esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Details
13C[1H] NMR (100 MHz, CDCl\({}_{3}\)): \(\delta\) 146.44, 146.17, 132.71, 132.64, 126.05, 123.51, 118.60, 116.25, 88.21, 85.61, 71.57, 70.27, 69.11. _4-bromo-N,N-bis(4-(ferrocenylethynyl)phenyl)aniline_, **5e**: tribromotriphenylamine (640 mg, 1.33 mmol) was dissolved in minimal THF: NE\({}_{3}\) (1:1, 14 mL) in a microwave vial. The solution was sparged for 30 min. Ethynyl-ferrocene (2.1 eq., 590 mg, 2.79 mmol) was added dropwise and the solution was then sparged for an additional 5 min before CuI (0.1 eq., 30 mg, 0.16 mmol) and Pd(dppf)Cl\({}_{2}\) (0.1 eq., 110 mg, 0.15 mmol) were added to the reaction mixture. The microwave vial was then sealed, and the reaction was heated to 65 \({}^{\circ}\)C overnight. The reaction mixture was dried in vacuo and the crude product was dissolved in DCM and washed with saturated aqueous NH\({}_{4}\)Cl and ascorbic acid solution. The product was dried over MgSO\({}_{4}\) and then the solvent was removed in vacuo. The product was isolated by column chromatography using heptane:DCM (4:1) as the eluent (R\({}_{\text{f}}\) = 0.25). The product was an orange solid. (300 mg, 0.40 mmol, 30.4%). 1H NMR (400 MHz, CDCl\({}_{3}\)): \(\delta\) = 7.40 (H\({}_{\text{g}}\), H\({}_{\text{h}}\), d, 3\({}_{\text{HH}}^{\text{J}}\) = 8 Hz, 6H), 7.08-6.96 (H\({}_{\text{f}}\), H\({}_{\text{l}}\), multi, 6H), 4.53 (H\({}_{\text{j}}\), s, 4H), 4.28 (H\({}_{\text{k}}\), H\({}_{\text{l}}\), s, 14H). 13C[1H] NMR (100 MHz, CDCl\({}_{3}\)): \(\delta\) 146.44, 146.15, 132.73, 132.63, 126.05, 123.51, 118.58, 116.26, 88.22, 85.65, 72.27, 70.32, 69.40.
## 2 THz Imaging Character of Parabolic Antennas
Considering the above analysis and the need to conserve computational resources, we have limited our simulation calculations to single scattering for this study. \[N=P\cdot R_{W}\cdot R_{V}\cdot R_{U}\cdot M=P^{{}^{\prime}}\cdot M \tag{12}\]3 Parabolic antenna imaging characteristics. (**a**) Three typical observation apertures. (**b**) Scattering intensity versus azimuth angle. (**c**) The specular point. (**d**) The edge pair-points. (**e**) The ellipse arc. For the CAD (in the second row) and corresponding imaging characteristics (in the first row), we give three typical different apertures to observe, as shown in \[N=P\cdot R_{W}\cdot R_{V}\cdot R_{U}\cdot M=P^{{}^{\prime}}\cdot M \tag{12}\]4 All observation apertures are with the same breadth, 3\({}^{\circ}\), and the observing center angles \(\varphi\) are 10\({}^{\circ}\), 46\({}^{\circ}\), and 70\({}^{\circ}\). From left to right, they are the three typical imaging characteristics, namely, specular point, edge pair-points, and ellipse arc, respectively. It can be found that the main body and solar panels are clearly discernible in all three observation apertures. In the case of the specular reflection point, as shown in Figure 6a, the parabolic antenna is not visible in the image; the specular scattering point occupies the majority of the energy, which is located at left to the satellite, and aligns with the analysis of component prior. In the case of the non-specular direction, there are two statuses of the parabolic antenna, corresponding to Figure 6b,c. It is easy to see that the parabolic antenna can be distinctly found in the image when it appears in the shape of an ellipse, which is shown in Figure 6c; and the parabolic antenna will be discretized into two endpoints when there are other strong scattering points in the observation aperture, as shown in Figure 6b. The readers can verify the position of the paraboloid by comparing the optical image with the corresponding viewing angle. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Scattering Component** & **Description** & **Scattering Mechanism** \\ \hline \multirow{3}{*}{Parabolic antenna} & Dish-shaped structure that is designed to receive and transmit signals & Single/multiple reflection \\  & Crucial component that provides electrical power & Single/multiple reflection \\  & to the satellite & Single/multiple reflection \\  & Box-like structure that & \\ \multirow{2}{*}{Main body} & houses the majority of the satellite’s instruments, & Single/multiple reflection \\  & electronics, and systems & \\ \multirow{2}{*}{Lens} & Cavity structure that is a & \\  & key component in cameras & Multiple reflection \\  & or telescopes & \\ \multirow{2}{*}{Propeller} & Cavity device that converts & \\  & rotational motion & \\  & into thrust & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Scattering mechanism of each component of satellite. Figure 5: Satellite CAD model with 5 main scattering components (**left**) and its geometry and size (**right**).
# Advances in Emerging Memory Technologies: From Data Storage to Artificial Intelligence
## 4 New Systems with New Memories
### Emerging Memories for Non Von Neumann Systems
#### 4.2.2 In/Near Memory Computing  
In-memory computing uses nonvolatility and the ability to couple computing with data, such as through Ohm's law to perform multiplication. Artificial neural networks take advantage of this by reducing the amount of data movement compared to von Neumann architectures. The expected performances strongly depend on the application and targeted system. However, general guidelines can be drawn. First, co-integration and persistence of the memory are prerequisite, while endurance and capacity are the key expected features. In order to move computing tasks in the emerging memory, endurance has to remain as close as possible to SRAM performances: the higher the endurance, the more important the amount of computing that can be done in the emerging device. Then, increasing the memory capacity will enable improving system complexity and performance. Again, the gain in memory capacity has to be significant with respect to what can be achieved with SRAM. As endurance and capacity increase, the system efficiency and performance will be improved.  
Figure 9: Performance vs. energy efficiency in computing systems.  
Today, RRAM and PCRAM endurance is insufficient to perform computing in a device, and only specific computing tasks can be achieved with a limited number of cycles. On the contrary, MRAM appears to be a promising technology due to its excellent endurance. The challenge of this technology will be to improve its capacity, using more scalable spin-based concepts [103], in order to envisage the development of more complex and better performing systems.  
Ternary content addressable memory (TCAM) can also be listed as a computing architecture. TCAM performs parallel searches by comparing input searched data with data stored in the memory and returning the data address when a match occurs. TCAMs provide a lookup response in a single clock cycle making them faster and more energy-efficient than random access memory-based search systems. Emerging memories and in particular RRAMs are a promising solution to implement TCAMs and can offer more area- and energy-efficiency with respect to static random access memory (SRAM)-based TCAMs [104].
# Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
## 1 Introduction
### Real-World Pathways  
Since the Paris Agreement was adopted in 2015, nations worldwide have made major efforts to implement decarbonized power grids. For example, in the United States, over200 cities and counties plan to use only renewable energy to meet all of their electricity needs over the next decades [5]. Four states have already enacted 100% clean energy mandates, and a further thirteen are drafting similar legislation [6]. Six more states have introduced nonbinding goals of 100% renewable electricity [4]. South Korea has recently finalized a plan to achieve carbon neutrality by 2050 [7; 8; 9], mainly by restricting coal and natural gas power plants and replacing internal-combustion-engine vehicles (ICEVs) with electric vehicles (EVs) and hydrogen-powered vehicles (HPVVs). Two scenarios were considered when evaluating the sectors of electricity, transportation, and hydrogen, for example. The details differ; flexible adaptive measures can be taken when unexpected environmental and business events arise. Specifically, in the first scenario, all thermal power plants using coal, natural gas, and oil are scrapped. The second scenario abolishes coal-fired power plants but retains natural gas-fired plants to ensure grid reliability, creating 20.7 million metric tons of carbon dioxide in 2050 (92.3% less than the 269.6 million metric tons of 2018). In the second scenario, the transformation of coal-fired plants to natural gas-fired plants is considered; carbon-capture and storage (CCS) technologies will be deployed to compensate for the carbon emissions. The proportion of renewable energy is planned to increase from 6.2% in 2018 to 30.2% in 2030, while the share of nuclear energy will increase marginally to 23.9% in 2030 from 23.4% in 2018. In terms of the transportation sector, the scenarios require that 97% and 85% (respectively) of all vehicles are EVs or HPVs. The remaining ICEVs use environmentally friendly fuels and are fitted with direct-air-capture devices. It is envisaged that the transportation sector will respectively emit up to 2.8 and 9.2 million metric tons of carbon dioxide in 2050, thus 97.1% and 90.6% less than the 98.1 million metric tons of 2018. Other countries are planning along similar lines [10], although small grid-specific differences are apparent.
## 4 Experimental Results
### Kinematic Tracking Accuracy  
In addition to the stop and go simulated movement, we also conducted dynamic experiments on some devices to track a moving agent that freely moves around with normal walking speed. The user was asked to press the \"_checkpoint_\" button in the CPS App to record the timestamps every time he entered into a new cell. Both the checkpoint time and Wi-Fi scan time use the same nano time of the android system.  
Figure 8 gives an example of the recorded Wi-Fi scan time sequences of Device 4. The average time difference between the checkpoint time and the Wi-Fi scans is 1.22 s. In Figure 8, there are some latencies between the checkpoint timestamps and the Wi-Fi scans since one Wi-Fi scan can take 0.6-3.7 s depending on devices. Considering that a Wi-Fi scan can take few seconds and a user can change the position while scanning is done. Thus, while a user is moving across cells, there will always be a blur in the Wi-Fi scan and the exact cell ID.  
Two kinematic trajectories were designed and repeated by different users. Each trajectory was defined as the sequence of the cell IDs along with the movement. Devices 1, 2, 3, 6 and 7 repeated Trajectory 1, while Devices 4 and 9 repeated Trajectory 2. Seven users carried the devices starting from the same cell (normally started from Cell 1), and repeated each trajectory several times. Note that the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Trajectory** & **Number of Covered Cells** & **Acc with Conditional Observation Likelihood** \\ \hline
1 & 16 & 96.53\% \\
2 & 17 & 98.16\% \\
3 & 21 & 98.04\% \\
4 & 23 & 97.02\% \\
5 & 25 & 97.05\% \\
6 & 35 & 97.50\% \\
7 & 20 & 97.20\% \\
8 & 19 & 96.99\% \\
9 & 27 & 97.16\% \\ \hline Average Accuracy & 23 & 97.29\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Matching accuracy for stop and go trajectories.  
Figure 7: Conditional likelihood function contributes to the improvement of localisation performance.
## 3 Results and Discussion
### Parameter identification  
[PERSON] et al. [29] introduced an effective length scale \(\psi\) that considered the component's shape and dimensions during solvent debinding. For a long solvent duration or supercritical fluid debinding (\(t>>0\)), and introducing the effective length scale for a cylinder, the fraction of the remaining soluble polymer can be approximately expressed as:  
\[F=\frac{C_{a}}{C_{i}}=\exp\left(-\frac{Dt\pi^{2}}{\psi^{2}}\right), \tag{6}\]  
\[\psi=\frac{\pi d^{2}\frac{L}{4}}{\pi D\left(L+\frac{d}{2}\right)}, \tag{7}\]  
where \(C_{a}\) is the average concentration during the extraction, \(C_{i}\) is the initial concentration, and \(d\) is the diameter of the cylindrical component. Introducing the diffusion coefficient \(D\), Equation (6) can be expressed as a linear relationship:  
\[\ln(-\ln(F))=\ln\left(\frac{D_{o}t\pi^{2}}{\psi^{2}}\right)-\left(\frac{E}{k} \right)\left(\frac{1}{T}\right), \tag{8}\]  
Figure 4 shows a plot of \(\ln(-\ln(F))\), according to the inverse of the temperature (\(I\)/\(T\)). Considering that the diffusivity of the solute at each temperature is constant [20], a linear regression using an ordinary least-squares method was conducted to minimize the sum of squared differences between the observed and predicted values. It was used to identify the diffusion coefficient as a function of the temperature. An apparent activation energy \(E\) of 20 kJ/mol and a pre-exponential factor \(D_{o}\) of 2.4 \(\times\) 10\({}^{-3}\) were identified from the linear regression, shown in Figure 4, with \(\psi^{2}\approx\) 2.8 mm\({}^{2}\) calculated from Equation (7).  
The diffusion coefficient \(D\) is calculated with Equation (2) using the identified activation energy and the pre-exponential factor. For a supercritical debinding at 423.15 K at 40 MPa, the diffusion coefficient \(D\) is equal to 7.46 \(\times\) 10\({}^{-6}\) cm\({}^{2}\)/s. This value is lower than the diffusion coefficient identified by [PERSON] et al. [25], which was 9.5 \(\times\) 10\({}^{-6}\) cm\({}^{2}\)/s for paraffin-wax extraction from ceramic components under 28 MPa at 345.15 K. The diffusion coefficient depends on the component shape, the powders' volume loading, and the powder size. The PEG used in this study has a high molar weight. The calculated diffusion coefficient is used for the numerical simulations of the supercritical debinding stage to predict the PEG removed from a cylindrical component.  
Figure 4: Temperature dependence and linear regression of PEG extraction after four hours’ supercritical debinding under 40 MPa.
## 6 Halophile PGPB for Sustainable Agriculture
### Biofertilization  
PGPB can successfully be manipulated in the agro-farming system as alternative strategies to control most of the stated abiotic stresses faced by crops and to increase their yields and would lead to the minimal use of synthetic fertilizers [146]. Arbuscular mycorrhize fungi and plant growth promoting bacteria are beneficial microbes in the soil which provide assistance to plants in many ways. Directly they are concerned with hormones and organic compounds synthesis while indirectly they reduce salinity stress. They can be employed in agriculture as biofertilizers [147]. Improved plant growth under saline soils has been attained with the use of PGPB [148]. Significant increase in growth attributes of rice plants with _Bacillus amyloliquefaciens_ as compared to non-inoculated control plants under salinity stress [149]. _Bacillus licheniformis_ SA03 inoculated with _Chrysanthemum_ plants grown under saline-alkaline conditions significantly decreased saline-alkaline stress in plants along with augmented biomass and survival rates via mediating cellular ABA (abscisic acid) levels [150].
## 4 Conclusions  
There is no doubt that the mechanical properties of the supporting materials play a critical role in supporting deep high-stress soft rock roadways. The mechanical properties and failure characteristics of basalt fiber-rubber granular concrete (BFRGC) are studied based on the triaxial compression tests. The supporting effects of the BFRGC with different ratios in the field application are further investigated using the finite difference method. The optimum content of basalt fibers and rubber particles in BFRGC is determined based on the results of triaxial compression tests and numerical simulation. The linear strain-softening equations of the BFRGC are deduced and then compiled in the finite-difference software to effectively simulate its post-peak strain-softening process. Therefore, the computed results are more reasonable compared with that without considering the strain-softening of supporting materials. Some main conclusions were reached and are listed below:  
1. The maximal yield strength of the BFRGC under triaxial compression tests is obtained when the mass fraction of basalt fiber in BFRGC is 0.4%. Additionally, basalt fiber can inhibit the development of fractures in plain concrete and rubber concrete, and mixing rubber particles into plain concrete can also inhibit the development of fractures of concrete.
2. Compared with the stress-strain curves of plain concrete under triaxial compression tests, the decreasing trend of BFRGC after the peak strength is increasing gentle, it is illustrated that the BFRGC has a good post-peak performance.
3. The optimum contents of basalt fibers and rubber particles in BFRGC are 0.4% and 5-10%, respectively, and the BFRGC with optimum ratios can effectively reduce the plastic region in surrounding rock of roadways and deformation of the rigid supporting structures. It may provide technical guidance for the support of deeply buried high-stress soft rock roadways.
4. The slight difference of the deformations among different supporting ways obtained by the finite difference method is partially due to the small support thickness of concrete. It is expected that the difference will be greater and that the advantage of the BFRGC with optimum proportions will become more obvious if the support thickness is increased.  
Figure 11: Controlling the effect of the basalt fiber-rubber granular concrete (BFRGC) with the optimum ratios in the surrounding rocks along the roadway.  
**Author Contributions**: Conceptualization, D.W. and Z.L.; methodology, D.W.; software, D.W., J.L. and H.S.; validation, D.W., B.P. and Z.L.; formal analysis, D.W. and Z.L.; investigation, D.W. and J.L.; data curation, D.W. and H.S.; writing--original draft preparation, D.W.; writing--review and editing, D.W. All authors have read and agreed to the published version of the manuscript.  
**Funding**: This research received no external funding.  
**Conflicts of Interest**: The authors declare no conflict of interest.
# A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization
## 3 Methodology
### PV Error Handling  
The comparison between the voltage and current predicted by the genetic optimization algorithm (\(V_{gen}\), \(I_{gen}\)) in Section 3.3 and the actual voltage and current (\(V_{act}\), \(I_{act}\)) involves a meticulous process, as depicted in the following flow chart (see Figure 7). This flow chart illustrates the systematic evaluation of the PV module's or PV array's operation by quantifying the error between predicted and actual values. The process begins with the extraction of predicted values (\(V_{gen}\), \(I_{gen}\)) and actual values (\(V_{act}\), \(I_{act}\)) from the genetic optimization algorithm and experimental measurements, respectively.  
The next step involves the subtraction of actual values from predicted values, resulting in the computation of the error for both voltage and current. This error is then compared against a predefined constant \(c\), typically set experimentally to 0.1 for enhanced precision. The decision block assesses whether the error exceeds the established threshold. If the error surpasses the threshold, the system flags the occurrence of an anomaly or deviation from expected performance. In case the error is within the acceptable range, the system proceeds to the output phase, indicating that the PV module or array is operating within the desired parameters. This systematic comparison and error analysis serve as a critical step in validating the effectiveness and accuracy of the genetic optimization algorithm in predicting the electrical characteristics of the photovoltaic system.  
The overall process encapsulated in this flow chart ensures a comprehensive assessment of the algorithm's performance, enabling researchers and engineers to identify and address discrepancies between predicted and actual values, thereby refining the modeling and optimization of the photovoltaic system (Figure 7).  
This flow chart provides a visual representation of the systematic comparison and error analysis between predicted and actual values in the context of photovoltaic system modeling and optimization. The pseudocode for the PV error-handling algorithm is presented below (Algorithm 2).  
Figure 6: Flip mutation example.  
Figure 7: Flow chart of PV error handling.  
```
1:functionhandlePVError(\(V_{gen}\), \(I_{gen}\), \(V_{act}\), \(I_{act}\), threshold)
2:\(\triangleright\) Calculate error for voltage and current
3:\(error\_V\gets V_{gen}-V_{act}\)
4:\(error\_I\gets I_{gen}-I_{act}\)
5:\(\triangleright\) Check if error exceeds the predefined threshold
6:if\(|error\_V|>\) threshold or \(|error\_I|>\) threshold then
7:\(\triangleright\) Anomaly detected, flag the occurrence
8:\(system\_status\leftarrow\) \"Anomaly Detected\"
9:else\(\triangleright\) System operates within desired parameters
10:\(system\_status\leftarrow\) \"Normal Operation\"
11:endif\(\triangleright\) Output the results
12:return\(system\_status,error\_V,error\_I\)
13:endfunction
```  
**Algorithm 2** PV Error Handling
## 3 Results and Discussion
### Evaluation of the Composites' Behavior Subjected to Physical Attacks
#### 3.2.1 Wetting-Drying in Water  
Figure 7a shows the modulus of rupture for composites before (0 cycles, the samples with SC) the wetting-drying process and after 10, 25, and 50 cycles. After SC (0 cycles), the composites with the pozzolanic substitution had lower strength values than the control (12.79 MPa). The pozzolanic composites with Acc had slightly higher strength values than the control. This was because the pozzolanic reaction better progressed with the acceleration process. This allowed a larger quantity of hydration products to form. After 10 cycles, a significant drop in the mechanical behavior of composite C was noted (9.32 MPa), but its value remained after 50 cycles. Similar behavior has been reported [14] for the control GRC: after 50 cycles, the modulus of rupture lowered from 17 MPa to 13 MPa. On the other hand, the pozzolan-containing composites had similar or higher values than 12 MPa after cycles, and greatly exceeded composite C's behavior. A large toughness difference (Figure 7b) was observed between composite C and the other composites with pozzolans. In the presence of pozzolans, toughness progressively decreased with the number of cycles, and very significant embrittlement was observed for C after 10 cycles. After all the cycles, it was noteworthy that the toughness for the pozzolanic composites with SC was always greater than it was for the composites with the accelerated pozzolanic reaction (Acc). This behavior means that previous curing strongly influenced the development of composite properties in this physical attack type.  
Figure 7: Modulus of rupture (**a**) and toughness (**b**) of the GRC composites subjected to wetting–drying tests in water.
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 5 Recent MM Techniques and 5G Architecture
### Considerations for SDN-based MM Solutions
#### Considerations for radio access network as service  
The RAN-as-a-service model fulfills parameters DL4 for dependability criteria. Next, parameters AL1, AL2, AL4 and AL5 fulfill the adaptability criteria. Furthermore, parameters EL2, EL3, and EL5 fulfill the extensibility criteria. Lastly, parameters PL4 and PL5 are fulfilled for power-saving criteria.
# Secure K-Means Clustering Scheme for Confidential Data Based on Paillier Cryptosystem
## 8 Performance Analysis
### Experimental Setup  
We evaluate our protocol's efficiency across computational time and communication overhead using four benchmark datasets with varying characteristics. All experiments were conducted on Ubuntu 20.04 with 16-core Intel Xeon CPUs (2.4 GHz) and 32 GB RAM. Table 2 presents the dataset specifications and experimental configurations.  
Dimensionality reduction via PCA (95% variance retention) for 2D-4D projections. Full 8D analysis on Yeast dataset to evaluate high-dimensional performance.