esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Noise Mitigation System Using Lightweight Graded Micro-Porous Material
## 1 Introduction
### Non-Acoustic Flow Properties and Calculation Methods
\begin{table}
\begin{tabular}{c c c} \hline \hline
**JCAL** & **JCAPL** & **PSD** \\ \hline Porosity, \(\varphi\) & \(\varphi\) & \(\varphi\) \\ \hline Static air flow resistivity, \(\sigma\) & \(\sigma\) & \(\sigma\) \\ Ns.m\({}^{-4}\) & \(\sigma\) & \(\sigma\) \\ \hline Tortuosity, \(\alpha_{\infty}\) & \(\alpha_{\infty}\) & \(\alpha_{\infty}\) \\ \hline Viscous characteristic length, & & \\ \(\Lambda\), \(\mu\)m & & Median pore size, \(\overline{r}\) \\ \hline Thermal characteristic length, & & \\ \(\Lambda^{\prime}\), \(\mu\)m & & \\ \hline Static thermal permeability, \(k^{\prime}_{0}\), m & \(k^{\prime}_{0}\) & - \\ \hline - & Static viscous tortuosity, \(\alpha_{0}\) & - \\ \hline - & Static thermal tortuosity, \(\alpha^{\prime}_{0}\) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Non-acoustic flow properties of three acoustic models. (3) acoustic properties is critical in managing noise mitigation. To design effective acoustic materials, a combination of micro-porous material systems with a complex pore-size distribution that offers porosity, tortuosity, and particles with surface roughness (increasing turbulence and sound dissipation) and direct measurements or computation models to determine the flow properties, along with a suitable acoustic model predicting the acoustic response, are required.
# Unidirectional Slow Light Transmission in Heterostructure Photonic Crystal Waveguide
## 3 Model Design and Simulation Results  
### The Unidirectional Waveguide and the Edge Modes  
Our proposed design is based on an interface between two types of PC structures and the schematic configuration is depicted in the inset in Figure 1. The lower half plane is a square lattice of MOPC consisting of Yttrium-Iron-Garnet (YIG) rods (\(\varepsilon=15\epsilon_{0},\ r=0.11a\)) [28], where \(a=38.69\) mm is the lattice constant. With an externally applied magnetic field \(H_{0}=1.27\times 10^{5}\) A/m normal to the plane (along the \(+z\) direction), the induced magnetic permeability sensor is given as [45]  
\[\mu=\left[\begin{array}{ccc}\mu_{r}&i\mu_{k}&0\\ -i\mu_{k}&\mu_{r}&0\\ 0&0&1\end{array}\right]\mu_{0} \tag{3}\]  
\[\mu_{r}=1+\frac{\omega_{m}\omega_{0}}{\omega_{0}^{2}-\omega^{2}},\ \mu_{k}=\frac{ \omega_{m}\omega}{\omega_{0}^{2}-\omega^{2}}. \tag{4}\]  
Here \(\omega,\ \omega_{0}=\mu_{0}\gamma H_{0}\) and \(\omega_{m}=\mu_{0}\gamma M_{s}\) indicate the operating frequency of the microwave field, the procession resonance frequency and the characteristic frequency, respectively (\(\gamma=1.759\times 10^{11}\) C/kg is the gyromagnetic ratio). The saturation magnetization of the YIG rods is \(M_{s}=1.42\times 10^{5}\) A/m. If the external magnetic field applied along \(-z\) direction, both \(\omega_{0}\) and \(\omega_{m}\) change signs accordingly. According to Equation (4), it can be inferred that \(\mu_{r}\) remains unchanged, but \(\mu_{k}\) becomes its opposite value. As the edge modes at the interface of the MOPC exist inside the light cone and thus will be leaky in the free space. A titled 45\({}^{\circ}\) square lattice of Alumina PC (the upper half plane) with an aligned band gap (\(\varepsilon=10\epsilon_{0},\ R=0.115a\)) has to be attached to the interface of the MOPC. Therefore, the excited edges are evanescent in both two types of PC structures.  
The dashed rectangle in the inset in Figure 1 indicates a supercell of the waveguide. Using a finite element eigenfrequency analysis and imposing period boundary conditions at the supercell's borders, we can get the band diagram of the waveguide. Theoretically, the supercell must contain infinite rods in \(y\) direction, while a supercell of \(10\times 1\) unite cells of the waveguide can satisfy the accuracy requirements in practical calculations. With an externally applied magnetic field along the \(+z\) direction, the projected band diagram of the unidirectional waveguide for transverse magnetic (TM) modes (electric field is polarized along \(z\)-axis) are given in Figure 1. The shadow regions are the bulk modes of full MOPC and Alumina PC and the red curve denotes the unidirectional edge mode. As shown in Figure 1, the slope of the red curve (group velocities) has only positive values. It indicates that the waveguide support only forward transmission and the opposite propagation is completely prohibited. This unique feature insures that the edge modes are robust against interfacial disorders or defects. It is of significant importance for slow light systems.
# Interface Characterization within a Nuclear Fuel Plate
###### Abstract  
To predict the performance of nuclear fuels and materials, irradiated fuel plates must be characterized efficiently and accurately in highly radioactive environments. The characterization must take place remotely and work in settings largely inhospitable to modern digital instrumentation. Characterization techniques based on non-contacting laser sensing methods enable remote operation in a robust manner within a hot-cell environment. Laser characterization instrumentation can offer high spatial resolution and remain effective for scanning large areas. A laser shock (LS) system is currently being developed as a post-irradiation examination (PIE) technique in the hot fuel examination facility (HFEF) at the Idaho National Laboratory (INL). The laser shock technique will characterize material properties and failure loads/mechanisms in various composite components and materials such as plate fuel and next-generation fuel forms in high radiation areas. The laser shock-technique induces large amplitude shock waves to mechanically characterize interfaces such as the fuel-clad bond. As part of the laser shock system, a laser-based ultrasonic C-scan system will be used to detect and characterize debonding caused by the application of the laser shock. The laser shock system has been used to characterize the resulting bond strength within plate fuels which have been fabricated using different fabrication processes. The results of this study will be to select the fabrication process that provides the strongest interface.  
laser shock; laser ultrasonic NDE; sensor; nuclear fuel plate +
Footnote †: journal: Applied Physics Letters
# Study on the Performance of Optical Lenses under High Fluxes of Solar Radiation
###### Abstract  
This work compares the performance of optical lenses made of silica glass or borosilicate glass (BK-7) when submitted to high-flux radiation emitted by a xenon arc lamp or provided by a high-concentration solar tower. Each irradiation test lasted for 60 min, with continuous monitoring of the radiation-flux incident on the lenses and the temperature generated in their vicinity. All silica glass lenses showed a good performance with both irradiation sources, xenon arc lamp and natural solar radiation, contrary to what was observed with the lenses made of borosilicate glass which fractured when irradiated with a xenon arc lamp. The negative behavior observed with the borosilicate lenses is attributed to the fact that the radiation spectrum of a xenon arc lamp contains certain wavelengths, in the near ultraviolet (UV) region, that are not present in the natural solar radiation spectrum at sea level.  
concentrated solar radiation; optical lenses; borosilicate glass; silica glass +
Footnote †: journal: Applied Sciences
## 4 Results and Analysis
### Data Transmission  
Data transmitted is calculated for a node that is allocated its requested GTS to transmit its data. In this section, data transmission by the legitimate node is calculated for a complete communication session. The results are evaluated for varying numbers of GTS-requesting nodes, for varying transmitting power, and for varying bandwidth capacity.  
Results shown in Figure 5 are observed for varying numbers of GTS-requesting nodes received by the fog node. It is evident from the results that when the number of GTS-requesting nodes is 50 then GTSs allocated to the nodes in TMPAD select nodes in such a way that maximum data are transmitted within a session. This difference with other competitors increases with the increase in data-requesting nodes with the same number of data slots. It is because the proposed scheme by applying TOPSIS and the knapsack algorithm allows more data to be transmitted within a session; however, the other three standards do not scrutinize data-requesting nodes intelligently and almost the same amount of data is transmitted with the increase in data-requesting nodes.  
Results in Figures 6 and 7 show the amount of data transmitted by nodes for varying trust probability of the networks calculated and for varying transmission power of 100 data-requesting nodes. It is evident from the results that the data transmitted in a session by the proposed TMPAD is significantly more than the other three standards. Figure 6 shows that when GTS-requesting nodes have varying channel bandwidth, the higher the channel bandwidth, the more the data can be transmitted by a node. These results show that with the increase in channel bandwidth, the data transmitted by all session nodes increases. However, TMPAD allows significantly more data as compared to the other three algorithms.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Coverage area & \(50\times 50\) m \\ \hline Maximum duration of a session (s) & 1 \\ \hline Number of nodes & 50–150 \\ \hline Maximum data rate (kbps) & 250 \\ \hline Maximum number of data slots & 232 \\ \hline Maximum slot capacity (kB) & 4 \\ \hline Slot duration (s) & 0.004 \\ \hline Contention access period (s) & 0.064 \\ \hline Channel bandwidth & 2000–10,000 \\ \hline Data transmission power & 10–30 \\ \hline Trust threshold probability & 0.3 \\ \hline Number of legitimate nodes & 48–146 \\ \hline Number of malicious nodes & 2–4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.  
The results in Figure 7 are observed when the transmitting power of the GTS-requesting node is increased. The results show that increased transmission power increases the data transmission capability of a node. It is evident from the results that TMPAD allows more data-transmitting nodes to transmit their data within a session as compared to the other three standards for all different values of transmission power.  
Figure 5: Data transmitted by legitimate nodes in a session for varying number of nodes.  
Figure 6: Data transmitted by legitimate nodes in session for varying channel bandwidth.
## 2 Cost of Reactive Power Production from DERs
### Synchronous Machine-Based DGs
\begin{cases}\rho_{0}&-Q_{mand}^{A}\leq Q_{0 DG}\leq Q_{mand}^{A} \\ \rho_{0}-\rho_{1}(Q_{1 DG}+Q_{mand}^{A})&Q_{DG}^{min}\leq Q_{1 DG}\leq-Q_{mand}^{A }\\ \rho_{0}+\rho_{2}(Q_{2 DG}-Q_{mand}^{A})&Q_{mand}^{A}\leq Q_{2 DG}\leq Q_{A}\\ \rho_{0}+\rho_{2}(Q_{B}-Q_{mand}^{B})+\rho_{Adj}\cdot\left|\Delta P^{Adj} \right|&Q_{A}\leq Q_{3 DG}=Q_{B}\leq Q_{DG}^{max}\end{cases}  \tag{1}\]  
where \(\Delta P^{Adj}\) is:  
\[\Delta P^{Adj}=P_{B}-P_{A} \tag{2}\]  
In order to model the constraints corresponding to the capability diagram of a DG, the curve between \((0,Q_{DG}^{max})\) and \((P_{DG}^{max},Q_{DG}^{x})\) in the capability diagram can be approximated by linear expression, as will be used in Equation (6). Therefore, the DSO will need to know the values of \(P_{DG}^{max}\), \(Q_{DG}^{max}\), \(Q_{DG}^{min}\), and \(Q_{DG}^{x}\) for each DG unit. From a mathematical viewpoint, Equation (1) can be modeled using the binary variables \(W_{0}\), \(W_{1}\), \(W_{2}\), and a set of algebraic relations as follows:  
\[COST(Q_{DG})=W_{0}\cdot\rho_{0}+W_{1}\cdot(\rho_{0}-\rho_{1}(Q_{1 DG}-Q_{mand}) )+W_{2}\cdot(\rho_{0}+\rho_{2}(Q_{2 DG}-Q_{mand}))+\rho_{Adj}\cdot\left|\Delta P ^{Adj}\right| \tag{3}\]  
\[-W_{0}\cdot Q_{mand}\leq Q_{0 DG}\leq W_{0}\cdot Q_{mand} \tag{4}\]  
\[W_{1}\cdot Q_{min}\leq Q_{1 DG}\leq-W_{1}\cdot Q_{mand} \tag{5}\]  
\[W_{2}\cdot Q_{mand}\leq Q_{2 DG}\leq W_{2}\cdot\left(Q_{DG}^{max}-\frac{Q_{DG }^{max}-Q_{DG}^{x}}{P_{DG}^{max}}(P_{0}+\Delta P^{Adj})\right) \tag{6}\]  
\[Q_{mand}=(P_{0}+\Delta P^{Adj})\cdot tan(cos^{-1}(pf_{mand})) \tag{7}\]  
\[Q_{DG}=Q_{0 DG}+Q_{1 DG}+Q_{2 DG} \tag{8}\]  
\[W_{0}+W_{1}+W_{2}\leq 1 \tag{9}\]  
In Equation (7), when the DG is operated in region III, the \(\Delta P^{Adj}\) variable is activated; otherwise it will be zero. Equation (9) ensures that the DG operates in only one of the three defined regions. Finally, Equation (3) can be rewritten as:_Appl. Sci._**2015**, \(5\)  
\[COST(Q_{DG})=C_{DG}(Q_{DG})+\rho_{Adj}\cdot\left|\Delta P^{Adj}\right| \tag{10}\]  
where \(C_{DG}(Q_{DG})\) represents the cost of reactive power support regardless of LOC as follows:  
\[C_{DG}(Q_{DG})=W_{0}\rho_{0}+W_{1}\cdot(\rho_{0}-\rho_{1}(Q_{1 DG}-Q_{mand}))+W_ {2}\cdot(\rho_{0}+\rho_{2}(Q_{2 DG}-Q_{mand})) \tag{11}\]
# Small-Sample Bearings Fault Diagnosis Based on ResNet18 with Pre-Trained and Fine-Tuned Method
## 2 Relevant Theoretical Approaches
### ResNet  
ResNet denotes an architecture for deep neural networks. It was proposed by [PERSON] and his team in 2015 at Microsoft Research Asia [37]. The core innovation of the network design is the introduction of the concept of residual learning, which aims to address issues of gradient disappearance and performance degradation when the network depth increases in the training process. Traditional deep neural networks stack multiple layers to construct the depth model. However, with an increase in the number of network layers, the gradient gradually disappears in the process of back propagation, resulting in difficulty in training. ResNet makes it easier for the network to learn identity mapping through the residual learning mechanism; that is, the input is directly transferred to the output, thus reducing the problem of gradient disappearance. The basic building block of ResNet is the residual block. A schematic depiction of the residual block is depicted in Figure 2. Each residual block contains two convolution layers and a skip connection. By adding the input \(x\) directly to the final convolution layer output, the direct flow of information and the effective propagation of gradients are realized.  
Figure 1: (a) Raw vibration signal; (b) schematic diagram of SDP transformation.  
Applying the Rectified Linear Unit (Relu) activation function in the depth network can improve the training speed [38]. Due to the excellent performance of Relu in alleviating gradient disappearance and gradient explosion, improving computational efficiency, achieving sparse activation and experiments, it has become the preferred activation function of residual networks [39]. The equation of Relu is as follows [40].  
\[\text{Relu(x)}=\text{max}(0,\text{x}) \tag{8}\]  
There are two types of jump connections, which mainly depend on whether the output of the residual block needs to be changed in size. As shown in Figure 3, Figure 3 illustrates the output without any processing, whereas Figure 3 requires an adjustment in the output channel size using \(1\times 1\) convolution.  
In the variant network of ResNet, ResNet18 has good performance in image classification and other tasks. Compared with deeper networks, such as ResNet50, it usually needs less training time and fewer parameters, so as to achieve a balance between performance and efficiency to a certain extent. Therefore, this study selects the ResNet18 model for small-sample FD. The ResNet18 model is mainly composed of four groups of residual  
Figure 3: Two different residual structures. (**a**) Basic residual block (**b**) Bottleneck residual block.  
Figure 2: Residual block.  
blocks. The internal feature dimensions of each group of residual blocks gradually increase. Figure 4 illustrates the architecture of ResNet18.
## 3 Results and Discussion
### Part Classification Results
#### 3.1.2 Image Segmentation Results by K-Means Clustering  
Plenty (a red sweet pepper variety), President (a yellow sweet pepper variety), and Derby (an orange sweet pepper variety) varieties were used in this study. In the average image of the fruit area, the fruit and the background were differentiated by back-projecting the histogram of color and saturation. For stems and leaves, the canopy of sweet pepper and the background were differentiated using the NDVI for the images of 735 and 660 nm bands.  
Figure 12: Differences of visible spectrum image at 695 (a), 735 (b), and 800 nm (c) wavelength by 470, 500, 525, 635, 660 nm.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Wavelength**} & \multicolumn{2}{c}{**695 nm**} & \multicolumn{2}{c}{**735 nm**} & \multicolumn{2}{c}{**800 nm**} \\  & **Mean** & **SD 1** & **Mean** & **SD** & **Mean** & **SD** \\ \hline
470 nm & 8.33309 & 0.0229 & 5.46441 & 0.0949 & 8.62191 & 0.0339 \\
500 nm & 8.20943 & 0.0294 & 6.51014 & 0.0837 & 7.81063 & 0.0306 \\
505 nm & 8.30828 & 0.0118 & 7.75226 & 0.0999 & 8.68881 & 0.0673 \\
525 nm & 8.2368 & 0.0971 & 7.57739 & 0.0283 & 7.53663 & 0.0667 \\
635 nm & 7.98845 & 0.0248 & 7.62452 & 0.0393 & 7.66518 & 0.0538 \\
660 nm & 7.84794 & 0.0298 & 8.71625 & 0.0315 & 7.70154 & 0.0178 \\ \hline \hline \end{tabular} \({}^{\dagger}\) SD: standard deviations.  
\end{table}
Table 3: PSNR values at different wavelengths.  
The result of image segmentation using k-means clustering with the number 3 clusters separated from the background is shown in Figure 13. To determine losses of fruits, stems, leaves, and suckers in the image through image segmentation, an image of a sweet pepper at 1288 \(\times\) 964 pixels was divided into four parts horizontally and vertically. Numbers of fruits, stems, leaves, and suckers damaged in the area were measured.  
Table 4 shows actual numbers of fruits, stems, leaves, and suckers in the cultivation image and numbers of fruits, stems, leaves, and suckers obtained through the image segmentation process. As a result, in the case of fruits, through the image segmentation process, the images of 6 fruits, 1421 stems, 2188 leaves, and 37 suckers were removed from the background.
## 3 Antenna Performance Enhancement Techniques
### Multielements  
The gain of an antenna can further be increased by using the antenna with multielement. It also improves the bandwidth and efficiency of an antenna, as in [40]. In applications where a single element antenna cannot fulfill the high gain and wide bandwidth requirements, a multielements antenna is the best solution and is more effective in this case.
# A Survey on LoRaWAN Architecture, Protocol and Technologies
## 2 LoRa & LoRaWAN Technologies
### MAC Layer--LoRaWAN
#### 2.4.2 Physical Message Formats  
UL messages are transmitted from end nodes to the medium and received by the all GW in range. The network server eliminates duplicate frames and the single packet is processed.  
UL messages use explicit mode in LoRa modulation as described in Section 2.3. UL frame format is shown in Figure 10.  
DL messages are sent from the network server to the devices through the GW. DL messages are sent to one single end node. LoRaWAN is a single hop protocol and does not support routing or multi-hop packet delivery. DL frame format is shown in Figure 11.  
Receive windows is a slotting access control initiated via a UL message from the end-nodes. First DL receive window RX1 is opened until RECEIVE_DELAY1 seconds (+/\(-\)20 microseconds) using the same channel as shown in Figure 12. RX2 has a configurable data rate and frequency via MAC commands.
# Internet of Food (IoF), Tailor-Made Metal Oxide Gas Sensors to Support Tea Supply Chain
## Appendix A
\begin{table}
\begin{tabular}{c c} \hline
**RT** & **Compound Name** & **Abundance Mean** \\ \hline
4.239 & 9-Tetradecen-1-ol, acetate, (E)- & 81,718 \\
5.184 & 2-Octyn-1-ol & 825,999 \\
5.211 &.beta.-Myrcene & 501,590 \\
5.665 & 2-Heptanone & 127,653 \\
5.912 & D-Limonene & 201,138 \\
5.918 & Cyclohexene, 4-ethenyl-1,4-dimethyl- & 109,577 \\
6.190 & 3-Pyridinecarbonitrile, 4-(methoxymethyl)-6-methyl-2-(2-propenyloxy)- & 64,607 \\
6.706 & 2-Hexanal & 151,942 \\
6.714 & (1-Allylcyclopropyl)methanol & 133,747 \\
6.957 & N-(Trifluoroacetyl)-N,O,O’,O’,O’,-tetrakis(trimethylsilyl)norepinephrinephrine & 55,876 \\
7.511 & Furfuryl heptanoate & 99,402 \\
7.556 &.alpha.-Pinene & 97,319 \\
7.630 & 4-Teriphenyl acetate & 61,282 \\
8.220 &.beta.-Cimene & 235,507 \\
8.486 & Undecane, 3,8-dimethyl- & 261,838 \\
8.499 & Dodecane, 4,6-dimethyl- & 334,363 \\
8.784 & Octane, 2,3,3-trimethyl- & 140,125 \\
9.045 & (+)-4-Carene & 55,258 \\
10.420 & trans.-beta.-Teripinyl pentanoate & 98,048 \\
10.485 & Methoxyacetic acid, 2-methylpropyl ester & 72,844 \\
10.729 & Undecane, 2-methyl- & 153,807 \\
10.739 & Nonane, 5-(2-methylpropyl)- & 210,592 \\
10.963 & Butanamide, 3-cyclohexylamino-4-hydroxy-N-cyclohexyl- & 233,226 \\
10.972 & Z-1,8-Dodecadiene & 197,182 \\
11.190 & Nonane, 1-iodo- & 843,66 \\
11.265 & 2,4-Pentanedione, 3-ethyl- & 65,334 \\
11.453 & 1-Pentene, 5-chloro- & 326,317.5 \\
11.595 & Undecane, 2,8-dimethyl- & 101,042 \\
11.910 & 5-Hepten-2-one, 6-methyl- & 361,742.5 \\
12.318 & Trichloroacetic acid, tridecyl ester & 59,185 \\
14.315 & Diazene, dicylochexyl-, 1,2-dioxide & 61,943 \\
14.698 & Nonanal & 167,658 \\
15.146 & 2-Decen-1-ol & 146,286 \\
15.591 & Heptadecane, 2,6,10,14-tetramethyl- & 59,042 \\
15.627 & Dodecane, 2-methyl- & 75,909 \\
16.206 & 5-Tideeene, (Z)- & 68,987 \\
16.267 & 2-Octyn-1-ol & 71,981 \\
18.250 & 2,3-Epoxybutane & 411,295 \\
18.445 & 6-Nonanal, (Z)- & 530,076 \\
18.452 & Imidazole, 2-amino-5-[(2-carboxy)vinyl]- & 245,253 \\
18.654 & Furfural & 266,284.5 \\
19.036 & Nonane, 5-(1-methylpropyl)- & 138,397 \\
19.039 & Nonane, 3-methyl-5-propyl- & 243,464 \\
19.698 & 2,3-Hexadiene, 2-methyl- & 277,170 \\
20.606 & 1-Pentanol, 2-ethyl-4-methyl- & 129,402 \\
20.995 & Benzaldehyde & 165,013 \\
21.054 & 1-Benzamido-N-benzyl-1-[-alpha-(2-pyridylthio)benzylidene]acetamide & 119,058 \\
21.205 & Nonane, 4,5-dimethyl- & 83,886 \\
21.426 & R-Limonene & 119,271 \\
21.758 & Dodecane, 4-methyl- & 78,992 \\
22.139 & 1,5-Heptadiene, 2,3,6-trimethyl- & 209,903 \\
23.194 & Heptanal, 2-methyl- & 278,302 \\
23.262 & 2-Octen-1-ol, 3,7-dimethyl- & 450,603 \\
23.615 & 2-Heptafluorobutyroxydodecane & 70,417 \\
24.087 & 1-Bromo-3,7-dimethyl-2,6-octaldiene & 9,842,404 \\
25.104 & Oxirane, decyl- & 134,290 \\
25.211 & Dihydrocarryl acetate & 90,710 \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._
# Deep Learning for Medical Image Cryptography: A Comprehensive Review
## 5 Discussion
### Limitations of Deep Learning for Medical Image Cryptography and Future Prospects
#### 5.2.3 Computationally Expensive  
Deep learning models used for medical image cryptography sometimes need expensive hardware and protracted training periods. This may make them less useful, particularly in real-time contexts or contexts with limited resources. Future work should focus on creating hardware accelerators, optimization methods, and more efficient algorithms to lessen the computational load and accelerate cryptographic operations.
## 2 Materials and Methods
### PSC Device Characterization  
The electrical characteristics of our fabricated PSCs were measured under one-sun optical illumination, i.e., 100 mW cm\({}^{-2}\), using the Oriel LSH-7320 LED solar simulator connected to a source meter unit from Ossila (Model: X200). The light was calibrated with a standard mono-Si solar cell (PVM-396, PV Measurements Inc., Boulder, CO, USA) certified by the US National Renewable Energy Laboratory (NREL).