esa-sceva/satcom-chunk-collection huggingface dataset:
# Chiral Metasurfaces: A Review of the Fundamentals and Research Advances
###### Abstract  
Chirality, the absence of mirror symmetry, is predominant in nature. The chiral nature of the electromagnetic field behaves differently with chiral matter for left circularly polarized and right circularly polarized light. The chiroptical behavior in the sensing of naturally occurring chiral objects is weak, and improving the chiroptical response enhances the chiral sensing platform. This review covers the fundamental concepts of chiral metasurfaces and various types of single- and multi-layered chiral metasurfaces. In addition, we discuss tunable and deep-learning-based chiral metasurfaces. Tunability is achieved by manipulating the meta-atom's property in response to external stimuli for applications such as optical modulation, chiral photonics, advanced sensing, and adaptive optics. Deep-learning modeling techniques, such as CNNs and GANs, offer efficient learning of the complex relationships in data, enabling the optimization and accurate prediction of chiral metasurface properties. The challenges in the design and fabrication of chiral metasurface include achieving broadband performance and scalability and addressing material limitations. Chiral metasurface performance is evaluated by optical rotation, circular dichroism enhancement, and tunability, which are quantified through the spectroscopic measurement of circular dichroism and optical rotation. Chiral metasurface progress enables applications, including metabolography, metalenses, and chiral sensing. Chiral sensing improves the detection of pharmaceuticals and biomolecules, increasing the sensitivity and accuracy of analytical diagnostics.  
chroptical response; chiral metasurface; enantiomers; chiral sensing; circular dichroism (CD); single-layered chiral metasurface; multi-layered chiral metasurface; tunable chiral metasurface; reconfigurable chiral metasurface; deep learning
# Free-Space QKD with Modulating Retroreflectors Based on the B92 Protocol
## 5 Conclusions  
We propose an MRR-QKD scheme using the B92 protocol. Based on the superposition principle of perpendicular polarization states, this system realizes polarization modulation by means of intensity modulation. Compared with RM2018, our system not only simplifies the structure, but also improves the tolerance of the MRR-QKD system for extinction ratio and optical misalignment error. This novel, cheaper and smaller system can be expected to be widely applied in mobile platforms with limited payloads, such as cube satellites, unmanned aerial vehicles, automobiles, etc.  
**Author Contributions**: Conceptualization, M.Z.; methodology, M.Z. and M.H.; software, M.Z.; validation, M.Z., M.H. and B.G.; formal analysis, M.Z. and M.H.; investigation, M.Z.; resources, B.G.; data curation, M.Z.; writing--original draft preparation, M.Z.; writing--review and editing, M.Z., M.H. and B.G.; visualization, M.Z. and M.H.; supervision, B.G.; project administration, B.G.; funding acquisition, B.G. All authors have read and agreed to the published version of the manuscript.  
**Funding**: This research was funded by Key-Area Research and Development Program of Guangdong Province (Grant No.2018B030325002).  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
**Acknowledgments**: Special thanks goes to [PERSON], [PERSON], [PERSON] and [PERSON] for their helpful discussions.  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 3 Results  
The data mining modeling and accuracy assessments used in this study were developed using the WEKA environment ([[http://www.cs.waikato.ac.nz/mL/weka/](http://www.cs.waikato.ac.nz/mL/weka/)]([http://www.cs.waikato.ac.nz/mL/weka/](http://www.cs.waikato.ac.nz/mL/weka/))), which is a free and open-source platform. A four-stage step was designed to explore the separability between landslide sources and runout signatures. The first stage, as reported in Section 3.1, compared the topographic attributes between the landslide source and runout samples extracted from different sizes of landslide inventory polygons that had an area of \(\geq\)100,000 or 50,000-100,000 m\({}^{2}\). In the second stage, the RF and cost-sensitive analysis-based data mining algorithms were used to construct the models polygon by polygon, with the limited training datasets (the five largest area sizes in the landslide inventory polygons were considered), as reported in Section 3.2. The major parameter in the RF-based computation was the number of trees (Ntree). [PERSON] et al. [60] determined that 10-200 Ntree did not have any effect on the results, but an increase in Ntree increased the computation loading. Therefore, 100 trees were adopted in this study, as suggested by WEKA, to develop the RF-based models. In the third stage (Section 3.3), the constructed models were applied to predict other landslide source and runout samples extracted from the polygons with an area of \(\geq\)100,000 and 50,000-100,000 m\({}^{2}\), respectively. The developed models were compared with the commonly used methods, namely SVM and LR. The fourth stage consisted of visualizing the spatial distributions of predicting the landslide source and runout areas to assess the performance of the constructed model, as reported in Section 3.4.
# Impact of Sound and Image Features in ASMR on Emotional and Physiological Responses
## 2 Materials and Methods
### Materials
#### 2.1.3 Image Features  
Image features were extracted from 22 variables, encompassing multiple color spaces and spatial characteristics. These included RGB (gray, red, green, blue), HSV (hue, saturation, value), and LAB (lightness, alpha, beta). The RGB color space, which combines red, green, and blue light, is fundamental for digital displays and is known as \"additive mixing\", where colors become brighter as they are combined. The HSV color space represents color through hue, saturation, and brightness, and is often used to capture properties such as rainbow-like, vivid, or monochromatic tones [17]. The LAB, designed to mimic the human visual system, is valuable for consistently representing color and detecting color differences across devices [18]. Since color can evoke emotional responses, we extracted variables from these three color spaces to assess whether emotional effects differ across different methods of color representation.  
For each variable, both the mean and standard deviation were calculated to capture the central tendency and dispersion of image characteristics. In the LAB color space, the alpha and beta values indicate the deviation of the mean values of the a and b channels from the central point of 128. A positive alpha value signifies a strong red component, whereas a negative value indicates a strong green component. Similarly, a positive beta value reflects a strong yellow component, while a negative value suggests a strong blue component.
## 2 System Model and Channel Modeling Methods
### System Model  
#### 2.1.1 System Model for LOS Scenarios  
Take the case that \(V_{1}\) is located on the right side of the Rx axis as an example. The V2I-VLC system model in the LOS scenario is shown in Figure 2. The definitions of left or right are based on the driver's viewpoint. The vehicle width of \(V_{1}\) is \(v_{w}\). The road width is \(L_{w}\). The PD-based Rx is outside the road and facing the center of the road where \(V_{1}\) is located. The emergent angles at Tx1 and Tx2 are denoted as \(\alpha_{1}\) and \(\alpha_{2}\), respectively. The incident angles at Rx are denoted by \(\beta_{1}\) and \(\beta_{2}\). Moreover, considering that Txs and Rx have the same height concerning \(RS\), it follows that \(\alpha_{1}=\beta_{1}\) and \(\alpha_{2}=\beta_{2}\). In addition, due to the characteristics of VLC, the light irradiated to Rx must satisfy a certain condition in order to receive the LOS signal, i.e., \(\cos\alpha_{j}\geq\cos\Psi_{PD}\), where \(\Psi_{PD}\) is the field-of-view (FOV) of Rx. The longitudinal distance of light propagation between \(V_{1}\) and Rx is \(I_{V_{1}-PD}\), and the transverse distance is \(L_{h}\) (using the direction of vehicle travel as the longitudinal direction, and vehicle movement from side to side as the transverse direction). Additionally, the lateral displacement of the vehicle corresponding to the \(j^{th}\) Tx is \(L_{h_{j}}\). Therefore, the LOS propagation distance for the \(j^{th}\) Tx can be expressed as \(D_{LOS,j}^{V_{1}-PD}=\sqrt{L_{h_{j}}\ ^{2}+I_{V_{1}-PD}\ ^{2}}\). Furthermore, in the proposed model, to emphasize the realistic traffic environment, the movement of vehicles is considered to be random. Consequently, the range of values of \(L_{h}\) can be expressed as \(0\leq L_{h}\leq(L_{w}/2-v_{w}/2)\).  
Figure 1: Schematic of a typical mountain road V2I–VLC communication scenario.  
Figure 2: Schematic of V2I–VLC system model for LOS scenario.
## 3 Results  
The results of the AF measurements are presented in Figure 4a,b. The number of the density fraction corresponds to the cells age (the higher the layer number the older the cells). The enhancement of AF with the increase in RBC age for RBCs in autologous plasma was found (Figure 4a). It is important to mention that these data are from five healthy volunteers and for each volunteer the same pattern was observed. Furthermore, for each donor, the same number of cells were measured/analyzed to avoid bias towards a particular donor. Statistically significant differences were found between all density fractions. Interestingly, the older the cells, the less alteration of AF was observed. For example, the standard deviation of the first fraction was 3.5 times higher than that of the fourth fraction (Figure 4a). The wide distribution of the first fraction was mostly related to the different means of the AF of different donors. It seems that the older the cells of different donors, the more similar they become in terms of AF. In addition, we performed a donor-based statistic and plotted the AF against the cell density (as a surrogate for the cell age; Supplemental Figure S1) and found a significant correlation (\(p=0.0035\)).  
The dependence of AF on the RBC age for RBCs in the dextran 70 kDa solution at the concentration of 50 mg/mL is presented in Figure 4b. Statistically significant differences were found between the first fraction and the others. No statistically significant differences were found between the second, third and fourth fractions (as well as there no significant correlation of the AF against the cell density (Supplemental Figure S1)). However, an increase in the AF was observed here for the oldest RBC compared to the youngest RBC.
## 4 Related Work  
Privacy protection for deep learning is an important research topic and has attracted significant attention. A number of techniques have been proposed in the literature, such as homomorphic encryption and secure multi-party computation. In this section, we discuss the related research.  
[PERSON] et al. [6] proposed a heuristic privacy-preserving CNN for image classification. Instead of pursuing perfect ciphertext-based non-linear operations, they applied a rough approximation to evaluate the CNN's non-linear transformation layers. It achieved promising performance, since the complex ciphertext-based computation was avoided. However, the accuracy of object classification was compromised, with a 4% degradation of classification accuracy. Our vePOD targets the more complex object-detection tasks and it keeps the same detection accuracy as the original deep-learning networks.  
[PERSON] et al. [3] and [PERSON] et al. [4] applied homomorphic encryption to deep learning on network-connected servers. The former devised a secure CNN model to process encrypted data and generate results in cipher text that only the owner of the data can decrypt. The latter focused on secure image-classification CNNs using fully homomorphic encryption. These approaches aim to protect the input data and inference results. However, homomorphic encryption causes prohibitive computation overhead and drastic performance degradation. For example, an implementation of fully homomorphic encryption for deep learning suffered from a slowdown by four orders of magnitude [7]. The preceding works are for image classification. The performance degradation of homomorphic encryption for object detection is even worse, making it impractical for real-world deployment.  
Secure machine learning [26] provides secure protocols for training ciphertext-based CNN models using linear regression and logistic regression. A privacy-preserving deep-learning framework [27] included secure protocols for connected servers to share model parameters. In the framework, no encryption or decryption was conducted on data, and there was no guarantee that adversaries could not use those crucial parameters to attack the system.  
Object detection is vital for autonomous driving and is more complex than image classification. Privacy protection for object detection in a vehicle-edge environment has not been studied. In this paper, we tackle this new problem and present privacy-preserving object-detection networks that are run on edge servers to process secret shares from vehicles to achieve enhanced data privacy and uncompromised detection accuracy.
# Photonic Integrated Reconfigurable Linear Processors as Neural Network Accelerators
## 3 Reconfigurable Linear Optical Processors
### Silicon Nitride Device  
A second four-input four-output device, shown in Figure 4, has been fabricated and packaged by LioniX International in a multi-project wafer run of the silicon nitride TriPleX platform based on asymmetric double-strip waveguides [38]. It is composed of ten thermally tuned balanced MZI elements [5], each of them realized using two 3 dB directional couplers and two phase shifters. The first six MZIs, arranged in a rectangular shape according to \"Clements\" architecture, implement the \(4\times 4\) unitary transformation matrix (i.e., SU(4)), while the last four MZIs implement the diagonal matrix multiplication section (i.e., \(\Sigma\)) used to control the optical power at the four outputs. The four inputs and the four outputs of the linear optical processor are terminated at the chip edge with spot size converters that guarantee low-loss coupling to single-mode fibers.  
MZIs are designed with relatively long low-loss phase shifters (8 mm for the inner shifter, 4 mm for the external one) in order to be driven with low currents (<20 mA). MZI internal and external arms have a spiral shape (radius \(\geq 105\)\(\upmu\)m) to reduce their footprint. Nonetheless, the overall circuit is larger compared to its silicon-on-insulator counterpart, having a size of 16\(\times\) 8 mm\({}^{2}\), considering also the optical and electrical I/O. The single MZI  
Figure 4: Silicon nitride-based \(4\times 4\) linear optical processor. (**a**) Realized chip (PIC footprint: \(16\times 8\) mm\({}^{2}\); the numbers identify the MZI elements); (**b**) packaged device with optical and electrical I/O.  
Figure 3: Silicon-on-insulator-based \(4\times 4\) linear optical processor. (**a**) Mask layout (the numbers identify the MZI elements); (**b**) photo of the packaged chip (PIC footprint: \(5\times 1.5\) mm\({}^{2}\)).  
element is 2.4 mm long and 1.64 mm wide. As depicted in Figure 4b, the chip has been placed on a submount, which is wirebonded and pigtailed with two 16-fiber fiber arrays, one of which is used for the linear optical processor.
## 4 Results and Discussion
### Cutting Force Analysis
Figure 12 shows the cutting force trajectories for six diverse layouts, each deploying a triangular prism cutter in the front row. The data underscore that using a triangular prism cutter at the forefront and an axe cutter in the rear row results in a reduction in overall cutting force fluctuation and peak. When contrasted with the cylindrical cutter and axe cutter arrangement, this setup experiences a comparatively lower cutting force. This can be attributed to the effective crumble impacts on the rock exerted by the triangular prism cutter, which facilitate the breakdown of the granite with relative ease. Yet, when an axe cutter is positioned in the front row, the total cutting force reaches its optimal low point. This observation substantiates the analysis depicted in Figure 9 concerning the rock-fracturing attributes of the cutter.
## 3 Model Validation with Experimental Data  
In order to validate the model, the simulation results were compared with our experimental measurements published in [18]. An innovative patented system was used to measure the position of the sensors within the office, as described in [29].  
In order to evaluate the accuracy of the numerical model, 33 positions randomly spread inside the room (see Figure 4a) were considered, and temperature and velocity values were both compared with the profiles obtained via the simulation. Figure 4b shows the temperature values obtained with the measurements (considering an error bar of \(\pm 0.5\)\({}^{\circ}\)C) compared with the corresponding values obtained with the simulation.  
There was good agreement regarding temperature distribution with a mean relative error of about 2.2%. The highest relative errors of 6.3% and 7.2% were in Positions 22 and 10, respectively. Both these positions were located in the middle of the room at a height above 2.36 m. For the remaining positions, the measured and simulated values could be fully compared.  
Table 3 shows the comparison between the measured and simulated air velocity values at different height and distances from the fan-coil unit.  
The velocity values recorded from the set of measurements were reasonably modelled in the simulation. The above comparisons lead to the conclusion that the numerical model was validated, so any further change in the input parameters would return a trustable picture of reality.  
Figure 4: (a) Positions of the sensors for model validation. (b) Comparison of measured and simulated temperature values in the winter condition.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Position**} & **Distance from** & **Distance from** & \multirow{2}{*}{\(\upsilon_{measured}\left(\frac{m}{s}\right)\)} & \multirow{2}{*}{\(\upsilon_{simulated}\left(\frac{m}{s}\right)\)} \\  & **Fan Coil (m)** & & & \\ \hline a & 0.6 & 1 & 0.05 & 0.05 \\ b & 0.6 & 1.5 & 0.11 & 0.10 \\ c & 0.6 & 2 & 0.52 & 0.48 \\ d & 1.1 & 1.5 & 0.01 & 0.02 \\ e & 1.1 & 2 & 0.02 & 0.03 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of measured and simulated velocity values.
# A Coaxial Dielectric Barrier Discharge Reactor for Treatment of Winter Wheat Seeds
## 3 Results and Discussion
### Discharge Characterization of the DBD Reactor Operating with Different Feed Gases
Figure 2: Current and voltage waveforms of the discharges generated in (**a**) argon and (**b**) helium along with pictures of the discharges in (**c**) argon and (**d**) helium. The emission spectrum of the helium discharge presented in Figure 4b exhibits very intensive bands of nitrogen (second positive system). The OH band at 308 nm originated from ambient moisture is very weak in comparison with the argon discharge spectrum (Figure 4a). Only a weak atomic He line can be observed at 706 nm. The low-intensity He line in Figure 4b confirms the efficient energy transfer from helium metastable species to nitrogen molecules prioritizing the formation of reactive nitrogen species (RNS) [67]. This shaking DBD reactor acts as an open system allowing ambient air to permeate the discharge region. Hence, reactive oxygen and nitrogen species (RONS) are produced in the discharge after admixture inside the reactor [68]. According to Figure 4a,b, different intensity levels of excited oxygen and nitrogen were observed for each feed gas, suggesting that, on one hand, the argon plasma might generate more reactive oxygen species (ROS) than reactive nitrogen species. While, on the other hand, the higher intense peaks of excited nitrogen in the helium plasma (Figure 4b) indicates the opposite (the biggest production of RNS when compared to ROS). Thus, both discharges provide different chemically active states of the system, which might impact the seeds in distinct ways. The percentage of air and the concentration of produced reactive species are important parameters to be considered when upscaling the reactor for agricultural applications. In such a case, the exact amount of air admixture should be previously determined to ensure results reproducibility. Thus, such open questions (reactive species production according to different percentages of air admixture) will be discussed and answered in future work. Figure 4: Optical emission spectra of the discharges generated in (a) argon and (b) helium. Figure 3: Dependence of the discharge power on applied voltage for argon (black) and helium (red).
# A Maximum Likelihood Estimation Method for Underwater Radiated Noise Power
## 3 Beamforming-Based Power Estimation Methods  
For comparison, the commonly used beamforming-based power estimation methods for underwater radiated noise are first reviewed. First, the beamforming is performed to the array received data, and the output signal is expressed as [35]  
\[Y_{k}=\mathbf{w}^{\text{H}}\mathbf{X}_{k}=\mathbf{w}^{\text{H}}\mathbf{G}S_{k}+\mathbf{w}^{\text{ H}}\mathbf{N}_{k}, \tag{5}\]  
where \(\mathbf{X}_{k}\) is a simplified notation of \(\mathbf{X}(k,f)\), \(\mathbf{w}\) denotes the weighting vector, and the superscript H represents the conjugate transpose. Then the beamforming output power is expressed as  
\[\begin{split} E\Big{(}\left|Y_{k}\right|^{2}\Big{)}& =E\big{(}\mathbf{w}^{\text{H}}\mathbf{X}_{k}\mathbf{X}_{k}^{\text{H}}\mathbf{w} \big{)}=\mathbf{w}^{\text{H}}\mathbf{R}_{x}\mathbf{w}\\ &\approx E\Big{(}\mathbf{w}^{\text{H}}\mathbf{G}S_{k}S_{k}^{\text{H}}\mathbf{ G}^{\text{H}}\mathbf{w}\Big{)}=\sigma_{s}^{2}\Big{(}\mathbf{w}^{\text{H}}\mathbf{G}G^{ \text{H}}\mathbf{w}\Big{)}.\end{split} \tag{6}\]  
In Equation (6), \(\mathbf{R}_{x}=\text{E}\big{(}\mathbf{X}_{k}\mathbf{X}_{k}^{\text{H}}\big{)}=\mathbf{R}_{s}+ \mathbf{R}_{n}=\sigma_{s}^{2}\mathbf{G}\mathbf{G}^{\text{H}}+\mathbf{R}_{n}\) denotes the covariance matrix of the received data, where \(\text{E}(*)\) is the expectation operator, \(\mathbf{R}_{s}=\sigma_{s}^{2}\mathbf{G}\mathbf{G}^{\text{H}}\) is the covariance matrix of the received radiated noise, and \(\mathbf{R}_{n}\) is the covariance matrix of the ambient noise. The approximation means that the power of ambient noise is negligible,which is reasonable when the SNR of the beamforming output is high. So the power of the radiated noise is given by  
\[\sigma_{s}^{2}=\frac{\mathbf{w}^{\mathrm{H}}\mathbf{R}_{x}\mathbf{w}}{\mathbf{w}^{\mathrm{H}}\mathbf{ G}\mathbf{G}^{\mathrm{H}}\mathbf{w}}. \tag{7}\]