esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Analysis of the Bottom Thickness of Closed Rectangular Tanks Used as Pontoons
## 3 Results
### Calculation Results of Buoyancy and Stability  
Following the recommendations of Australian standard AS 3962-2001 [31] and using the dependencies detailed and described in the paper [32; 33; 34], buoyancy at self-load and at service load evenly distributed on the upper plate, amounting to 3.0 kN/m\({}^{2}\), was calculated for the analysed tanks differing in bottom thickness. Moreover, the metacentric height and tilt angle were calculated with half of the upper plate loaded 1.0 kN/m\({}^{2}\).  
The following data was used for detailed calculations: length l\({}_{\rm x}\) = 10 m, width l\({}_{\rm y}\) = 2.5 m, height l\({}_{\rm z}\) = 1.25 m, thickness of tank walls and the upper plate h = 0.08 m, reinforced concrete class C35/45, Styrofoam filling with a volumetric weight 0.45 kN/m\({}^{3}\).  
The results of the calculations are summarised in Table 3.  
Figure 9: Distribution of bending moments in the cross-section in the middle of the tank length.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Bottom Thickness of the** & \multicolumn{4}{c}{**Pontoon Buoyancy**} & \multicolumn{2}{c}{**Pontoon Stability with Half of the Upper Plate Loaded 1.0 kN/m\({}^{2}\)**} \\ \multicolumn{1}{c}{**Analysed Tanks**} & \multicolumn{2}{c}{**At Self-Weight Load**} & \multicolumn{2}{c}{**At Uniform Load**} & \multicolumn{2}{c}{**Pontoon Stability with Half of the Upper Plate Loaded 1.0 kN/m\({}^{2}\)**} \\ \multicolumn{1}{c}{**(m)**} & \multicolumn{2}{c}{**Inmersion Depth**} & \multicolumn{2}{c}{**Freeboardh**} & \multicolumn{2}{c}{**Inmersion Depth**} & \multicolumn{2}{c}{**Freeboardh**} & \multicolumn{2}{c}{**Metacentric Heighth**} & \multicolumn{2}{c}{**Title Angle**} & \multicolumn{2}{c}{**Freeboardh**} \\  & \(\mathbf{h_{d}}\) **(m)** & \(t\) **(m)** & \(\mathbf{h_{d}}\) **(m)** & \(t\) **(m)** & \(\mathbf{m_{c}}\) **(m)** & \(\mathbf{\varphi}\) **(\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{    \mathbf{    }}}}}}}}}}}}\)) & \(t\) **(m)** & \(\mathbf{\varphi}\) **(\(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{     \mathbf{   \mathbf{  }}}}}}}}}}}}}\)) & \(t\) **(m)** \\ \hline
0.08 & 0.66 & 0.59 & 0.96 & 0.29 & 0.35 & 7.16 & 0.38 \\
0.10 & 0.71 & 0.54 & 1.01 & 0.24 & 0.37 & 6.38 & 0.35 \\
0.12 & 0.75 & 0.50 & 1.05 & 0.20 & 0.38 & 5.86 & 0.32 \\
0.14 & 0.80 & 0.45 & 1.10 & 0.15 & 0.41 & 5.15 & 0.29 \\
0.16 & 0.84 & 0.41 & 1.14 & 0.11 & 0.42 & 4.77 & 0.26 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of buoyancy and stability calculations of the analysed tanks.
## 4 Corrosion Potential Behavior with Test Parameters
### Relationship between OCP and the Parameters  
#### 4.2.1 Effects of the Cover Depth and \(w/c\) Ratio on the OCP with Increasing Chloride Content  
It is difficult to quantify the OCP values at each measurement point by comparing them with the factors affecting corrosion (\(w/c\), cover depth, and chloride concentration). The OCP change tendency according to the factors can be clearly identified by averaging the OCP values measured for two years. Figure 11 shows the average values with increasing cover depth. The corrosion potential decreased as the cover depth increased and the \(w/c\) ratio decreased. When the OCP ratio was analyzed based on a corrosion potential of \(-\)450 mV, it was found to be approximately 18.4% even under the most unfavorable conditions (30 mm, \(w/c\) 0.6) at a chloride concentration of 0.0%. At 3.5%, however, it significantly increased to 40.1 \(\sim\) 67.4% for \(w/c\) 0.6, 27.6-37.7% for \(w/c\) 0.5, and 28.0-39.1% for \(w/c\) 0.4. This tendency increased from \(\sim\)49.8 to \(\sim\)76.5%, 33.8% to 50.8%, and 28.0% to 37.3%, respectively, at a chloride concentration of 7.0%.  
Figure 11: _Cont._  
When each average OCP value was evaluated through linear regression analysis, the gradient can be assumed as the degree to affect the OCP. When the x-axis (cover depth) in Figure 11 was subjected to linear regression analysis with respect to the OCP, the gradient can be summarized as shown in Table 5. As can be seen from the table, the OCP recovery through the cover depth was rapidly improved in all cases as the \(w/c\) ratio increased. This tendency indicates that securing the cover depth is an important protection mechanism that can reduce the corrosion risk in the condition of low-quality concrete and high external chloride concentration. The results can provide useful information for durability design such as determining the minimum cover depth and maximum \(w/c\) ratio of concrete. A critical chloride content inducing corrosion is usually adopted for service life evaluation; however, it is only assumed, and the conservative index since corrosion initiation occurs very complicatedly and the value varies a lot with experimental conditions [1; 2; 6].
# Research on Wellbore Trajectory Optimization and Drilling Control Based on the TD3 Algorithm
## 4 Experimental Design and Result Analysis
### Borehole Design Under Multi-Source Obstacle Coupling Based on Reinforcement Learning  
In actual drilling engineering, the downhole environment is often complex and changeable, usually accompanied by the existence of obstacles such as faults, abandoned wellbores, and high-pressure abnormal zones. If the wellbore path crosses or approaches these areas, it may not only cause engineering accidents, such as lost circulation and well collapse, but also greatly increase drilling costs and safety risks. Therefore, the design of an obstacle avoidance trajectory is of great significance for ensuring drilling safety and improving wellbore quality. Obstacle avoidance design not only requires avoiding risk areas but also takes into account the constructability and target orientation of the trajectory. One of the core problems of modern intelligent wellbore trajectory planning is to ensure a smooth trajectory and accurate target entry while meeting the safe space distance.  
In order to verify the obstacle avoidance ability of the trajectory design model in a complex obstacle environment, two typical obstacle bodies were set up. The horizontal inclined obstacle was used to simulate the crossing of the interlayer abandoned well belt, and the vertical obstacle was used to simulate the dense mining well group. The three-dimensional paths of all training trajectories and optimal trajectories are shown in the Figures 7 and 8, and their projection effects on the horizontal plane (XOY) and two vertical planes are given. As depicted in Figure 7, the wellbore design training process under multi-source obstacle coupling is illustrated. As shown in Figure 8, the optimal trajectory under multi-source obstacle coupling is presented.  
For unavoidable obstacles, the algorithm triggers emergency replanning:  
**Step 1:** Backtracking to the last safe state to regenerate the trajectory.  
**Step 2:** Adjusting reward function weights (e.g., increasing progress reward) to allow moderate target deviation for obstacle bypass.  
On the whole, the trajectory successfully realized the avoidance of the complex environment and the stable docking of the target point under the premise of both obstacle avoidance and curvature smoothing.  
Figure 8: Optimal trajectory under multi-source obstacle coupling.  
Figure 7: Wellbore design training process under multi-source obstacle coupling.
## 3 Geocasting Based on Local Coverage Areas
### Geocasting Using Local Coverage Areas
#### 1) Execute Decision:  
It basically checks if the node that receives the query is inside the region of interest, _i.e._, if point \(p_{i}\) is inside the polygon \(\mathbf{R}\). The point-in-polygon problem is a well-known problem in computational geometry and many solutions and implementations have been proposed [31]. Complexity is reduced when the region of interest \(\mathbf{R}\) is a _convex_ polygon. To check if the coordinate \(p_{i}\) is inside a convex polygon \(\mathbf{R}\), node \(i\) needs to check for every line segment the coordinate \(p_{i}\) is left of the line segment. If so, the query needs to be executed (see Figure 2(a) and [6] for more details). In the simulations we assume that the target region is a circle having a radius \(r_{C}\). The node needs to check the distance between its coordinate \(p_{i}\) and the coordinate of the center of the area of interest. If the distance is smaller than \(r_{C}\), the node should execute the query.
# Solar Ultraviolet Irradiance Characterization under All Sky Conditions in Burgos, Spain
## 3 Results and Discussion
### Analysis of the 10 min GHUV/GHI Ratio  
Figure 4 shows the evolution of the 10 min values of _GHUV_, _GHI_, and its ratio (_GHUV/GHI_), which were measured in Burgos throughout the experimental campaign. It should be noted that the gray areas that appear in the three plots in Figure 4, for each day between sunrise and sunset, correspond to missing data, either because they were not recorded or because they did not pass the QC described in Section 2. With regard to Figure 4c, the _GHUV/GHI_ ratios assume values between 1% and 10%; however, the highest values are clearly concentrated in the early hours of the day, and they decrease significantly during the central hours, although there is no clear trend throughout the day. This evolution is analyzed in detail in Section 3.3.  
Although the scale used to represent the _GHUV/GHI_ ratio values in Figure 4c reaches up to 10%, there are some values that considerably exceed this threshold (up to 15.21%): however, as can be seen in Figure 5a, which shows the Cumulative Distribution Function (CDF) of the 10 min _GHUV/GHI_ ratios, the probability of this ratio exceeding 10% is extremely low (less than 0.05%). Figure 5a also shows the good fit between the empirical CDF (blue line) and the CDF adjusted to a normal distribution function (yellow line).  
Figure 4: Ten minute experimental data of (**a**) _GHUV_ (W-m\({}^{-2}\)), (**b**) _GHI_ (W-m\({}^{-2}\)), and (**c**) the _GHUV/GHI_ ratio (%) throughout the experimental campaign.  
This can also be seen in Figure 5b, which shows the empirical Probability Distribution Function (PDF) of the 10 min _GHUV/GHI_ ratios, and the adjusted PDF that shows a normal distribution.
# An Automatic ECG Signal Quality Assessment Method Based on Resnet and Self-Attention
## 3 Materials and Methods
### Comparison of Parameters and Calculations of Attention Modules  
In this subsection, we compare the proposed self-attention module with two typical attention modules in terms of parameter amount and calculation amount. One of them is the squeeze and exception network (SE-Net), which refers to the attention mechanism added on the channel dimension [47]. The key operations are \"squeeze\" and \"exception\". Figure 4a shows the module structure of SE-Net. Compared with using one full connection layer directly, using two full connection layers in the citation operation introduces more computation and parameter quantities. The other module is the non-local attention mechanism, which uses the information of the surrounding points when extracting a feature. The \"surrounding\" can be either temporal or spatial. The non-local module can be used as a component to complete video classification, image segmentation, target detection, and other tasks together with other network results, and the effect is good. In this design, when extracting a heartbeat feature, we also use other heartbeat information around it as auxiliary judgment information, so we follow the idea of the non-local module to optimize and design a self-attention module.  
Figure 4: (**a**) SE-Net module structure; (**b**) Non-Local module structure.  
The proposed self-attention module only needs to perform one convolution operation at the input side in comparison to the non-local model, which decreases the computation and number of parameters of the attention module as well as the number of convolution operations. The computation and number of parameters for the proposed self-attention module, SE block module, and non-local module are compared in Table 2. As can be seen, the suggested self-attention module has the least computations and parameters, allowing the attention module to minimize the introduction of computations and parameters to the overall network. It is more suitable for training lightweight network models for deployment into mobile embedded devices.
## 4 Discussion
### Distribution and Age of the CORBs  
The Late Cretaceous oceanic red beds (CORBs) are prominently developed across the North Tethys Himalaya (NTH) in southern Tibet, China, with discontinuous outcrops spanning from east to west in the regions of Yamdrok, Gyangze, Kangmar, Angren, Gyirong, Zhongba, Zhada, and Zanskar (Figure 14). Regional stratigraphic correlations, based on lithologic associations and fossil assemblages (primarily planktic foraminifera and radiolaria), indicate that the ages of CORBs in this region exhibit significant spatial variability. Specifically, the CORBs in the Gyangze region are dated to the late Santonian to Campanian ([PERSON] et al., 2005) [43], while those in the Kangmar region span from the late Santonian to Maastrichtian ([PERSON] et al., 2011) [9], whereas in Gyirong, they extend from the Campanian to the Paleocene ([PERSON] and [PERSON], 2002; [PERSON] et al., 2005; [PERSON] et al., 2017) [17; 18; 43; 44], and the Zhada region records CORBs of early Paleocene age ([PERSON] et al., 2005) [18].  
A notable trend emerges in the age distribution of CORBs across the NTH, with a general tendency for younger ages towards the east. For instance, in Zanskar, Kangmar, and Gyangze, the CORBs are primarily Campanian, while in Gyirong and Zhada, they range from Maastrichtian to early Paleogene (Figure 14).  
The depositional environments of the CORBs were traditionally interpreted as being confined to continental slope and deep basin settings, with their absence in shelf or shelf margin environments being widely accepted (e.g., [PERSON] et al., 2005; [PERSON] et al., 2005; [PERSON] et al., 2011) [4; 18; 43]. However, our study in the Gyangze region and adjacent areas reveals that CORBs are not limited to deep marine settings but instead occur across a broad spectrum of depositional environments, ranging from deep basins to slopes and even shelf settings (e.g., Figure 12). This finding aligns with the recent identification of shallow-marine, purple limestone beds within the upper Campanian in the Guru area of Yadong ([PERSON] et al., 2021) [5], which is tectonically situated within the Southern Tethys Himalaya (STH) (Figure 14).  
Paleogeographic reconstructions further demonstrate that Campanian-aged CORBs were geographically extensive, spanning from shelf to basin environments (Figure 15).  
Figure 14: Distribution and stratigraphic correlation of the CORBs in Tethyan Himalayan area (after [PERSON], 1964 [19]; [PERSON] and [PERSON], 2002 [17]; [PERSON] et al., 2005 [18,43]; [PERSON] et al., 2011 [9]; [PERSON] et al., 2017 [44]; [PERSON] et al., 2021 [5]; and this paper).  
However, we emphasize that the thickness and temporal duration of the CORBs in different paleogeographic settings likely exhibit significant variability. This variability is primarily controlled by the extent to which Fe(II)-rich (or ferruginous) seawater infiltrated oxic depositional environments, highlighting the complex interplay between geochemical conditions and depositional processes during the Late Cretaceous.
# A Low-Ambiguity Signal Waveform for Pseudolite Positioning Systems Based on Chirp
## 2 Mathematical Models
### Power Spectrum Density  
The PSD can be derived from the following expression [25]:  
\[S_{chp}(f)=\frac{1}{T_{c}}|P(f)|^{2}S_{c}(f) \tag{10}\]  
where, \(|P(f)|\) is the amplitude spectrum of \(p(t)\); \(S_{c}(f)\) is the PSD of the PRN sequence \(\{c_{m}\}\) as follows:  
\[S_{c}(f)=\sum_{k=-\infty}^{\infty}R_{c}(k)e^{-j2\pi/kT_{c}}=1 \tag{11}\]  
After a complicated derivation (the detailed derivation is shown in Appendix A), \(P(f)\) is:  
\[P(f)=\frac{1}{2}\sqrt{\frac{T}{B_{s}}}\left\{\begin{array}{ll}\exp\biggl{[}- j\frac{T_{c}\pi(f-f_{  
\(C(X)\) and \(S(X)\) are the Fresnel cosine and sine integrals, respectively. Simplifying (12) yields:  
\[P(f)=\sqrt{A^{2}+B^{2}+2 AB\cos(\theta_{1}-\theta_{2})}\exp\biggl{(}j\text{arctan} \biggl{(}\frac{A\sin(\theta_{1})+B\sin(\theta_{2})}{A\cos(\theta_{1})+B\cos( \theta_{2})}\biggr{)}\biggr{)} \tag{13}\]  
where:  
\[A=\frac{1}{2}\sqrt{\frac{T_{c}}{B_{\text{s}}}}\sqrt{\left(C(X_{1})+C(X_{2}) \right)^{2}+\left(S(X_{1})+S(X_{2})\right)^{2}}\]  
\[B=\frac{1}{2}\sqrt{\frac{T_{c}}{B_{\text{s}}}}\sqrt{\left(C(X_{3})+C(X_{4}) \right)^{2}+\left(S(X_{3})+S(X_{4})\right)^{2}}\]  
\[\theta_{1}=-\frac{\pi T_{c}(f-f_{0})^{2}}{B_{\text{s}}}+\text{arctan}\biggl{(} \frac{S(X_{1})+S(X_{2})}{C(X_{1})+C(X_{2})}\biggr{)}\]  
\[\theta_{2}=\frac{\pi T_{c}(f+f_{0})^{2}}{B_{\text{s}}}-\text{arctan}\biggl{(} \frac{S(X_{3})+S(X_{4})}{C(X_{3})+C(X_{4})}\biggr{)}\]  
Substituting (11) and (13) into (10) yields the PSD expression:  
\[S_{chp}(f)=\left(A^{2}+B^{2}+2 AB\cos(\theta_{1}-\theta_{2})\right)/T_{c} \tag{14}\]
## 4 Experiments
### Ablation Study
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset Names**} & \multicolumn{3}{c}{**AOC-Caps (Dynamic Routing)**} & \multicolumn{3}{c}{**AOC-Caps (EM Routing)**} \\ \cline{2-11}  & **ACC** & **PRE** & **REC** & **F1** & **AUC** & **ACC** & **PRE** & **REC** & **F1** & **AUC** \\ \hline PathMNIST & 0.794 & 0.790 & 0.782 & 0.783 & 0.952 & **0.859** & **0.840** & **0.837** & **0.845** & **0.960** \\ \hline DermaMNIST & 0.732 & 0.420 & 0.368 & 0.390 & 0.883 & **0.786** & **0.485** & **0.417** & **0.447** & **0.890** \\ \hline OCTMNIST & 0.707 & 0.788 & 0.760 & 0.773 & 0.950 & **0.750** & **0.820** & **0.802** & **0.819** & **0.955** \\ \hline PneumoniaMNIST & 0.859 & 0.862 & 0.843 & 0.847 & 0.985 & **0.931** & **0.914** & **0.893** & **0.895** & **0.990** \\ \hline OrganMNIST\_Axial & 0.802 & 0.799 & 0.773 & 0.785 & 0.950 & **0.931** & **0.920** & **0.903** & **0.919** & **0.973** \\ \hline OrganMNIST\_Coronal & 0.794 & 0.803 & 0.892 & 0.899 & 0.940 & **0.907** & **0.911** & **0.904** & **0.904** & **0.965** \\ \hline OrganMNIST\_Sagittal & 0.739 & 0.680 & 0.665 & 0.669 & 0.967 & **0.783** & **0.731** & **0.717** & **0.723** & **0.980** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance of different capsule forms and routing. The results in Tables 9-11 have demonstrated the effectiveness of EM routing, max-pooling in the AOC-Layer, and attention modules. By putting them together, we find the optimal combination to implement AOC-Caps based on the following comparative experiments.
# Research on Data-Driven Performance Assessment and Fault Early Warning of Marine Diesel Engine
## 2 Introduction to Performance Assessment and Fault Warning Methods
### Fault Early Warning Method
#### 2.4.2 Yamamoto Detection Method  
The Yamamoto detection method identifies abrupt changes by assessing the statistical significance of mean differences between pre- and post-test point subsequences [26]. This methodology conceptualizes abrupt change detection as a hypothesis-testing problem concerning population mean equivalence. For a given series \({}_{x}\), let \(n_{1}\) and \(n_{2}\) represent the sample sizes of the pre- and post-test subsequences, respectively. The signal-to-noise ratio (SNR) is defined as  
\[SNR=\frac{\left|\overline{x_{1}}-\overline{x_{2}}\right|}{s_{1}+s_{2}} \tag{7}\]  
In the equation, \(\overline{x_{1}}\) and \(\overline{x_{2}}\) represent the mean values of the pre- and post subsequences, respectively, with \(s_{1}\) and \(s_{2}\) denoting their standard deviations.  
If the signal-to-noise ratio (SNR) is greater than 1.0, it is considered that an abrupt change occurs at that moment. If the SNR exceeds 2.0, the abrupt change is classified as a strong mutation. If the value of the SNR is set to 10, it corresponds to a confidence level of 95%, while a value of 14 corresponds to a confidence level of over 99.5%.
# TEDL: A Text Encryption Method Based on Deep Learning
## 1 Introduction
The contributions of our paper are as follows:  
* To the best of our knowledge, although there exists some work with respect to the combination of deep learning and information security [20; 21; 22], TEDL is among the first to utilize the uninterpretability and time-consuming training features in deep learning to realize encryption. Moreover, it is the first time that the word embedding based on deep learning model is used for encryption. * TEDL has time-varying and self-updating characteristics, which are greatly beneficial for reducing the frequency of key redistribution. The time-varying refers to the variation of codebook as information is transmitted. Consequently, for the identical word, its representation varies every time. To some extent, it is close to the one-time pad. Besides, the concept of self-updating means that both sender and receiver can reconstruct codebook by revising synthetic corpus, without changing the key. * TEDL is sensitive to the change of corpus. We prove that the process of skip-gram hierarchical softmax (SGHS) is equal to implicit matrix decomposition, beneficial to the better understanding of the word embedding process. Moreover, it means that minor changes in the corpus can cause a wide range of adjustments in training results, just as changes in a small number of elements in a matrix lead to a wide variation in matrix decomposition results. It directly supports the feasibility of our method. * TEDL has a two-stage structure: codebook construction stage and communication stage. The former needs a long time, and the brute-force crack is performed at this stage. The latter is always time-saving, for it only involves a search operation. Furthermore, communication is mainly carried out at the second stage. In this way, both parties are able to achieve relatively high-speed communication while attackers still need more time to crack. * TEDL performs well for security and high efficiency concluded by experiments and relevant analyses, which involve the recoverability, time cost for a brute-force attack, frequency analysis, correlation analysis, sensitivity analysis, efficiency, and generality. The rest of the paper is organized as follows. In Sections 3 and 4, we outline our method and give some preliminaries, respectively. In Section 5, we illustrate the key design. Section 6 details the encryption/decryption process. Section 7 introduces the self-updating mechanism in TEDL. We prove the feasibility of TEDL in Section 8 and the security analysis is shown in Section 9 with experiments in Section 10. Section 11 reveals the limitations of TEDL. Section 2 discusses the related work. Finally, Section 12 draws conclusions and further work.