esa-sceva/satcom-chunk-collection huggingface dataset:
# LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis
## 3 Online Rendering and Tracking
### Formal Description
#### 3.2.3. Pose Estimation Using PnP  
The final step is to compute the camera's current pose \(\mathbf{T}_{t+1}\) by leveraging the set of \(N\) 3D-2D correspondences \(\{(\mathbf{X}_{i},\mathbf{u}^{r}_{i})\}_{i=1}^{N}\) obtained from the matching step. The PnP algorithm is employed to find the rotation \(\mathbf{R}_{t+1}\) and translation \(\mathbf{t}_{t+1}\) that minimize the reprojection error.  
The reprojection error is defined as the sum of the squared distances between the observed 2D keypoints in the real image and the projected 3D points from the world point cloud. The objective function to be minimized is:  
\[E(\mathbf{R}_{t+1},\mathbf{t}_{t+1})=\sum_{i=1}^{N}\|\mathbf{u}^{r}_{i}-\pi( \mathbf{R}_{t+1},\mathbf{t}_{t+1},\mathbf{X}_{i})\|^{2} \tag{3}\]  
where \(\pi(\cdot)\) is the projection function that maps the 3D point \(\mathbf{X}_{i}\) onto the image plane given the pose \((\mathbf{R}_{t+1},\mathbf{t}_{t+1})\):  
\[\pi(\mathbf{R}_{t+1},\mathbf{t}_{t+1},\mathbf{X}_{i})=\begin{pmatrix}f_{x} \frac{\mathbf{x}^{r}_{j}}{Z_{j}^{s}}+c_{x}\\ f_{y}\frac{\mathbf{x}^{s}_{j}}{Z_{j}^{s}}+c_{y}\end{pmatrix} \tag{4}\]  
and where \([X^{r}_{i},Y^{r}_{i},Z^{r}_{i}]^{T}=\mathbf{R}_{t+1}\mathbf{X}_{i}+\mathbf{t}_ {t+1}\).  
This non-linear least-squares problem is typically solved using iterative algorithms, such as the Levenberg-Marquardt algorithm. To ensure robustness against outlier matches, the PnP solver is often embedded within a RANSAC (Random Sample Consensus) scheme. The resulting pose \(\mathbf{T}_{t+1}=[\mathbf{R}_{t+1}|\mathbf{t}_{t+1}]\) is then used as the initial guess for the subsequent frame, continuing the tracking process.
# A Low-Complexity Security Scheme for Drone Communication Based on PUF and LDPC
## 3 Security Architecture
### Error Correction Schemes  
Firstly, concerning the parity matrix \(H=\left[h_{i,j}\right]_{k\times n^{\prime}}\), define the symbol set \(N_{i}=\left\{j,1\leq j\leq n,h_{i,j}=1\right\}\) for the i-th parity equation. It is worth pointing out that in Table 5, the column index result \(p_{r_{A}}\in N_{i}\) represents the set of symbols for the j-th symbol (including information bits and parity bits) involved in the set of parity equations \(K_{j}=\left\{i,1\leq i\leq k,h_{i,j}=1\right\}\), i.e., the row index.  
\begin{table}
\begin{tabular}{l} \hline \hline
**Generation of LDPC Parity Matrix** \\ \hline Firstly, take the modulus of the target row number \(r_{P}\) to calculate its position \(r_{I}\) in the sub-matrix. Take the integer dividing to calculate the position \(r_{A}\) of the base matrix, where \(r_{P}=r_{A}\times b+r_{I}\), and \(b\) is the size of the cyclic matrix. \\ \hline Secondly, calculate the base offset of the base matrix \(O_{r_{A}}=(p_{1}+r_{A})\text{mod}a\), where \(p_{1}\) is the first line of the index of the base matrix and \(a\) is the number of columns of the base matrix; Calculate the initial offset of the final matrix \(O_{r_{I}}=O_{r_{A}}\times b\). \\ \hline Finally, the current basis matrix cycle offset \(c_{r_{A}}\) can be found according to Formula (9). The final column index is \(p_{r_{P}}=(c_{r_{A}}+r_{I})\text{mod}b+O_{r_{I}}\). \\ \hline \hline \end{tabular}
\end{table}
Table 5: Generation of LDPC parity matrix.  
Figure 2: Shape of the final parity-check matrix.
# Coupled Vibration Analysis of Multi-Span Continuous Cable Structure Considering Frictional Slip
## 2 Nonlinear Coupled Vibration Model
### Main Cable Subsystem Model  
With the direct stiffness method, the main cable subsystem model of the cable hoisting system is established. According to what was assumed earlier, when the crane passes through the main cable with speed \(V\), the main cable is subjected to the force of gravity \(F_{g}=(m_{1}+m_{2}+m_{3}+I_{3})g\) and hoisting cable elastic force \(P_{S}=k_{v}(u_{v}-u_{b})\big{|}_{x=Vt}\). The damping force of the hoisting cables is \(P_{D}=c_{v}\big{(}\dot{u}_{v}-\ddot{u}_{b})\big{|}_{x=Vt}\):  
So, \(F_{b}=\delta(x-Vt)\big{[}m_{v}g+k_{v}(u_{v}-\ddot{u}_{b})+c_{v}(\ddot{u}_{v}- \ddot{u}_{b})\big{]}\). The equation of motion of the main cable under a uniformly moving load can be expressed as  
\[[M_{b}]\big{\{}\ddot{u}_{b}\big{\}}+[C_{b}]\big{\{}\dot{u}_{b}\big{\}}+[K_{b} ]\big{\{}u_{b}\big{\}}=\{F_{b}\} \tag{2}\]  
where \(M_{b}\) is the mass matrix of the main cable subsystem model, \(C_{b}\) is the damping matrix of the main cable subsystem model, \(K_{b}\) is the stiffness matrix of the main cable subsystem model, \(F_{b}\) is the external load vector.  
In order to consider the nonlinearity and slip effects of the main cable of the cable hoisting system, the whole main cable is now discretized into two three-node catenary slip cable elements (i.e., span anchorage node, slip node at the top of the cable tower and node of the mid-span cable closest to the cable tower) and m\(-\)2 two-node catenary cable elements. The tangent stiffness matrices and damping matrices of the two types of elements are briefly described in the following subsections.
# Exploring CeO\({}_{2}\)-Doped Co/SBA-15 Catalysts for Acetic Acid Oxidative Steam Reforming
## 4 Conclusions  
This study highlights the significant potential of CeO\({}_{2}\)-doped Co/SBA-15 catalysts for AAOSR, as bio-oil aqueous phase representative compounds. The characterization findings revealed that Ce-doped catalysts preserved the well-ordered mesostructure of the SBA-15 support while increasing the catalyst's reducibility. Additionally, the incorporation of CeO\({}_{2}\) improved cobalt dispersion, as evidenced by smaller crystallite sizes, thus enhancing the catalytic performance and reducing the coke formation rate. Catalysts with 10-20 wt.% CeO\({}_{2}\) exhibited the best catalytic performance, reaching H\({}_{2}\) yields close to thermodynamic equilibrium and nearly complete acetic acid conversion at 550 \({}^{\circ}\)C. Furthermore, these catalysts maintained the H\({}_{2}\) yield above 55% and high acetic acid conversion with a low coke formation rate. These findings underscore the importance of using CeO\({}_{2}\) as a promoter in the catalyst formulation, highlighting that Co/10 CeO\({}_{2}\)-SBA-15 and Co/20 CeO\({}_{2}\)-SBA-15 catalysts are promising for reforming purposes.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app15116376/s1](https://www.mdpi.com/article/10.3390/app15116376/s1)]([https://www.mdpi.com/article/10.3390/app15116376/s1](https://www.mdpi.com/article/10.3390/app15116376/s1)), Figure S1: N\({}_{2}\) physisorption isotherms at 77 K of calcined supports; Figure S2: UV-vis spectra of calcined supports; Figure S3: Application of Tauc method for the determination of band-gap energy in A. 5 CeO\({}_{2}\)-SBA-15, B. 10 CeO\({}_{2}\)-SBA-15, C. 20 CeO\({}_{2}\)-SBA-15, and D. 30 CeO\({}_{2}\)-SBA-15 supports; Table S1: Physical-chemical characteristics of xCeO\({}_{2}\)-SBA-15 supports; Table S2: Hydrogen yield in acetic acid steam oxidative reforming; Table S3: Acetic acid conversion in oxidative steam reforming.  
C.A.C.: Investigation, Formal analysis, Data curation, Writing--original draft, Writing--review and editing. A.M.d.l.C.: Investigation. P.J.M.: Methodology, Formal analysis, Data curation, Writing--review and editing. A.J.V.: Formal analysis, Writing--review and editing, Funding acquisition. J.A.C.: Conceptualization, Writing--review and editing, Funding acquisition. A.C.: Conceptualization, Formal analysis, Writing--review and editing, Funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research has been financially supported by the Spanish Ministry of Science and Innovation (Projects TED2021-131499B-I00 and PID2020-117273 RB-I00).  
The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 5 Case Study
### Simulation Results
#### 5.3.1 Effect of BSN Density  
Figure 3 shows the CDF of PER in the peak time scenario, moderate time scenario, and off-peak time scenario respectively. Each curve shows the CDF of PER with a specified duty cycle. The PER increases with the duty cycle. This is reasonable because when the duty cycle gets higher more BSNs transmit simultaneously resulting in higher PER. When comparing the three graphs in Figure 3, the PER decreases as the BSN density decreases from 2 square meters per person in peak period (see Figure 3a) to 6 square meters per person in off-peak period (see Figure 3c). This is caused by less severe interference due to lower density BSNs.  
\begin{table}
\begin{tabular}{|c|c|c|c|} \hline \(m\) & 12 & \(k\) & 52 \\ \hline \(d_{\theta}\) (cm) & 10 & \# of channels & 1 \\ \hline \(\sigma\) (dB) & 6.2 & \(\Omega_{\mu}\) (dBm) & 0 \\ \hline \(\Omega_{\mu}\) (dBm) & -80 & \(\Omega_{s}(d_{\theta})\) (dB) & 35.7 \\ \hline \(\alpha_{o}\) & 3.38 & \(\alpha_{t}\) & 2.6 \\ \hline \end{tabular}
\end{table}
Table 2: The parameter settings of the simulation in case study.  
Figure 3: _Cont._From Figure 3, we obtain the reliability level of data transmission in BSNs. The reliability level is defined as the ratio of the number of successfully received data packets (with PER less than a certain target PER requirement \(PER_{required}\)) to the number of transmitted data packets. The reliability level can be calculated as \(\Pr(PER<PER_{required})\). Assuming that reliability requires PER being less than  
Figure 3: The cumulative distribution function (CDF) of packet error rate (PER) for three scenarios. (**a**) Peak time scenario; (**b**) Moderate time scenario; (**c**) Off-peak time scenario.  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|} \hline Duty Cycle (\%) & 10 & 20 & 30 & 40 & 50 \\ \hline Peak time scenario (58 BSNs) & 57.3 & 21.9 & 7.7 & 2.5 & 0.7 \\ \hline Moderate scenario (40 BSNs) & 81 & 49.9 & 23.3 & 11.5 & 5 \\ \hline Off-peak scenario (16 BSNs) & 90.8 & 68.5 & 47.3 & 29.1 & 14.8 \\ \hline \end{tabular}
\end{table}
Table 3: Reliability level (%) with PER lower than 0.05.  
0.05 (\(PER_{required}=0.05\)), the reliability level is listed in Table 3. For instance, in the case of _Duty cycle = 0.2_, under the scenario of peak time, the reliability level is only 21.9%. At the off-peak time, the reliability level is 68.5%.
## 4 Experimental Results
### Result and Discussion
#### 4.4.2 Linear Regression Method  
The linear regression model was tested with various humidity levels at 10%, 50%, and 100%, and their results are listed in Table 6. It was observed from the Figure 8 that the LR method gives better results at the 50% humidity level. Based on the humidity level, the performance differs from machine learning models.
## 3 Hybrid Stochastic Approach to Location Estimation
### Inductive Approach for Location Estimation  
On the one hand, the _inductive approach_ estimates the _likelihood function_ measuring directly the signal strength in each place. That is, several measurements are taken for each training place; then, the function p(\(o|l\)) is estimated. The main drawback of this approach is the time consuming nature of the training phase. We denote as \(T\) the set of training data, formed by \(t\) observations with their respective locations. Each training data, _T\({}_{i}\)_, it is represented as (_l\({}_{i}\)_, _o\({}_{i}\)_), where \(i\) can be from 1 to \(t\). Several alternatives has been proposed in the literature to estimate p(\(o|l\)) from _T_: the histogram method [30; 32], the Bayesian method [33] or the kernel method [30; 34]. Another drawback is that this model only returns one of the locations from the training set. In order to solve this problem, several proposals can be found in reference [35].
## 4 Strength Properties
### Effects of Lime-Cement Stabilization on the Resistance to Loss in Strength of the Clay Soil  
The resistance to strength loss, a critical durability parameter assessed through water immersion testing that compares 7-day cured plus 7-day soaked specimens to 14-day cured specimens, increased dramatically from 9% at 0% additives to 76% at 15% lime and 6% cement through calcium-induced pozzolanic reactions forming calcium silicate hydrate and calcium aluminate hydrate gels with enhanced hydrophobic properties (Figure 14). This substantial improvement results from these secondary cementitious compounds creating water-resistant inter-particle bonds that effectively prevent water-induced degradation by replacing weak clay particle associations with robust chemical linkages that maintain structural integrity under adverse environmental conditions. The linear improvement pattern from 20% to 60% resistance over 0-20% lime and 0-6% cement indicates optimal cation exchange and progressive gel formation within this range, while diminished effectiveness beyond the 15% lime and 6% cement threshold suggests a critical saturation point where excess additives disrupt optimal crystalline structure formation and incomplete utilization of binding agents occurs. This transformation demonstrates the precise balance required between chemical reactivity and mechanical resilience in soil stabilization, where controlled additive proportioning fundamentally alters the material's response to environmental stressors by creating a quasi-cemented matrix that resists water penetration and maintains long-term durability under field conditions.  
The effects of Fly Ash and GGBS in clay soil stabilization demonstrate distinct strengths and durability compared to lime-cement systems. Fly Ash (Class C/F) exhibits long-term pozzolanic activity, achieving compressive strengths of 915.5 kPa at 28 days in geopolymer composites in clay stabilization [59], though its early-age performance (e.g., 180 kPa at 7 days) lags behind cement-based controls [60]. GGBS shows significant strength improvements, with 71% enhancement at 20% replacement in binder mixes [61], and optimizes CBR values at 20% dosage [62]. However, lime-cement stabilization (15% lime + 6% cement) achieves a 76% resistance to strength loss, surpassing Fly Ash and GGBS due to calcium-mediated cation exchange and pozzolanic reactions [60]. While Fly Ash reduces moisture absorption in lime-stabilized soils and GGBS/FA blends lower mass loss, lime-cement systems remain more effective in mitigating durability losses under immersion. Combinations of Fly Ash and GGBS (e.g., 50:50 mixes) show promise in balancing workability and strength [63], but lime-cement retains superior efficacy for clay stabilization.
## 3 Results and Discussion  
### Structural and Chemical Composition Analysis via XRD and XPS  
The crystalline structures and chemical compositions of the samples with the Ta and W buffer and capping layers were examined by XRD and XPS, respectively. Figure 2a shows the XRD scans of the samples with the Ta and W buffer and capping layers. Both samples with the Ta and W buffer and capping layers have broad and low peaks, indicating they are in an amorphous state. This is because the B content constrained the nucleation of CoFe, resulting in the absence of crystallization [53]. Figure 2b,c show the XPS depth profiles of the Ta/CoFeB/MgO/Ta and W/CoFeB/MgO/W samples, respectively. The XPS depth profile confirms the presence of B in the CoFeB layer, which explains the amorphous states of both samples.
## 3 Results
### Quantitative Analysis of Isothermal Gas Adsorption  
According to the N\({}_{2}\) (B)H desorption branch) isotherm adsorption data (Figure 5a,b), the mesopore and macropore parameters could be calculated, combining with the micropore parameters derived from CO\({}_{2}\) (based on DFT density functional theory) isotherm adsorption (Figure 5c,d). The average micropore and mesopore volume of marine shale are 0.00877 cm\({}^{3}\)/g and 0.03066 cm\({}^{3}\)/g (Table 3), respectively. In contrast, the micropores and mesopores of continental shale are lower than that of marine shale, only 0.00595 cm\({}^{3}\)/g and 0.02304 cm\({}^{3}\)/g, respectively. The average macropore volume of continental shale is 0.01166 cm\({}^{3}\)/g, which is higher than that of marine shale by 0.00932 cm\({}^{3}\)/g. The specific surface area of micropores and mesopores of marine shale is larger than that of continental shale, while the specific surface area of macropores is smaller than that of continental shale. Furthermore, the total specific surface area of marine shale is 1.93 times that of continental shale, which is consistent with the average pore width of the two types of rocks (7.13 nm  
Figure 4: SEM images of marine shale samples (**a**–**c**) continental shale samples (**d**–**f**). OM = Organic matter, InterP = interparticle, IntraP = intraparticle, Py = pyrite, Qtz = Quartz. In detail, micro fracture developed in the marine shale (**a**); OM pores supported by the detrital quartz (**b**); OM pores distributed in rigid quartz framework (**c**); the lithological stratification of clay layer, OM layer, and pyrite layer (**d**); interparticle pores developed along with clay and quartz (**e**); the oriented direction of clay minerals and the main pore type (inorganic pore in clay mineral) in the continental shale (**f**).  
for marine shale and 16.56 nm for continental shale). According to the classification of IUPAC [28], all the hysteresis loops of marine shale and continental shale belong to type IV(a), which manifest that the complexity of the pore structure leads to the phenomenon of capillary condensation.  
Based on the total pore volume and total specific surface area of different pore diameters, the mesopores of 2-50 nm are the main pore types of marine and continental shale, accounting for 63% and 56%, respectively, and correspondingly contributing 60% and 77% specific surface area (Figure 6). In addition, the micropore volume in marine shale only accounts for 18% of the total pore volume but contributes 38% of the specific surface area. In contrast, the micropore volume in continental shale accounts for 14.5% of the total pore volume but only contributes 18% of the specific surface area. It shows that marine shale has a more complicated microporous spatial structure at the nanoscale than continental shale.
# A Hybrid DNN Multilayered LSTM Model for Energy Consumption Prediction
## 5 Experimental Results
### Evaluation of the Proposed Model, DNN Model, and LSTM Model
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **RMSE** & **MAE** & **MAPE** & \(R^{2}\) \\ \hline CNN multilayered bidirectional LSTM [29] & 0.565 & 0.346 & 0.2910 & - \\ Linear regression [30] & 0.6517 & 0.5022 & 83.74 & - \\ CNN-BiGRU [32] & 0.42 & 0.29 & - & - \\ GRU [28] & 0.22 & 0.19 & 60.00 & - \\ CNN-ESN [33] & 0.0472 & 0.0266 & - & - \\ R-CNN with ML-LSTM [34] & 0.0325 & 0.0144 & 1.024 & 0.9841 \\ Proposed model: DNN multilayered LSTM & & 0.02410 & 0.01565 & 0.01826 & 0.99911 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model comparison with various models that used the IHEPC dataset. The hybrid model was also compared with other LSTM methods to determine its performance. It was put against LSTM, bidirectional LSTM, multiple LSTM, and WT multiple LSTM. The purpose of the comparison was to establish how the changes in LSTM affected the model. The comparison also fit well because all the models used the same parameters and structure. If the model had LSTM alone or if it was a bidirectional LSTM alone, then multiple LSTM alone, multiple LSTM with WT, or our model, multiple LSTM with a DNN, was used. The indicators used for the comparison were the RMSE, MAPE, and \(R^{2}\). The results are shown in Table 5.  
Table 5 shows that the values of all the three indicators improved as the LSTM model improved. The RSME values moved from 0.041 for a lone LSTM model but decreased with the multiple LSTM models. This is good because lower values indicate a more stable model. The MAPE values also decreased while moving from the lone LSTM to the multiple LSTM. The proposed model's value was low but was not the closest to 0 in the comparison. However, the results showed that multiple LSTM had better accuracy than the other models. The model performed best with the coefficient of determination. The value was close to 1, which makes the model have the best-fitting effect. Based on the comparison results, multiple LSTM or multilayered LSTM models provide an improved fitting effect and prediction accuracy.
## 4 Discussion
### Comparative Analysis of Thermal Discharge Diffusion Ranges in Different EFZs  
According to the national marine functional zoning, the sea area adjacent to HYNPP is designated as an industrial and urban construction sea use area. During development  
Figure 6: Seasonal variations in EFZ warming induced by thermal discharge at HYNPP. (**a**) Spring; (**b**) Summer; (**c**) Autumn; (**d**) Winter.  
and utilization, this area adheres to Type II sea water quality standards and Type III water temperature standards. However, the temperature rise within the thermal mixing zone is not explicitly limited. Referencing the sea water quality, Type III sea water quality stipulates that human-induced sea water temperature rise should not exceed 4 \({}^{\circ}\)C compared to local ambient temperatures.  
To assess the spatial overlap between different _TRIs_ of HYNPP thermal discharges and EFZs, maximum temperature rise envelopes were plotted for each season from the start of NPP operation to November 2014 (Figure 7). These envelopes represent the potential temperature increase from _R+1_ to _R+4_. Table 4 summarizes the distribution areas of these _TRIs_.  
HYNPP's thermal discharge exhibits distinct seasonal spatial patterns, with a larger influence range and lower temperature rise in summer, and a smaller influence range but higher temperature rise in winter. Two primary factors contribute to this phenomenon:  
1. Temperature rise of discharged water. Winter thermal discharge exhibits a higher temperature rise (10.79 \({}^{\circ}\)C) compared to summer (8.09 \({}^{\circ}\)C) due to HYNPP design. This elevated temperature rise in winter concentrates the high-temperature water near the discharge point, resulting in a significantly larger influence area with higher temperature rises.
2. Water body mixing. Winter's stronger vertical mixing within the water body accelerates the dispersion and cooling of the thermal discharge, reducing its influence range. Conversely, summer's pronounced thermal stratification inhibits mixing and cooling, resulting in a larger area of influence and a more persistent thermal plume [23].  
These factors collectively shape the spatial distribution pattern of thermal discharge influence on HYNPP, with a larger extent in summer and a smaller extent in winter.