esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In conclusion, a novel photon spin routing device with a simple structure was proposed. It has good anti-interference ability and dynamic tunability because it is based on nondiffracting surface plasmon beam launching and the launching direction of the nondiffracting SPP beam can be dynamically rotated to couple to different output waveguides by changing the incident angle. For the single non-diffracting SPP-beam directional launching device, the SPP beam can reconstruct itself after passing through an obstacle, and the transmission distance is larger than 20 mm. Moreover, the device also has a good broadband response. The simulation results show that the SPP intensity contrast ratio is larger than 8 dB between 950 and 1070 nm. For the multi-nondiffracting SPP-beam launching device, the non-diffracting transmission distance is larger than 15 mm. The SPP intensity contrast ratio of the main lobe is larger than 9 dB within 140 nm (935 to 1075 nm), and the SPP intensity contrast ratio of the side lobe is larger than 6.5 dB within 50 nm (930 to 980 nm) broadband. These results may be useful for practical on-chip photon spin routing and spin-controlled multicasting.  
Conceptualization, Z.Q., H.L.; investigation, Z.Q., H.L.; writing--original draft preparation, Z.Q., H.L.; writing--review and editing, H.L., L.Y.; funding acquisition, H.L., L.Y. All authors have read and agreed to the published version of the manuscript.  
Figure 11: **(a)** The coupling effect of three nondiffracting SPP beams to three PMMA SPP waveguides when the inclined incidence angle is 0\({}^{\circ}\). The inset in **(a)** is the structure scheme of the routing device. **(b)** The SPP intensity contrast ratio between the points (\(-\)15.5 \(\upmu\)m, 4.6 \(\upmu\)m) and (15.5 \(\upmu\)m, 4.6 \(\upmu\)m) in the incident wavelength range of 700 to 1200 nm. **(c)** The SPP intensity contrast ratio between the points (\(-\)15.5 \(\upmu\)m, 0.09 \(\upmu\)m) and (15.5 \(\upmu\)m, 0.09 \(\upmu\)m) in the incident wavelength range of 700 to 1200 nm. **(d)** The SPP intensity contrast ratio between the points (\(-\)15.5 \(\upmu\)m, \(-\)4.5 \(\upmu\)m) and (15.5 \(\upmu\)m, \(-\)4.5 \(\upmu\)m) in the incident wavelength range of 700 to 1200 nm. The red and black lines represent the SPP intensity, and the blue line represents the SPP intensity contrast ratio.  
**Funding:** This work was supported by the National Key Research and Development Program of China (Grant No. 2019 YFB2203903), the National Natural Science Foundations of China (Grant No. 62065006), the Natural Science Foundation of Guangxi (Grants No. 2019 GXNSFAA245024 and No. 2020 GXNSFBA159059), the Science and Technology Project of Guangxi (Grant No. AD19245064), Guangxi Key Laboratory Project of Optoelectronic Information Processing (Grant No. GD20103), and Guangxi Key Laboratory Project of Automatic Detection Technology and Instrument (Grant No. YQ20103).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## Materials and Methods  
### Collection of Bacterial Strains and Seed Inoculation  
The phytohormone producing rhizobacterial strains FN13, FN14 and FN16 previously isolated and characterized by [PERSON] et al. [37] were collected from the Soil Microbiology and Biotechnology Laboratory, Department of Soil Science, Islamia University of Bahawalpur, (IUB), Pakistan. _Brassica juncea_ (L.) seeds of latest commercial variety (Pioneer 45S42), widely accepted in the farmer community, were collected from Oilseeds Research Station, Khanpur, Pakistan, surface sterilized using 2% solution of sodium hypochlorite. The surface sterilized _Brassica juncea_ (L.) seeds were rinsed with sterile distilled water for decontamination.  
The strains were grown in Dworkin and Foster (DF) minimal media modified with Cd (1000 \(\upmu\)g mL\({}^{-1}\) of Cd) and incubated at 30 \(\pm\) 1 \({}^{\circ}\)C in shaking (100 rpm) incubator (Model SIBR-2, Shellab, Riverside, OH, USA) for 48 h. After incubation, the bacterial cells were harvested by centrifugation at 9000 rpm and 22 \({}^{\circ}\)C for 20 min (Model: UNIVERSAL 320R, Hettich, Germany). The pellets were re-suspended in sterilized distilled water and the washing procedure was repeated. The pellets were dissolved in sterilized distilled water to obtain uniform cell density (OD\({}_{600}\) = 0.45; cell count 10\({}^{8}\) colony-forming units (CFU) mL\({}^{-1}\)). The final cultures with a uniform population were taken in sterile flasks to be used as inoculant.  
The surface-disinfected seeds were inoculated with 48-h old culture of respective bacterial strains by dipping in broth culture for 30 min. For uninoculated control, _Brassica juncea_ (L.) seeds were dipped in sterile distilled water for 30 min.
# RJ-RRT: Improved RRT for Path Planning in Narrow Passages
## 4 Simulation and Result
### Narrow Passage  
Figure 9 shows the planning performance of the three algorithms in a narrow passage environment. The experimental results show that when Basic-RRT faces a configuration space containing narrow passages, due to the lack of identification of the particular configuration of the narrow passage, it is not only challenging to find the entrance of the narrow passage but also challenging to expand inside the passage, so successful path-planning usually requires a large number of sampling points and more time. From Table 2, it can be concluded that, in this environment, both RRV and RJ-RRT perform better than RRT, while RJ-RRT requires the least amount of time and takes the least number of samples, on average 1.276 s, and generates an average of 227 sampling nodes. Compared with RRV, it saves 45% of planning time and 76% of the generation of the number of nodes. This means that the RJ-RRT can quickly plan a feasible path and occupy fewer memory resources for a narrow passage environment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Average Time (s)** & **Min Time (s)** & **Max Time (s)** & **Average Number of Collisions** & **Average Number of Nodes** \\ \hline RRT & 1.982 & 0.452 & 2.741 & 2338 & 1542 \\ RRV & 3.143 & 1.310 & 5.593 & 2975 & 1094 \\ RJ-RRT & 0.239 & 0.127 & 0.874 & 103 & 55 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of three algorithms in complex environment.  
Figure 8: The performance of the three algorithms in complex environments. (**a**) RRT; (**b**) RRV; (c**) RJ-RRT.
# W-Band Modular Antenna/Detector Array for the Electron Cyclotron Emission Imaging System in KSTAR
## 4 Fabrication and Measurement  
### Fabrication  
Top and bottom views of the fabricated antenna/detector array are shown in Figure 7a. The unit antenna/detector modules are separately installed in an array frame to form 12 channels. The bias voltages of the unit antenna/detector module (for the diode: +0.7 V, for the LNA: +5 V) are provided through the bias connectors on the bias PCB. In Figure 7b, a picture of the fabricated unit antenna/detector module is shown. The antenna/detector PCB is attached to the unit frame, and the elliptical mini-lenses are mounted individually on the top side of each unit antenna/detector module using HDPE dielectric screws.
## 3 Orthogonal Simplex Chebyshev-Laguerre Cubature Kalman Filter
### Orthogonal Spherical Simplex Rule  
According to [PERSON] and [PERSON] [24; 25], the cubature rule for Equation (14) can be obtained from the transformation group of the regular simplex with vertices, that is:  
\[\mathbf{\alpha}_{j}=\big{[}\alpha_{j,1},\alpha_{j,2},\ldots,\alpha_{j,n_{x}}\big{]} ^{T}, \tag{16}\]  
where \(j=1,2,\ldots,n_{x}+1\), and  
\[\alpha_{j,m}=\left\{\begin{array}{ll}-\sqrt{\frac{n_{x}+1}{n_{x}(n_{x}-m+2) (n_{x}-m+1)}}&m<j\\ \sqrt{\frac{(n_{x}+1)(n_{x}-j+1)}{n_{x}(n_{x}-j+2)}}&m=j\\ 0&m>j\end{array}\right., \tag{17}\]  
Accordingly, the third-degree spherical simplex rule is constructed as follows:  
\[S_{3}(r)=\sum_{j=1}^{n_{s}}w_{j}^{s}f(\cdot)=\frac{A_{n_{x}}}{2(n_{x}+1)}\sum_ {j=1}^{n_{x}+1}\big{(}f\big{(}r\mathbf{\alpha}_{j}\big{)}+f\big{(}-r\mathbf{\alpha}_{ j}\big{)}\big{)}, \tag{18}\]  
where \(n_{s}=2(n_{x}+1)\), \(A_{n_{x}}=2\sqrt{\pi^{n_{x}}}/\Gamma(n_{x}/2)\), and \(\Gamma(n_{x})=\int_{0}^{\infty}x^{n_{x}-1}\mathbf{e}^{-x}dx\).  
Compared with the cubature rule based on [PERSON] et al. [18], two more sample points are needed in the spherical simplex rule, but it has been proven that the spherical simplex rule is more accurate [21]. According to Equation (1), the highly nonlinear nature of the nonlinear system model may lead to the nonlocal sampling effects, with the covariance being easily affected by unknown high order behavior (especially when \(n_{x}>3\)). Thus, we introduce the orthogonal method to tune the high order information of the spherical simplex transformation. In other words, Equation (18) can be updated as follows:  
\[S_{3}(r)=\frac{A_{n_{x}}}{2(n_{x}+1)}\sum_{j=1}^{n_{s}}f\big{(}r\mathbf{\gamma}_{ j}\big{)}, \tag{19}\]  
where \(\mathbf{\gamma}=[\gamma_{1},\gamma_{2},\ldots,\gamma_{2n_{x}+2}]=\mathbf{B}\times[\bm {\alpha}\ -\mathbf{\alpha}]\) and \(\mathbf{B}=[\mathbf{B}_{1},\mathbf{B}_{2},\ldots,\mathbf{B}_{n_{x}}]\) is an \(n_{x}\times n_{x}\) orthogonal matrix, where \(\mathbf{B}_{i}=[\beta_{i,1},\beta_{i,2},\cdots,\beta_{i,n_{x}}]^{T}\). Therefore, \(\mathbf{\gamma}\) is a \(n_{x}\times(2n_{x}+2)\) matrix.  
The method to select an orthogonal matrix can be seen in the works of [26; 27]. In this paper, we construct \(\mathbf{B}\) according to [PERSON] [28]:  
\[\beta_{i,2p-1}=\sqrt{\frac{2}{n_{x}}}\cos\frac{(2p-1)i\pi}{n_{x}}, \tag{20}\]  
\[\beta_{i,2p}=\sqrt{\frac{2}{n_{x}}}\sin\frac{(2p-1)i\pi}{n_{x}}, \tag{21}\]  
where \(p=1,2,\cdots,\ \max\{p\in\mathbf{Z}|p\leq n_{x}/2\}\). If \(n_{x}\) is odd, \(\beta_{i,n_{x}}=(-1)^{i}/\sqrt{n_{x}}\).  
From Equations (18)-(21), it can be seen that the orthogonal method does not introduce additional computational costs to the spherical simplex rule, because the new orthogonal sample points can be computed offline in advance.
## 3 Control Strategy
### Particle Swarm Optimization (PSO)
#### 3.1.1 PSO-Based Optimizer-1  
In this study, PSO-based optimization was used for two purposes. The first of these was to optimize the \(\mathrm{K_{p}}\), \(\mathrm{K_{i}}\) and \(\mathrm{K_{d}}\) values of the PID controllers for each motor to control the route-tracking performance of the AGV. The block diagram of the algorithm created for this purpose is given in Figure 5a. In this optimizer, 12 parameters are adjusted depending on the error between the reference and actual translational and rotational motions. As can be seen in the figure, the PSO algorithm determines the PID controller parameters for each motor that provide the best performance by optimizing in line with the determined initial conditions and the objective function, and the control input is created in line with the incoming error. Then, the determined optimal values for PID coefficients are used to adjust to AGV motion by using the relationship between the angular velocities of the wheels and control input voltage of the motors. The procedure of the PSO algorithm for finding best values of controllers' coefficients to obtain best tracking performance is shown in Figure 5b.  
The limit values of the optimized parameters are defined in the PSO algorithm, and the optimizers try to find the optimal values of parameters for reaching the goals in these ranges. For Optimizer-1, the limits of PID controllers' parameters \(\mathrm{K_{p}},\mathrm{K_{ij}}\) and \(\mathrm{K_{D}}\) (j = 1, 2, 3, 4) are given in Table 5.
# SADG: Self-Aligned Dual NIR-VIS Generation for Heterogeneous Face Recognition
## 4 Experiment
### Quantitative Comparison  
We compared our proposed self-aligned dual generation (SADG) method with the state-of-the-art approaches on three datasets in this part, as recorded in Table 2. Note that DVG is our baseline method.  
As for the CASIA NIR-VIS 2.0 dataset, our proposed self-aligned method slightly improved the VR@FAR = 0.1% of the baseline from 99.4% to 99.6% while maintaining the performance of the Rank1 accuracy and VR@FAR = 1%. Compared with the baseline, our method boosted the performance of VR@FAR = 0.1% and VR@FAR = 1% by 0.4% and 0.3%, respectively, on Oulu. Moreover, our model reached 97.3% in VR@FAR = 0.1% in the BUAA dataset, higher than the baseline model by 0.7%. However, the performance of our model slightly dropped by 0.1% in Rank1 on Oulu and VR@FAR = 1% in the BUAA dataset, compared with the strong baseline (DVG).  
On the whole, our method impressively gained improvement compared with the VGG [40] and Light CNN [31] databases, the PACH [27] and PCFH [9] models and the baseline. This demonstrates the better performance of our proposed method. Numerous better-aligned generated NIR-VIS images can tremendously benefit the performance of heterogeneous face recognition network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **CASIA 2.0** & **DVG** & **Ours** \\ \hline MLD5 & 5.2 & 4.7 & 1.4 \\ MLD68 & 6.5 & 6.2 & 3.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of mean landmark distance (MLD) with the CASIA NIR-VIS 2.0 dataset, DVG and our results. Lower values are better.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**CASIA 2.0**} & \multicolumn{3}{c}{**Oulu**} & \multicolumn{3}{c}{**BUAA**} \\ \hline
**Method** & **Rank1** & **FAR = 1\%** & **FAR = 0.1\%** & **Rank1** & **FAR = 1\%** & **FAR = 0.1\%** & **Rank1** & **FAR = 1\%** & **FAR = 0.1\%** \\ \hline VGG [40] & 62.1 & 71.0 & 39.7 & - & - & - & - & - \\ LightCNN [31] & 96.8 & 99.1 & 94.7 & 96.7 & 92.4 & 65.1 & 96.5 & 95.4 & 86.7 \\ PCFH [27] & 98.5 & 99.6 & 97.3 & 100 & 97.7 & 86.6 & 98.4 & 97.9 & 92.4 \\ PACH [9] & 99.0 & 99.6 & 98.5 & 100 & 97.9 & 88.2 & 98.6 & 98 & 93.5 \\ DVG [8] & 99.9 & 99.9 & 99.4 & 100 & 98.5 & 92.9 & 99.9 & 98.5 & 96.6 \\ SADG (Ours) & 99.9 & 99.9 & 99.6 & 99.9 & 98.9 & 93.2 & 99.9 & 98.4 & 97.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of Rank1 accuracy (%) and verification rate (%) on the CASIA NIR-VIS 2.0, Oulu-CASIA NIR-VIS and BUAA VIS-NIR databases.
## 3 Results
### Phosphorus Equilibrium Adsorption  
Figure 5 shows the phosphorus adsorption curve of different particle size fractions. There was little difference between the bulk soils. The adsorption of clay with respect to phosphorus increased very rapidly, while that of the other three fractions increased relatively slowly. With increasing initial concentration, the order of adsorption of different particle size fractions was always clay > silt > fine sand > coarse sand. At a higher initial concentration, the adsorption of different particle size samples varied more. For example, at the initial concentration of 30 mg\(\cdot\)L\({}^{-1}\), the adsorption of clay was 0.577 mg\(\cdot\)g\({}^{-1}\), while those of silt, fine sand, and coarse sand were 0.224, 0.155, and 0.094 mg\(\cdot\)g\({}^{-1}\), respectively. The adsorption capacity of clay was, thus, far greater than that of other fractions (sixfold greater than that of coarse sand).  
Table 6 shows the parameters of the Langmuir and Freundlich isotherm models. The constant, \(n\), of the Freundlich isotherm curve equation did not change much, while the \(K_{F}\) value (indicating the adsorption per unit concentration) decreased with an increase in particle size. The maximum adsorption (q\({}_{\rm m}\)) of variation fractions was as follows: clay (0.900 mg\(\cdot\)g\({}^{-1}\)) > silt (0.384 mg\(\cdot\)g\({}^{-1}\)) > fine sand (0.304 mg\(\cdot\)g\({}^{-1}\)) > coarse sand (0.204 mg\(\cdot\)g\({}^{-1}\)).
## 3 Experimental Validation
(**a**) For various polarizations, (**b**) for various incidence angles (TE mode), and (**c**) for various incidence angles (TM modes). Lastly, the performance of the proposed absorber is compared with previously reported ITO-based EM absorber designs in terms of fractional bandwidth and thickness in Table 1. A figure-of-merit (FOM) was set to be the fractional bandwidth over the electrical thickness. As seen, the proposed work exhibits the highest FOM when compared to other published works.
# A Privacy-Preserving Record Linkage Method Based on Secret Sharing and Blockchain
## 4 Methods  
The privacy-preserving record linkage method based on secret sharing and blockchain (SSB-PPRL) integrates technologies such as secret sharing, Bloom filters, blockchain, and IPFS to achieve secure matching of multi-party data records. Its core components include the system architecture and three main modules: the data preparation and generation module, the approximate matching module, and the output and audit module. The system architecture leverages the synergy of blockchain and IPFS, using smart contracts to manage data distribution, computation, and verification processes, ensuring both privacy protection and efficiency. The data preparation and generation module are responsible for encoding raw data and decomposing it into distributed shares. The approximate matching module performs secure similarity computations, while the output and audit module verify results and provide traceability.
## 4 Results
### Classification of Papers According to Study Categories
#### 4.1.3 Application Areas  
In the \"Application Areas\" category, the 238 selected papers were tagged based on information extracted from their titles and abstracts. This category directly provides the data needed to address the research question concerning the general civil and military application areas of unmanned surface vehicles. The identified areas were classified into 10 groups, as presented in Table 11. Firstly, there is a group of 128 papers categorized as purely academic works. These papers focus on designs or algorithms at the modeling and simulation level without specifying a particular application field. Furthermore, the lower construction and operational costs of USVs, coupled with their ability to access restricted areas, have driven the development of studies with practical real-world applications, such as data acquisition for environmental monitoring, bathymetry, oceanography, and hydrog  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Level of Autonomy (IMO)** & **Number of Papers** \\ \hline Fully autonomous vessel & 126 \\ Remotely controlled uncrewed vessel & 23 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Number of papers labeled in the category “Autonomy Levels”.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Application Environments** & **Number of Papers** \\ \hline Maritime & 89 \\ Lacustrine & 22 \\ Coastal & 20 \\ Fluvial & 18 \\ Other (ponds, pools, reservoirs) & 42 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Number of papers labeled in the category “Application Environments”.  
raphy. Additional practical applications are directed toward the naval, fishing, disaster management, and transportation sectors.
# An EV SRM Drive and Its Interconnected Operations Integrated into Grid, Microgrid, and Vehicle
## 4 G2V/V2G Operations
### Experimental Evaluation
#### 4.3.2 V2G Operation  
The output real power \(P_{o}^{*}\) of the developed inverter under V2G mode is set at 1 kW. The measured \((v_{gs},v_{ds},i_{Lp},i_{Ls})\) of the CLLC resonant converter are shown in Figure 21a, the voltage and current of battery and DC-link \((v_{b},\ i_{b},v_{dc},i_{dc})\) are shown in Figure 21b, and the phase voltage \(v_{an}\) and three line currents \((i_{a},\ i_{b},\ i_{c})\) are shown in Figure 21c. Furthermore, the measured results with \(P_{o}^{*}=1\) kW and \(Q_{o}^{*}=-1\) kVAR are shown in Figure 22, and the results with \(P_{o}^{*}=1\) kW and \(Q_{o}^{*}=1\) kVAR are shown in Figure 23. The normal operations and good performances of the CLLC resonant converter and the 3P3W inverter under various scenarios are seen in the results. The preset real and reactive powers are successfully sent back to the utility grid.
# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 4 Suitability of Legacy Techniques and Standards for 5G and Upcoming Networks
### Received Signal Strength (RSS) Technique for AP Selection  
Received signal strength (RSS) selects the base station by comparing the sensed BS link features, such as reference signal received quality (RSRQ), RSRP, or RSSI level [25, 50, 51]. The said method is simple and easy to implement and does not require higher signaling or processing overhead. However, some concerns may afflict such a method, as BSS with good RSS value may be overburdened, while the rest of the stations may not be adequately utilized. To review the RSS-based AP selection technique, we present the advantages, drawbacks, and discussion in light of the defined parameters in Table 2.  
**Advantages**  
* The RSS approach is simple and straightforward because a small number of signals are exchanged and low processing is required.
* Due to its simplicity, its implementation is also straightforward, as it is already being used by 3 GPP [30, 52].  
**Drawbacks**  
* This approach is unfeasible in dense environment (most populated) [51], especially during frequent handoffs.
* The approach is one-dimensional and unreliable, owing to the uncertainty regarding the context of a user, network load, network heterogeneity, etc. [30, 51, 52].  
In light of the discussion, the RSSI-based technique is simple in its implementation and deployment point of view. Hence, it fulfills the parameters EL1, EL3, and EL5 of Table 2 concerning extensibility. Similarly, it also meets the parameters mentioned in PL2 and PL5 of Table 2, an energy-saving point of view. Next, the RSSI-based technique can also guarantee the movement between multiple RATs, fulfilling the parameter DL3 of Table 2 for dependability.
## 4 Design of TLQoS
### Routing Potentials
#### 4.3.4 Temperature Potential Field  
The temperature potential field is defined as  
\[P_{n_{i}}^{t}=T(n_{i}) \tag{9}\]  
Here, \(T(n_{i})\) denotes the normalized temperature at node \(n_{i}\) which is defined as  
\[T(n_{i})=\frac{T_{n_{i}}^{avg}}{T^{thr}} \tag{10}\]  
Here, \(T_{n_{i}}^{avg}\) refers to the average temperature at node \(n_{i}\), and \(T^{thr}\) denotes the temperature threshold for hotspot detection. If the value of \(T_{n_{i}}^{avg}\) exceeds \(T^{thr}\), then \(T^{thr}\) will be assigned to \(T_{n_{i}}^{avg}\).  
Then, we define the temperature potential force from a node \(n_{i}\) to \(n_{j}\in NB(n_{i})\) as  
\[F_{t}(n_{i},n_{j})=\frac{P_{n_{i}}^{t}-P_{n_{j}}^{t}}{c_{n_{i},n_{j}}} \tag{11}\]  
This force drives a packet to traverse along the nodes having least temperature on its path to the \(BC\). Similar to delay and reliability potential, it is also time-variant that might change dynamically, also the routing loop issue is addressed the similar way to delay and reliability potential field as to be discussed later.