esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Evaluation and Application of the PA-LM Model
### Application of the PA-LM Model  
In this section, we showed two field applications of the established PA-LM model. Two typical horizontal wells of CSS (B1H and B5H) were selected from the Lvda 21-2 reservoirs in Bohai Bay, China. These two horizontal wells only conducted one cycle of CSS. Therefore, the COP values of the first cycle of the horizontal wells were utilized to evaluate the PA-LM model's accuracy. As shown in Figure 10, good agreements between the predicted and actual COP values are obtained in the two cases. Table 5 shows that the MAPEs of the COP values for the two wells are 8.66% and 5.93%, respectively. Although the geological, well, and operational parameters are different for the two horizontal wells, the optimized PA-LM model presents the reliable predictions in the two horizontal wells. The results indicate that the developed PA-LM model has a good generalization. In conclusion, the aforementioned evaluations based on the initial data set and actual production data proved the PA-LM model's accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**The Types of Data Sets and Model** & **MAPE (\%)** & **MSE** & **RMSE** & **R2** \\ \hline The training set of the LM model & 7.68 & 8876.4\({}^{2}\) & 8876.4 & 0.9135 \\ The testing set of the LM model & 9.33 & 10,124.8\({}^{2}\) & 10,124.8 & 0.9027 \\ The training set of the PA-LM model & 2.34 & 3931.3\({}^{2}\) & 3931.3 & 0.9913 \\ The testing set of the PA-LM model & 5.06 & 6054.33\({}^{2}\) & 6054.33 & 0.9807 \\ \hline \end{tabular}
\end{table}
Table 4: Comparisons of MAPE, MSE, RMSE, and R2 of the initial data sets for the LM and PA-LM models.  
Figure 9: Comparisons of the COP values predicted by the LM and PA-LM models: (**a**) typical data 1; (**b**) typical data 2; (**c**) typical data 3; (**d**) typical data 4.  
However, it is noted that more COP data need to be collected from the following cycles or other CSS wells to further verify the PA-LM model's accuracy with the subsequent development of the reservoirs in Bohai Bay, China. In addition, the developed PA-LM model in this study can only be used to predict the COP values of horizontal CSS wells in Bohai Bay, China, because the model was developed based on the parameters of actual reservoirs in Bohai, Bay, China. When the method is used in other offshore heavy oil reservoirs, the establishment process of the PA-LM model shown in this study should be re-conducted based on the practical reservoir conditions.  
Figure 11 shows the predicted COP values of the two CSS horizontal wells in the eight cycles by the optimized PA-LM model. The predicted COP values of the eight cycles of two CSS horizontal wells are 226,753 and 147,117 m\({}^{3}\), respectively, which are expected to provide important references for decision-makers in developing offshore heavy oil reservoirs.
## 4 Respiratory Motion Correction Strategies  
Respiratory motion correction can be either prospective or retrospective, where prospective correction involves estimating the motion before every acquired _k_-space segment, using previously described motion estimation techniques, and correcting for it by updating the gradient, excitation and acquisition parameters accordingly. Retrospective correction involves correcting for the motion after the scan is completed, which relaxes the real-time constraints for motion estimation, but prohibits adjustment of slice position due to through-plane motion. It is worth mentioning that prospective motion correction is limited to affine corrections, a set of linear transformations including translation, rotation, scaling and shearing which can be applied for prospective motion correction by adapting the linear gradient waveforms [82], whereas retrospective motion correction can be used for correction with more degrees-of-freedom, such as non-linear correction [69]. Although respiratory motion estimation typically precedes motion correction, this is not a strict requirement. A method has been developed whereby the respiratory motion is iteratively corrected without prior motion estimation by maximizing an edge sharpness metric in image space [83].
# A Comprehensive Study on Deep Learning-Based 3D Hand Pose Estimation Methods
## 3 Deep Learning Methods  
In this paper, we propose a new taxonomy that groups each work based on the modality of the input data. As such, there are three distinct categories: (a) depth-based, (b) RGB-based, and (c) multimodal approaches.  
Each category is further divided into more sub-categories. The depth-based approaches are grouped based on whether they directly use 2D depth maps or process them to extract 3D data representations, such as point clouds or 3D voxel representations. The RGB-based approaches are categorized based on whether they directly regress the 3D hand joint locations or a heatmap of the joints or employ a predefined hand model. The last category describing multimodal approaches is divided according to the input modality, required during evaluation, to unimodal and multimodal evaluation methods. A visualization of the proposed taxonomy can be observed in Figure 1.  
Figure 1: Proposed taxonomy. Each work is grouped by the modality of the input data, i.e., RGB, depth, and multimodal data, and then by the per-category distinct characteristics that best describe them.  
In order to improve the comprehensibility of this work, an introduction of hand models is deemed necessary. In general, a hand model imposes a geometrical prior of feasible poses and possible joint rotations. Usually, hand models are employed to further refine the predicted pose and consequently constrain the neural network's predictions to humanly possible hand poses. Such models may be developed explicitly by the authors or instead use an already existing hand model.  
Many hand models have been proposed that use different ways to represent a hand, such as assembled geometric primitives [56], sum of Gaussians [79], spheres [80], or triangulated meshes [81].  
Recently, a differentiable triangulated hand model has been introduced, namely the MANO hand model [82], and is gaining a lot of traction. The hand is generated by a differentiable function \(M(\beta,\theta)\), where \(\beta\) are the shape parameters and \(\theta\) the pose parameters. The complete function is defined as:  
\[M(\beta,\theta)=W(T(\beta,\theta),J(\beta),\theta,w), \tag{1}\]  
where \(W\) is a Linear Blend Skinning function (LBS) which is applied to a rigged template hand mesh \(T\) with 16 joints \(J\). The parameter \(w\) denotes the blend weights. By default, the MANO hand model's differentiable nature makes it very convenient to use alongside DNN-based methods; thus, most recent deep learning 3D hand pose estimation methods that employ a hand model utilize the MANO model.
# LPBERT: A Protein-Protein Interaction Prediction Method Based on a Pre-Trained Language Model
## 3 Results
### Analysis of Sequence Length Parameters  
To evaluate LPBERT's performance on varying protein sequence lengths, we analyzed the length distribution of sequences from two BioGRID [39] datasets previously used in the DeepTrio study. The _H. sapiens_ dataset comprised 38,869 sequences, and the _S. cerevisiae_ dataset contained 17,015 sequences. The sequence length distributions are shown in Figure 3.  
The scatter plot in Figure 3 shows the distribution of the sequence lengths, where most protein sequences fell within the range of 0 to 1500 residues. Based on this distribution, we evaluated LPBERT's performance using sequence lengths of 500, 1000, and 1500 on both the _H. sapiens_ and _S. cerevisiae_ datasets from BioGRID. The comparative results are presented in Table 3.  
Table 3 shows that LPBERT achieved its best overall performance on both datasets with a sequence length of 1500. Although the precision and specificity peaked at a sequence length of 1000 for the _S. cerevisiae_ dataset, the performance difference compared with the length of 1500 was negligible. Given that the protein sequence length used by RAPPPID and DeepTrio was 1500, we selected this as the input length for LPBERT.  
Figure 3: Statistical analysis of the sequence lengths in the BioGRID dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Species** & **Length** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **MCC** \\ \hline \multirow{3}{*}{_H. sapiens_} & 500 & 98.50 \(\pm\) 0.13 & 98.59 \(\pm\) 0.20 & 98.41 \(\pm\) 0.09 & 98.59 \(\pm\) 0.20 & 97.00 \(\pm\) 0.27 \\  & 1000 & 98.76 \(\pm\) 0.11 & 99.05 \(\pm\) 0.21 & 98.47 \(\pm\) 0.13 & 99.05 \(\pm\) 0.22 & 97.53 \(\pm\) 0.22 \\ \cline{1-1}  & 1500 & **98.93 \(\pm\) 0.13** & **99.23 \(\pm\) 0.19** & **98.62 \(\pm\) 0.13** & **99.23 \(\pm\) 0.19** & **97.85 \(\pm\) 0.26** \\ \cline{1-1}  & 500 & 97.50 \(\pm\) 0.26 & 98.15 \(\pm\) 0.55 & 96.83 \(\pm\) 0.54 & 98.17 \(\pm\) 0.56 & 95.01 \(\pm\) 0.53 \\ \cline{1-1}  & 1000 & 97.71 \(\pm\) 0.20 & **98.64 \(\pm\) 0.31** & 96.76 \(\pm\) 0.60 & **98.67 \(\pm\) 0.31** & 95.45 \(\pm\) 0.38 \\ \cline{1-1}  & 1500 & **97.94 \(\pm\) 0.16** & 98.60 \(\pm\) 0.42 & **97.27 \(\pm\) 0.46** & 98.61 \(\pm\) 0.43 & **95.89 \(\pm\) 0.31** \\ \hline \hline \end{tabular}  
This table reports the mean and standard deviation of the three test results. The best values are in bold.  
\end{table}
Table 3: Performance comparison of LPBERT at various sequence lengths (%).
# Characteristic Analysis and Decision Model of Lane-Changing Game for Intelligent Connected Vehicles
## 5 Simulation Results and Analysis
### Analysis of Security  
The TTC (Time-To-Collision) [32] indicator is used to portray the collision time between vehicles, reflecting the risk conflict between vehicles; when the rear vehicle speed is greater than the front vehicle, the smaller TTC indicates that the risk collision is more serious.  
\[TTC_{i}=\frac{x_{i-1}-x_{i}-l_{car}}{v_{i-1}-v_{i}} \tag{25}\]  
where \(TTC_{i}\) is the time of collision between the vehicle and the front vehicle; \(v\) is the speed of the vehicle; \(x\) is the position of the vehicle; and \(l_{car}\) is the length of the vehicle.  
To display the trend of the TTC value, we limit the TTC value within [50; 51; 52; 53]. When the TTC value is larger, the vehicles will not collide; when the TTC value is less than 0, it means that the rear vehicle speed is less than the front vehicle speed, and they will not collide. In Figure 7, the black line represents the SL2015 lane-switching model, the red line represents the decision-conflict situation in the mixed-strategy game model, and the blue line is the evolutionary game model proposed in this paper. It can be seen in Figure 7, in the SL2015 lane-changing model and mixed strategy game of the vehicle lane-changing process, the TTC value quickly decreases to within 15 and tends to 0, indicating that the collision risk between the rear vehicle and the front vehicle is larger, while in the evolutionary game-based lane-changing model, the TTC value is usually greater than 15 or less than 0. When the TTC value is less than 0, it means that the rear vehicle speed is less than the front vehicle speed, and the two sides will not collide when driving at the current speed. In Figure 7, we can see that in the evolutionary game model, the rear car decides to give way  
Figure 6: Comparison of lane-change trajectories: (**a**) SL2015; (**b**) The model of mixed strategy game; (**c,d**) The model of this paper.  
to reduce the risk of vehicle collision, the TTC value is reduced to less than 0, and the two sides will not collide.  
Although the mixed strategy game model has optimal solutions and is usually used to predict each other's decisions, it still has a certain probability of decision conflict, which puts vehicle safety at risk; therefore, the model needs strong constraints to reduce the occurrence of decision conflict. The evolutionary game model, on the other hand, converges on feasible strategies from the perspective of dynamic evolutionary equations and excludes unstable strategies, thus reducing the risk of vehicle conflict.
# A Hybrid Linear Quadratic Regulator Controller for Unmanned Free-Swimming Submersible
## 3 Mathematical Structure of UFSS
### Dynamical Modeling of Head  
The head system steers the UFSS vehicle and acts as an input. It also consists of different subsystems (i.e., head gain, rudder actuator, and vehicle dynamics) as shown in Figure 4. The transfer function of the head system is calculated in the same manner as of the pitch control system by considering the product of all subsystems, i.e., head gain, rudder actuator, and vehicle dynamics, as given in Equation (7). Recalling again, we use the vehicle dynamics from a case study as referred in Section 3.1. The open-loop system of the head is shown in Figure 4. The transfer function after derivation of the open-loop system is given in Equation (8). The feedback to the closed-loop system is provided by a head rate sensor, thus we will acquire the closed-loop transfer function in the next step.  
\[\frac{\Psi(s)}{\delta_{rc}(s)}=(\frac{-0.125(s+0.437)}{(s+0.193)(s^{2}+1.29s)}) (\frac{-2K_{1}}{s+2}) \tag{7}\]  
Similar to dynamical modeling of the pitch, the head system involves two closed-loop transfer functions calculated by using a general equation as follows \(\frac{G(s)}{1+G(s)H(s)}\). It also involves two closed-loop transfer functions and its implementation also requires two iterations. Therefore, the implementation of the first iteration is given in Equation (8). The complete closed-loop system of pitch is illustrated in Figure 5. Like pitch, here, also, we can see two variables (\(K_{1}\) and \(K_{2}\)) that are utilized in head gain and yaw rate sensor. By implementing the closed-loop formula on Equation (8) and by substituting the value of \(K_{2}=1\), we achieve the transfer function as given in Equation (9). Then, we substitute the unity feedback gain (such as \(K_{1}=1\)) in Equation (9) to achieve Equation (10). Similarly, the transformation of Equation (10) to state-space model is given in Equations (11) and (12).  
\[G(s)=\frac{0.25K_{1}s+0.1093K_{1}}{s^{4}+3.487s^{3}+3.214s^{2}+0.4979s+(0.25s+0. 1093)K_{2}s} \tag{8}\]  
\[\frac{\Psi(s)}{\Psi_{c}(s)}=\frac{0.25K_{1}s+0.1093K_{1}}{s^{4}+3.487s^{3}+3.46 5s^{2}+(0.607+0.25K_{1})s+0.109K_{1}} \tag{9}\]  
\[\frac{\Psi(s)}{\Psi_{c}(s)}=\frac{0.25s+0.1093}{s^{4}+3.487s^{3}+3.465s^{2}+0. 857s+0.109} \tag{10}\]  
\[\begin{bmatrix}x_{1}\\ x_{2}\\ x_{3}\\ x_{4}\end{bmatrix}=\begin{bmatrix}0&1&0&0\\ 0&0&1&0\\ 0&0&0&1\\ -0.109&-0.856&-3.464&-3.483\\ \end{bmatrix}\begin{bmatrix}x_{1}\\ x_{2}\\ x_{3}\\ x_{4}\end{bmatrix}+\begin{bmatrix}0\\ 0\\ 0\\ 1\end{bmatrix}u \tag{11}\]  
\[y=\begin{bmatrix}0.1093&0.25&0&0\end{bmatrix}\begin{bmatrix}x_{1}\\ x_{2}\\ x_{3}\\ x_{4}\end{bmatrix} \tag{12}\]  
Figure 3: Closed-loop representation of the pitch control system.
## 2 System Model Discussion
To this end, we consider a ZFBF precoder that can be normalized as [53]  
\[\mathbf{v}_{k}=\sqrt{\frac{p_{k}}{\left|\left|\left(\hat{\mathbf{H}}^{\mathrm{ T}}\right)^{;k}\right|\right|_{2}^{2}}}(\hat{\mathbf{H}}^{\mathrm{T}})^{;k}, \tag{3}\]  
where \(\mathbf{v}_{k}\) denotes the precoding vector of user \(k\) and parameter \(p_{k}=||\mathbf{v}_{k}||_{2}^{2}\) represents a scalar value of the power that can be allocated to user \(k\), which satisfies \(\sum_{k=1}^{K}p_{k}=P_{\mathrm{d}}\), and \(\hat{\mathbf{H}}^{\mathrm{T}}\) denotes the pseudo-inverse of matrix \(\hat{\mathbf{H}}\) that represents the DL CSI estimation with \(\hat{\mathbf{H}}^{\mathrm{T}}=[\hat{\mathbf{h}}_{1},\dots,\hat{\mathbf{h}}_{ K}]\). AS such, the DL achievable rate, Rate, can be written as  
\[\mathrm{Rate}=\left(1-\frac{T_{\mathrm{tr}}}{\tau_{\mathrm{c}}}\right)\sum_{ k=1}^{K}\mathbb{E}\{\log_{2}(1+\mathrm{fl}_{k})\}\quad[\mathrm{bit/s/Hz}]. \tag{4}\]  
The per-user received signal in (2) depends on the statistic information of the channel, the CSI estimation, and the precoder used at the BS. Note that the expectation in (4) is determined using different channel variations based on Monte Carlo simulations.
## 3 Results and Discussion
### Regression Analysis
In our analysis, we also considered the potential for multicollinearity among the independent variables. Multicollinearity can lead to unreliable and unstable estimates of regression coefficients, inflating the variance and making it difficult to determine the individual effect of each predictor. To assess and address multicollinearity, we calculated the variance inflation factor (VIF) for each independent variable. A VIF value below 5 generally indicates that multicollinearity is not a serious concern. In all three scenarios, the models without interaction terms showed high \(R^{2}\) values and statistically significant \(p\)-values (below 0.05) for each parameter. Additionally, the VIF values for all parameters in these non-interaction models were 1.0, indicating no multicollinearity issues. Therefore, we chose to use the simpler models without interaction terms for clarity and consistency, ensuring robust and valid findings.
# Optimization of Intelligent-Reflecting-Surface-Enabled Covert Communication in Multi-Sensor Systems
## 3 System Model
### Problem Formulation and Optimization Method Proposed  
In this work, we aim to maximize the transmission rate in (9) while satisfying the covertness constraint in (13). To alleviate the complexity and burden associated with IRS covert communication, we employ the 2-BPSK codebook proposed in [8] instead of the Gaussian codebook or the BPSK codebook. [PERSON] randomly selected either sub-codebook from the 2-BPSK codebook for communication. To maximize the transmission rate, optimization is performed on the transmission amplitudes \(\beta\) and phase angles of the two sub-codebooks, i.e., \(\theta_{1}\) and \(\theta_{2}\). The problem in this work can be formulated as:  
\[\text{P1}:\quad\max_{\beta,\{\theta\}} \mathbb{I}(\mathbf{Y}_{b}^{n};\mathbf{c}^{n})\] (16) s.t. \[\mathcal{D}(Q_{1}^{(n)}\|Q_{0}^{n})\leq\epsilon, \tag{17}\] \[0<\beta,\] (18) \[0<\theta_{1}<\pi,\] (19) \[0<\theta_{2}<\pi. \tag{20}\]  
To solve the problem P1, we adopted an optimization approach different from previous works, as illustrated in Figure 2. In Section 3, we formulated this problem, which is a two-parameter optimization problem. We decomposed the problem into scaling the optimization objective (16) and constraints (17) and approximated mutual information and KL divergence in Sections 4 and 5. In Section 4, we approximated KL divergence using a Taylor series expansion method, transforming the complex integration process for computing KL divergence into a summation. Similarly, in Section 5, we approximated mutual information using a Taylor series expansion method, converting the intricate integration process for computing mutual information into a summation. In Section 6, we integrated the approximated optimization objective mutual information and the constraint KL divergence, reformulating the problem into P2. Then, we proposed a gradient descent optimization algorithm with random initialization to solve problem P2. Finally, by combining the optimal results from P2, the original problem P1 can be completely resolved.  
Figure 2: Optimization methodology diagram in this work.
## Data Acquisition and Model Construction
### Inductor Series Resistance Characterization and Modelling
#### 2.2.2 Large Signal Loss Characterization and Modelling
\(k_{hr}\) and \(P_{hr}\) are obtained by cost function minimization coded in MATLAB with the cost function calculated as \(\sum\ (P_{measur}-P_{calc})^{2}\). To calculate the Power losses \(P_{calc}\), the losses caused by each individual harmonics are calculated independently and summed as in Equation (8). \[P_{calc}=P_{calc}=\sum_{f=f_{raw}}^{N*f_{raw}}\hat{1}_{L}\ (f)^{2}*R_{total}(f) \tag{8}\]  
In order to check the validity of the model with respect to the ripple current magnitude, another set of measures is conducted at 100 kHz frequency for ripple current ranging from 0.2 up to 2A. Figure 9 provides a set of curves showing in black the measured AC losses, in blue the losses estimated with the impedance analyser modelling technique and in red the model derived from the AC losses measurements. The bottom plot presents the relative errors between the models. It is possible to see that the model derived from the AC losses measurements is robust to ripple current frequency and magnitude variations. At low ripple current magnitudes, the modelling errors are significant. This is certainly due to the fact that losses are low and harder to measure accurately. With this model, there is still a risk to make an estimation error since we did not characterize the inductor losses when a strong DC current is applied in addition to the ripple current. Indeed, the addition of the DC current plus the ripple current magnitudes may exceed the saturation current magnitude. In this case, the inductor behaviour is no more linear. In this paper, we are considering that the inductor is kept used in its linear region, away from the saturation current. In this case, it is possible to consider that the extra losses coming from the DC part of the current will only produce extra Joules losses from the DC resistance of the inductor. From this characterization procedure, it is now possible to extend it to the entire inductor series from the selected manufacturer in order to develop models that can represent  
Figure 9: Evolution of losses into an MSS1210-473 47 \(\mu\)H inductor at 100 kHz over a ripple current magnitude from 0.2 up to 2 A. The upper curve represents the losses into the inductor measured and calculated with models of Equation (4) in blue and (6) in red. The bottom curves present the absolute relative errors between the impedance analyser model and the AC loss model. the evolution of the losses of any inductor with respect to the applied frequency and ripple current magnitude.