esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Sharp-Switching Band-Modulation Devices
## 5 Alternative Applications of Sharp Switching  
### Static Memory Cell: Operation as Capacitorless SRAM  
The DC hysteresis observed when sweeping the Z\({}^{2}\)-FET anode voltage back and forth while monitoring the current suggests the possibility of using the Z\({}^{2}\)-FET as a single-transistor static RAM cell [26]. The static memory window, i.e., the valid anode voltages to operate the cell as an SRAM, is defined as the range between the DC current switching points V\({}_{\text{ON}}\) and V\({}_{\text{OFF}}\), see Figure 16. The high-current '1'-state is set by applying V\({}_{\text{A}}\) \(>\) V\({}_{\text{ON}}\), to overcome the injection barriers and forward-bias the cell. In contrast, the low-current '0'-state is programmed with a low anode voltage V\({}_{\text{A}}\) \(<\) V\({}_{\text{OFF}}\); the barriers are rebuilt and block the current flow. In either case, after setting the '1'- or '0'-state, the anode bias must be maintained within the limits of the memory window, V\({}_{\text{OFF}}\) \(<\) V\({}_{\text{A}}\) \(<\) V\({}_{\text{ON}}\), to avoid flipping the memory state.  
Figure 15: Current switch in response to a gate pulse showing the impact of (**a**) front-gate voltage (for V\({}_{\text{A}}\) = 0.8 V) and (**b**) ground-plane voltage V\({}_{\text{BG}}\) (V\({}_{\text{A}}\) = 1.1 V, V\({}_{\text{FG}}\) = 0.5 V, adapted from [32]). The gate pulse has a 0.5 V amplitude and varies, as in Figure 14b, between the indicated V\({}_{\text{FG}}\) value and a lower level.  
The large hysteresis, with excellent stability during memory cycling, the ease of operation, with only the anode contact as a control terminal, and the unrivalled current margin between states would seem to make the \(Z^{2}\)-FET cell a promising candidate for SRAM applications. The critical drawback is related to the power consumption in the '1'-state. Unlike the '0'-state, where the current is practically zero, the '1'-state is associated with a relatively high I\({}_{\text{ON}}\) since the anode bias cannot be reduced below V\({}_{\text{OFF}}\). Unless the \(Z^{2}\)-FET is somehow combined with a current blocking device, the power consumption would be unsustainable in billion-cell SRAM chips.
# A Metamaterial Inspired AMC Backed Dual Band Antenna for ISM and RFID Applications
## 3 Results  
### Results of the Antenna without AMC Backing  
A prototype antenna was being designed and developed. The true image and structure is represented in Figure 3c,d. The antenna without the AMC backing structure was designed in FR4 material and simulated using CST software. The simulated and fabricated results of S-parameters and VSWR of the antenna without AMC construction is shown in Figure 13a,b. The antenna without the AMC Backing exhibited resonance peaks at 0.915 GHz and 2.45 GHz that cover the bandwidth of 20 MHz and 130 MHz for simulation. The measured values exhibit 20 MHz and 130 MHz bandwidth at 0.912 GHz and 2.45 GHz frequency bands. Hence, the antenna is applicable for RFID applications (902-928 MHz) as well as WiMAX (2.31-3.49 GHz) and Bluetooth (2.346-2.906 GHz) applications. Figure 13c shows a photograph of measured values of the prototype antenna using VNA.  
The simulated gain of the antenna in the absence of AMC Backing is represented in Figure 14. In such a situation, it was detected at 0.915 GHz and 2.45 GHz frequencies as 1.53 dBi and 4.49 dBi, respectively. The simulated bandwidth obtained at the lower frequency band (0.905-0.923 GHz) was 0.018 GHz, and at the higher frequency band (2.382-2.516 GHz) it was 0.13 GHz. Further, it was observed that, the square metallic patch close to the feed line increased the performance at high frequency.  
Figure 13: Simulated and Measured Results of Antenna without AMC Backing (**a**) Reflection Coefficient, (**b**) VSWR, (**c**) Snapshot of antenna results using VNA.  
At 0.915 GHz and 2.45 GHz frequencies, radiation patterns are depicted in Figure 15. Figure 15a represents the radiation profiles which were obtained by simulation and fabrication process along the E-plane at two resonating frequencies, i.e., at 0.915 GHz and 2.45 GHz. Figure 15b represents the simulated and measured results of radiation patterns in the H-plane at 0.915 GHz and 2.45 GHz frequencies. The blue line represents the radiation pattern at 0.915 GHz, while the red line shows the radiation pattern at 2.45 GHz frequency bands. It is evident that the simulated radiation patterns at 0.915 GHz along the E-Plane had a bi-direction nature, and along the H-Plane, it was omnidirectional. The simulated radiation patterns at 2.45 GHz along the E-Plane was a dipole, and along H-Plane it was nearly omnidirectional.
## 2 Materials and Methods  
### The Initial Situation  
#### 2.1.1 Rail Traffic and Handling Facilities  
It is possible to change the path of trains from one railway line to another, by using a specialized device called a track changer. The railway switch is a crucial component of the track changer, consisting of point blades, sliding supports, and two fixed rails. The handling device operates the point blades through a traction bar. To ensure that the switches work correctly, even in harsh conditions such as heavy snow or low temperatures, each switch is equipped with a heating system, comprising four resistive elements mounted on both fixed and movable parts [15]. Figure 1 displays a switch equipped with a resistive heating system.  
Switches are located at various points in railway stations, including entry points from open lines, access to different lines within stations, and exit points, for changing the train's path. The distance between the entry switches and the exit switches of a station determines the size or limit of the station. Figure 2 shows a double-line railway station and displays the normal position and numbering of switches, and the distribution of the power supply panels (PSP) for the switch heaters.  
At present, the process of coupling or decoupling the heaters' power supply, involves the use of control keys situated within the railway station, as well as cables that connect to the PSPs using wires. Due to the elevated danger of control cable malfunction in the vicinity of the railway, alternative methods for transmitting this command have been suggested [16]. This paper presents a potential solution to address this issue, by proposing the use of a SCADA-compatible GSM support as a replacement for the unreliable physical control method [17]. The method consists of interfacing a GSM modem with a SCADA RTU (remote terminal unit), and controlling said GSM modem through the SCADA central station. This expands on the previous use for GSM technology, namely for the control of electrical installations of resistor heaters in railway switches, where control cable failure led to decommissioning [18].  
Figure 1: Diagram of a switch equipped with a resistive heating system. PSP = power supply panel.
# Higher-Order Belief Propagation Correction Decoder for Polar Codes
## 2 Preliminaries
### Belief Propagation Correction Decoder  
The BPC decoder, proposed in [22], considers the perturbation on code bits located at the rightmost stage of the factor graph, which are corrupted by channel noise. When a decoding failure occurs in the original BP decoder, the decoding attempts after correction are performed to generate a valid estimate. Specifically, the correction operations assign both \(+\infty\) and \(-\infty\) to the \(L_{n+1,j}\) of identified code bits in turn, and additional decoding attempts are executed until the estimate fulfills the error detection and termination check (EDTC) criterion. Each decoding attempt involves a one-bit correction. Compared with the flipping on information bit, the simulation results show that the correction on code bit has a greater impact on the decoding result. The correction set \(\mathcal{S}\) is established based on the reliabilities of the left-propagating message determined by the channel LLRs. The decoder selects \(T\) indices with the lowest magnitude of \(L_{n+1,j}\), i.e., \(\mathcal{S}\gets j\in\{1,2,...,N\}\) of \(T\) smallest \(|L_{n+1,j}|\). The stopping tree [22] is utilized to reduce the sorting range. The CRC is selected as the EDTC. Let the symbol in bold denote the matrix for LLR messages. Algorithm 1 gives a brief description of the BPC decoding.  
```
0: LLR(\(y_{1}^{N}\)), \(I_{max}\), \(T,\mathcal{I}\).
1: Initialize L and R by Equations (3) and (4)
2:\(\hat{u}_{1}^{N}\leftarrow\) conventional BP decoding with CRC termination and \(I_{max}\).
3:if CRC(\(\hat{u}_{1}^{N}\)) = true then
4:return\(\hat{u}_{1}^{N}\).
5:else
6: Store the messages of current state in vectors \(\mathbf{L}^{\prime}\) and \(\mathbf{R}^{\prime}\).
7: Obtain the correction set \(\mathcal{S}=\{j_{1},j_{2},...,j_{T}\}\).
8:for\(k=1\) to \(T\)do
9: Refresh the L and R by \(\mathbf{L}^{\prime}\) and \(\mathbf{R}^{\prime}\). And assign the \(\pm\infty\) to \(L_{n+1,j_{k}}\) in turn.
10: Perform additional BP iterations with CRC termination and get \(\hat{u}_{1}^{N}\).
11:if CRC(\(\hat{u}_{1}^{N}\)) = true then
12:return\(\hat{u}_{1}^{N}\).
13:endif
14:endfor
15:endif
16:\(\hat{u}_{1}^{N}\).
```  
**Algorithm 1** The BPC decoding algorithm.
## 3 Results and Discussion
### Effect of the Adsorbent Dose on the Adsorption Capacity of X-PR and MGC  
The study of the effect of the dose on the adsorption rate of the two dyes X-PR and MGC under experimental conditions is given by Figure 12a,b for the two materials BS-HVL and BC-HVL. The adsorption of the two dyes increases with the increase of the adsorbent dose during its range of study. This is due to the increased number of free sites on the adsorbent surface. The increase is quite large in the range of 0.01 to 0.07 g, but for adsorbent masses higher than 0.07 g, the variation becomes very small.
# Advancing the State of the Fog Computing to Enable 5G Network Technologies
## 7 Convergence of 5G and Fog Computing
### The Needs for Fog and 5G Network Technologies
#### 7.1.6 Management of Resources  
Resource management is paramount in fog computing for leveraging and managing fog services and resources. It has been noted that there is normally restricted energy remaining in fog nodes and devices at the edge. Therefore, the management and allocation of resources has a significant influence on the durability and performance of fog network. However, to improve the processes of low-latency and mobility in fog computing, the methods of resource management need to be concentrated. It is especially required, when factors such as fog nodes, migration, placement, device strengthening at edge, application module needed to be well studied. Moreover, these factors have their adverse effects during processing of latency and when decision is meant to be made. To resolve this issue, some solutions are suggested as; the resource virtualization of fog nodes may improve resource management competency. Another approach involves enabling context-awareness technology for services and management of resources in fog computing. The management of energy and resources within the domain of context-awareness technology may ensure improved usage of resources and energy conservation [65]. It is important to note that resource discovering and sharing in fog computing is paramount for application performance enhancement. It can alternate flooding and centralized modes naturally by conserving energy from diverse networks. A robust mobile cloud computing resource discovery approach was proposed to transform between flooding and centralized strategies in saving energy from diversified networks automatically [66].
# Practical Performance Analyses of 5G Sharing Voice Solution
## 4 Voice Services Based on 5G Sharing
### SA Sharing EPS Fallback Process
#### 4.2.2 EPS Fallback Based on Redirection  
As can be seen in Figure 6B, after the 5G registration process, the pre-process is the same as the handover-based EPS fallback. After the gNodeB responds to the 5 GC that the UE context setup is done, it will send a blind A2 event measurement control to the UE.  
Figure 6: EPS Fallback flow based on handover/redirection.  
Then, the UE responds that the RRC connection reconfiguration is completed. For the blind A2 event, gNodeB sends RRC release with target frequency and other information to UE, which performs random access to the target LTE cell based on the target frequency and other information. UE receives the random access response from the target LTE cell. At this point, the redirection-based EPS fallback process is completed.  
Equation (5) is the calculation method of voice access delay under redirection.  
\[T_{EPSFB-redirection}=T_{redirection\ judgment}+13\delta+2\eta+3\gamma \tag{5}\]  
\(T_{redirection\ judgment}\) is the redirection judgment delay. There are thirteen communications between UE and gNodeB, two communications between UE and eNodeB, andthree times of communications between gNodeB and 5 GC. The meanings of the remaining parameters in Equation (5) are the same as those investigated in Table 1.  
Subsequently, EPS Fallback voice service is taken over by VoLTE; 5G only conducts data service and only conducts interoperability with 4G to ensure voice service continuity. It should be noted that EPS fallback allows 5G UE to reside in 5G NR but does not provide voice service. Due to the delay in the fallback process, the call setup time increases.
# Research on New Drilling Downlink Communication Based on Anisotropic Magnetoresistive Technology
## 4 Experimental Test and Analysis
### Test and Analysis of the Factors Affecting the Quality of Download  
In order to further study the influence factors of downlink communication quality, the influence factors (emission voltage, electrode distances and temperature) were tested, respectively. The BER of the communication system under different transmission conditions is obtained, and the data results are analyzed to provide a reference for further reducing the BER.
## 2 Scientific Background  
The K-means clustering algorithm is a partitional clustering technique that splits a dataset into \(k\) number of clusters using a specific fitness measure. That is, given a dataset \(X,\ X\) is divided into \(K\) non-overlapping groups \(C=\{c_{1},\ c_{2},\ldots,c_{k}\},c_{i}\
eq\varnothing,\ i=1,\ldots,\) and such that \(\cup_{1}^{k}c_{i}=X;c_{i}\cap c_{j}=\varnothing,\ i,j=1\ldots k\) and \(i\
eq j.\) The partitioning process is handled as an optimization problem with the fitness measure taking as the objective function such as minimizing the distances between data objects or maximizing the correlation between data objects [10]. Mathematically, the optimization problem for cluster analysis is defined as follows:  
Given a dataset \(X=\{x_{i}\},\) where \(i=1,2,\ldots n\) of d-dimension data points of size \(n,\ X\) is partitioned into '\(k\)' clusters such that  
\[J(c_{k})=\sum_{x_{i}\in c_{k}}||x_{i}-\mu_{k}||^{2} \tag{1}\]  
with the objective function: minimize the sum of the square error over all the \(k\) clusters. That is, minimize  
clustering algorithm seeks to optimize the number of combinations in the assignments of \(N\) objects into \(k\) clusters. This is mathematically represented as:  
\[S(N,K)=\frac{1}{K!}\sum_{i=0}^{K}(-1)^{K-i}\left(\begin{array}{c}K\\ t\end{array}\right)t^{N} \tag{3}\]  
In finding the optimal cluster number, the search space is mathematically represented as:  
\[B(N)=\sum_{K=1}^{N}S(N,K) \tag{4}\]  
The task of finding an optimal solution for this problem when \(k>3\) is NP-hard [6; 13] and this makes the search computationally expensive for moderately sized problems [6; 14]. In recent times, there has been an immeasurable increase in the magnitude of data being generated. The current real-world datasets are characterized as being high dimensional and massive in size. Automatic clustering of such datasets with no background knowledge of the features of the data objects can be termed a difficult task. Without prior domain knowledge, it is difficult to determine the appropriate number of clusters for a massive, high-dimensional dataset. Moreover, due to the enormous size of data objects in real-world datasets, the distribution of data objects into appropriate clusters to produce an optimal cluster result is computationally intensive and time-consuming.
# A Comparative Evaluation of Cement and By-Product Petrit T in Soil Stabilization
## 3 Results and Discussion
### Water Content and Density
For further investigation of the curing time effect on the affinity between soil moisture content and the liquidity index, it is observed that the increase in curing time cause a further decrease in the liquidity index and the soil moisture content as illustrated in Figure 6. Thus, adding cementitious binder (cement or Petrit T) results in a reduction in the water content towards the plastic limit of soil after treatment. As mentioned by [32], an increase in soil strength is observed when the soil water content is reduced from the liquid limit to the plastic limit. Consequently, the relationship between the consistency limits and soil strength after treatment will be presented and discussed in more detail later in this study. The effect of the added cement and Petrit T on the bulk density of the soil after being treated with various percentages of binder is presented in Figure 7. For both binder types, the soil density increases as the binder content and curing time is increased. The bulk density of the cement specimens is higher than for the Petrit T specimens due to adding 1 to 4% binder content. In contrast, a slight increase in the specimen density of Petrit T compared to cement is observed at 7% binder content and longer curing period (see Figure 7). Consuming water content due to hydration and pozzolanic reactions produces a large amount of solid particles in the soil, which consequently leads to an increase in the  
Figure 5: Binder content versus liquidity index of soil after one hour of treatment. Figure 6: Liquidity index versus soil water content for all curing times. density of the soil after treatment. The increase in specimen's density is attributed to the production and deposition of CSH and CAH, which subsequently fill the pore voids [13; 31; 32; 46].
## 3 Method
### Volume Reconstruction  
The last phase is the volume reconstruction of studied cases. We reconstructed the lungs' volumes at first then the infection volumes in order to calculate the volume ratio of each case to obtain at last the correspondent infection rate. For this step, we used the platform Thermo Scientific Amira Software [46], which is a powerful multifaceted 2D-5D platform for visualizing, manipulating and understanding data from many image modalities including CT, MRI, and others. Below, Table 3 demonstrates the volume ratios calculated using the lung and infection reconstructed volumes of the 20 patients from the used dataset which respects the fact that the proportion of infections in the lungs range from 0.01% to 59% as stated in [41].  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Slices** & **Lungs** & **Infection** & **Volume** & **Infection** \\  & **Number** & **Volume** & **Volume** & **Ratio** & **Severity** \\ \hline
**P1** & 301 & 3312776 & 408920 & 0.1234 & Moderate \\ \hline
**P2** & 200 & 4622672 & 181422 & 0.0392 & Mild \\ \hline
**P3** & 200 & 3498208 & 1015819 & 0.2903 & Severe \\ \hline
**P4** & 270 & 4536387 & 59529.4 & 0.0131 & Mild \\ \hline
**P5** & 290 & 4746608 & 80654.7 & 0.0169 & Mild \\ \hline
**P6** & 213 & 4430680 & 125977 & 0.0284 & Moderate \\ \hline
**P7** & 249 & 2916567 & 88225.8 & 0.0302 & Moderate \\ \hline
**P8** & 301 & 3818469 & 278690 & 0.0729 & Moderate \\ \hline
**P9** & 256 & 2639075 & 101621 & 0.0385 & Moderate \\ \hline
**P10** & 301 & 2274445 & 396574 & 0.1743 & Severe \\ \hline
**P11** & 39 & 5516346 & 1067853 & 0.1935 & Severe \\ \hline
**P12** & 45 & 5736035 & 1044140 & 0.1820 & Severe \\ \hline
**P13** & 39 & 4617109 & 156231 & 0.0338 & Moderate \\ \hline
**P14** & 418 & 8186436 & 263209 & 0.0321 & Moderate \\ \hline
**P15** & 110 & 4138735 & 1023851 & 0.2473 & Severe \\ \hline
**P16** & 66 & 7882785 & 777185 & 0.0985 & Moderate \\ \hline
**P17** & 42 & 3136991 & 9.01817 & 0.003 & Mild \\ \hline
**P18** & 45 & 5729041 & 46320.6 & 0.0080 & Mild \\ \hline
**P19** & 45 & 6664643 & 148246 & 0.0222 & Mild \\ \hline
**P20** & 93 & 4842892 & 2844976 & 0.5874 & Severe \\ \hline \end{tabular}
\end{table}
Table 3: Quantitative results of the volume reconstruction phase for three different patients from the used dataset.  
As the table shows, the used dataset contains a total of six mild infected patients having a volume ratio inferior to 0.02, eight moderately infected patients having a volume ratio between 0.02 and 0.15 and six severely infected patients having a volume ratio superior to 0.15. This clearly demonstrates that we used a well-balanced dataset. Figure 6a-c below illustrate the volume reconstruction of the lungs and its corresponding infection regions of the most representative three patients, respectively. Volume reconstruction for all patients is given in Figure 7.  
Figure 6: Volume reconstruction of the lungs and its correspondent infection regions of: (**a**) a patient presenting a mild infection; (**b**) a patient presenting a moderate infection; (**c**) a patient presenting a severe infection.
## 4 Conclusions  
In conclusion, we have studied lensless GI with a TGSM beam in simulated oceanic turbulence. The turbulence in the calculation is assumed to be homogeneous and isotropic. Analytical expressions for the FOCF between \(D_{1}\) in path one and \(D_{2}\) in path two are derived by applying a tensor method and Rytov approximation. Based on the derived formulae, the influences of the oceanic turbulence and the twist phase on the quality and visibility of image are analyzed in detail through numerical examples. Our results show that the turbulence always plays a negative role which degrades the  
Figure 7: Image of double slits at several different propagation distances \(z\) (m) (**a**) in free space and (**e**) in oceanic turbulence. The variation of CC with the propagation distance (**b**) in free space and (**f**) in oceanic turbulence. The variation of visibility with the propagation distance (**c**) in free space and (**g**) in oceanic turbulence. Evolution of the coherence width with the propagation distance in turbulence at several values of twist factors (**d**) in free space and (**h**) in oceanic turbulence.  
quality and visibility of the image. Compared to \(\varepsilon\) and \(\omega\), the turbulence parameter \(\chi_{T}\) is the main factor in determining the quality/visibility of the image. The twist phase can improve the visibility of the image under the same condition, but the quality is decreased. The quality/visibility with larger twist factor is less affected by those with smaller twist factor. Thus, one may modulate the characteristics of the ghost image by adjusting the twist factor of the beam. Our results will be useful in underwater communication/imaging that uses a GI system to transmit the image or information for reducing the oceanic turbulence.  
Data curation, Y.L.; writing--original draft, Y.L., X.L., L.L., F.W., and Y.C.; supervision, L.L., F.W., Y.Z., and Y.C.; project administration, Y.C. All authors read and approved the final manuscript.  
This work was supported by the National Natural Science Foundation of China (91750201, 11525418, 11774251, 11804198, 11874046) and the Natural Science of Shandong Province (ZR2019 BA030).  
The authors declare no conflict of interest.