esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In summary, 7-12 GHz, stable, frequency-doubling, and tunable microwave carriers have been theoretically and experimentally demonstrated at 2 \(\upmu\)m wavelengths by applying the DSB-CS modulation method. A similar experimental setup has also been built for the 1.55 \(\upmu\)m/1.31 \(\upmu\)m wavelengths region consisting of the RF modulation signal power (18 dBm), light side band power (\(--\)7.7 dBm), and spectral resolution (0.05 nm). The same indium-rich InGaAs photodetector covering the working band until 2 \(\upmu\)m wavelengths was used. The microwave carrier power was normalized to the wavelength-corresponded squared external quantum efficiency, and the ratio of generated microwave carrier power between the results at 2 \(\upmu\)m and the C/O-band is obtained as 1.31/1.98, demonstrating a power conversion efficiency increesment of \(\sim\)1.16 dB/\(-\)2.98 dB due to the higher detector photoresponsivity at longer wavelengths, which aligns with the theory. That proves the higher power conversion efficiency at 2 micron wavelengths based on the same method at the same frequency. Additionally, we will put even more attention on expanding the working RF frequency to the 5G/6G region or even the terahertz level, developing more RF-generation techniques for comparisons and noise issues at 2 micron wavelengths involving the use of the available mode lock lasers to obtain lower phase noise, applying an optical filter to further suppress the ASE noise induced by the optical amplifier.  
Conceptualization, D.J. and N.Y.; methodology, D.J. and N.Y.; software, D.J. and Z.H.; validation, D.J. and Z.H.; formal analysis, D.J. and Z.H.; investigation, D.J. and Z.H.; resources, D.J. and Z.H.; data curation, D.J. and Z.H.; writing--original draft preparation, D.J. and Z.H.; writing--review and editing, D.J. and N.Y.; visualization, D.J. and Z.H.; supervision, N.Y.; project administration, F.P. and Y.S.; funding acquisition, F.P., Y.S. and N.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key Research and Development Program of China (2021 YFB2900800, 2019 YFB1802901), by the Natural Science Foundation of China (NSFC) (61420106011, 61601277, 61601279), by the Science and Technology Commission of Shanghai Municipality (205111102400, 20 ZR1420900), by 111 project (D20031), by STCSM (SKLSFC2019-02), by the Shanghai Fujang Program (20 PJ1404100), and by the National Natural Science Foundation of China (Grant No. 62175143).  
Not applicable.  
We also thank [PERSON] for providing the optical amplifier at 2 micron wavelengths and the technical support of [PERSON].  
The authors declare no conflict of interest. The funders agree to publishing the results.
# A Novel L-Shaped Metalens for Ultra-Wide Band (UWB) Antenna Gain Improvement
## 4 Experimental Section
The measured E-plane (solid curve) and H-plane (dashed curve) half-power bandwidths of the AVA (black curve) and AVA L-shaped#1 (green curve) as a function of the frequency are illustrated in Figure 15. The HPBW of the antenna with metalens is reduced over the whole band in both planes, with a maximum HPBW reduction of \(\Delta HPBWmax=31.3^{\circ}\) at a frequency of \(f=14\) GHz  
Figure 14: Comparison between full-wave simulation (solid curves) and measurement (dotted curves) of the AVA (black curves), and AVA L-shaped#1 (green curves): (**a**) modulus of the scattering parameter \(|S_{11}|\) as a function of frequency, and (**b**) realized gain (\(G\)) and gain increase (\(\Delta G\)) of frequency. Figure 13: Prototypes of antipodal Vivaldi antenna without and with metalens: (**a**) AVA, and (**b**) AVA L-shaped#1. for the E plane and of \(\Delta HPBWmax=28.3^{\circ}\) at a frequency of \(f=5\) GHz for the H plane. The difference between the beamwidths in the E and H planes is reduced after the insertion of the metalens, hence the radiation pattern is more symmetrical. Figure 16 shows the E-plane and H-plane radiation patterns of AVA (dashed curve) and AVA L-shaped#1 (full curve), measured at frequencies \(f=14\) GHz and \(f=5\) GHz where the maximum HPBW reduction occurs. Figure 16: Measured (**a**) E-plane and (**b**) H-plane radiation patterns of AVA L-shaped#1 (solid curve) and AVA (dashed curve) at frequencies of \(f_{1}=5\) GHz and \(f_{2}=14\) GHz. Figure 15: Measured E-plane (solid curve) and H-plane (dashed curve) half-power bandwidth of AVA (black curve) and AVA L-shaped#1 (green curve) as a function of frequency. A comparison between AVA L-shaped#1 and recent similar Vivaldi antennas with metalens is shown in Table 3. The comparison with the literature is not trivial if size, bandwidth, and gain are simultaneously considered. However, by comparing the measured performances of the proposed AVA L-shaped#1 with the state of the art, the size is smaller to parity of gain. As an example, the size of the AVA with a metalens [12] is \(120\times 260\) mm, and a director element is employed in addition to the metalens. In this paper, the size of AVA L-shaped#1 is \(48\times 121\) mm, without a director.
# Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function
## 2 Multi-Wavelength QKD with a PUF
### Multi-Wavelength QKD
#### 2.1.1. Ternary Protocol  
The ternary scheme introduces unused states and can be applied to traditional QKD protocols; one advantage of unused states is that their presence can indicate eavesdropper interference without [PERSON] and [PERSON] sacrificing a portion of the key, as is necessary in some QKD protocols such as BB84. Our modified ternary protocol chart (Figure 1a) has three data states: \(-\), \(+\), and zero. Notice that two states--\(135^{\circ}\) for wavelength 1 and \(90^{\circ}\) for wavelength 2--were not used. These unused states will not be sent intentionally and act as \"unused states\"; in theory, they should not show up unless an eavesdropper is present. However, in practice, the unused states may show up due to various error sources. If the number of unused state cases is significantly large, then we conclude that an eavesdropper is present.  
Problems and Improvements to the Ternary Protocol  
One weakness of the ternary encoding scheme is that if the eavesdropper measures the wavelength, then she can obtain information about the key, thus reducing security. However, we can solve this by changing the unused states to reduce the amount of information an eavesdropper can obtain. The main issue with the ternary protocol as described above is that in the \(+\) basis, the green wavelength is only used when sending a zero state. Similarly,  
Figure 1: Ternary (**a**) and quaternary (**b**) encoding schemes [6]. The red font represents wavelength one, and the green font wavelength two.  
in the \(\times\) basis, the red wavelength is only used when sending a zero state. This means that by knowing the bases choices (which becomes public information) and the wavelength of each photon, Eve can deduce the zero trits in the final key. To help reduce this security issue, the unused states can be used 50% of the time [6]. For example, we can use the red wavelength 90\({}^{\circ}\) to send a \(+\) trit in the \(+\) basis 50% of the time, and we can use the green wavelength 90\({}^{\circ}\) to send a \(+\) trit in the \(+\) basis the other 50% of the time. This means that an eavesdropper cannot assume that the green wavelength in the \(+\) basis signifies a 0 trit because sometimes the green wavelength can indicate a \(+\) trit. Similarly, we can do the same for the \(\times\) basis \(+\) trit using the red and green wavelength 135\({}^{\circ}\) 50% of the time. By changing the unused states, we can reduce the amount of information an eavesdropper can obtain and thus strengthen the protocol.
# Efficient Collaborative Edge Computing for Vehicular Network Using Clustering Service
## 5 Implementation  
The framework has subjected to real-time implementation with three real servers to meet the proof of concept of the claimed idea of this work. The CCVEC framework was designed and implemented using OpenStack [27] as a cloud and edge computing logic. OpenStack is an open source cloud operating system, which was found to manage and control the entire computer infrastructure, such as storage, resources, and network. Further benefits stem from the fact that OpenStack also provides services and platforms for end-users or enterprises. In this research, the Senlin clustering service is utilized and well configured in order to create and manage the cluster nodes with high orchestration. Initially, the cluster profile was created at the basic level for all cluster nodes using SopenStack cluster profile create myserverProf. Next, using Senlin, the cluster space is created using the predefined profile and name by using $ senlin cluster-create-p myserverProf clusterName. Later, we put the required size for the cluster to three nodes using $ senlin cluster-resize-capacity 3 clusterName. It is worth mentioning here that all the cluster nodes and the cloud are functioning with a basic profile to avoid the complexity and trade-off performance. In other words, we have kept all the settings in default just to obtain the proof of concept of our claim.  
This work makes use of the receiver service in OpenStack which aims to capture events of vehicles connecting to a cluster node. In this work, the API was used in order to share vehicles' information (e.g., vehicle_ID) between the current and the next node. Figure 4 shows the passing message between network components.
## 3 The Experimental Results and Analysis  
The measurement system of the proposed method is shown in Figure 8. It mainly consists of a DLP (Light Crafter 4500) and a CCD camera (HXC20). In the experiment, the three non-zero grayscale binary gratings share the same width in a period, which means that the duty cycle is 1/3. In our measurement system, the illumination of DLP4500 is LED with the illuminance of 150-500 \(\mathrm{lx}\) the height measurement range is about 40 mm. The size of the figure captured by CCD is 1624 \(\times\) 1236 pixels, which corresponds to the real field of a view size of 192.6 \(\times\) 146.5 mm when the distance between the reference plane and the CCD camera is 700 mm, the lateral resolution of this system is about 0.118 mm. The width of the fringe period we project onto the object is 9 pixels, which expanded to 76 pixels in a deformed pattern, so that the height resolution could be calculated by the phase-to-height relationship which is about 0.013 mm. The measurement rate of this system is 300 frames per second.
# Digital Twins' Application for Geotechnical Engineering:
## 3 Challenges in Geotechnical Digital Twin Development
There remains a conspicuous absence of a unified information management platform comprehensively covering the geotechnical engineering lifecycle:  
Currently, numerous information platforms have emerged in geotechnical engineering, primarily serving critical functions including visualization, construction safety assurance and quality control, resource allocation and cost management, monitoring data integration, and construction progress tracking. These platforms aim to fulfill diverse requirements such as design communication, project demonstration, and construction simulation. However, most existing platforms are confined to specific project phases, failing to achieve comprehensive lifecycle management. Their capabilities in design optimization refinement and on-site construction guidance require further enhancement and development. Standardization frameworks for geotechnical digital twin data remain critically underdeveloped:  
As geotechnical digital twins are still in their nascent stage, there exists an urgent need to establish a comprehensive standard system encompassing six key dimensions: fundamental common standards, core technical specifications, tool/platform requirements, evaluation metrics, security protocols, and application guidelines. Each category demands intensified research efforts to ensure systematic development. A universally recognized theoretical framework for geotechnical digital twin modeling is notably lacking:  
Given the cross-disciplinary nature of digital twin technology and its inherent integration complexity, current implementations suffer from insufficient interdisciplinary collaboration and the absence of a generalized technical infrastructure. For example, there are some problems in geotechnical engineering: incompatibility between BIM (Building Information Modeling) and geotechnical data formats, e.g., IFC vs. GeoSciML; lack of protocols for reconciling multi-scale data, e.g., integration of surface models derived from LiDAR with borehole logs; and ethical and legal implications of data ownership in multilateral DT projects. The exploration of geotechnical digital twins remains in its preliminary stages, necessitating immediate establishment of consensus-driven standards and theoretical systems to provide coherent technical direction and implementation guidance.
# A Fuzzy Consensus Clustering Algorithm for MRI Brain Tissue Segmentation
## 2 Methodology
### Pre-Processing  
We perform three pre-processing steps, namely Registration, Bias Field correction, and Skull Stripping. Registration is the process of spatially aligning two or more images of the same content taken from a different view and/or at a different time, and alignment of the multi-modal image of the same patient is required. Bias Field refers to a low-frequency signal which corrupts the MRI images due to inhomogeneities in the magnetic field of the MRI machines. Bias field leads to intensity inhomogeneity, and in turn, it affects the segmentation accuracy. Hence, the bias field needs to be corrected before performing the segmentation. Skull Stripping is the process of removing non-brain tissues such as fat, skull, and neck. These non-brain tissues have an intensity that overlaps with the intensity of the other brain tissues. Thus, the brain tissues have to be extracted before the brain segmentation. There are many skull stripping methods such as Brain Extraction Tool (BET) [30], Brain Surface Extraction (BSE) [31], AFNI (\"Analysis of Functional NeuroImages\" (AFNI) software package publicly available at [[https://afni.nimh.nih.gov/](https://afni.nimh.nih.gov/)]([https://afni.nimh.nih.gov/](https://afni.nimh.nih.gov/)) (accessed on 19 April 2022)), BridgeBurner [32], GCUT [33], and ROBEX [34]. Among all these methods, ROBEX provides significantly improved performance [34].  
All the aforementioned steps are optional and depend on the image data used for the study. Hence, in this paper, different pre-processing steps are performed for different datasets. The pre-processed brain images are segmented using consensus clustering. The following subsection presents a detailed description regarding segmentation.
## 2 Materials and Methods
### Data Processing
#### 2.4.3 NDVI and Variations in Gross Primary Productivity (GPP)  
The orthomosaic hyperspectral reflectance values were corrected using an empirical line correction method by selecting two pseudo-invariant features in the scene: an unshaded fixed white instrument box and a patch of dark bare soil that were identified from the composite true-color hyperspectral images. Then the LAI ([-]) and canopy chlorophyll content (CCC, [g/m\({}^{2}\) ground area]) were retrieved from inversion of PRO4 SAIL model (available at [[http://teledetection.ippg.jussieu.fr/prosail/](http://teledetection.ippg.jussieu.fr/prosail/)]([http://teledetection.ippg.jussieu.fr/prosail/](http://teledetection.ippg.jussieu.fr/prosail/)), accessed on 8 April 2021) with the corrected hyperspectral reflectance, pixel by pixel at 2.25 cm resolution (resampled from 4.5 mm to match thermal images). The NDVI was calculated using the NIR band (843 nm) and the red band (664 nm): NDVI = (NIR \(-\) red)/(NIR \(+\) red). The NDVI has been shown to have a linear relationship with the fraction of photosynthetically active radiation (fAPAR) absorbed by plant canopy [35; 36]. The rice plant variables GPP, LAI, NDVI, and CCC were used as rice growth indicators in this study. The GPP can be simulated using a light use efficiency model [37] with the photosynthetically active radiation (PAR) absorbed by canopy chlorophyll [38]. Based on these relations and assuming other factors constant across biochar treatments, we inferred the relative GPP variation (\(\Delta GPP/GPP\)) after biochar application from the variation in NDVI and CCC using a propagation approach [39] (see Supplementary Material for further details):  
\[\frac{\Delta GPP}{GPP}=\frac{\Delta NDVI}{NDVI}+\frac{\Delta CCC}{CCC}. \tag{3}\]
# Theoretical Study on a Cable-Bracing Inerter System for Seismic Mitigation
## 2 Theoretical Analysis of a Cable-Bracing Inerter System
### Motion Governing Equation of SDOF System with CBIS
**MX+CX+KX=F**, (8)  
where **M**, **C**, **K** and **F** respectively represent the mass matrix, damping matrix, stiffness matrix and external excitation vector of the SDOF system with a CBIS. Equation (8) is converted into the state space form:  
\[\overset{\cdot}{\mathbf{A}\overset{\cdot}{\mathbf{\overline{X}}}}+\mathbf{B} \overset{\cdot}{\mathbf{\overline{X}}}=\left\{ \begin{array}{c}\mathbf{F}\\ \mathbf{0}\end{array} \right\}, \tag{9}\]  
where **0=\(\{\mathbf{0},\mathbf{0}\}^{T}\)**, **A**, **B**, \(\mathbf{\overline{X}}\) and \(\mathbf{\overline{X}}\) are determined as:  
\[\mathbf{A}=\left[ \begin{array}{cccc}c&0&m&0\\ 0&c_{\mathrm{d}}r_{0}^{2}&0&J\\ m&0&0&0\\ 0&J&0&0\end{array} \right]\mathbf{B}=\left[ \begin{array}{cccc}k+k_{\mathrm{b}} \cos^{2}\theta&-k_{\mathrm{b}}r_{0}\cos\theta&0&0\\ -k_{\mathrm{b}}r_{0}\cos\theta&k_{\mathrm{b}}r_{0}^{2}&0&0\\ 0&0&-m&0\\ 0&0&0&-J\end{array} \right]\mathbf{\overline{X}}=\left\{ \begin{array}{c}u \\ \varphi\\ \dot{u}\\ \dot{\varphi}\end{array} \right\},\mathbf{\overline{X}}=\left\{ \begin{array}[] {c}\dot{u}\\ \dot{\varphi}\\ \ddot{u}\\ \ddot{\varphi}\end{array} \right\}. \tag{10}\]  
Assume that the solution of Equation (9) has the form of:  
\[\mathbf{\overline{X}}=\psi\mathbf{e}^{\lambda t},\overset{\cdot}{\mathbf{ \overline{X}}}=\psi\lambda\mathbf{e}^{\lambda t}, \tag{11}\]  
where \(\psi\) is the eigenvector, substituting \(\psi=[\psi_{1}\ \psi_{2}]^{\mathrm{T}}\) into Equation (9), whereby we obtain:  
\[(\mathbf{A}\lambda+\mathbf{B})\left\{ \begin{array}{c}\psi\\ \psi\lambda\end{array} \right\}=0. \tag{12}\]  
The characteristic equation can be expressed as:  
\[\mathrm{det}|\mathbf{A}\lambda+\mathbf{B}|=0. \tag{13}\]  
The \(j\) th pair of eigenvalues are \(\lambda_{2j-1}\) and \(\lambda_{2j}\), and the \(j\) th fundamental angular frequency \(\omega_{j}\) can be obtained as:  
\[\omega_{j}=\left|\lambda_{2j-1}\right|=\left|\lambda_{2j}\right|. \tag{14}\]
## 5 Calculus Analysis
### Calculation Results and Analyses
#### 5.2.4 Effect of SNR on System Reliability
This increases the symbol error rate (SER) of the system, affecting the quality of wireless communication and the reliability of the system. Therefore, an important difference between wired communication and wireless communication is that the quality of wireless communication is easily affected by weather factors, especially in adverse weather conditions. When choosing the access network communication method for the distribution network, the actual weather type of the geographical area should be considered. During extreme weather events, information systems can ensure the reliability of communication systems by appropriately increasing wireless signal transmission power and reducing frequency.
## 4 Methods
### Cluster Establishment Phase
#### 4.1.1 Coarsely Clustering All Nodes to Obtain K Initial Clustering Centers  
In this paper, the Canopy algorithm is used to pre-cluster nodes, and to quickly obtain the k-value and \(k\) initial cluster centroids. Then, the output is used as the input of the initial parameters for the K-means algorithm to cluster nodes in WSN.  
The process of the Canopy algorithm in DCK-LEACH for optimizing the initial clustering centers is as follows.  
Step 1: Randomly choose a node in the set N, N = {Node (1), Node (2),..., Node (n)}, denoted as P1 and delete P1 in N. Mark P1 as a cluster center point of Canopy, and mark it as Canopy (1). Save the node position information into the set C = {Canopy (1), Canopy (2),... Canopy (\(i\))}.  
Step 2: Set the value of thresholds T1 and T2 (T1 = 50, T2 = 0.5 T1), traverse the set N, and calculate the distances of the nodes in N to the nodes in the set C using Equation (4), respectively. If the distance of node \(i\) to a Canopy (\(i\)) in the set C is less than T1, the cluster ID of the node \(i\) is named \(i\). If the distance of node j to a Canopy (\(i\)) in the set C is less than T2, it means that node j is very close to Canopy (\(i\)), and the node j is removed from N to avoid repeatedly joining it to other clusters.  
Step 3: if the distance of the node j to all existing Canopy centroids is greater than T1, we add node j to the set C as a new Canopy centroid.  
Step 4: Iterate through the residual nodes in N, continue with Step 2 and Step 3 until N is empty, and get the set C of all Canopy centroids and the number of clustering centers (K). Use the above result as the input of the initial parameter of the K-means algorithm for further clustering.
# Landslide Susceptibility Prediction Based on a CNN-LSTM-SAM-Attention Hybrid Model
## 4 Model Realization and Result Analysis
### Results and Analysis
#### 4.2.3 ROC Curves and PR Curves  
In landslide prediction tasks, the model's performance is further validated through Receiver Operating Characteristic (ROC) curves and Precision-Recall (PR) curves.  
1. ROC Curve and AUC Value  
The ROC curve, with false positive rate (FPR) as the horizontal axis and true positive rate (TPR) as the vertical axis, reflects the model's ability to discriminate between landslides (positive category) and non-landslides (negative category) under different thresholds, and the closer the AUC (area under the curve) is to 1, the better is the model's classification performance. A high AUC value indicates that the model can effectively reduce the \"misreporting of non-landslides as landslides\" (low FPR) and maximize the \"correct detection of real landslides\" (high TPR), which is crucial to guarantee the reliability of disaster warning.  
2. PR curves with AUC values  
The PR curve, with Recall on the horizontal axis and Precision on the vertical axis, focuses on the prediction quality of the positive category (landslide). The AUC value of the PR curve in this model reaches 0.9740 (close to 1), which indicates that the model can still achieve both high Precision (reduce false alarms) and high Recall (reduce missed alarms) in a scenario with a high degree of category imbalance (sparse landslide samples).  
The ROC curve and PR curve of the model in this paper are shown in Figure 8.  
The high AUC value (0.9703) of the ROC curve reflects the strong overall classification ability of the model, while the high AUC value (0.9740) of the PR curve further validates its excellent performance in category-imbalanced data. The combination of the two suggests that the model in this paper is not only able to accurately distinguish landslides from non-landslides but also maintains a high confidence in its early warning ability when landslide samples are scarce.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Training Set & 93.35\% & 94.57\% & 89.84\% & 0.9214 \\ Test Set & 91.66\% & 92.73\% & 87.64\% & 0.9012 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of key metrics between training and test sets.  
Figure 8: The ROC curve and PR curve of the CNN–LSTM-SAM–Attention model.