esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface Plasmon Resonance-Based Fiber Optic Sensors Utilizing Molecular Imprinting
## 5 Molecular Imprinting
### MITs for MIP Synthesis
#### 5.2.3 Nano-MIT  
The combined approach of the molecular imprinting and nanotechnology results in the synthesis of nanostructured MIPs [93]. This combinational method greatly enhances the applicability of MIPs because of the improvement in site accessibility and binding capacity of the imprinted materials. N-MIPs in different kinds of nanostructures such as nanoparticles, nanoflowers and nanotubes have been reported by many research groups using different nanofabrication methods [94; 95; 96]. The abovementioned advantages increase significantly the sensitivity as well as selectivity for broad varieties of analytes.  
In the development of molecular imprinting technology, several special imprinting methods were developed. With the need for simultaneous recognition of two templates, multi-template imprinting technology has emerged [97]. In this series several other techniques such as multi-functional monomer imprinting [98], segment imprinting [99], dummy imprinting [100], etc. have been developed. The characterization methods are used for the morphological analysis, monomer template interaction analysis and screening of the monomers of the prepared MIPs. Table 1 shows the various characterization methods and their requirements for the confirmation of MIPs.
## 4 Finite Element Modeling  
### Model Description  
The horizontal influence range of the caisson extends to 3.0 times the caisson diameter, while vertically, it reaches approximately 30 m below the bottom of the caisson [14]. Therefore, in the FE model, the long side (\(x\)-axis) of the soil is 144 m, and the short side (\(y\)-axis) is 58.85 m, as shown in Figure 3. To characterize the slipping and separation phenomena at the interface between the caisson and the soil, contact elements were placed at the interface, with a penalty method used for tangential direction [15], a friction coefficient set at 0.4, and hard contact employed in the normal direction. The soil in the model follows the  
Figure 2: Displacement and strain monitoring at different locations.  
Mohr-Coulomb yield criterion [16], while concrete structures adopt the concrete damaged plasticity (CDP) model [17]. With the elastic modulus of steel far exceeding that of concrete, it exhibits elastic-plastic behavior before reaching its yield strength and is therefore approximated as a linear elastic material.  
The loess-like light silty soil layer in the model contains a dry-wet separation surface, which acts as a permeable free surface allowing unrestricted settlement with a pore pressure of 0.0. The left and right boundaries constrain horizontal displacements, while the bottom boundary restrains vertical displacements of the soil. The annual fluctuation curve of the groundwater level is simplified into a sinusoidal function. After coding in Fortran2012 programming software, the DISP subroutine was embedded into the FE model to incorporate boundary conditions for calculation. The groundwater fluctuation functions are shown in Equations (1) and (2):  
\[P_{0}=10000\times(-23.19-y) \tag{1}\]  
\[U(1)=P_{0}+10000\times H\times\sin(\pi t/6) \tag{2}\]  
where \(P_{0}\) is the initial groundwater pressure that varies with burial depth, \(H\) is the amplitude of the annual change in groundwater level, and \(t\) is the time of change in groundwater level (in months).
## 4 Results and Discussions
### Sampling Partitioning and Ptimization Based on the Information Value Model  
In this research, grid cells are selected as assessment units, and the study area is divided into grids with a spatial resolution of 30 m. Landslides are represented as point data in the GIS platform; thus, the quantity of landslides corresponds to the total grid cells they encompass. Utilizing the frequency ratio values of the influencing factors (Table 1) and the calculation formula of the information value method, the information value for each classification is computed. Subsequently, raster layers corresponding to each classification are obtained, and a susceptibility index map based on the information values is generated. Employing the natural break method, susceptibility is divided into five levels: very high, high, medium, low, and very low. The levels thus generate the landslide susceptibility zoning map (Figure 5).  
Figure 5: Landslide susceptibility zoning based on information value model.  
To prevent negative sample points from being located within potential landslide areas, an optimization of negative sample sampling methods was conducted. Based on the landslide susceptibility zoning generated using the information value model, the sampling area was further divided. The areas of very high and high susceptibility were combined into Zone 1, while areas with medium, low, and very low susceptibility were grouped into Zone 2. Through random selection, 1000 non-landslide grid cells were chosen from Zone 1 and Zone 2 combined, as well as 1000 from Zone 2 alone, resulting in Sampling I and Sampling II (Figure 6). Various machine learning models were utilized to assess the effects of different sampling methods on landslide susceptibility modeling predictions.
# The Interface of Privacy and Data Security in Automated City Shuttles: The GDPR Analysis
## 4 The Interface of Privacy and Data Security in ACSs
### Privacy-Preserving Techniques Overview  
The GDPR introduces pseudonymization and anonymization as prominent countermeasures to protect personal data since they lower the risk of linking personal data to their related data subjects. In legal terms, such schemes are forethought differently and independently, while, technically, they offer incommensurable levels of privacy-preserving.  
We observed an ongoing debate regarding the effectiveness of privacy-preserving approaches and the inherent risk to re-identify data by using specific reverse engineeringtechnologies or by combining anonymized data with other information [44; 45]. The opinion 05/2014 flagged three main risks related to non-robust privacy-preserving methods [35]:  
* \"Singling out\": when the data subject's data are isolated to identify the natural person attributes or track their localization.
* \"Linkability\": when correlation of multiple records, at least two, of the same individual leads to the identification of the person.
* \"Inference\": when a data subject's data are deducted from a dataset or through additional information leading to their identification [30].  
By extending the opinion 05/2014 risks, we present a clear categorization and highlight the limitations of the most relevant pseudonymization and anonymization schemes for the ACS environment.
# Performance Analysis of Quantum Key Distribution Technology for Power Business
## 4 Structure of Power QKD Network
### QKD-Based Video Conference System  
The large-scale popularity of the video conference system has improved the efficiency of administrative negotiations, cross-regional consultations and cross-regional exchanges, while reducing the costs of meetings. Accompanying system security issues have gradually become apparent and major decision-making consultations face third-party threats. At the same time, video information has the characteristics of large data volume, controllable encoding rate and strict synchronization. The encryption method is different from text encryption. Combined with the complicated overhead power lines in operation, in order to guarantee the performance of quantum key distribution, QKD equipment with fast deviation feedback function is equipped, and quantum relay equipment is used to guarantee the key rate on large-span lines. As a result, it satisfies the need for secure transmission of large amounts of video data. Finally, high-efficiency, high-security data protection is achieved, and the level of security protection for video conference scenarios in fixed venues and outdoor emergency is improved. Figure 9 shows the operation chart of video conference.
## 3 Prototype, Measurements, Discussion, and Assessment of Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**[Ref.]** & **Feeding Mechanism** & **CP Is Achieved by** & \begin{tabular}{c} **Volume of the HDRA** \\ **(in Terms of A at f\({}_{\text{r}}\))** \\ **(L \(\times\) W \(\times\) H\({}_{\text{eff}}\))** \\ \end{tabular} & \begin{tabular}{c} **f\({}_{\text{r}}\) (or) CP Bands of** \\ **Resonance (GHz)** \\ \end{tabular} & **Gain (dBic)** \\ \hline \multirow{3}{*}{[23]} & \multirow{3}{*}{Penetrated coax feed} & \begin{tabular}{c} Quasi-self- \\ complementary \\ characteristic of the \\ metasurface \\ \end{tabular} & 6.08 \(\lambda\times\) 4 \(\lambda\times\) 0.06 \(\lambda\) & 24.65–26.06 & 6.03 \\ \cline{3-5}  & & \begin{tabular}{c} Due to plus shaped \\ unit-cells based \\ metasurface and \\ rectangular DR \\ \end{tabular} & 0.93 \(\lambda\times\) 1.29 \(\lambda\times\) 0.16 \(\lambda\) & 3.6–6.6 & 6-7.2 \\ \hline \multirow{3}{*}{[26]} & \multirow{3}{*}{Aperture coupled} & \
\begin{tabular}{c} Having a feeding \\ network composed of \\ four microstrip lines, \\ where the four slots \\ are geometrically \\ arranged to ensure \\ circular polarization \\ \end{tabular} & 0.8 \(\lambda\times\) 0.8 \(\lambda\times\) 0.12 \(\lambda\) & 1.08–1.82 & 5 \\ \cline{3-5}  & &
\begin{tabular}{c} Due to plus shaped \\ unit-cells based \\ metasurface and \\ rectangular DR \\ \end{tabular} & 0.98 \(\lambda\times\) 1.29 \(\lambda\times\) 0.16 \(\lambda\) & 3.6–6.6 & 6-7.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of proposed antenna with the existing literature.
## 3 SAR Environment Modeling  
### Environment Model  
The environment is formulated as 2D grid worlds of size \(m\times m\in N^{2}\) with a cell size of \(c\) and a set of all possible positions \(M\). The buildings and walls that the UGV cannot occupy are given by the set  
\[W=\left\{\left[x_{i}^{W},y_{j}^{W}\right]^{T}:\left[x_{i}^{W},y_{j}^{W}\right] ^{T}\in M\right\} \tag{4}\]  
The traps where the UGV will be punished are given by the set  
\[B=\left\{\left[x_{i}^{B},y_{j}^{B}\right]^{T}:\left[x_{i}^{B},y_{j}^{B}\right] ^{T}\in M\right\} \tag{5}\]  
The environment can be described by functions \(f_{1},f_{2},\ldots,f_{e}\), and the eigenvector at position \(p\in M\) is  
\[F_{p}=\left(f_{1}(p),f_{2}(p),\ldots,f_{e}(p)\right) \tag{6}\]  
The state of the target is described through its position:  
\[p_{\text{target}}=\left[x_{\text{target}},y_{\text{target}}\right]^{T}\in M-W \tag{7}\]  
The target tends to appear in positions within the feature interval, such as  
\[F_{\text{low}}\leq F_{p_{\text{target}}}\leq F_{\text{up}} \tag{8}\]  
The UGV moves within the limits of the grid world, and the state of the UGV is described through the following:  
* Its position;
* Its operational status, either inactive or active;* Its field of view (FOV), the grid world size with the cell size, and the set of all possible positions.  
The distance that the UGV walks in a mission's online time slot \(\delta_{t}\) is equivalent to the cell size \(c\). The position of the UGV evolves according to the motion model given by  
\[p(t+1)=\left\{\begin{array}{ll}p(t)+a(t),&\quad\varphi(t)=1\\ p(t),&\quad\text{otherwise}\end{array}\right. \tag{9}\]  
The evolution of the operational status \(\varphi(t)\) of the UGV is given by  
\[\varphi(t+1)=\left\{\begin{array}{ll}0,&\quad\varphi(t)=0\lor p(t)=p_{ \text{target}}\\ 1,&\quad\text{otherwise}\end{array}\right. \tag{10}\]  
The end time point \(T\) is defined as the time slot when the UGV reaches its terminal state without actively operating.The following constraints restrict the UGV mobility model:  
\[\left\{\begin{array}{ll}p(t)\
ot\in W,&(a)\\ t\leq T,&(b)\\ \varphi(0)=1,&(c)\end{array}\right. \tag{11}\]
## 2 Numerical Methodology  
### Geometry  
The present study numerically investigates the unsteady aerodynamic pressure of an enclosed housing for sound emission alleviation when a high-speed train passes through. CFD simulations are conducted using ANSYS Fluent software. A geometric model of a high-speed train is simplified as shown in Figure 1a. The details of the train, such as the pantograph, bogies, and wheels, are ignored. As the most common trains, the train model consists of eight coaches, including the head and tail coaches, and six intermediate coaches, with a total length (\(L_{T}\)) of 203 m and a width (\(W\)) and height (\(H\)) of 3.38 m and 3.7 m, respectively. Figure 1b shows the geometry of the enclosed housing for sound emission alleviation. The model is the preliminary design of a project in a high-speed railway line. The length of the enclosed housing for sound emission alleviation is 840 m, the radius of the cross-section is 6.913 m, the height is 9.637 m, the track spacing is 5.0 m and the cross-sectional area of the enclosed housing for sound emission alleviation is 110.5 m\({}^{2}\). A universal beam (H 500 \(\times\) 300 \(\times\) 12 \(\times\) 25) is set up every two meters inside the housing for sound emission alleviation.
# Estimation of Hand Motion from Piezoelectric Soft Sensor Using Deep Recurrent Network
## 4 Result and Discussion
### Moving State Estimation  
To evaluate the performance of the proposed network, we compared the result \(m_{p}\) with \(m_{h}\) from Equation (14). Table 3 shows an identical ratio of \(m_{p}\) and \(m_{h}\) with \(m_{gt}\). In Table 3, it is evident that the coincidence between \(m_{p}\) and \(m_{gt}\) is 5-25% higher than the coincidence between \(m_{h}\) and \(m_{gt}\). This shows that the predicted \(m_{p}\) is more similar to the ground-truth data than \(m_{h}\). In addition, while \(m_{h}\) confuses curvature change with noise, as shown in Figure 8d, the result in Figure 8c shows that \(m_{p}\) rarely confuses noise with sensor movement and recognizes only sensor movement.  
Based on this result, we demonstrate that the network exhibits good performance for moving state estimation in real-time, multiple sensor data as single pre-acquired data. The result indicates that the proposed network can also predict moving state in real-time system, and chasing \(\bar{k}(0)\) for offset renewing in a real-time system is also facilitated with the proposed network.  
Figure 7: The actual picture of motions used in the test. Pictures of: (**a**) a hand before making a motion; (**b**) a hand when bending the thumb; and (**c**) a hand clenching fist except for the thumb.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **thumb1** & **thumb2** & **thumb3** & **index1** & **index2** & **index3** \\ \hline proposed method (\(m_{p}\)) & 80.02\% & 84.44\% & 83.25\% & 84.47\% & 81.12\% & 82.62\% \\ heuristic method (\(m_{h}\)) & 61.25\% & 63.58\% & 78.41\% & 65.79\% & 54.31\% & 53.90\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Coincidence of moving state estimations with leap motion controller.
# Advances in Deep Space Probe Navigation
## 3 Exploring Further Advances in Deep Space Probe Navigation
### Pulsar Navigation  
The recent flight experiments with the Neutron Star Interior Composition Explorer (NICER) and Insight-Hard X-ray Modulation Telescope (Insight-HXMT) have demonstrated the feasibility of X-ray pulsar-based navigation (XNAV) in space. Ref. [7] proposes a fast algorithm for on-orbit estimation of the pulse phase of the Crab pulsar called X-ray pulsar navigaTion usIng on-orbiT pulsAr timiNg (XITIAN), which is computationally efficient and has the potential to be employed for onboard computation. Ref. [8] proposes an adaptive grid search (AGS) method for estimating the pulse phase and the Doppler frequency.  
Epoch folding is a classical period estimation method in the time domain. In order to reduce the computational complexity, Ref. [9] improves the fast folding algorithm through segment correlation and amplitude accumulation, which is based on the post-order traversal of a binary tree. In order to overcome the time-varying frequency problem caused by the acceleration of spacecraft in the process of photon time-of-arrival data, Ref. [10] proposes an improved method called nonuniform epoch folding (NUEF). The pulsar's directional error and the onboard clock error are two types of systematic errors that seriously reduce navigation accuracy. To solve this problem, Ref. [11] proposes a star angle/double-differenced pulse time of arrival (SA/DDTOA) integrated navigation method.
# Improved Automatic License Plate Recognition in Jordan Based on Ceiling Analysis
## 1 Introduction  
The rapid growth in the number of vehicles has led to the continuous need for more use of the Intelligent Transportation System (ITS) to address many security- and traffic-management challenges, including finding stolen cars, banning violations, managing parking lots, monitoring cars at traffic lights, and others. The ITSs that aim to make the use of transportation networks safer and smarter have been benefiting from recent advances in image processing and machine intelligence techniques towards the development of more intelligent roads, vehicles, and users [1, 2]. License plate recognition (LPR) is typically one of the key components of the ITS.  
LPR is one of the essential elements of a mass surveillance method. It is sometimes known as a license plate recognition system (LPRS), automatic license plate reader (ALPR), automatic vehicle identification (AVI), mobile license plate reader (MLPR), or vehicle license plate recognition (VLPR). The design and format of the license plates vary in different countries. However, these systems typically comprise four main components: image capturing, detection, segmentation, and recognition.  
The work presented in this paper extends on and improves on the performance of an ALPR that was previously reported in [3]. The primary contributions of this paper can be summarized as follows:  
1. A ceiling analysis is carried out to identify potential enhancements in each stage of the ALPR that have a significant impact on the overall system performance.  
2. Based on the obtained ceiling analysis results, the following enhancements are suggested: \(\odot\) In the detection stage, new processing units are suggested; a vertical-edge histogram analysis and size estimation of the candidate regions. \(\odot\) In the segmentation stage, a new processing unit is suggested to de-rotate the misaligned license plate images.  
These improvements have resulted in significant improvements in overall system performance despite only a 1% increase in execution time.  
The remainder of this paper is organized as follows. Works related to ALPR systems are reviewed in Section 2. The style and attributes of Jordanian license plates, as well as the dataset used in this study, are detailed in Section 3. The suggested ceiling analysis and ALPR system are shown in Sections 4 and 5, respectively. The obtained results are presented and discussed in Section 6. Finally, the work is concluded in Section 7.
## 2 Materials and Methods
### Data Acquisition  
Before any UAV imaging, the first step of data acquisition involved marking and collecting CP and RP on a local geodetic basis. A total amount of 7 CPs were collected, which were used to check the accuracy of the UAV's direct georeferencing system. The RPs where the UAV's positioning device or base station iBase was placed, were also collected. All points were measured in the official projection coordinate system of the Croatian Terrestrial Reference System (HTRS96/TM). The next step was related to the planning of the UAV missions, which was carried out using the QBase 3D software (Figure 3). MS imaging mission was carried out with a front and side overlap of 70%, while considering the terrain morphology and the desired level of detail, the relative flight altitude was set to 100 m. Settings provided a ground sampling distance (GSD) for the UAVMS of 6.94 cm/px (Figure 3). On the other hand, the settings of the second mission related to the RGB sensor were set to front and side overlap of 75% and the relative flight altitude of 140 m. The  
\begin{table}
\begin{tabular}{l l} \hline
**Sensor Type** & Multispectral (MS) \\ \hline \multirow{4}{*}{**Spectral Bands**} & Coastal blue (444 nm), blue (475 nm), green \\  & (531 nm), green (560 nm), red (650 nm), red (668 nm), red (668 nm), red edge (705 nm), red edge (717 nm), red edge (740 nm), near-infrared (842 nm) \\ \hline
**Ground Sample Distance** & 8 cm per pixel (per band) at 120 m \\ \hline \end{tabular}
\end{table}
Table 1: MicaSense RedEdge-MX Dual MS specifications.  
Figure 2: The research methodology framework.  
expected GSD for DOP was 1.81 cm/px. Before the flight, the process of calibration of the UAV's initial measurement systems (IMUs) was carried out, followed by the radiometric calibration of the MS sensor, using the appropriate reference calibration panel. After the processes, aerial photogrammetric imaging of the selected research area was performed. The radiometric calibration of the MS sensor was repeated after a mission to determine any differences in the atmospheric conditions.
## 2 Materials and Methods
### Data Analysis
#### 2.3.4 Statistical Analyses  
An unpaired two-sample t-test was conducted to assess the effect of sex on the total minimum distance traveled by satellite-tracked reef manta rays across the study regions. Before performing the t-test, we conducted a Shapiro-Wilk normality test to verify that the data were normally distributed, followed by an F-test to evaluate the homogeneity of variances. Additionally, we examined the impact of sex on the home ranges of the satellite-tracked individuals. We hypothesized that there would be no significant difference in the total minimum distance traveled between sexes, aligning with the findings of [PERSON] et al. [12], who reported no significant differences in the movements of acoustically tagged reef manta rays in Dampier Strait, Raja Ampat. Furthermore, we hypothesized that sex would not influence the home ranges (95% UD) of the reef manta rays. A one-way ANOVA was also conducted to evaluate the effect of region on the average movement speed of the satellite-tracked reef manta rays. In our study, all the means are reported with \(\pm\) one standard deviation (mean \(\pm\) SD).
## 1 Introduction
Many equations have been recommended by researchers and design codes [26; 27; 28; 29]. However, most of these equations were built for monolithic beams or members prestressed by steel tendons. For PSCB-IUCFRP, the discontinuity of the segmental joints causes different deformation modes from those seen in monolithic beams and further influences the tendon stress increment and flexural capacity. The applicability of previous equations to PSCB-IUCFRP needs to be carefully discussed. This paper focuses on the simplified method for predicting the flexural capacity of PSCB-IUCFRP. Firstly, a numerical analysis comparison study is carried out with the developed numerical model. The failure modes and deformation characteristics of PSCB-IUCFRP are discussed and compared with those of monolithic beams. Then, a parametric analysis study containing 162 models is carried out, and the influence of the crucial parameters is presented. The results of the 162 numerical models are used to evaluate the applicability of three widely used simplified prediction methods. Finally, new prediction equations for PSCB-IUCFRP are derived and verified based on the deformation and failure mechanism analysis. Furthermore, a balanced prestressing reinforcement ratio prediction equation is put forward for the PSCB-IUCFRP system. The research in this paper provides suggestions for the structural design of PSCB-IUCFRP. Flexural Behaviors of Precast Segmental Concrete Beam Prestressed with Internal Un-Bonded CFRP Tendons