esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### VR as a Tool to Evaluate Universally Accessible Housing  
Based on the results obtained in this research, it is possible to project a series of practical applications that can significantly contribute to the development of more inclusive housing solutions. These applications not only optimize design time and resources but also allow homes to be tailored to the specific needs of people with disabilities, anticipating accessibility issues before physical construction. Some of the potential uses emerging from the proposed approach are outlined below:  
* Realism of Experience: The VR environment achieved a generally effective visual representation of the housing model. However, developing precise interactions and animations requires considerable time and effort. Lighting and material choices were also found to be critical--both factors directly impact the sense of immersion. Poor material choices can flatten the perceived realism of the environment.
* Wheelchair Maneuverability: The VR-based wheelchair simulation provided realistic movement and interaction, in part thanks to using a physical wheelchair during testing. Nevertheless, the lack of deformable objects in the virtual world limits collision realism. Enhancing this with motion sensors to synchronize virtual and real wheelchair movement could significantly boost immersion and fidelity.
* Collision Detection and Parameter Evaluation: Although Unreal Engine easily registers collisions, the current system allows the virtual wheelchair to keep moving after impact instead of stopping--reducing realism. Also, objects do not deform upon collision, which would improve feedback and realism. Introducing controller vibration during contact and improving physical feedback would further enhance the experience. Despite these issues, the tool remains functional for data collection.
* Data Collection Performance: The VR tool successfully captured the intended parameters, such as collision counts and user dwell time in different spaces. However, collecting these data is labor-intensive, requiring each interactive object to be tagged individually. Improving automation would streamline future evaluations.  
Overall, the issues raised by experts can be addressed through extended development time and iteration. Importantly, all experts agreed that the tool effectively evaluates circulation and access zones within accessible homes. They also consistently noted that certain spaces, particularly bathrooms and smaller rooms, did not meet accessibility standards--aligning with the heatmaps and collision data gathered during the study.
# A Review on Internet of Things for Defense and Public Safety
## 3 Target Scenarios for Mission-Critical IoT
### Logistics
#### 3.3.1 Fleet Monitoring and Management  
Fleet monitoring can be represented by aircraft and ground vehicle fleets with on-board sensors that monitor performance and part status. For example, they track vehicle status and subsystems, and indicate when resupplying low-stock items (i.e., fuel or oil) is needed. Sensors would issue alerts, potentially reducing the risk of fatal failures. The aim is to facilitate condition-based maintenance and on-demand ordering of parts, reduce maintenance staff, and decrease unanticipated failures or unnecessary part replacements. Although IoT deployment carries up-front costs, it can enable significant long-term savings by transforming business processes across logistics. Defense has an opportunity to take advantage in the auto and industrial sectors, and exploit performance data on existing data links, like Blue Force Tracker transponders (already in place on many military vehicles) to limit new security risks. By extension, IoT-connected vehicles could also share information, for example, about available spare parts.  
Real-time fleet management includes geolocation, status monitoring, speed and engine status, total engine hours, fuel efficiency, and weight and cargo sensors. Besides, when tracking shipments, the position and status of the containers can be monitored to identify potential problems.  
Regarding aircraft, modern jet engines are equipped with sensors that produce several terabytes of data per flight. This information combined with in-flight data can improve engine performance to reduce fuel costs, detect minor faults or shorten travel duration [59]. Furthermore, it enables preventive maintenance resulting in a long lifecycle (slowing or preventing breakage) and less downtime spent in repairs. The flight data can be tracked in real-time by operators and analysts on the ground.
# Study of the Error Caused by Camera Movement for the Stereo-Vision System
## 2 Principle  
As shown in Figure 1, the stereo-vision system consists of two cameras, which are used to capture the images of the same object from different positions at the same time. For any spatial point, the 3D world coordinate is denoted by \(P(X_{w},Y_{w},\ Z_{w})\), and the pixel coordinates are \(p_{l}(u_{l},v_{l})\) in the left camera and \(p_{r}(u_{r},v_{r})\) in the right camera. The left camera and right camera coordinate systems are defined as \(C_{l}-X_{cl}Y_{cl}Z_{cl}\) and \(C_{r}-X_{cr}Y_{cr}Z_{cr}\), respectively, and their origins \(C_{l}\) and \(C_{r}\) are located at the optical centers.  
According to the pinhole imaging principle [38], we have  
\[\left\{\begin{array}{c}\left[\begin{array}{c}X_{cl}/Z_{cl}\\ Y_{cl}/Z_{cl}\\ 1\end{array}\right]=\left[\begin{array}{c}x_{nl}\\ y_{nl}\\ 1\end{array}\right]=A_{l}^{-1}\left[\begin{array}{c}u_{l}\\ v_{l}\\ 1\end{array}\right]\\ \left[\begin{array}{c}X_{cr}/Z_{cr}\\ Y_{cr}/Z_{cr}\\ 1\end{array}\right]=\left[\begin{array}{c}x_{nr}\\ y_{nr}\\ 1\end{array}\right]=A_{r}^{-1}\left[\begin{array}{c}u_{r}\\ v_{r}\\ 1\end{array}\right]\\ \end{array}\right., \tag{1}\]  
where \((x_{nl},y_{nl})=(X_{cl}/Z_{cl},Y_{cl}/Z_{cl})\) and \((x_{nr},y_{nr})=(X_{cr}/Z_{cr},Y_{cr}/Z_{cr})\) are denoted as the image coordinates after focal length normalization, \(A_{l}\) and \(A_{r}\) are the intrinsic matrices.  
Figure 1: Geometric model of stereo-vision 3D measurement.  
Without loss of generality, the world coordinate system coincides with the left camera coordinate system. Thus, the imaging model of the stereo-vision system can be described as  
\[\left\{\begin{array}{c}Z_{w}\left[\begin{array}{c}x_{nl}\\ y_{nl}\\ 1\end{array}\right]=\left[\begin{array}{c}X_{w}\\ Y_{w}\\ Z_{w}\end{array}\right]\\ Z_{cr}\left[\begin{array}{c}x_{nr}\\ y_{nr}\\ 1\end{array}\right]=[\mathbf{R}_{lr}|\mathbf{T}_{lr}]\left[\begin{array}{c}X _{w}\\ Y_{w}\\ Z_{w}\\ 1\end{array}\right]\ \,\ \text{with}\ \mathbf{R}_{lr}=\left[\begin{array}{cc}r_{11}&r_{12}&r_{13}\\ r_{21}&r_{22}&r_{23}\\ r_{31}&r_{32}&r_{33}\end{array}\right]\ \text{and}\ \mathbf{T}_{lr}=\left[\begin{array}{c}T_{x}\\ T_{y}\\ T_{z}\end{array}\right],\end{array}\right. \tag{2}\]  
where \(\ \mathbf{R}_{lr}\ \text{ and}\ \ \mathbf{T}_{lr}\) denotes the rotation matrix and translation vector from the left camera coordinate system to the right camera coordinate system. From Equation (2), \(P(X_{w},Y_{w},\ Z_{w})\) in the world coordinate system can be derived as.  
\[\left\{\begin{array}{c}X_{w}=x_{nl}Z_{w}\\ Y_{w}=y_{nl}Z_{w}\\ Z_{w}=\frac{r_{23}-r_{23}}{x_{nr}(x_{31}x_{nl}+x_{32}y_{nl}+r_{33})-(r_{11}x_{ nl}+r_{12}y_{nl}+r_{13})}\\ =\frac{r_{23}-y_{nr}z_{nr}}{y_{nr}(x_{31}x_{nl}+r_{32}y_{nl}+r_{33})-(r_{21}x_ {nl}+r_{22}y_{nl}+r_{23})}\end{array}\right., \tag{3}\]
## 4 Test Results
### Strength  
The measured strengths of the specimens are listed in Table 3. In this table, the completed stage refers to the completion of two cycles with the same displacement amplitude and without strength degradation. All of the specimens strengthened with an inner restrainer showed an obviously higher compressive strength than the tensile strength. The specimens with an outer restrainer thickness of 9 mm exhibited a higher strength than those with an outer restrainer thickness of 6 mm. The hardening phenomenon observed in the compression zone of Figure 11 was caused by the friction between the steel core bent in the higher buckling mode and the mortar filler enclosing the steel core; furthermore, the constraint on the Poisson expansion of the steel core by the mortar filler is another reason [20; 21]. The specimens with the inner restrainers exhibited a more prominent hardening in compression than those without because the inner restrainers constrained the steel core more strongly than the mortar filler alone. The specimens with a partially increased unbonding material thickness did not exhibit a distinct additional strengthening effect. However, although the difference in the compressive strength was significant, the difference in the tensile strength was negligible.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Specimen**} & \multicolumn{3}{c}{**For Completed Cycles**} & \multicolumn{3}{c}{**For Entire Test**} \\ \cline{2-6}  & **Compression (kN)** & **Tension (kN)** & **Compression (fN)** & **Tension (kN)** & **Tension (kN)** \\ \hline NR-T6 & 1070 & 927.9 & 1.15 & 1070 & 1070 \\ \hline R-T6S4 & 1271 & 962.0 & 1.32 & 1289 & 975.0 \\ \hline R-T6S4-G & 1248 & 951.8 & 1.31 & 1304 & 966.6 \\ \hline NR-T9 & 1178 & 987.1 & 1.19 & 1345 [2] & 987.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Maximum strength of specimens (1).  
The Korean Seismic Design Standard for Steel Structures (KDS 14 31 60) stipulates cyclic tests for the qualification of BRBs [19]. The acceptance criteria presented in the standard require that the compression strength adjustment factor (= maximum compressive force divided by maximum tensile force), abbreviated as the CSA factor, shall not exceed 1.3. The ANSI/AISC 314-16 Seismic Provisions for Structural Steel Buildings stipulate 1.5 as the upper limit of the CSA factor [1]. The CSA factors based on the completed stages are listed in Table 3. Both NR-T6 and NR-T9, to which no enhancements are applied, satisfy the acceptance criteria of KDS 14 31 60, with a compression strength adjustment factor of 1.15-1.19. In contrast, the CSA factor of the specimens strengthened with inner restrainers range from 1.26 to 1.50, which are relatively high values compared to those of specimens with no inner strainers. R-T6S4, R-T6S4-G, and R-T9S2 have CSA factors that are less than or almost equal to 1.3, conforming to KDS 14 31 60, whereas the other enhanced specimens only satisfy the requirements of ANSI/AISC 314-16. Notably, the CSA requires further improvement.
## 3 System Architecture
### Overall Architecture
#### 3.1.5 User  
The user can be any type of terminal device and can initiate a service request at any time through the respective client to receive the processed results. Each user will be assigned a unique user identifier (UID) by the client and have a corresponding network address. The client interacts with the ICN name resolution system to register, query, and deregister the identifier and its corresponding network address. The ICN name resolution system maintains the mapping between them. In this paper, the user's needs involve delay and success rate requirements. Considering the limited space in the flow table during the subsequent data plane matching stage, the user's original needs are converted into interval levels. The higher the delay requirement, the smaller the level number. The client performs the necessary protocol conversion so that these can be represented through the ICN transport and network layer protocol extension headers.
# Unsupervised Learning-Based Spectrum Sensing Algorithm with Defending Adversarial Attacks
## 1 Introduction
* A large number of simulation experiments are conducted to evaluate the performance of the algorithms. Experimental results show that the USDAA algorithm has a better defense effect against PGD and FGSM white box attacks than the DAED algorithm [12]. The number of labeled samples used in USDAA is only 11% of the DAED algorithm. When the false alarm probability is 0.1 and the signal-to-noise ratio (\(SNR\)) is greater than \(-8\) dB, the detection probability of the USDAA algorithm for the FGSM and PGD attack samples can reach more than 96%, which is more than 10% higher than that of the DAED algorithm. And the USDAA algorithm is more robust than the DAED algorithm; when the perturbation value increases from 0 to 0.14, the detection probability of the USDAA algorithm for attack samples decreases by 10% at most, while the detection probability of the DAED algorithm for attack samples decreases by 29% at most.
## 2 Optimal Lane Change Path Planning Based on the NSGA-II and TOPSIS Algorithms
### NSGA-II Multi-Objective Optimization and Decision Algorithm
#### 2.3.4 Multiple Objective Decision Making  
On the basis of the Pareto optimal solution set, this section takes three objective optimization functions as evaluation indicators and introduces the COWA [37,38] operator to calculate the weight of the three indicators, which can effectively weaken the impact of extreme solutions in a Pareto optimal solution set on objective weighting. Then, the TOPSIS [39] method is adopted to make decisions to obtain the solutions in the top five orders in the Pareto optimal solution set. The specific decision-making calculation process is as follows:  
1. Take three objective optimization functions as indicators to evaluate \(t\) solutions in the Pareto optimal solution set and obtain the indicator matrix \(I\): \[I=\left(i_{mn}\right)_{t\times 3}(m=1,2,\cdots t;n=1,2,3)\] (13) where \(i_{mn}\) is the value of the \(n\)th objective function of the \(m\)th solution.
2. Standardize each element of indicator matrix \(I\): \[i^{*}_{mn}=\frac{i_{mn}}{i_{\max,n}}\] (14)  
Figure 7: Pareto optimal solution set diagram.  
where \(i_{\max,n}\) is the maximum value of the \(n\)th objective function.
3. Using the COWA operator to calculate the weight of the three objective functions \(\omega_{n}\): \[\omega_{n}=\frac{\omega_{n}^{*}}{\sum\limits_{n=1}^{3}\omega_{n}^{*}}\] (15) where \(\omega_{n}^{*}=\sum\limits_{m=1}^{t}\tau_{n}\cdot i_{mn}^{*}\), \(\tau_{m}=\frac{C_{t-1}^{m-1}}{i_{t-1}^{2}}=\frac{C_{t-1}^{m-1}}{2^{t-1}}\), \(C_{t-1}^{m-1}\) is the combination number of \(m-1\) elements arbitrarily extracted from \(t-1\) elements.
4. Assign weight to each element of indicator matrix \(I\) to obtain the weighting matrix \(K\): \[K=\left(k_{mn}\right)_{t\times 3}(m=1,2,\cdots,t;n=1,2,3)\] (16) where \(k_{mn}=i_{mn}^{*}\cdot\omega_{n}\)
5. Take the minimum element of the \(m\)th column in the weighting matrix \(K\) as the optimal solution \(S_{n}^{+}\) and the maximum element as the worst solution \(S_{n}^{-}\), respectively: \[S_{n}^{+}=\min(k_{1n},k_{2n},\cdots,k_{3n})\] (17) \[S_{n}^{-}=\max(k_{1n},k_{2n},\cdots,k_{3n})\] (18)
6. Calculate the distance between elements \(k_{mn}\) in the weighting matrix \(K\) and \(S_{n}^{+}\), \(S_{n}^{-}\)----\(D_{m}^{+}\), and \(D_{m}^{-}\), respectively: \[D_{m}^{+}=\sqrt{\sum\limits_{n=1}^{3}\left(k_{mn}-S_{n}^{+}\right)^{2}}\] (19) \[D_{m}^{-}=\sqrt{\sum\limits_{n=1}^{3}\left(k_{mn}-S_{n}^{-}\right)^{2}}\] (20)
7. Calculate the proximity index \(R_{m}\) between the \(m\)th solution in the Pareto optimal solution set and the optimal level: \[R_{m}=\frac{D_{m}^{-}}{D_{m}^{+}+D_{m}^{-}}\] (21) Sort the results in descending order. The solution with the largest \(R_{m}\)-value is optimal.
# Metal Artifact Reduction in X-ray CT via Ray Profile Correction
## 3 Results and Discussion  
To test the proposed MAR framework, we used clinical CT projection data obtained during an image-guided surgery procedure on a cervical spine region using a Medtronic O-Arm surgical imaging CT scanner. The scanner has a source to axis distance of 647.7 mm and a source to detector distance of 1147.7 mm. It is equipped with a flat X-ray detector with \(1024\times 384\) bins and an active area of \(393.432\times 290.224\) mm\({}^{2}\). During the scan, 360 projections were collected uniformly distributed over \(360^{\circ}\). The 3D reconstruction used a filtered back-projection algorithm [1] and produced a \(512\times 512\times 192\) volume with a voxel size of \(0.415\times 0.415\times 0.83\) mm\({}^{3}\).  
Figures 7 and 8 show some results we obtained using our metal artifact reduction algorithm. The spine has two pedicle screws implanted. Figure 7 shows one of them in transverse, sagittal, and coronal views. Note that the sagittal and coronal views are horizontal and vertical cut slices passing through the screws, respectively. Figure 8 shows the other implanted screws in the same manner. Overall, the proposed method effectively removes the metal artifacts (dark/bright bands and streaks) and reveals clear outlines of the implanted pedicle screws, which are suitable for evaluating their placements after the surgery. For example, in Figure 7 (top and bottom rows), the yellow arrow indicates a pedicle screw where only the corrected image (Column b) can reveal that is has been correctly inserted into the bone without extending into the tissue. Likewise, the yellow arrow in the middle row in Figure 7 shows a volume feature that was previously hidden by the beam hardening artifacts (Column a) but is now readily visible.  
Figure 6: An example of seamless in-painting: (**a**) original; (**b**) corrected; and (**c**) in-painted projection data.  
Figure 7: Case Study I. From top to bottom, transverse, coronal, and sagittal views. (**a**) uncorrected images, (**b**) corrected images, (**c**) uncorrected - corrected (**a,b**).  
One side effect of the proposed method is the tendency of blurring the anatomical structures near metal objects. The difference images (Column c) between the uncorrected and corrected images show: (1) the removed artifacts; (2) a bright version of the metal pieces (as mentioned, our method lowers their projection values in the sinogram); and (3) some incorrectly removed details. The latter causes the blurring effects (annotated by the red arrow in Figures 7 and 8). We think this is primarily because of the distance-based artifact region prediction model in Equation (6) where the model estimates the artifact regions based on the distance (\(p_{i}^{dt}\)) from a point to the nearest metal boundary along a ray path regardless of whether the point is corrupted by metal artifacts or not. One way to mitigate the blurring effect is by adding an additional stage at the end of our MAR framework to exploit the information hidden in low- and high-pass filtered sinogram [36] or images [37].
# Pairwise Likelihood Estimation of the 2 PL Model with Locally Dependent Item Responses
## 1 Introduction
### Pairwise Maximum Likelihood (PML) Estimation with Excluded Item Pairs to Handle Local Dependence  
It has been pointed out in Section 2.4 that the specification of the dependency structure for item residuals \(\varepsilon\) is indeterminate. Hence, the estimated item discriminations and item difficulties will depend on the distributional assumption of the item residuals. Hence, consistent parameter estimates of the 2 PL normal copula model with MML estimation (Section 2.6) or PML estimation (Section 2.7) cannot be guaranteed if the multivariate vector item residuals follow a different distribution. In this section, we describe a variant of PML estimation that does not require the specification of the dependence structure of item residuals.  
As highlighted in (10), the item responses of items from different testlets are conditionally independent. Hence, it can be expected that marginal item response functionscan be consistently estimated if weights \(w_{2,X_{i}X_{j}}\) in pairwise likelihood estimation in (6) are set to 0 for an item pair with items within the same testlet but set to 1 for items within different testlets.  
Let us illustrate the particular PML specification utilizing a toy example. The test contains seven items arranged in three testlets. Testlet 1 contains Items 1, 2, and 3; Testlet 2 contains Items 4 and 5; and Testlet 3 contains Items 6 and 7. The weight matrix \(W_{2}=(w_{2,X_{i}X_{j}})\) for PML estimation required in (6) can be defined by  
\[\mathbf{W}_{2}=(w_{2,X_{i}X_{j}})=\left(\begin{array}{cccc|c|c|c}0&&&&\\ 0&0&&&&\\ \hline 1&1&1&&\\ 1&1&1&0&&\\ \hline 1&1&1&1&1&\\ 1&1&1&1&1&0\end{array}\right) \tag{28}\]  
By applying PML estimation based on only evaluating the bivariate frequencies of item pairs for items within different testlets, the local independence assumption for the bivariate probabilities holds. Hence, consistent item parameter estimates from this modified PML estimation can be expected because the ability variable \(\theta\) receives a marginal interpretation, which is used in the univariate frequency PML terms \(L_{1,X_{i}}\) and bivariate frequency PML terms \(L_{2,X_{i}X_{j}}\) that do not receive a weight of zero.  
The standard errors can be obtained in the same way using the sandwich formula as for PML estimation with correlation parameters in the normal copula model (see (25) in Section 2.7).  
The PML estimation with excluded item pairs is attractive because no specification of the dependence structure and its distribution is required. In fact, the dependence parameters \(\mathbf{\rho}\) are treated as nuisance parameters that are not of interest and are therefore eliminated from the estimation. Note that a misspecified distribution of the local dependence structure in the IRT model can also bias the item parameters. In this regard, our proposed modified PML estimation approach offers advantages over the copula modeling approach. However, there is still a normal distribution assumption for \(\theta\), which might be required to obtain consistent parameter estimates.
# A Review of Progress and Hydrodynamic Design of Integrated Motor Pump-Jet Propulsion
## 5 Discussion
### Optimization Technology of IMP Thrusters  
Previous studies pay more attention to the design goals of IMP propulsion efficiency, cavitation performance, blade strength, and noise performance. The introduction of multiobjective optimization design methods is aimed at the occasions that require multiple performance indicators. Generally, more multiobjective methods are being used, such as the dualobjective optimization design of propulsion efficiency and cavitation performance, and performance optimization under multiple operating conditions.  
During the optimization design process of IMP propellers, many scholars have analyzed the sensitivity of IMP propeller design parameters to design goals through design of experiment (DOE), Sobol sensitivity analysis, and regression analysis. Studies have shown that the maximum chord length of the blade is the main factor affecting the cavitation performance, and the dimensionless radius of 0.7-0.9 has a greater impact on the low-frequency broadband noise of the IMP propeller. In addition, the thinner the blade, the more obvious the effect of improving the efficiency of the propeller, but thinner blades have an adverse effect on the strength and other mechanical properties of the blade. The influence of the parameters of IMP propellers on the performance and the restrictive relationships between the parameters are more easily reflected in the optimization design process. It is very important to apply modern optimization design theory to IMP propeller design optimization.