esa-sceva/satcom-chunk-collection huggingface dataset:
# Satellite-Based Sea Ice Navigation for Prydz Bay, East Antarctica
## 4 Results
### Navigation through Landfast Ice  
Typically, the extent and average thickness of the land-fast ice off Zhongshan Station are approximately 30 km and 1.5 m in early December. Under these conditions RV _Xuelong_ was in full ice breaking operation in a land-fast ice zone. The highest risk was the grounded ice ridges and icebergs, which can often be detected by the ice crew onboard. The seasonal variation of land-fast ice thickness between March and November during the 2011-2013 was estimated using a modified version of [PERSON]'s law (as mentioned before). Results are presented in Figure 6. The average air temperature between March and November during the 2011-2013 seasons are \(-11.6\)\({}^{\circ}\)C, \(-11.9\)\({}^{\circ}\)C, and \(-12.4\)\({}^{\circ}\)C, respectively. The maximum land-fast ice thickness in the three years remained around 1.6 m. The maximum ice thickness in November is a proxy to be taken into account on whether a higher class ice breaker would be needed to assist RV _Xuelong_.  
Figure 5: Six-hour MSLP fields (color) with 10-m wind vectors, at 00:00 UTC on 25, 27 and 29 November in 2011–2013.  
Figure 6: Land-fast ice thickness based on modified version of [PERSON]’s law from 1 March to 15 November 2011–2013.
## 2 System Architecture  
### System Requirements Analysis  
Each greenhouse is equipped with one wireless sensor network node, and the node is connected to the temperature, humidity and soil moisture sensors to measure their values inside and outside greenhouses, respectively. An antenna stretches out of each greenhouse to collect data on a predefined interval, meanwhile an _ad-hoc_ wireless network is built. Consequently data are transmitted to a base station, where they are packed and sent to the data center in Beijing on a predefined schedule in order to achieve real-time data release in the WEB. The base station is placed where farmers can easily access real-time monitoring data [7].  
This system can achieve the following functions: (1) automatic collection of monitoring data for all greenhouses; (2) periodical transmission of the monitoring data and any alarm messages through matching the greenhouse ID to the greenhouse owner's phone number; (3) rolling and displaying the information on the screen of the base station; (4) acquisition of the monitoring data of the specified greenhouse with text messages being sent by the manager; (5) sending of the real-time greenhouse monitoring data to the Beijing data center via the GPRS network.
## 3 Methodology
### Overall Architecture  
MSSTTN is improved based on STTNs [19], which is structured by stacked spatial-temporal blocks (ST-Blocks). Each of the ST-Blocks are comprised of a spatial transformer and a temporal transformer. The spatial transformer extracts the dynamic spatial dependencies using self-attention mechanism and fixed dependencies using a GCN layer. For long-range temporal dependencies, the temporal transformer employs self-attention mechanism to utilize more information in the time series history. In addition, MSSTTN makes multi-step predictions directly that alleviate the propagated error.  
Inspired by STTNs [19], MSSTTN learns the complicated spatial and temporal dependencies by constructing a series of spatial-temporal blocks in which every block contains both a spatial and temporal module. The comprehensive structure is presented in Figure 1. The spatial-temporal block comprises a spatial transformer and temporal autotormer that enable the extraction of both spatial and temporal dependencies. After the extraction operations from the stacked spatial-temporal blocks, the model makes the prediction using the last block. We use \(\mathcal{X}^{i}\in R^{B\times N\times T_{h}\times C_{s}}\) and \(\mathcal{Y}^{i}\in R^{B\times N\times T_{h}\times C_{t}}\) to denote the input and output of the \(i\)-th spatial-temporal block. The input to the first spatial-temporal block \(\mathcal{X}^{1}=(\mathcal{X}_{t-h+1},\mathcal{X}_{t-h+2},\ldots,\mathcal{X}_ {t})\in R^{B\times N\times T_{h}\times 1}\) is the historical spatial-temporal data, where \(B\) denotes the batch size. Then, the \(k\) spatial-temporal blocks will be used to extract spatial-temporal dependencies from \(\mathcal{X}^{1}\). For the \(i\)-th spatial-temporal block, the input \(\mathcal{X}^{i}\) is equal to \(\mathcal{Y}^{i-1}\). \(\mathcal{X}^{S_{i}}\) and \(\mathcal{X}^{T_{i}}\) are the spatial and temporal dependencies extraction results of the spatial transformer and temporal autotormer in the \(i\)-th spatial-temporal block. Due to \(\mathcal{X}^{S_{i}}\) being the input of the temporal autotormer of the \(i\)-th spatial-temporal block, \(\mathcal{X}^{T_{i}}\) also contains the spatial dependencies extraction results. The output of the \(i\)-th spatial-temporal block can be described as \(\mathcal{Y}^{i}=\mathcal{X}^{T_{i}}\). The prediction results can be described as \(\mathcal{Y}=Conv(\mathcal{Y}^{k})\in R^{B\times N\times T_{p}\times 1}\), where \(Conv()\) is a convolution layer and \(k\) denotes the number of spatial-temporal blocks. By utilizing stacked spatial-temporal blocks, the MSSTTN captures the spatial-temporal dependencies and then makes predictions in the last block.
## 4 Results
### Rice Biomass Estimation  
Tables 5 and 6 show all the results of rice biomass inversion performance of MLR, SVR, and RF. The best performance is obtained by the MLR method using dual-polarization VV and VH model: \(R^{2}=0.85\), RMSE = 206 g\(\cdot\)m\({}^{-2}\) (22%). For SVR and RF methods, the best performance is obtained by using the VH polarization model: \(R^{2}=0.87\), RMSE = 175 g\(\cdot\)m\({}^{-2}\) (19%) for the SVR method: \(R^{2}=0.9\), RMSE = 162 g\(\cdot\)m\({}^{-2}\) (18%) for the RF method, respectively.  
Figure 8 presents the effectiveness of three methods to estimate rice biomass. The correlation coefficients \(R^{2}\) are 0.85, 0.87, 0.90 for MLR, SVR and RF methods, respectively. In addition, the RMSE values are 206 g\(\cdot\)m\({}^{-2}\) (22%), 175 g\(\cdot\)m\({}^{-2}\) (19%), and 162 g\(\cdot\)m\({}^{-2}\) (18%), respectively.  
As seen in Figure 9, for example, at two reference plots 1G3 and 3M1, the scatter plots between retrieved and in-situ rice biomass indicate the same behavior along the growth stage with continuous line for rice biomass retrieval and circles for in-situ rice biomass.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**AIC**} & \multicolumn{3}{c}{**BIC**} \\ \hline
**Methods** & **VV** & **VH** & **VV and VH** & **PCA (VH and VV)** & **VV** & **VH** & **VV and VH** & **PCA (VH and VV)** \\ \hline MLR & 242.2 & 239.4 & **239.4** & 240.8 & 241.3 & 238.5 & **238.2** & 239.6 \\ SVR & 231.0 & **230.3** & 241.6 & 236.6 & 230.1 & **229.4** & 240.4 & 235.4 \\ RF & 228.3 & **227.0** & 232.8 & 232.1 & 227.4 & **226.0** & 231.6 & 230.9 \\ \hline \hline \end{tabular}
\end{table}
Table 6: AIC and BIC for rice biomass estimation. The smallest values are in bold.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & **VV** & \multicolumn{3}{c}{**VH and VV**} & \multicolumn{2}{c}{**PCA (VH and VV)**} \\ \hline
**Methods** & \(R^{2}\) & **RMSE (g\(\cdot\)m\({}^{-2}\))** & \(R^{2}\) & **RMSE (g\(\cdot\)m\({}^{-2}\))** & \(R^{2}\) & **RMSE (g\(\cdot\)m\({}^{-2}\))** & \(R^{2}\) & **RMSE (g\(\cdot\)m\({}^{-2}\))** \\ \hline MLR & 0.81 & 230 & 0.81 & 216 & **0.85** & **206** & 0.83 & 213 \\ SVR & 0.86 & 178 & **0.87** & **175** & 0.86 & 207 & 0.86 & 193 \\ RF & 0.90 & 167 & **0.90** & **162** & 0.88 & 177 & 0.89 & 174 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Rice biomass estimation from five folds of cross-validation. The best results are in bold.  
Figure 7: Rice height retrieval and in-situ measurements at two reference plots 2M2 and 4M2.
# Structural Assessment of Endodontic Files via Finite Element Analysis
###### Abstract  
A methodology for the structural assessment of Nickel-Titanium (Ni-Ti) endodontic files and a novel approach to predict their fatigue behavior using finite element method (FEM) were proposed. ProTaper-Universal F1 and F2 endodontic files were selected due to availability of extensive test data needed for the validation of the methodology. Bending and torsional loadings were analyzed since these provide essential data for the structural integrity assessment for the endodontic files. High-definition FEM models and their computationally efficient idealized versions were developed. The results for the bending and torsional stiffness of the F1 endodontic file agreed with the literature data validating the proposed methodology. Hysteresis energy density was shown to give promising results as a predictor of low cycle fatigue failure. The predictions with the idealized models matched those of the high-definition models, justifying the proposed idealizations. The validated models demonstrated that F2 has 60% higher bending and torsion resistance and 7% higher hysteresis energy density per cycle with respect to F1, leading to the conclusion that F1 has a lower structural stiffness but a longer fatigue life as compared to F2. In summary, the developed methodology allows for the structural and durability evaluation of various design parameters for Ni-Ti endodontic files.  
root canal treatment; endodontic file; finite element analysis; shape memory alloy +
Footnote †: journal: Applied  
0023
# Dynamic and Active THz Graphene Metamaterial Devices
## 2 Fundamentals of Graphene
### Nonlinear Optical Property
#### 2.2.2 High Harmonic Generation  
As mentioned above, the strong THz field incident on graphene increases the carriers' temperature, which causes a decrease in the graphene conductivity. Graphene's carriers are restored to their original state by phonon emission. In the process, ultrafast carriers heating and cooling lead to nonlinear temporal modulation of THz absorption and make the instantaneous graphene conductivity modulate nonlinearly. The nonlinear conductivity leads to the generation of the nonlinear current of graphene (\(j(t)=\sigma E(t)\)) [25]. If the frequency of the incident THz is \(f_{0}\), the generated nonlinear current will contain high harmonics for the driving frequency. Due to the centrosymmetric graphene, even-order harmonic generation (\(2f_{0}\), \(4f_{0}\)... ) is forbidden, and the generated current contains contributions of odd-order harmonics (\(f_{0}\), \(3f_{0}\), \(5f_{0}\)... ), which leads to the electromagnetic reemission of high harmonics, as shown in Figure 2b. Unlike the high harmonic generation of the optical band, the high harmonic generation of the THz band relies on the nonlinear current driven by the nonlinear conductivity, which is based on the intraband transition.
# Grid Anchor Lane Detection Based on Attribute Correlation
## 3 Methods
### Grid Anchor Representation Method for Lane Lines
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathbf{x}^{p}_{i,j}\) & \(\mathbf{y}^{p}_{i,j}\) & **Direction** & \(\mathbf{T}^{s}_{n+1,i,j}\) & \(\mathbf{T}^{s}_{n+2,ij}\) \\ \hline - & - & No lane & 0 & 0 \\ \(i\frac{W}{W_{s}}\) & \(\left[j\frac{H}{H_{s}},\frac{(j+1)H}{H_{s}}\right)\) & Left & 1 & 0 \\ \(\left[j\frac{W}{W_{s}},\frac{(i+1)W}{W_{s}}\right]\) & \(\frac{(j+1)H}{H_{s}}\) & Down & 1 & 1 \\ \(\frac{(i+1)W}{W_{s}}\) & \(\left[j\frac{H}{H_{s}},\frac{(j+1)H}{H_{s}}\right)\) & Right & 0 & 1 \\ \hline \hline \end{tabular}  
* The table defines the values of the direction attribute based on intersection coordinates. The symbol “-” indicates that the anchor is classified as background, in which case the intersection coordinates are not considered.  
\end{table} 
Table 2: Direction attribute value calculation table. Figure 3: Illustration of the grid anchor data structure: (**a**) the grid anchor employs multiple channel layers to represent the added attributes, where different color channels correspond to different attributes; (**b**) taking the anchor \((s,i,j)\) as an example, the meanings of the attributes in each channel layer are explained. arbitrary shape and position, just like segmentation methods. Grid anchors adopt the anchor box concept from anchor-based methods, discretizing the image into regions and using fewer data points to represent lane lines. Grid anchors use attribute values to represent the classification results of pixels within an image region, which reduces computational complexity and improves speed compared to segmentation methods. Conventional anchor-based methods typically use predefined anchors with fixed shapes to represent lane markings. However, predefined anchor shapes are often difficult to fit various lane marking shapes. To address this, anchor-based methods use offsets to correct positional errors, but the prediction results may be influenced by features outside the anchor region, which increases the difficulty of training and prediction. The detection method of grid anchors employs a series of grid anchors to jointly represent a single lane marking. Since grid anchors are selected and combined by the model based on features, they do not have fixed positions or shapes, making them flexible enough to represent lane markings of arbitrary shape and position. In terms of data format, grid anchors share the same resolution as the feature map, and the feature and classification data at corresponding positions align with the image features. The prediction results of the grid anchors are computed from the features at corresponding positions on the feature map, allowing the use of convolution for direct computation. This not only reduces the complexity of model training and prediction tasks but also eliminates the need for the complex process of selecting anchor boxes. Furthermore, grid anchors incorporate a directional attribute to represent the internal relationships within a lane line--an aspect not present in other methods.
# FAIDM for Medical Privacy Protection in 5G Telemedicine Systems
###### Abstract
5G and IoT will become important parts of the next generation smart medical healthcare [1]. Medical privacy is of the utmost importance. Once leaked, it not only brings huge economic losses and loss of credibility to hospitals and other related institutions, but also does potential harm to patients, and, more importantly, it can even endangers lives of patients, which will seriously damage the healthy development of medical industry [12]. Unfortunately, the healthcare industry has lagged to meet users' expectations. The health data, which is stored in conventional system, are very difficult to share due to varying standards and data formats, i.e., current healthcare ecosystem is ill-suited for the instantaneous needs of modern user. Maintaining privacy of user data is very important and failure to this will result in implications related to financial as well as legal sectors [13]. If a person's medical information is the key to finding clinical treatment, how to maintain the privacy of health records is a central issue that determines the success of medical practice. Increasingly, people interact with health-care providers, using digital media technologies [14, 15, 16]. Accompanying the acceleration of medical data collection are rapid advancements in algorithmic computing capacities to aggregate, analyze, and draw sensitive inferences about individuals from their health data [15, 17, 18, 19]. Since the above description, federated anonymous identity management (FAIDM) for medical privacy protection in telemedicine systems is proposed in this paper, which can provide a secure way to protect medical privacy. There are some properties below. (i) The proposed scheme provides federated identity management which can manage identity of devices in a hierarchical structure efficiently. (ii) Identity authentication will be achieved by mutual authentication. (iii) The proposed scheme provides session key to secure transmitted data which is related to privacy of patients. (iv) The proposed scheme provides anonymous identities for devices in order to reduce the possibility of leaking transmitted medical data and real information of device and its owner. (v) If one of devices transmit abnormal data, the proposed scheme provides traceability for servers of medical institute. (vi) the proposed scheme provides signature for non-repudiation. The rest of this paper is organized as follows. We introduce related works in Section 2, including telemedicine, healthcare certificate, ID-based cryptosystem, definitions of Chebyshev chaotic maps, and chaotic maps-based signature which we apply in our scheme. In Section 3, we describe the proposed scheme. We discuss the security and performance analysis of proposed scheme in Sections 4 and 5, respectively. Finally, some concluding remarks are presented.
## 3 Testing Set-Up and Data Acquisition
### Experiments to Measure Horizontal Displacement
#### 3.1.2 Lateral Displacement Measurement Using LiDAR  
LVDT sensors were mounted on one side of the concrete wall, while the opposite surface was scanned using LiDAR as shown in Figure 6. To use the LiDAR equipment, installation of the Intel Realsense SDK 2.0 software is necessary. This exclusive software, shared by Intel, is GUI-based and does not allow for detailed variable adjustment. For instance, the scanning frequency is fixed at 30 Hz, resulting in a high capacity charge due to the measurement data that is more than necessary. To address this, MATLAB-based measurement software with adjustable scanning intervals was created using a source code provided by Intel. This allowed for the collection of point cloud data with three-dimensional coordinates of (\(x\), \(y\), \(z\)) and RGB data. The scanning interval within the software was also  
Figure 4: Precast concrete walls designed to withstand lateral loads include the following: (**a**) a conventional uniform flat wall and (**b**) a wall featuring 450 \(\times\) 600 openings.  
Figure 5: Loading plan for concrete wall.  
adjustable, and the concrete object was measured using LiDAR with a 15 s interval setting. The set-up for surface laser scanning of concrete using LiDAR, such as that shown in Figure 6, is simple, and it can save time and manpower. In addition, if only the measurable range is observed, the distance between the LiDAR equipment and the wall does not need to be precisely adjusted. Of course, if the distance becomes too far, it is not recommended because the resolution for the point spacing decreases. Figure 7 shows the RGB data and point cloud data obtained by measuring Wall A and Wall B using LiDAR before applying the load.  
Line fitting was used to reduce noise in the point cloud data. The left boundary of the concrete wall was found to be linear, and the point cloud was reconstructed using the 1D line fitting method. In Figure 7, the orange points represent the left boundary point clouds of the concrete extracted from the raw data, the orange line is a straight line fitted based on the extracted point cloud, and the blank area on the right should be where the original concrete surface is located. The outlier points, which deviated up to 11 mm, were removed through 1D line fitting. The lateral displacement monitoring test for concrete walls resulted in the extraction of displacement data from LVDT #1 and LiDAR measurements calculated using the developed algorithm. The data were analyzed by converting them to a drift ratio for quantitative analysis. Figures 8 and 9 show the drift ratio results for Wall A and Wall B, respectively.  
Figure 6: An example of non-contact measurement of displacement using LiDAR.  
Figure 7: Combined image of measured grayscale RGB and point cloud data; some points are lost due to noise at the edges of the LiDAR equipment.
## 4 Methodology
### Bayesian Risk Scoring  
In the context of high-speed railway operations, ensuring the security of train control systems is critical. Although IDS can effectively identify potential malicious activities, their outputs typically lack quantitative assessment of attack consequences, limiting their usefulness for informing system-level response strategies. To address this, we propose a Bayesian risk scoring method that follows the fundamental principle of quantitative risk assessment--representing overall risk as the product of relevant risk factors. According to [PERSON] and [PERSON] [34], security risk evaluations commonly adopt multi-factor multiplicative models such as \(R=L\times A\times V\times C\), where each factor represents risk (\(R\)), likelihood of an attack (\(L\)), attractiveness of an asset (\(A\)), vulnerability in the facility (\(V\)), and the potential consequence (\(C\)) of a successful attack, respectively.  
Inspired by this principle, our Bayesian risk scoring model integrates multiple critical factors derived from intrusion detection results in the T2G context. Specifically, it combines the impact score (\(I_{i}\)), posterior probability of the attack type(\(P_{i}\)), and anomaly score (\(A_{i}\)), along with a context adjustment coefficient (\(C_{i}\)) that enhances sensitivity to high-confidence threats. This yields a standardized risk score and corresponding risk level label for each sample, calculated as  
\[Risk_{i}=I_{i}\times P_{i}\times A_{i}\times C_{i}\times 100 \tag{20}\]  
where \(Risk_{i}\) denotes the risk score of the \(i\)-th sample, normalized to the range [0, 100]; \(I_{i}\) reflects the potential impact of the detected attack on the T2G system; \(P_{i}\) is the posterior probability of the \(i\)-th sample belonging to the predicted attack class, i.e., the confidence output by the DOA-BPNN; \(A_{i}\) quantifies behavioral deviation from normal patterns; and \(C_{i}\) is a context adjustment coefficient used to amplify risks under high-risk conditions. The thresholds for \(A_{i}\) and \(C_{i}\) are set to 0.8 and 1.2, respectively, guided by the Pareto Principle and expert knowledge [35]. When the anomaly score exceeds 0.8, the sample is highly likely to represent a real threat that warrants special attention. The context adjustment coefficient \(C_{i}\) increases the risk weighting by 20% in such cases, aiming to balance between avoiding excessive false alarms and ensuring that high-risk threats receive sufficient priority. This design allows the system to allocate resources more effectively toward detections most likely to represent genuine security risks.  
For samples predicted as normal, the risk score is simplified as  
\[Risk_{i}=A_{i}\times 100 \tag{21}\]  
Finally, the resulting risk scores are categorized into four predefined risk levels, as shown in the corresponding Table 3.