esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dimensional Analysis
### Leveee Overtopping Risk Curve  
The leveee overtopping risk curve utilizes the Froude number, which is the ratio of superelevation to leveee height, and the water depth to leveee height as its primary variables. This study examines the relationships between these dimensionless variables to predict the risk of leveee overtopping. The Froude number is shown on the x-axis, while the ratio of superelevation to leveee height is on the y-axis. Overtopping is considered likely when the sum of the ratio of superelevation to leveee height and the water depth to leveee height exceeds 1.  
Figure 3 illustrates the leveee overtopping risk curve, showing that as the ratio of superelevation to leveee height or the Froude number increases, the risk of levee overtopping also rises. For example, suppose the ratio of superelevation to leveee height is 0.3 and the ratio of water depth to leveee height is 0.6. In that case, the sum of these two variables is 0.9, indicating a low risk of overtopping. However, if the ratio of water depth to leveee height increases to 0.8, the sum becomes 1.1, suggesting a high risk of overtopping.  
The overtopping risk curve presented in this study serves as a standardized tool for predicting overtopping conditions across levees of various sizes and characteristics. This curve enables the quantitative assessment of the likelihood of overtopping under specific flow conditions during leveee design. By providing a consistent basis for comparing different leveee designs and operational conditions, the curve enhances the efficiency of the design process. By utilizing this overtopping risk curve, the risk of levee overtopping can be consistently assessed regardless of the leveee's scale. This tool is instrumental in preemptively identifying conditions that may lead to overtopping, thereby helping to prevent potential damage. Through this curve, the safety of each leveee can be quantitatively evaluated, and effective response strategies can be developed to minimize damage.  
Figure 3: Leveee overtopping risk curve (dimensionless parameter).
# Depth Segmentation Approach for Egocentric 3D Human Pose Estimation with a Fisheye Camera
## 3 Our Methods
### Architecture
#### 3.1.1 Two Branch Architecture  
In designing the architecture, we initially faced the question of how many branches our architecture should have. Most previous studies on egocentric 3D pose estimation [8; 9; 10], had two branches. Mo2 Cap2 [10] opted for a two-branch architecture because they regressed two heatmaps individually using two zoom-level input images. xR-EgoPose [8] used dual branches because they regressed two types of outputs: 3D joint positions and a heatmap with 2D information. [PERSON]'s approach [9] implemented two branches to extract 2D body features with a heatmap and body-excluded scene depth features with a depth map. Given our need to extract both 2D body features and body depth features, we designed a dual-branch architecture following their approaches.  
Figure 1: Overview of our framework. The RGB image (\(R^{256\times 256\times 3}\)) is input to generate 2D pose heatmaps, segmentation maps (\(R^{256\times 256\times 3}\)), and depth maps (\(R^{256\times 256\times 1}\)). The joint position module combines the heatmap features, SegDepth features, and an additional convolutional feature (\(R^{128\times 1}\)) extracted from the depth map to estimate the final 3D pose (\(R^{16\times 3}\)).  
With the dual-branch architecture, we considered whether our network should start with two branches, like Mo2 Cap2 [10], or begin with one branch and split into two decoding branches as in xR-EgoPose [8]. This difference comes from the number of features that are used for 3D pose estimation. xR-EgoPose [8] extracts one feature, the heatmap feature, and uses it to regress 3D joint positions. Therefore, xR-EgoPose [8] requires one branch for the heatmap feature extraction at startup. Mo2 Cap2 [10], extracts two heatmap features from two different input images (showing zoom-in and zoom-out views of the user's body), thus requiring two branches at runtime. Similarly, [PERSON]'s approach [9] extracts two features--heatmap and depth map features--with two branches at runtime. Since our main idea was to use two features--heatmap and depth map features--to regress the 3D pose, we followed [PERSON]'s structure, starting with dual branches, but we used the body depth map feature instead of the scene depth feature. As a result, our architecture consists of the SegDepth and heatmap branches (see Figure 1).
# A Deep Learning Method for DOA Estimation with Covariance Matrices in Reverberant Environments
## 1 Introduction
Thus, this paper focused on the spherical microphone array, and three different input features and two different strategies are proposed for improving the accuracy of the localization task. The remainder of this paper is organized as follows. In Section 2, a data model of DOA estimation with spherical arrays is given. Our proposed method is described in Section 3. The architecture of the network is indicated in Section 4. Simulations and performance evaluation are shown in Section 5. Section 6 shows the computational complexity. Finally, conclusions are drawn in Section 7.
## 3 Staff Detection and Tracking System
### Staff Line Smoothing  
After the tracking process is complete for a selected staff, its resemblance can be jagged at certain points due to false detections, errors, etc., but also to the task itself, i.e., an approximately horizontal segment could be tracked by a saw-tooth pattern due to nature of the discretized images themselves (see Figure 7c). This type of pattern is not expected for a staff line. To avoid this, a smoothing spline is constructed for each of the staff lines found. The smoothed staff line \(i\) is the curve \(s_{i}^{*}(m)\) that minimizes [29]:  
\[s_{i}^{*}(m)=\min_{s_{i}(m)}\left(p\sum_{m}(g_{i}(m)-s_{i}(m))^{2}+\ (1-p)\int_{ \mathcal{C}_{m}}\left(\frac{d^{2}s_{i}(x)}{dx^{2}}\right)dx\ )\right. \tag{14}\]  
where the smoothing parameter is \(p=10^{-4}\). The second derivative is obtained at the data points by means of its discrete approximation [29]. After the procedure is complete, the curve data are rounded to the nearest integer:  
\[L_{i}(m)=round(s_{i}^{*}(m)) \tag{15}\]  
Note that this procedure will also help remove some isolated spurious detections.  
Figure 7 illustrates the application of the smoothing scheme. Figure 7a displays a detail of an original image composed of four staff lines. Figure 7b shows an enlarged view of the fourth staff line displayed in Figure 7a. Figure 7c presents the corresponding tracked staff line, this image shows clearly the saw-like structure of a two pixel wide tracked line derived from the uncertainty of the location of the center of the staff line due to its scanned thickness. Figure 7d shows the final result for the same staff line after the smoothing operation: the staff line finally obtained is a much better representation of the original staff line.  
Figure 6: Simplified diagram of the staff tracking scheme.
## 4 Design and Results of the Experiments
### Results of the Experiments  
#### 4.3.1 Experiment 1  
Six groups of hyper-parameters are tested on FERPLUS-10, FERPLUS-20, and FERPLUS-30. The first five groups are randomly selected within the range, and the sixth group does not exert influence. The proposed lightweight model is used for dynamic noise detection and adjustment. The experimental parameters are shown in Table 2.  
The noise detection evaluation values are counted according to Section 3.1.3, as shown in Figure 6.  
From the change in the F1 Score, it can be judged that the defined hyper-parameters can dynamically adjust the detection range of noise samples with high precision and accuracy and minimal sample loss in the process. Moreover, the proposed parameters show a certain positive correlation with the detection effect in different noise ratio datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **DC** & **RC** \\ \hline
1 & [1.03, 1.03, 1.08, 1, 1.02, 1, 1.08] & 0.01 \\
2 & [1.05, 1.065, 1.023, 1.01, 1.02, 1.01, 1.08] & 0.002 \\
3 & [1.066, 1.08, 1.04, 1, 1.033, 1.02, 1.024] & 0.008 \\
4 & [1.2, 1.3, 1.1, 1.1, 1.1, 1.2, 1.08] & 0.02 \\
5 & [1.05, 1.3, 1.05, 1.06, 1.3, 1, 1.01] & 0.03 \\
6 & [1, 1, 1, 1, 1, 1, 1] & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters.
## 3 Results
This observation is consistent with expectations given the original LBP's high sensitivity to noise, which proves suboptimal for identifying benign nodules due to their homogeneous texture. This sensitivity typically results in a higher rate of false positives, thereby diminishing the descriptor's specificity. In assessing the efficacy of our proposed model, we utilized several performance metrics, as outlined in Table 4. These evaluation criteria encompass accuracy, sensitivity, specificity, and the Area Under the Receiver Operating Characteristic (ROC) curve. Table 4 provides a detailed overview of these performance indicators, showcasing the superiority of our approach. The outcomes are detailed for each discrete experiment and also in an aggregated form, illustrating the model's comprehensive performance. Additionally, we performed a comparative analysis to juxtapose our methodology against existing approaches that have also engaged the LIDC dataset. Given the difficulty in finding studies with precisely matching criteria for selecting nodule samples, our comparison focused on studies that adopted similar, albeit stringent selection criteria and had a dataset size comparable to ours. This strategy of comparison serves to underscore the advantages of our proposed methodology while positioning it amidst the wider landscape of current research in this field as shown in Figure 9. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Evaluation Metrics**} \\ \hline  & **Accuracy** & **Sensitivity** & **Specificity** & **AUC** \\ \hline
3D-LOP & 96.23 & 97.55 & 93.71 & 0.9894 \\
3D-CNN & 93.52 & 95.57 & 89.89 & 0.9697 \\ Fusion & 97.84 & 98.11 & 94.73 & 0.9912 \\ \hline [PERSON], et al. [25] & 92.70 & 90.90 & 94.50 & - \\ [PERSON], et al. [26] & 87.30 & 86.20 & 88.50 & 0.873 \\ [PERSON], et al. [27] & 90.77 & 85.37 & 95.04 & - \\ [PERSON], et al. [24] & 96.17 & 97.14 & 95.33 & 0.9832 \\ [PERSON], et al. [28] & 93.17 & 90.38 & - & 0.9753 \\ [PERSON], et al. [29] & 90.39 & 90.32 & 93.65 & 0.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification results. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{4}{c}{**Evaluation Metrics**} \\ \hline  & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline Original 3D-LBP & 89.33 & 92.43 & 83.56 \\ Adjustable 3D-LBP [22] & 91.74 & 94.62 & 89.60 \\ Resolved Ambiguity 3D-LBP [23] & 92.20 & 90.32 & 93.60 \\
3D-LTP [24] & 92.19 & 93.33 & 91.21 \\ Proposed 3D-LOP & 96.23 & 97.55 & 93.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative Analysis of Various 3D-Local LBP Descriptor Variations.
## 2 Materials and Methods
### Experimental Set-up Description
#### 2.1.1 Sample Collection and Preservation  
Seven porcine corneas were used in the experiments. They were taken from a local slaughterhouse immediately after the animals' deaths and preserved in a cold solution of sodium chloride (1%) for 10 h until the tests began. An ophthalmologist surgeon checked for tissue abnormalities and extracted the corneas using a section ring. The selected corneas were left at room temperature for 30 min to thermally stabilize them. Then, the corneas were fixed inside the pressurization chamber, and the tests began. The corneas were submitted to a preconditioning process of three increasing and lowering pressure cycles. The pressure was gradually built up to the maximum value to be used in the experiments. The initial pressure to start testing was maintained for 10 min in the last cycle.
# Efficient Security Scheme for Disaster Surveillance UAV Communication Networks
## 6 Security Analysis
### Confidentiality  
Although messages' confidentiality is not a primacy security concern for UAV networks, it is still a crucial requirement. If the messages are exchanged in plaintext, then the malicious adversaries can intercept/eavesdrop and falsify its content to do damage or make the situation worst in the disaster area. Thus, our proposed scheme guarantees the confidentiality of the exchanged messages between different parties in the network. Outside adversaries cannot extract the contents of the transmitted messages because it is encrypted by the powerful LWE encryption scheme. If \(\mathcal{A}\) manages to intercept a message \((\tilde{h},\tilde{z})\), he/she cannot obtain the plaintext reading \(ms_{i}\) because \(\mathcal{A}\) does not have access to the private key of \(CS\,\tilde{r}_{2 cs}\) and consequently cannot decrypt \(m_{j}\). Even with compromising certain drones, capturing/falsifying their messages, or preventing them from reaching to \(CS\), \(CS\) still receives the readings from other honest drones in the group; in addition, certain other groups scan the subarea and send similar data so that compromising drones or blocking messages does not have a significant impact on the final results.  
Based on the hardness of Ring-LWE problem, it is an NP-hard problem to extract the plaintext messages from the encrypted versions. If \(R=\mathbb{Z}[X]/(X^{n}+1)\) for \(n\) a power of two, and \(R_{q}=R/qR\), where elements of \(R_{q}\) are polynomials of degree \(<n\) with mod-\(q\) coefficients. It is an NP-hard problem to find secret ring element \(s(X)\in R_{q}\), given:\[a_{1}\to R_{q}, b_{1}=a_{1}.s+e_{1}\in R_{q},\] \[a_{2}\to R_{q}, b_{2}=a_{2}.s+e_{2}\in R_{q},\] \[a_{3}\to R_{q}, b_{3}=a_{3}.s+e_{3}\in R_{q},\]  
\[\vdots\]  
where \((a_{i},b_{i})\in R_{q}\times R_{q}\) are uniformly random subject to \(b_{i}-a_{i}.s\approx 0\) and the error \(e_{i}\in R\) are small values. \(\mathcal{A}\) cannot compromise the \(CS\)'s secret key \(\tilde{\tau}_{2 cs}\) even via a quantum computer [30].  
In summary, the integrity and availability of the received information by \(CS\) are guaranteed because each subarea is monitored by several groups of drones and encrypted versions of their readings are sent to \(CS\) via different relays. Then, data redundancy and network's reliability assures the UAV communication security.  
Table 1 summarizes the security aspects of our proposed scheme.
## 5 Case Study
### Project Implementation
#### 5.3.1 Implementation of Overall Business Process
The relevant personnel of the reserve calculation received the reminder of the standardized business process of geological modeling at work site A and created and initiated the reserve calculation standardized business process. The reserve calculation engineer started the geological modeling software through the business software entrance on the mining technology collaboration platform and the geological modeling software automatically loaded the result data of geological modeling from database according to configuration information (including borehole database, pit exploration database, orebody model, level model, stope model and shaft and laneway engineering model) and completed the reserve calculation work under his job duty. The result data (including block model, level ore reserve, stope ore reserve, stope engineering ore reserve) are saved to the database. According to the standardized business process of reserve calculation, it flows to the department head and chief engineer of the reserve calculation successively. The department head and chief engineer review the reserve calculation results data according to their job duties, until the results data are approved and archived. The relevant personnel in mining planning and design received reminders of the completion of the standardized business processes for geological modeling and reserves calculation and created, initiated and completed the business processes for mining planning, mine development design, mining and cutting design in sequence. The relevant personnel in surveying received reminders of the completion of the standardized business processes for mining planning and design and created, initiated and completed business processes such as the underground control survey and construction lofting. Therefore, the 38 business tasks involved in the Fujian Makeng Iron Mine are strictly handled and managed in accordance with the business process flow method and the organizational structure of the participants involved in the process is optimized and flattened; additionally, the duties of all professional post and its input and output data are clarified and the business data are standardized. The centralized access, interoperability and high sharing of the business data and the high-speed flow of the business are realized.
# Seismic Identification and Characterization of Deep Strike-Slip Faults in the Tarim Craton Basin
## 6 Discussion
### Seismic Attribute Fusion Technology
To address these issues, future research could explore integrating deep learning techniques with our multi-attribute fusion method, potentially leading to breakthroughs in fault detection accuracy and scalability. Such advancements would improve the method's applicability across different geological settings and enhance its robustness against noise, ultimately contributing to more reliable subsurface imaging and resource development.
###### Abstract  
This study presents the observations of midlatitude plasma irregularities over Eastern Asia during a moderate magnetic storm on 16 July 2003. Multi-instrumental observations, including the ground-based ionosondes, the GNSS networks, and the CHAMP and ROCSAT-1 satellites, were utilized to investigate the occurrence and characteristics of midlatitude plasma irregularities. The midlatitude strong spread F (SSF) mainly occurred in the midnight-morning sector as observed by ionosondes over Japan during this storm. SSF was related to plasma depletions, which is also recorded by GNSS network in the form of the enhancement of the rate of total electron content (TEC) change index (ROTI). The possible mechanism for the generation of SSF is that the enhanced eastward electric fields, associated with the prompt penetration electric fields and disturbance dynamo electric fields, cause the uplift and latitudinal extension of equatorial plasma bubbles (EPBs) to generate the observed midlatitude SSF further. Meanwhile, plasma density increased significantly under the influence of this storm. In addition, other common type of spread F, frequency spread F (FSF), was observed over Japan on the non-storm day and/or at high latitude station WK545, which seems to be closely related to the coupling of medium-scale traveling ionospheric disturbances (MSTIDs) and sporadic E (Es) layer. The above results indicate that various types of midlatitude spread F can be produced by different physical mechanisms. It is found that SSF can significantly affect the performance of radio wave propagation compared with FSF. Our results show that space weather events have a significant influence on the day-to-day variability of the occurrence and characteristics of ionospheric F-region irregularities at midlatitudes.  
Keywords: Ionospheric F-region irregularity; spread F; midlatitude plasma depletions; magnetic storm; Eastern Asia +
Footnote †: journal: remote sensing
# Considering Inter-Frequency Clock Bias for BDS Triple-Frequency Precise Point Positioning
## 3 Results
### Data Acquisition  
In order to achieve a good coverage of different latitude and longitude areas, we select 35 stations. The geographical distribution of the selected stations is shown in Figure 2. The green points refer to MGEX stations, which can offer B1/B2/B3 BDS-2 tracking. The international GNSS Monitoring and Assessment System (iGMAS) stations, which are marked in blue and yellow in Figure 2, can track the B1, B2 and B3 signals of BDS-2 satellites. In addition, the yellow ones are also capable of tracking the B1 and B3 signals of BDS-3 satellites. The two stations marked in black, including the iGMAS  
Figure 1: Ground tracks of 14 BDS-2 satellites and 4 BDS-3 satellites on 13 July 2016. This figure is plotted by the Matlab mapping package M_Map [34].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Generation** & **Band** & **Frequency (MHz)** & **Chip Rate (Mcps)** \\ \hline \multirow{3}{*}{BDS-2} & B1 & 1561.098 & 2.046 \\  & B2 & 1207.140 & 2.046 \\  & B3 & 1268.520 & 10.23 \\ \hline \multirow{3}{*}{BDS-3} & B1 & 1561.098 & 2.046 \\  & B1C & 1575.420 & 1.023 \\ \cline{1-1}  & B2a & 1176.450 & 10.23 \\ \cline{1-1}  & B2b & 1207.140 & 10.23 \\ \cline{1-1}  & B3 & 1268.520 & 10.23 \\ \hline \hline \end{tabular}
\end{table}
Table 3: BDS signals.  
station XIA1 and the station SGG1 operated at the School of Geodesy and Geomatics (SGG) of the Wuhan University in Wuhan, China, have the capability of tracking all the BDS signals listed in Table 3, except for the B2 signal for station XIA1. Observations from a 7-day interval from 7-13 July 2016 for station SGG1, a 14-day interval from 2-15 September 2016 for station XIA1 and a 32-day interval from 15 August-15 September 2016 for other stations are selected as the core datasets for this study.
## 3 QoE-Aware Geographic Routing Protocol for Video Streaming over VANETs
### Mobility Metrics  
The GeoQoE-Vanet protocol uses two mobility metrics which are: distance from the destination and the link expiration time. In computing these metrics, the predicted positions of neighbours are used to increase the probability to select the most stable relay vehicle. The detailed descriptions of these metrics are given in the following subsections.
## 4 Experiment and Result Analysis
### Experimental Design and Environment
#### 4.1.2 CNN Classification Model Settings  
To verify the reconstruction ability of the model, a pre-trained model is built using a custom CNN, and the original and reconstructed images are classified. The parameters of the CNN structure are listed in Table 1. The learning rate is set to 0.01, the optimization function is SGD, the activation function is ReLU, and max pooling is chosen for pooling. After convolution and pooling, the fully connected layer makes the feature map into vectorization. The classification accuracy rate (ACC) is used as the evaluation index.  
In this network, two convolutional and pooling layers are designed to extract the features. After the convolutional pooling, the data enters into a fully connected layer and the final features are extracted for image classification tasks.