esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Ammonia (Nh\({}_{3}\))  
The best model obtained for monitoring NH\({}_{3}\) concentration involved eight LVs and used the 1 st Der + MSC as the pre-processing method in three spectral regions (Table 1), one of which (1439.2-1189.0 cm\({}^{-1}\)) comprising Raman shifts previously attributed to N-H in plane deformation (1400 and 1425 cm\({}^{-1}\)), and part of a band associated with NH\({}_{3}\) (1550-1428 cm\({}^{-1}\)) [39]. Despite the promising calibration parameters (R\({}^{2}\)Cal = 96.2% and RMSEC = 0.72 mgN L\({}^{-1}\)), the low R\({}^{2}\)CV and RDPCV values of 65.5% and 1.7, respectively, and the substantial RMSECV (1.89 mgN L\({}^{-1}\)) obtained imply that this model has a poor prediction capacity, being unable to extract the relevant information from the spectral data. This is evidenced by the scattering of data points around the regression line (Figure 2c) and the discrepancy between the measured and predicted NH\({}_{3}\) concentration values for some of the samples (Figure 3c). However, no further samples were excluded from the data set, as no clear outliers were detected. The significantly better calibration results in comparison to cross-validation suggest that a higher number of samples should be used for PLS model development in order to represent the whole NH\({}_{3}\) concentration range under study. Accordingly, is it possible that the prediction accuracy of the PLS model could be improved by including more samples to equally cover the total NH\({}_{3}\) concentration range. In fact, real-time knowledge on the NH\({}_{3}\) concentration in a photo-BNR reactor would allow the assessment of its nutrient removal capacity and the adaptation of the operational conditions when the treatment efficiency would not meet the discharge requirements.
## Nomenclature  
\begin{tabular}{l l} CFD & Computational fluid dynamics \\ CWE & Computational wind engineering \\ \(\alpha_{g}\) & volume fractions of the air phase (\(\%\)) \\ \(a_{s}\) & volume fractions of the sand phase (\(\%\)) \\ \(\rho_{g}\) & density of the air phase (kg/m\({}^{3}\)) \\ \(\rho_{s}\) & density of the sand phase (kg/m\({}^{3}\)) \\ \(v_{g}\) & velocity vectors of the air phase \\ \(v_{s}\) & velocity vectors of the sand phase \\ \(u_{g}\) & velocity of the air phase (m/s) \\ \(u_{s}\) & velocity of the sand phase (m/s) \\ \(\tau_{g}\) & surface stress tensors of the air phase (Pa) \\ \(\tau_{s}\) & surface stress tensors of the sand phase (Pa) \\ \(p\) & pressure of an ideal fluid (Pa) \\ \(p_{s}\) & solid pressure of the sand phase (Pa) \\ g & acceleration of gravity (m/s\({}^{2}\)) \\ \(f_{gg}\) & interaction force between the air and sand phases (N) \\ \(C_{D}\) & drag coefficient \\ \(d\) & equivalent diameter of the sand particle (m) \\ \(u_{r}\) & relative velocity between the air and sand phases (m/s) \\ RANS & Reynolds Average Navier-Stokes method \\ \(k\) & turbulent kinetic energy \\ \(\epsilon\) & turbulent dissipation rate \\ \(u_{i}\) & velocity components in the \(x\)-direction (m/s) \\ \(u_{j}\) & velocity components in the \(y\)-direction (m/s) \\ \(\sigma_{k}\) & Prandtl constant corresponding to the turbulent kinetic energy \\ \(\sigma_{\epsilon}\) & Prandtl constant corresponding to the turbulent dissipation rate \\ \(G_{k}\) & turbulent kinetic energy caused by the average velocity gradient \\ \(S_{d}^{k}\) & turbulent kinetic energy caused by particle motion \\ \(S_{d}^{\epsilon}\) & turbulent dissipation rate caused by particle motion \\ \(\mu_{t}\) & turbulent viscosity \\ C\({}_{1}\) & model constant \\ C\({}_{2}\) & model constant \\ C\({}_{\mu}\) & model constant \\ \(u(y)\) & horizontal wind speed at height \(y\) (m/s) \\ \(u\)- & friction wind speed (m/s) \\ \(\kappa\) & Karman constant \\ \(y_{0}\) & surface roughness (m) \\ \(v\) & inflow velocity (m/s) \\ \end{tabular}
## 2 Construction Overview  
### Project Introduction  
This paper takes the Ciqu station project along Beijing Metro Line 17 as the studied case. The horizontal net distance between the enclosure structure of the newly built station and the existing station is only 2.8 m, which is about 9 m deeper than the bottom plate of the existing station. There is a cross transfer with the existing Yizhuang line in Ciqu station, the station consists of open excavation section and concealed excavation section, the open section is sited in the north and south sides of the existing Ciqu station, while the concealed section lies in the middle of the station and directly cross through the existing station. The general layout diagram of the new station and the existing station is shown in Figure 1.  
The newly built Ciqu station is a kind of island station, in which the open excavation at both ends is the underground three-layer box frame structure, and the central part that underpasses the existing line is flat top and straight wall under concealed excavation construction. The total length of the station is 415 m, the standard section is 23.1 m in width, the structure net height is 22 m, the bottom plate buried depth of the station center line milee is 25 m, and the absolute elevation of the rail roof is 1.963 m. The existing station is divided into two separate foundation pits, the total length of the foundation pit is 286.1 m,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Project Name**} & **Excavation** & \multirow{2}{*}{**Adjacent Structure**} & \multicolumn{2}{c}{**Deformation of the Structure/mm**} \\ \cline{3-5}  & **Depth/m** & & **Horizontal** & **Vertical** \\ \hline Jing’an Temple Station of Shanghai Metro Line 7 & 23.4 & Shield tunnel & 3.2 & \(-\)1.3 \\ Zhuhai City Plaza in Guangzhou & 21 & Mining tunnel & 6.9 & - \\ Foundation pit of a building in Beijing & 12 & Auxiliary structure of & 1.8 & \(-\)5.3 \\ A hospital in Singapore & 15 & Shield tunnel & 6 & 3.8 \\ A comprehensive building in Guangzhou & 18 & Subway station & 5.8 & \(-\)6.8 \\ A control center in Hangzhou & 13 & Shield tunnel & 1.52 & \(-\)4.11 \\ Qianhaiwan station of Shenzhen Metro Line 5 & 18 & Subway station & 4.9 & \(-\)1.5 \\ Yueyang International Plaza in Shanghai & 15 & Subway station & 5.3 & 3.5 \\ Provident fund building in Suzhou Industrial Park & 12.2 & Shield tunnel & 1.05 & 6.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Cases of adjacent foundation pit engineering.  
Figure 1: General layout of secondary Ciqu stations.  
the depth of the existing station is 25.85 m and the width is 30 m, the depth of the ground wall is set to 13.8 m, which is inserted into the water barrier. The existing station is an open double-layer three-span frame structure with net height of 13.2 m, net width of 20.7 m and bottom plate buried depth of 16.2 m.
## 3 Results and Discussion
### Analysis of Artificial Lighting Energy Consumption  
Table 11 specifies the amount of energy production for the integrated LSPV and the total lighting energy consumption in each lighting control zone throughout the year pertaining to the climatic condition of Ha'il. For the analysis, it is assumed that lighting was switched on automatically after 8:00 a.m. and kept on until 5:00 p.m., with the dimming system set to 300 lux. The results show that the reference model (without LS) exhibited the lowest energy use compared to all LSPV configurations by a difference of only 20.6 kWh per year, while the lowest lighting energy consumption of 49.7 kWh was achieved for LSPV5, which is more than 50%. Peak lighting energy demand for the deep office reached up to 102 kWh for LSPV2.  
On the other hand, the energy output due to PV use was much higher than the lighting energy consumption. PV energy output ranged between 107 kWh for LSPV7 to 686 kWh for LSPV2. Consequently, the annual lighting-specific energy savings were calculated for two tilt angles (horizontal and 30\({}^{\circ}\)) and four PV coverages. The net savings were computed relative to the energy output of PV and lighting energy demands. In all the cases, the results indicated a reduction of at least 63% and, in some cases, up to 89%. Considering these observations, a light shelf using a solar module is advantageous concerning energy savings for lighting. Furthermore, the energy production can also compensate for a considerable fraction of other domestic energy needs, such as that required for cooling.
## 5 Fiber Transmission Performance Results and Discussion  
In this section, we first study the fiber transmission performance with high-baud-rate 16-QAM signals in the unamplified link. Then, we extend our experiment to the high-baud-rate 64-QAM signals. Finally, we summarize a transmission matrix showing capacity and distance for 16-QAM and 64-QAM signals at various baud rates.
## 5 Discussion
The percentage of predictability of distal rotation was 60%. These results are similar to those obtained by several studies [15; 16; 17; 18; 19], reporting that the ClinCheck generated a prognosis of greater distal rotation than actually realized. In a recent study, [PERSON] et al. [20] assessed that the mesial rotation of the upper first molars was around 42.9%. The difference in percentage predictability could be associated with maxillary arch expansion, which is essential to gain further space and increase the predictability of disto-rotational movement. However, the rotation accuracy achieved in the present study was inferior when compared with the one reported by [PERSON] et al. [15]. This difference is due to the fact that the predictability was tested at the end of the first set of aligners before any refinement. Regarding the expansion movements, recently, [PERSON] et al. [21] found a high predictability of the planned molar expansion with CA in permanent dentition. Our results showed that the same accuracy could be obtained in mixed dentition as well. Our data indicated that Invisalign CA are an effective tool for achieving transverse expansion as no statistical differences with the ClinCheck prediction were observed. This is, in the literature, the first prospective investigation to compare the T2-T1 changes of the maxillary first permanent meso-rotated molar position obtained by means of the Invisalign First System with the same disto-rotation protocol in all subjects with mixed dentition. Prior published data included case reports, material studies, technical articles, editorials, surveys, and studies assessing the predictability of multiple tooth movements. A limitation of the present investigation is its short-term nature and the small sample size of the treated groups. Therefore, further evaluations are necessary to increase the sample size and to analyze the stability of the results obtained in the long term. Figure 3: Intraoral occlusal evaluation pre-treatment (T1) and post-treatment (T2).
# Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning
## 4 Experiments and Results Analysis  
### Experimental Datasets and Environment  
Two datasets are used for verifications: MNIST and Metal fatigue strength data. For the MNIST handwritten dataset [50], it contains 60,000 training samples and 10,000 testing samples. In addition, the neural network model consists of 784 input layers, two hidden layers with a default number of 64 units, and an output layer with 10 output units.  
About metal fatigue data, it only has 437 records coming from the NIMS MatNavi open dataset [51]. MatNavi is one of the world's largest materials databases, including polymers, ceramics, alloys, superconducting materials, composites, and diffusion databases. Here, we select 437 pieces of metal fatigue strength data from MatNavi to build a regression model to test different metals, like carbon steel, low alloy steel, carburizing steel, and spring steel, under different testing conditions, such as different components, rolled product characteristics, and subsequent heat treatment. Each piece of metal fatigue data contains 15-dimensional features and 1-dimensional labels. According to the fatigue dataset, we divide it into four categories as shown in Table 2. The model structure used in the experiment includes one input layer with 15 units (15 dimensions), three hidden layers with 64 units, and one output layer with four units. The network structure of PFMLP is shown in Table 3.  
Here, the \(D_{n}^{Dataset}\) represents the \(n-th\) data of the \(Dataset\). In order to evaluate the PFMLP algorithm and its optimization methods, several comparative experiments were designed from three perspectives: (1) the prediction accuracy of the federated multi-layer perceptron and the single-node multi-layer perceptron; (2) the time consumption of the model training using different key length; (3) the time consumption of the model training with different sizes of hidden layer units; and (4) the impact of different numbers of learning clients on model performance.  
The experimental environment is in Windows 10, Python 3.6 with scikit-learn 0.21.3 and phe 1.4.0. We deployed a computing server, a KMC, and multiple clients in the local area network, and established communication between machines through the Socket. The specific network deployment is shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c} \hline
**Dataset** & **Data Range** & **Data Amount** \\ \hline \multirow{4}{*}{Fatigue} & \([200,400)\) & 56 \\  & \([400,500)\) & 147 \\  & \([500,600)\) & 148 \\  & \([600,\infty)\) & 86 \\ \hline \end{tabular}
\end{table}
Table 2: Fatigue dataset for multi-classification tasks.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Input Layer** & **Hidden Layers** & **Output Layer** \\ \hline MNIST & 784 (units) & 2 (layers) \(\cdot\) 64 (units) & 10 (units) \\ Fatigue & 15 (units) & 3 (layers) \(\cdot\) 64 (units) & 4 (units) \\ \hline \end{tabular}
\end{table}
Table 3: The network structure of PFMLP.
## 2 Dataset Description
### VIS-NIR Spectral Data Acquisition  
The experimental setup used to acquire the spectral data (line-scan hyperspectral imaging), as well as the procedure to compute the reflectance previously described by the authors in [7; 12; 13]. Thus, the reader is directed to additional references for a more detailed description. In summary, the system consisted of a hyperspectral camera, composed of a black and white camera and spectrograph, and a lighting source comprising a lamp holder to hold four 20 W, 12 V halogen lamps and two 40 W, 220 V blue reflector lamps. The acquired line-scan hyperspectral images had 1040 \(\times\) 1392 pixels, in which the 1040 pixels were related to the measured wavelength channels that had a width of approximately 0.6 nm (ranging from 380 to 1028 nm), and the 1392 pixels denoted the spatial dimension (one line over the sample) with a width of approximately 110 nm [7; 12; 13]. After imaging, a threshold-based segmentation method was applied to identify and extract the grape berries from the complete image. Figure 1 displays an example of a line-scan hyperspectral image acquired by the described setup and for three berries measured simultaneously.  
For a certain wavelength range, \(\lambda\), and position, \(x\), the reflectance values were obtained according to the following:  
\[R(x,\lambda)=\frac{GI(x,\lambda)-DI(x,\lambda)}{SI(x,\lambda)-DI(x,\lambda)}, \tag{1}\]  
where \(GI\) is the intensity of light reflected by the grapes; \(SI\) is the intensity of light coming from a white reflectance target (Spectralon) that reflects almost all the light reaching its surface in the ultraviolet, visible, and infra-red wavelengths; and \(DI\) is the dark current signal (electronic noise) measured by keeping the camera shutter closed. This electronic noise is independent of the object being imaged, and must be subtracted from the grape berries and the Spectralon in order to avoid tampering in the determination of the reflectance values.  
Each hyperspectral image was acquired over the berry's equator, considering the pedicel as the pole, and for three different positions of the berries, corresponding approximately to 120\({}^{\circ}\) rotation between positions [7; 12; 13; 14]. In order to minimize the measurement noise, 32 hyperspectral images were acquired. The final hyperspectral images were obtained by averaging the 32 images and, after identifying the grape berries, the reflectance measurements were computed. Finally, to create a unique reflectance spectrum for each sample, all  
Figure 1: Example of a line-scan hyperspectral image acquired before segmentation, considering three grape berries simultaneously imaged.  
berries' points were averaged over the spatial dimension and across all positions. All of the reflectance spectra gathered for the Touriga Franca variety are illustrated in Figure 2.
## 4 Overview of GNSSs
### BeiDou
#### 4.4.3 BeiDou Ground Segment  
The BDS ground control includes the main control station (MCS) and over 20 calibration stations. The MCS is responsible for receiving incoming signals, determining the satellite's orbit, transmitting signals, ionospheric correction, determining user location, and sending short messages to specific users. Calibration stations provide basic measurements for orbit determination, differential positioning over a large area, and calculation of user altitude.  
The OCS BDS provides command, control, and operation functions for three types of satellite constellations. The main functions of the OCS BDS are as follows:  
* Establishment and maintenance of data coordinates;
* Maintaining a time reference point;
* Measuring time synchronization;
* Precise determination;
* Prediction of orbits;
* Predicting the shift of the satellite clock;
* Processing of extended data;
* RDSS information processing;
* Monitoring, processing, and predicting ionospheric delays;
* Integrity monitoring;
* Uploading and downloading navigation messages.  
The OCS workflow can be described as the process of collecting data related to satellites and ground stations, processing and analyzing that data, managing communications between satellites and ground stations, and sending operational commands to ground stations [88].
# Bearing Fault Diagnosis Grounded in the Multi-Modal Fusion and Attention Mechanism
## 4 Experimental Design and Results
### Dataset Classification  
The public dataset includes the following fault types: normal, inner-race fault, outer-race fault, and ball fault. After data preprocessing, the experimental data were processed in units of 256 data points, normalized, and converted through convolution into grayscale images. After adding labels, the data were randomly divided into training and testing sets in a 7:3 ratio. The algorithm parameters were adjusted using the training set, and the performance metrics, such as accuracy, precision, recall, and F1 score, along with the confusion matrices and classification results, were calculated using the test set.
# PriSign, A Privacy-Preserving Single Sign-On System for Cloud Environments
### Contributions  
To address the above requirements, we propose a strong privacy-preserving single sign-on scheme called PriSign, which uses our proposed attribute-based credential with traceability (ABCT), attribute-based credential with blindness (ABCB), and threshold inner-product functional encryption (TIPFE) schemes along with Groth's structure-preserving signatures (SPS) scheme. The main contributions of our scheme are:  
**Fine-grained access control**: PriSign allows users to anonymously disclose a subset of user attributes to the ticket issuer, thereby obtaining different types of tickets for fine-grained access control.  
**Designated verifiers and token-hiding**: For the first time, we achieve perfect privacy protection for user service requests (i.e., access tokens); that is, users' access tokens can only be verified by a verifier with the designated policy, and are hidden from any non-designated verifiers.  
**Threshold key issuance for verifiers and proxy re-verification**: We innovatively deploy \(n\) policymakers in the PriSign scheme using threshold cryptography technology. As long as \(t\) policymakers are online, they can issue the verification key for proxy verifiers in order to achieve a stable proxy re-verification service in the unstable cloud environment.  
Similar to existing schemes, PriSign supports the unlinkability of user identities and double-spending detection. In addition, PriSign supports the traceability of users to achieve strong security control. We prove that PriSign satisfies the formal security definitions by reducing them to either well-known complexity assumptions or the security of proven cryptography primitives. The performance of PriSign is measured on a personal laptop, and our source code is made publicly available [10].
## 5 Conclusions  
The deep-seated landslide in the Lantai area, Taiwan, has a long history of landslide activity and damages the sole access road to the Tai-Ping Mountain National Forest Recreation Area from time to time. The high-resolution digital terrain model (DTMH) derived from UAV mounted LiDAR data were adopted for mapping the lineaments of the geological structures. Field investigation verified the locations of the geological structures, and fault gouge material was found. Comparing the location of geological structures to the borehole data, it was postulated that a shear zone was bounded by the geological structure surfaces and extended through the slope body. A slope model was constructed with the proposed shear zone in the slope body, and the numerical analysis was conducted using  
Figure 19: The distribution of the maximum shear strain increment from the numerical analysis of the slope model without geological structure (state 1). The distribution of the maximum shear strain suggested a deeper and larger potential sliding surface.  
the finite difference analysis. The failure mechanism was found to be quite different for the slope model with the proposed shear zone and geological structures compared to the slope model without the shear zone. It appeared that the shear zone bounded by geological structures affected the landslide behavior significantly. In addition, the proposed slope model resulted in landslide behavior that consisted well with mapped scarps, borehole data, and conformed with the event records. The identification of the geological structure lineaments would not have been possible without the high-resolution DTMH. The results of this study appeared to interpret the field data and evaluate the failure mechanism of the Lantai deep-seated landslide properly. These results could provide vital information supporting hazard mitigation strategy.  
Conceptualization, M.-L.L.; methodology, M.-L.L.; validation, M.-L.L. and Y.-C.C.; formal analysis, Y.-C.C. and Y.-H.T.; investigation, Y.-H.T. and K.-L.W.; data curation, Y.-C.C., Y.-H.T. and K.-J.C.; writing--original draft preparation, M.-L.L.; visualization, Y.-C.C. and Y.-H.T.; supervision, M.-L.L. All authors have read and agreed to the published version of the manuscript.  
This research was partly supported by the Ministry of Science and Technology, Taiwan (project number: MOST106-2119-M-006-009). The high-resolution LiDAR and borehole LTW05 data were provided by the Soil and Water Conservation Bureau (project number: SWCB-107-207), Council of Agriculture, Taiwan.  
The authors do not have the authority to distribute the remote sensing data, images, and geological map.  
The authors declare no conflict of interest.