esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Evaluation on KITTI Dataset
#### 4.2.3 Ablation Study  
We performed an ablation study to analyze the effects of the proposed loss functions. In (a) and (b) of Table 3, the base model computes only the image-reconstruction loss. Our network shows the lowest performance when using only the L1 loss and shows better performance when it is replaced with PieAPP. However, as in (c)-(e), when the left-right consistency loss and smoothness loss are applied together, there is a noticeable performance improvement. This is because each loss contributes to increasing the overall accuracy by removing the texture-copy artifacts of the disparity maps and making the disparities locally smooth. The experimental model (e) calculates all the losses (L1, PieAPP, left-right consistency loss, and smoothness loss) presented in this paper. (c) shows the perceptual loss, that is, the performance when PieAPP is removed. As compared to (d) and (e), the overall performance degradation is quite large. According to the table, the perceptual loss has a very large effect on the performance improvement.  
Figure 2: Qualitative results: comparison with several representative unsupervised learning-based models ([PERSON] et al. (2018) [25], [PERSON] et al. (2018) [24], [PERSON] et al. (2017) [6], [PERSON] et al. (2019) [1], [PERSON] et al. (2020) [7]).  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline
**Method** & **L1** & **PieAPP** & **Left–Right** & **Smooth** & _Abs_ & _Sq_ & _RMSE_ & _RMSE_ & \(\delta<\) & \(\delta<\) & \(\delta<\) \\  & & & **Consistency** & & _Rel_ & _Rel_ & & _log_ & **1.25** & **1.25\({}^{2}\)** & **1.25\({}^{3}\)** \\ \hline (a) Base (L1) & ✓ & & & & 0.197 & 1.849 & 6.104 & 0.278 & 0.722 & 0.887 & 0.944 \\ (b) Base (PieAPP) & & ✓ & & & 0.190 & 1.678 & 6.224 & 0.300 & 0.729 & 0.892 & 0.947 \\ \hline (c) Ours without PieAPP & ✓ & & & ✓ & ✓ & 0.149 & 1.134 & 5.559 & 0.227 & 0.799 & 0.937 & 0.973 \\ (d) Ours without L1 & & ✓ & ✓ & ✓ & ✓ & 0.137 & 1.014 & 5.212 & 0.211 & 0.835 & 0.950 & 0.975 \\ (e) Ours (full) & ✓ & ✓ & ✓ & ✓ & 0.116 & 0.873 & 4.805 & 0.198 & 0.869 & 0.953 & 0.977 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study for analyzing the effectiveness of our perceptual loss. (In the table, “Smooth” represents the smoothness loss.
## 4 Stability Evaluation
### Result Analysis  
#### 4.3.1 Deformation of the Surrounding Rock of the Cavity  
Figure 10 shows the cloud diagrams depicting the total displacement of the salt cavern's surrounding rock under different internal pressures. To facilitate the observation of the deformation of the salt cavern's surrounding rock in the area covered by sediment, the sediment layer is concealed in the figure. As the internal pressure rises, the deformation displacement of the salt cavern's surrounding rock gradually diminishes, indicating an improvement in the cavern's stability. In the large-height-difference BWSC, it is evident from the figure that the deep cavity experiences greater deformation displacement than does the shallow cavity. Under low-pressure conditions, the maximum difference between them reaches approximately 2 m, with the maximum displacement occurring at the waist of the deep cavity. Notably, at a pressure of 10.5 MPa, the maximum displacement of the deep cavity reaches 8.3 m, exceeding 10% of the salt cavern's maximum diameter. According to the allowable range mentioned in Reference [42], it is clear that a pressure of 10.5 MPa no longer meets the requirements. By contrast, at a pressure of 12 MPa, the  
maximum displacement is 7.3 m, which falls within the allowable range. Additionally, the deformation displacement of the cavity covered by sediment is smaller than that of the empty cavity, highlighting the sediment's inhibitory effect on the deformation of the cavity wall's surrounding rock. A similar phenomenon was also reported in Reference [43]. Figure 11 shows the curves representing the deformation displacement of the salt cavern's roof over time under different internal pressures. Evidently, as time progresses, the settlement displacement of the cavern roof gradually increases, albeit with a decreasing rate of increase. The figure also clearly demonstrates that, under the same pressure, the deep cavity's roof experiences greater deformation displacement than the shallow cavity's roof. It is worth emphasizing that the displacement of the deep cavity's roof at a pressure of 12 MPa even surpasses that of the shallow cavity's roof at a pressure of 10.5 MPa, which underscores the unique structural characteristics of the large-height-difference BWSC.  
Figure 10: Displacement distribution nephogram of surrounding rock under different internal pressures after 30 years of operation. **(a)** 10.5 MPa; **(b)** 12 MPa; **(c)** 25.5 MPa; **(d)** 29 MPa.
# A Probabilistic Hyperspectral Imagery Restoration Method
## 6 Discussion  
In this paper, an HSI-oriented low-rank denoising method is proposed. According to the experimental results, we can see that:  
(1) Low-rank based methods including the proposed, Godec based and SLrank based ones can function better compared with VBM3D based method, especially when confronting with missing values (e.g., dead lines or dead pixels, see from Figure 5 to Figures 6 and 8, Figures 9-11). This phenomenon implies that low-rank property is a powerful constraint for HSI restoration. In addition, the result is consistent with the fact that clean/noise-free HSI is a low-rank matrix but the noisy and incomplete HSI is not, thus low-rank property can be used to remove the noise as well as fill in the missing values.  
(2) Compared with Godec based method which uses a sparsity item and a Gaussian item to represent noise, mixture of Gaussian distribution introduced in the proposed method is more powerful to represent complex noise since it models each pixel independently. Thus, it guarantees the proposed method is more suitable for HSI restoration (e.g., noise varies across band) and obtains better restoration performance, which can be seen from Figures 3-11 and Table 1.  
Figure 9: Visual results on band 4 of terrain dataset: (**a**) Captured actual band, (**b**) Restoration results of VBM3D based method, (**c**) Restoration results of Godec based method, (**d**) Restoration results of SLrank based method, (**e**) Restoration results of the proposed method.  
(3) Considering the clean image can be decomposed into the multiplication of two matrices which has lower dimensionality than the original clean image, an endmember determination method is used to automatically determine the rank. It can facilitate the parameter initialization of the low-rank based method, whereas Godec based and SLrank methods need to assign the rank number manually.  
Figure 10: Visual results on band 2 of urban dataset: (**a**) Captured actual band, (**b**) Restoration results of VBM3D based method, (**c**) Restoration results of Godec based method, (**d**) Restoration results of SLrank based method, (**e**) Restoration results of the proposed method.
# A Cryptography-Powered Infrastructure to Ensure the Integrity of Robot Workflows
## 4 Application and Evaluation in a Real Robot System
### Application in an Industrial Robot System  
We apply our verifiable workflow approach to a use-case using our mobile manipulator CHIMERA. The task for the robot is to pick up material at one position, transport it to another and place it into the designated area. The use case is realized as a sequence of non-interrelated workflows, which means that the parameters of each workflow do not depend on the outcome of any previously executed workflow.
## 3 Results And Discussion
### Structural and Electronic Band Structures
This displacement is with respect to its equilibrium lattice constant. For example, the Po atom's displacements of \(0.02a_{eq}\) for the CaPoO\({}_{3}\), SrPoO\({}_{3}\), BaPoO\({}_{3}\), and RaPoO\({}_{3}\) were 0.087 (A), 0.088 (A), 0.089 (A), and 0.089 (A), respectively. The induced electric polarization was large and increased linearly to around 0.02 (C/m\({}^{2}\)) at \(0.02a_{eq}\) of the Po atom's displacement. The \(P_{e}\) increased slowly and saturated after the Po atom's displacement was higher than \(0.02a_{eq}\). We also found that the calculated bandgap decreased as the Po atom moved away from the center at the TI phase. This behavior was also reported in previous study of the halide CsPbI\({}_{3}\) perovskites [20]. Figure 4: Calculated electronic band structures of BaPoO\({}_{3}\) as a function of the lattice constant without (upper panel) and with (bottom panel) SOC. Lattice constant for (**a**,**f**) is 4.046 Å; (**b**,**g**) 4.246 Å; (**c**,**h**) is 4.446 Å; (**d**,**i**) is 4.846 Å; (**e**,**j**) is 5.246 Å. The external pressure induced by the change of lattice constant is from 60 GPa to \(-\)20 GPa as shown in Figure 3b. The Fermi level is shifted to zero as a reference. Without SOC effect, the BaPoO\({}_{3}\) perovskite is a normal metal and the band contributions at R point near the Fermi level is the same, except (**e**). When the SOC is taken into account, the band inversion occurs at lattice constant from 4.046 Å to 4.846 Å. The energy for the lowest unoccupied band is decreased when the lattice constant is decreased or increased, which results in a decrease in bandgap.
# Trends in Performance Limits of the HOT Infrared Photodetectors
## 1 Introduction
In the past decade considerable progress in development of interband quantum cascade infrared photodetectors (IB QCIP) based on T2 SLs, 2D material [12] and CQD photodetectors brought their performance close to commercial ones [13]. In 1999 [PERSON] et al. claimed that there is no fundamental obstacle to reach 300 K operation of photon detectors with background-limited performance even in reduced fields of view [14]. In this paper we attempt to reconsider the performance of different material systems for the HOT detection operation in IR spectral range. Theoretical estimates are collated with experimental data for different photodetectors.
## 3 Results and Discussion
### Optimization Results  
The optimal workpiece placement was determined based on the optimization problem described in (8) and optimisation constraints listed in Table 1. For optimisation purposes, only UP, RIGHT, and NON-FLIP robot's configuration will be considered.  
The optimal placement is named as \"Optim\", it is represented by the green-colored workpiece shown in Figure 5a-f. The optimal workpiece placement of each machining path is described by the transformation matrix \(T_{O,optim}^{B}\), as is shown in Table 2. For comparison, three other placements of the workpiece will be evaluated, named: \"Init\", \"1\", and \"2\". Additional workpiece placements will be added to the analysis in order to verify the effectiveness of the proposed objective function and thus demonstrate the relationship between the value of the objective function and the maximum measured joint velocities required for executing the predefined machining trajectory.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Optimization Constraint** & **Value** \\ \hline Positional joint limits of the UR5e robot & \(-360^{\circ}\leq\theta_{i}\leq 360^{\circ}\), \(i=1,\dots,6\) \\ Working area & \(-400\;\text{mm}\leq X\leq 400\;\text{mm}\) \\  & \(-200\;\text{mm}\leq Y\leq-800\;\text{mm}\) \\ Workpiece rotation & \(-180^{\circ}\leq\varphi\leq 180^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimization constraints.  
Figure 5: Workpiece placement on the mobile platform for the machining paths: (**a**) 1A, (**b**) 1B, (**c**) 2A, (**d**) 2B, (**e**) 3A, (**f**) 3B. Red-colored workpiece shows its initial placement “Init”, blue- and orange-colored workpieces show its intermediate placements, respectively, and green -colored workpiece shows its optimal position “Optim”.  
The workpiece placement \"Init\", shown as a red-colored workpiece, represents the initial placement of the workpiece defined by \(T_{O,init}^{B}\), while workpiece placements \"1\" and \"2\", shown as blue- and orange-colored workpieces, represent random workpiece placements on the mobile platform, defined by \(T_{O,1}^{B}\) and \(T_{O,2}^{B}\), respectively. Each placement is represented by the workpiece reference frame, where the red arrow represents the \(x\)-axis, the green arrow represents the \(y\)-axis, and the blue arrow the \(z\)-axis, respectively.  
For each analyzed workpiece placement, we calculated and displayed the maximum achievable linear velocity \(V_{max}\) at each waypoint on the robot's path, calculated by the DTF method (4). The results are presented in Figure 6 in the form of a swarm scatter chart for each machining path. From Figure 6, it can clearly be seen that the highest worst-case maximum achievable linear velocity \(V_{\max}^{path}\) is calculated for the optimal placement of the workpiece in all six cases.
# A CNN-MPSK Demodulation Architecture with Ultra-Light Weight and Low-Complexity for Communications
## 3 The Proposed CNN-MPSK Architecture
### Architecture Presentation
Applying ReLU, the output can be represented as  
\[f(j)=\max[0,z^{\prime}(j)],\,0\leq j\leq d-1. \tag{13}\]  
The pooling is often performed after activation for sub-sampling features. In general, the reason to subsample is that the an important feature of a sequence is seldom contained in adjacent data. The sub-sampling can produce features that are invariant to scale, translation, pose, and rotation changes. Max pooling selects the maximum value from the adjacent data, and thus we have  
Figure 4: The operation of the 1D convolution. \[p(m)=\max\{f[ms:(m+1)s-1]\},\,0\leq m\leq d/s-1, \tag{14}\]  
where \(s\) is the slide step size and \(p\) is the output from max pooling. To provide classification results, a full connection needs to integrate useful and hierarchical features. In a full connection, each unit is connected to all the previous input units. The connection can be expressed as a matrix multiplication, i.e.,  
\[o=\sum_{m=0}^{d/s-1}p(m)w_{f}(m)+b_{f}, \tag{15}\]  
where \(b_{f}\) is a parameter known as bias, \(w_{f}\) are weights, and \(o\) denotes a single unit. According to Equation (15), we can construct a structure to depict the full connection, as shown in Figure 5. In BPSK demodulation, we only need one output unit represented by bit \(1\) or \(0\). Thus, the full connection costs \(d/s\) multiplications and \(d/s\) additions for one output.
# Development of a Drone's Vibration, Shock, and Atmospheric Profiles
## 3 Results and Discussion
### \(G_{\rm rms}\) Values  
Table 5 illustrates the maximum \(\rm G_{rms}\) and the \(\rm G_{rms}\) values range where 68% of the vibration events fall in. The overall recorded \(\rm G_{rms}\) levels of the drones were significantly higher than those in the published air profile of the aircraft [17; 26]. The DJI Matrice 600 Pro tends to have a higher vibration \(\rm G_{rms}\) than DJI Matrice 100, especially in X-direction. The \(\rm G_{rms}\) recorded in the horizontal level are higher in general than that in the vertical direction.  
The \(\rm G_{rms}\) histogram indicated that overall \(\rm G_{rms}\) values are close to the normal distribution (Figures 8 and 9).  
Overall, the major vibration sources are inherited from the high-frequency motor and structure and not from the interaction between the drone and the airflow characterized by lower vibration frequency. This is different from the truck and railroad profiles where the high vibration intensity comes mainly from the interaction between the road and suspension to the products.  
Figure 8: Vibration \(\rm G_{rms}\) histogram for DJI 600.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline
**Drone** & \multicolumn{6}{c}{**DJI Matrice 600 Pro**} & \multicolumn{6}{c}{**DJI Matrice 100**} \\ \hline Data logger & \multicolumn{3}{c}{Top} & \multicolumn{3}{c}{Bottom} & \multicolumn{3}{c}{Top} & \multicolumn{3}{c}{Bottom} \\ \hline Axis & X & Y & Z & X & Y & Z & X & Y & Z & X & Y & Z \\ \hline Overall & 1.6 & 1.01 & 0.95 & 0.78 & 0.65 & 0.54 & 0.66 & 0.49 & 0.53 & 1.09 & 0.97 & 0.99 \\ \(\rm G_{rms}\) & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} & \multicolumn{3}{c}{} \\ No. of & 67.8\% & 64.5\% & 70.2\% & 68.9\% & 59.2\% & 68.8\% & 70.6\% & 73.5\% & 70.2\% & 72.9\% & 70.3\% & 78.3\% \\ Range of & 0.42–1.03 & 0.36–0.72 & 0.33–0.65 & 0.25–0.50 & 0.21–0.42 & 0.20–0.39 & 0.15–0.44 & 0.12–0.33 & 0.15–0.43 & 0.24–0.70 & 0.21–0.62 & 0.23–0.66 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Overall vibration \(\rm G_{rms}\) value and \(\rm G_{rms}\) values near 68%.
# Random Laser Action in Dye-Doped Polymer Media with Inhomogeneously Distributed Particles and Gain
## 2 Experimental  
Figure 1 shows a schematic of the random structures used for the RLs. For comparison, a conventional medium (Figure 1a) in which scatterers were distributed homogeneously (homogeneous medium) was also examined. The random gain media were fabricated using transparent silica particles with the diameters of 30, 40, 60, and 100 \(\upmu\)m as SPs. The scattering particles of TiO\({}_{2}\) with a diameter of 180 nm, together with SPs, were dispersed in a light-curable photopolymer (Adell K40) doped with Rhodamine 6G (R6G) laser dye with a density of \(6.7\times 10^{-3}\) mol/L. The refractive indices of the TiO\({}_{2}\) particles were in the range of 2.7-2.8 (depending on the wavelength), whereas they were 1.5 for both the silica particles and the K40 polymer. The liquid polymer medium was then cured via illumination for 8 min using 405-nm violet laser light. Two samples were fabricated for each configuration of SPs and scattering particles.  
A schematic of the experimental setup is shown in Figure 2. The sample was pumped with a frequency-doubled Nd: YAG laser beam with a wavelength of 532 nm and a pulse duration of 10 ns. We used two different pump configurations: A pump with a circular excitation pattern and a pump with a stripe excitation pattern. The pump beam was focused onto the surface of the sample using a convex lens with a focal length of 50 mm for the circular excitation pattern, and a cylindrical lens with the same focal length for the stripe excitation pattern. For the circular excitation, the spot diameter was 62 \(\upmu\)m and the excitation area was \(3.0\times 10^{3}\)\(\upmu\)m\({}^{2}\), whereas for the stripe excitation, the stripe length was 2.7 mm and the excitation area was 0.57 mm\({}^{2}\). The light emitted from the sample was detected using a spectrometer (Ocean Optics HR2000) through a collector lens and an optical fiber at an observation angle of \(\sim\)45\({}^{\circ}\). The focal lengths of the collector lens were 50 mm for the circular excitation and 60 mm for the stripe excitation. A notch filter located in front of the fiber was used to remove the pump light. The emission spectrum was measured with a spectral resolution of 0.2 nm. For each sample configuration, 200 spectral data sets were obtained by varying the position of the pump spot. We fabricated and measured the samples with 5%, 10%, and 15% volume filling fractions of the scattering particles (\(\phi_{\rm{sc}}\)). The volume-filling fractions of the SPs (\(\phi_{\rm{sp}}\)) were 0%, 5%, 10%, and 15%. The SPs of each size occupied the same volume (for example, 2.5% for samples with 10% SPs).  
Figure 1: (**a**) Homogeneous and (**b**) bubble-structure (BS) dye-doped scattering media. The white circles in (**a**,**b**) and the blue dashed circles in (**b**) represent scattering particles and spacer particles (SPs), respectively.
## 2 Materials and Methods
### Predictive Model Building
#### 2.3.2 Bayesian Information Criterion (BIC)  
The Bayesian information criterion is a well-known general approach to model selection that favors more parsimonious models (i.e., with fewer features) over more complex models. This is obtained by adding a penalty based on the number of parameters being estimated in the model [15; 16]. Operatively, the Bayesian information criterion, also known as Schwarz's Bayesian criterion (SBC), can be computed for one or several fitted model objects, for which a log-likelihood value is calculated using the following formula:  
\[\text{BIC}=-\log(\mathcal{L})+n_{par}*\log(n_{obs})\]  
where \(n_{par}\) represents the number of RFs, \(\mathcal{L}\) is the likelihood of the model, and \(n_{obs}\) is the number of observations in the fitted model. In our case, using the best lambda obtained from cross validation, the best BIC value (i.e., the minimum) was obtained with \(n_{par}=5\)  
Figure 1: Study design relies on the following steps: [1] identification of a dataset of 239 patients with high-resolution CT (Par-CT), grouped into training and test sets following a stratified 70/30 split, iterating the process 100 times. These datasets were used for the 100 LASSO-Cox feature selection. The optimal number of RFs for inclusion in developing the LASSO-Cox model was determined using the BIC values. The final model was validated using a dataset of 196 having high and standard CT reconstruction (Par-CT and Med-CT, respectively). In all the datasets, 175 RFs were extracted using SOPHiA DDM.  
and \(n_{obs}\) = 239, corresponding to the number of features in the fitted model trained on the entire Par-CT dataset, respectively.  
BIC is a variant of AIC with a stronger penalty for including additional variables to the model, it tries to find a parsimonious model that is more likely to be the \"truth\". The smaller the value that is considered, the better the BIC. According to [16] guidelines, if the differences in BICs between compared models are 0-2, 2-6, 6-10, or greater than 10, then the evidence in favor of the model with the smallest BIC can be considered 'weak', 'positive','strong', or'very strong', respectively.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 4 Results and Discussion
### Results after Varying the Packet Size
#### 4.1.1 Simulation Results of Friis Propagation Model regarding Packet Size  
The graph findings reveal that under the Friis propagation model, the mobile RSU outperforms the sRSU. Most notably, the mRSU performs well in terms of average latency since the receiving power is inversely proportional to the distance, and since the mRSU remains in movement with the vehicles, the length does not grow rapidly and the delay remains minimal. Consequently, as the packet size grows, the packet delivery ratio greatly increases and eventually becomes superior to that of the sRSU. Most importantly, in the Friis model or FreeSpace model, the energy consumption for the mRSU is the lowest, implying that by retaining the lowest energy quota, we may propagate far more data. The reason for this is that within the Friis model, power is inversely proportional to distance, and in the mRSU, the spacing among nodes and RSUs is smaller than in the sRSU; hence, less power is needed and much less energy is spent.