In the proposed short-packet URLLC network, in a typical industrial application, we formulated the general analytical framework of finite block-length uplink transmission, which is the downlink symmetry. Our proposed RFISSL model only needed about 0.52 ms to finish online physical-layer access with limited computing resources. This satisfies the requirement of practical URLLCs. Furthermore, we provided an evaluation of the error probability bound and network availability by utilizing saddle-point approximation. Lastly, numerical experiment results demonstrated that introducing RFI and massive MIMO into URLLCs is a promising technique to improve transmission efficiency and robustness.  
Figure 11: Performance of URLLC uplink transmission. (**a**) Error probability against number of antennas M with different channel correlation levels. (**b**) Network availability against average error probability with different channel correlation levels.  
**Author Contributions:** Conceptualization, T.Z., P.R. and D.X.; methodology, T.Z.; software, T.Z. and Z.R.; validation, T.Z.; formal analysis, T.Z. and Z.R.; investigation, T.Z. and Z.R.; resources, P.R. and D.X.; data curation, T.Z.; writing--original draft preparation, T.Z.; writing--review and editing, T.Z., P.R., Z.R. and D.X.; visualization, T.Z. and Z.R.; supervision, P.R. and D.X.; project administration, T.Z. and P.R.; funding acquisition, P.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The work was completed with the financial support of National Natural Science Foundation of China under grant no. 62071373 and the Innovation Talents Promotion Program of Shaanxi Province under Grant No. 2021 TD-08.  
**Institutional Review Board Statement:** not applicable.  
**Informed Consent Statement:** not applicable.  
**Data Availability Statement:** The datasets in this paper can be downloaded from [[https://github.com/ZhanyiRen/LTE-Datasets.git](https://github.com/ZhanyiRen/LTE-Datasets.git)]([https://github.com/ZhanyiRen/LTE-Datasets.git](https://github.com/ZhanyiRen/LTE-Datasets.git)) (accessed on 15 March 2022).  
**Conflicts of Interest:** the authors declare no conflict of interest.
# PV-Powered Charging Station for Electric Vehicles: Power Management with Integrated V2G
## 5 Conclusions  
This paper presents a power management strategy with integrated V2G to reduce the peak pressure of the public grid while meeting the needs of users. In the strategy, PV sources, battery storage, and the public grid are used to charge EVs in sequence; at the \"peak\" periods of the public grid, PV sources and EVs are discharged to the public grid one after another to play the role of V2G. The SPVA is proposed to judge the optimal charging/discharging start time of EVs, through its arrival time, departure time, \(SOC_{EV\_in}\), and \(SOC_{EV\_final\_lim}\), to achieve peak shaving and valley filling while reducing the cost of the public grid. In addition, in order to better maintain the stability of the public grid, the \"peak\" periods and \"valley\" periods of the public grid correspond to different power limits. Simulation results show that the charging station, based on a DC microgrid, can operate well considering the EV charging/discharging behaviors; the proposed SPVA is highly effective in reducing the cost of the public grid by calculating the optimal start operating time of EVs, while ensuring the stability of the public grid. The limitation of SPVA is that it does not consider the operating cost of the EV charging station. In the future, we can take into account the power prediction and load prediction through global optimization to minimize the operating cost of the EV charging station.  
With the popularity of EVs, there will be certain rules for EVs arriving at the charging station every day, thus, the EV prediction system will be designed which can predict the current situation of EVs based on the EV data of the past few days. In addition, PV data can be predicted based on the weather forecast. With these two predictions, EV charging stations will have predictions about the usage of the public grid and batteries, which can better control the system and improve the stability of the system, laying a solid foundation for the production of fully intelligent EV charging stations in the future.  
Conceptualization, D.W., M.S., and F.L.; methodology, D.W., M.S., and F.L.; software, D.W.; validation, D.W.; formal analysis, D.W., M.S., and F.L.; investigation, D.W., M.S., and F.L.; resources, D.W., M.S., and F.L.; writing--original draft preparation, D.W.; writing--review and editing, M.S.; supervision, M.S., and F.L.; project administration, M.S.; funding acquisition, M.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by ADEME France in the context of the call for projects APRED 2017, project MOBEL_CITY grant number #1766C0006.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline _Algorithms_ & **ICDA** & **SPVA** \\ \hline \(EV\) _number_ & 16 & 16 \\ _Number of finishing CH or DCH_ & 11 & 11 \\ _Energy cost of public grid power_ (\(c\mathbf{\epsilon}\)) & \(-\)280.4216 & \(-\)294.1568 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results comparison of ICDA and SPVA.
###### Abstract  
The weakness of the dispersion-managed link, which is combined with optical phase conjugation to compensate for optical signal distortion caused by chromatic dispersion and the nonlinear Kerr effect of the standard single mode fiber is, its limited structural flexibility. We propose a dispersion map that can simultaneously compensate for the distorted wavelength division multiplexed signal while increasing the configurational flexibility. Each residual dispersion per span (RDPS) in the former half of the proposed link is randomly determined, and in the latter half, the arrangement order of RDPS is the same as or inverted in the former half. We confirm that the dispersion maps in which the RDPS distribution pattern in the latter half is opposite to the arrangement order in the former half are more effective in compensation, and the compensation effect is better than in the dispersion map of the conventional scheme. The notable result of this study is that the flexibility can be increased by randomly arranging RDPS in the former half, and compensation improvement can be achieved by inversing the order of RDPS arrangement of the former half in the latter half, which makes the dispersion profile of each half link roughly symmetric with respect to the midway optical phase conjugator.  
dispersion management; mid-span spectral inversion; dispersion map; optical phase conjugator; residual dispersion per span; random distribution; chromatic dispersion; nonlinear Kerr effect; wavelength division multiplexed [1]Applied Sciences  
1][PERSON] [2] and [PERSON] [2]
## 2 Materials and Methods  
### Endophytic Bacteria Isolation  
A durum wheat root sample was collected from a cultivated saline field (EC = 6.50 dS/m) in Ghor Sweimeh (31\({}^{\circ}\)48\({}^{\prime}\)52\({}^{\prime\prime}\) N; 35\({}^{\circ}\)37\({}^{\prime}\)42\({}^{\prime\prime}\) E; elevation: \(-\)249 m below sea level), as described previously [16]. To isolate endophytic bacteria, root samples from a selected durum wheat plant were washed under tap water for 10 min to remove adhering soil particles. Subsequently, root tissues were disinfected by immersing them in 70% ethanol for 1 min, followed by rinsing with sterile distilled water. Surface sterilization was then performed using a 3% sodium hypochlorite solution with a small amount of Tween 20\({}^{\circ}\) (Sigma-Aldrich, Steinheim, Germany) for 10 min, followed by six rinses with sterile distilled water. The effectiveness of the root surface sterilization was confirmed by plating an aliquot (100 \(\mu\)L) from the sixth rinse solution onto nutrient agar (NA) supplemented with 10% NaCl and incubating for 5 days at 28 \({}^{\circ}\)C to check for microbial growth.  
After ensuring the roots were sterile, the surface-sterilized root tissue (1 g) was macerated with a mortar and pestle in 10 mL of 1% NaCl solution. Thereafter, serial dilutions of the macerate were then prepared. Thereafter, 1 mL of the root extracts and the dilutions were spread onto NA medium supplemented with 10% NaCl. Afterwards, the plates were incubated at 28 \({}^{\circ}\)C and observed for bacterial colony formation for up to one week, following established protocols [18].  
Distinct colonies showing unique morphological features were selected and purified through three rounds of subculturing on NA media supplemented with 10% NaCl and stored in a glycerol stock at \(-\)80 \({}^{\circ}\)C. One isolate, designated G34, was assigned a unique code indicating the collection site, with \"34\" representing the isolate number.
# Machine Learning for Physical Layer in 5G and beyond Wireless Networks: A Survey
## 3 Physical Layer in 5G
### Physical Layer Issues
NR primarily has two frequency ranges: (1) 450-6000 MHz is defined for FR1, and (2) 24,250-52,600 MHz is defined for FR2. Physical impairments have bad effects on signal propagation. To implement the solution, an estimation of transmission quality for the light path is required. Trained ML and DL play a vital role in optimizing the existing algorithms for the estimation process. ML and DL-based algorithms constantly rely on input features that link occupation, length of the path, modulation format, source and destination nodes, etc. More features assist in precise estimation, while the bit error rate of a light path remains under the quality of transmission. Hence, the signal propagation remains less affected [296]. The traffic metrics and light-path requests are used to predict the future demand, and the selected path will be cost-efficient. Furthermore, the estimation can cause errors; for example, the light path may be impractical if the bit error rate (BER) does not go beyond the threshold, and its complexity will be increased by increasing the size of networks. Figure 7: Fifth-generation frame, subframe, and subcarrier spacing. The increasing space dimension in resources enhances the crosstalk factor and affects service transmission quality (QoS). During the interval, multi-dimensional resource utilization declines, and the complexity of resource fragmentation increases, so the link quality may degrade [297]. Delay-sensitive applications may suffer from service provisioning where at first crosstalk is calculated, and then resource fragmentation is estimated. The whole process introduces more time; therefore, the delay in connection establishment increases, and the quality of service decreases [298]. Some other hybrid solutions involve saving the shortest paths throughout the routing process while considering the number of hops in the path and modulation format. ML-based approaches optimize transmission quality, and resource allocation has been implemented in [299]. Several ML-based algorithms can learn the distribution of input samples, which depends upon data types and availability, and classify the topology of the input vector.
# An Improved Opportunistic Routing Protocol Based on Context Information of Mobile Nodes
## 2 Proposed Protocol
The message M2 of node A is forwarded to node B, since both the average distance and average time values of node B are smaller than those of node A and the delivery predictability of node B is larger than that of node A, which is the forwarding condition when the hop count of a message is larger than 0 and both the average distance and average time information are available. Figure 3: Flowchart of the proposed protocol. time information are available, as in Equation (7). On the other hand, the message M3 of node A is not forwarded to node B, since the forwarding condition Equation (7) is not met, i.e., the delivery predictability of node B to the destination node D is smaller than that of node A, although both the average distance and average time values of node B are smaller than those of node A. The message M5 of node B is not forwarded to node A, since the forwarding condition Equation (4) is not met, i.e., the delivery predictability of node A to a node E, which is the destination node of M5, is not larger than that of node B and both average distance and average time values of node A are not smaller than those of node B. The message M7 of node B is forwarded to node A, since the delivery predictability of node A to node F, which is the destination node of message M7, is larger than that of node B, although both average distance and average time values of node A are not smaller than those of node B, as in Equation (6), in order to disseminate the message M7 more. Figure 4: An example of message deliver: (**a**) before contact, (**b**) after contact.
## 3 Model Violations
### Materials
#### 3.3.5 Thickness-Dependent Optical Constants  
The basic concept of optical thin film theory described so far relies on the possibility to perform a clear separation between material parameters (optical constants) and geometrical parameters (thicknesses) from each other [16]. This is a fundamental requirement, and it allows, for example, in a coating design task, the combining of optical constants and film thicknesses in an arbitrary manner. In fact, this is not always possible. Once the optical constants represent macroscopic parameters, obtained after averaging the atomic or molecular responses in a thermodynamically relevant volume fraction, this concept may collapse when the film thickness becomes too small. Thus, atomic or molecular monolayers are more reliably modeled in terms of the microscopic polarizability [135,136]. This is clearly an extreme example, but a \"thickness dependence\" of optical constants is also obtained in thin metal films, when the thickness becomes smaller than the mean free path of the conduction electrons in the metal [137; 138; 139]. Also, a clear separation between effective optical constants and thickness of a metal island film is problematic [140]. In a more recent publication, [PERSON] [141] proposed a design procedure for metal-island-film-based coatings while taking the thickness dependence of their effective optical constants into account explicitly.  
Presently, research is also directed on making use of nanolaminates as versatile building blocks in optical coating design [142; 143; 144; 145]. The hope is that because of quantum confinement effects [147; 148], new combinations of optical gap and refractive index may become accessible, thus overcoming the restrictions emerging from the Moss or Ravindra rules [41]. However, those nanolaminates are strongly anisotropic (see Figures 6 and 7), and the efficient exploitation of confinement effects may be restricted to special illumination and polarization geometries. Thus, in the case of normal incidence (Figure 6), the electric field strength vector \(\mathbf{E}\) is in the \(x\)-\(y\)-plane and cannot induce electric dipole transitions along the \(z\)-axis (i.e., between different discrete energy levels of the electron moving along the confinement direction in the quantum well; see the selection rules discussed in [148]). At oblique incidence and p-polarization, however, such transitions shall be possible, because the electric field strength vector has a non-vanishing component along the \(z\)-axis. Hence, even the absorption behavior of such nanolaminates is expected to be strongly anisotropic, which offers new development directions in the field of polarizing absorptive optics.  
Figure 6: Nanolaminate at normal light incidence.  
Figure 7: Nanolaminate at oblique light incidence and for p-polarization.
# A Compact Sub-GHz Wide Tunable Antenna Design for IoT Applications
## 2 Design Details
### Antenna Design Procedure  
This section provides a step-by-step antenna design procedure and optimization steps to obtain the final design. The first step to designing the proposed antenna was designing a rectangular slot-line antenna structure with dimensions 24 mm \(\times\) 15 mm, fed with 50 \(\Omega\) microstrip line. The antenna was resonating above the 2 GHz bands. The dimensions of the slot were optimized to make it resonate above 1.5 GHz by increasing the electrical length of the radiating slot. To further reduce the resonance frequency to lower bands, the rectangular structure was turned into a meandered structure to achieve a resonance frequency that is below 2 GHz, as shown in Figure 4a. For the given meandered structure, the slot structure was resonating at 1.75 GHz. The antenna was further optimized by increasing its electrical length, as shown in Figure 4b. The given structure was working at 1.305 GHz. The width of each slot as well as the distance between different meandered slots were optimized to tune the antenna to be effectively loaded with varactor capacitance to cover the maximum sub-GHz bands of the proposed antenna structure. For the same antenna structure as shown in Figure 4b, the theoretical resonance value calculated based on Equation 1 was 1.287 GHz. Hence, a very close agreement between the theoretical and simulated values was observed.  
A single varactor diode was utilized to bring the resonance frequency down to sub-GHz bands, as well as a continuous sweep of frequencies was obtained over a wide band. Figure 4a-c show the real(\(R_{e}\)) and imaginary(\(I_{m}\)) parts of input impedance (\(Z_{in}\)). It can be seen that Re\(\{Z_{in}\}\) is around 50\(\Omega\) while the Im\(\{Z_{in}\}\) is crossing the zero value at the resonating bands. The placement of the diode was very critical as the input impedance (\(Z_{in}\)) matching was dependent on it. The varactor diode's various locations resulted in different reactive loading effects on the radiating structure. To obtain an optimum Zin matching, several parametric analyses were performed, including its placen  
Figure 4: Zin curves (**a**) Stage-1 \(Z_{in}\) curves (**b**) \(Z_{in}\) curves of the optimized design (**c**) \(Z_{in}\) curves with varactor loading (**d**) Re\(\{Z_{in}\}\) for various capacitance values.  
The antenna optimized dimensions and the placement of diodes resulted in a continuous frequency sweep from 758\(\sim\)1034 MHz, which can thus support several narrow-band IoT applications below 1 GHz. Figure 4d shows the Re[\(Z_{in}\)] for different capacitance values of 0.84, 0.90, 1.24, 2.09, and 5.08 pF. It has been observed that the capacitive loading helped in matching the \(Z_{in}\) at different resonating bands for different values of reverse bias voltages across the varactor diode.
## 3 Data Pre-Processing
### Variational Auto-Encoder Based on Gaussian Mixture Model
#### 3.2.2 Gaussian Mixture Model  
The Gaussian model is a commonly used variable distribution model. The probability density function of one-dimensional Gaussian distribution is as follows:  
\[f\left(x\mid\mu,\sigma^{2}\right)=\frac{1}{\sqrt{2\pi\sigma}}exp\left(-\frac{ \left(x-\mu\right)^{2}}{2\sigma^{2}}\right) \tag{9}\]  
In the formula, \(x\) is a variable, \(\mu\) and \(\sigma^{2}\) are the mean and variance of Gaussian distribution, respectively.The essence of the Gaussian mixture model is to fuse several single Gaussian models to make the model more complex. The one-dimensional Gaussian mixture distribution is expressed as follow:  
\[p\left(x\right)=\sum_{k=1}^{K}\omega_{k}f\left(x\mid\mu_{k},\sigma_{k}^{2}\right) \tag{10}\]  
In the formula, \(K\) is the number of single Gaussian distribution in the Gaussian mixture model. \(\omega_{k}\) is the weight of the \(k\)th Gaussian distribution, and \(0\leq\omega_{k}\leq 1\), \(\sum_{k=1}^{K}\omega_{k}=1\). Theoretically, if there are enough single Gaussian models fused by the Gaussian mixture model and the weight design between them is reasonable enough, the Gaussian mixture model can fit arbitrarily distributed samples [34].  
Since we cannot directly observe which distribution the data \(x\) comes from, we introduce a latent variable \(z_{k}\) represents the probability that data \(x\) comes from the \(k\)th Gaussian distribution. Assuming that \(z_{k}\) is independent and identically distributed, then \(p\left(z\right)=p\left(z_{1}\right)p\left(z_{2}\right)\cdots p\left(z_{k} \right)=\prod_{k=1}^{K}\pi_{k}^{z_{k}}\). Where \(z_{k}\in\left\{0,1\right\},\sum_{k=1}^{K}z_{k}=1,p\left(z_{k}=1\right)=\pi_{k}\). The probability form of conditional probability based on latent variable \(z\) is as follows:  
\[p\left(x\mid z\right)=\prod_{k=1}^{K}N\left(x\mid\mu_{k},\sigma_{k}^{2}\right) ^{z_{k}} \tag{11}\]  
Thus, the form of \(p\left(x\right)\) is as follows:  
\[p\left(x\right)=\sum_{z}p\left(z\right)p\left(x\mid z\right)=\sum_{z}\left( \prod_{k=1}^{K}\pi_{k}^{z_{k}}N\left(x\mid\mu_{k},\sigma_{k}^{2}\right) \right)=\sum_{k=1}^{K}\pi_{k}N\left(x\mid\mu_{k},\sigma_{k}^{2}\right) \tag{12}\]
# Seismic Response Study of L-Shaped Frame Structure with Magnetorheological Dampers
## 5 Analysis and Interpretation of Results
### Results Analysis on Shear Force and Bending Moment of the Structural Columns  
Under the excitation of seismic waves, the maximum shear force and bending moment of the column on every floor of the L-shaped frame structure with and without MRDs are plotted in Figure 12. As shown in Figure 12, the maximum shear force and the bending moment of the column on each floor of the structure with MRDs are obviously lower than those without MRDs, and the shear forces at both ends of the column are equal. Taking the column (Nodes 20 and 34) on the second floor as an example, as shown in Figure 12a, under the excitation of the Taft wave, the horizontal bi-directional maximum shear forces of the column of structure without MRDs are 184.90 and 220.93 kN, and those with MRDs are 128.35 and 167.67 kN, which are decreased by 30.58% and 24.11%, respectively. As shown in Figure 12b, throughout the duration of the Kobe wave, the horizontal bi-directional maximum shear forces of the column of structure without MRDs are 265.29 and 302.03 kN, and those with MRDs are 195.07 and 247.98 kN, which are decreased by 26.47% and 17.90%, respectively.  
As shown in Figure 12, the inflection point of the column on the top floor appears in the lower middle of the column, the inflection point of the column on the ground floor appears in the upper middle of the column, and those on the other floors appear in the middle of the column. Throughout the duration of seismic waves, the maximum bending moments at both ends of columns on each floor of the structure with MRDs are obviously lower than those without MRDs. Taking the column (Nodes 20 and 34) on the second floor as an example, as shown in Figure 12a, during the action time of the Taft wave, the horizontal bi-directional maximum bending moments of the column bottom of structure without MRDs are 541.61 and 468.77 kN\(\cdot\)m, and those with MRDs are 413.81 and 325.01 kN\(\cdot\)m, which are decreased by 23.60% and 30.66%, respectively. As shown in Figure 12b, throughout the duration of the Kobe wave, the horizontal bi-directional maximum bending moments of the column bottom of structure without MRDs are 731.01 and 674.36 kN\(\cdot\)m, and those with MRDs are 602.17 and 496.72 kN\(\cdot\)m, which are decreased by 17.62% and 26.34%, respectively.