esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The modulus of elasticity, which is the measure of the stiffness of the aligner material, is crucial for the aligners to apply the necessary orthodontic forces while maintaining flexibility for comfort [8]. Generally, UV exposure can lead to photogradation and photo-oxidation, impacting the modulus of elasticity, with initial exposure increasing the modulus. Still, prolonged exposure potentially reduces it due to material degradation [22; 23]. Tan delta, a measure of the damping properties and viscoelastic behavior of the material, is also influenced by UV exposure [24]. This parameter is critical for understanding how the aligners dissipate energy as heat. UV exposure increases tan delta, indicating more energy dissipation, but extended exposure may decrease it due to the material becoming more brittle [25; 26]. The transition temperature, particularly the glass transition temperature (Tg), signifies the temperature at which the polymer transitions from a hard, glassy state to a soft, rubbery state [27; 28]. A suitable Tg is essential for aligners to perform effectively across various temperatures [8]. UV light exposure, especially its energy density and power density, can lower the Tg by increasing the degree of cross-linking and reducing chain mobility [29; 30; 31]. Understanding the effects of UV exposure on Tg helps in designing aligners that maintain their properties under different environmental conditions in the oral cavity. The degree of conversion (DC) reflects the extent to which monomers have transformed into polymers during the curing process. A higher degree of conversion typically results in better mechanical properties and chemical resistance of the aligners [11]. UV exposure affects the DC, with higher intensity and longer exposure times generally increasing the DC. However, excessive exposure can lead to over-curing or degradation, which may negatively impact the DC, resulting in compromised mechanical properties [29]. Proper control of UV exposure is necessary to achieve an optimal balance, ensuring the aligners have the desired mechanical properties without compromising their structural integrity. This paper aims to investigate the effects of different UV light exposure durations on the modulus of elasticity, glass transition temperature, tan delta, and the degree of the conversion of polyurethane-based aligners. By understanding these relationships, we can optimize 3D printing parameters to enhance the performance and durability of orthodontic aligners, ensuring they meet the required standards for effective orthodontic treatment.
# Characteristics of Plane Gate Vibration and Holding Force in Closing Process by Experiments
## 3 Results and Discussion
### Correlation between Gate Vibration and Holding Force  
Through the physical model test, we simultaneously measured the gate vibration and holding force. Gate vibrations in three directions are independent of each other. To explore the correlation between the gate vibration and holding force, we directly conducted the correlation and significance test between the two variables under two operation modes, respectively.
# A Security Framework for the Internet of Things in the Future Internet Architecture
## 6 Delegation-Based Key Provisioning Protocol
### Discussions and Security Analysis
#### 6.7.2 Lightweight Approach  
This key provisioning protocol is carefully designed for IoT networks as a light key solution. As one of the most important features, the protocol design favours the resource-limited Child device \(C\) in all possible means. The strategy is to minimize the number of messages \(C\) needs to process and shift the work load to those more capable devices, namely \(G\) and \(P\), as much as possible. Consequently, the key provisioning complexity is transferred to the edge device's Parent party and the Guardian device, making it possible for minimally complex IoT edge devices to operate with secure interactions.  
Compared with public key certificates, our protocol implements only lightweight symmetric key operations and secure storage for at least a single symmetric key and protected key operations with the stored key. Specifically, all of the security operations on the Child are symmetric cryptographic computations, which makes the protocol feasible for low-end IoT edge devices.  
_Timestamps_ and _nonces_ are two typical security measures to guarantee the freshness of a protocol instance and to prevent replay attacks. Timestamps require strict time synchronization, and consequently increases the hardware cost as an oscillator with high accuracy is not necessarily cheap. On the other hand, nonces require a (pseudo) random generator and some form of database to retrieve if a nonce appeared before. As a comparison, nonces are relatively cheaper, and hence we chose to use nonces to protect against replay attack. Unlike a Kerberos system, the Child device is not required to have secure clock synchronization.  
Furthermore, the protocol can be implemented with a minimal number of protocol messages handled by the child device. The protocol can thus minimize the computation and power costs to a Child device, such that lightweight devices like sensors, actuators, and microcontroller units (MCUs) can be provisioned with secret keys securely. In addition, the first four messages are sent as plaintext in open channels to reduce overhead. Modifying and/or replaying the messages in the first stage will result in protocol abortion later as each piece of session information will be verified in the following stage.
## 2 Materials and Methods
### Calculation Process  
An algorithm consisting of three parallel branches that conflate at a point has been constructed:  
* In the left branch, the power dissipated by aerodynamic drag is computed. For that, the speed of the vehicle and its thermodynamic data are entered. At that given speed, the tube diameter is calculated so that the Kantrowitz limit is prevented. According to the blockage ratio, the power dissipated by aerodynamic drag is computed.
* In the middle branch, the onboard batteries which feed the rotor of the linear motor are dimensioned. Their dimensioning comes from evaluating their energy density and their discharge time, which depends on the total travel time. In turn, the total travel time relies on the operating speed, acceleration and deceleration of the vehicle through kinematic relations.
* In the right branch, the power needed to propel and lift the vehicle is calculated. This calculus relies highly on the number of containers (which equals the number of capsules in the vehicle) and their individual masses, which depend on the filling factorof each container. These data determine how much mass is lifted and propelled and, thus, the power needed for that.  
These branches conflate, in order to determine the energy consumption of the vehicle. In this way, the energy consumption is linked to the mass transported and to the operating speed, which allows the finding of relations between the mass flow and the energy needed to maintain that mass flow (always considering that only one vehicle, the one that is to be optimized, is using the tube).  
The algorithm is shown in the Figure 1, which shows how the different equation blocks are interrelated. Equation blocks referring to the main subsystems (aerodynamics, electromechanics and thermodynamics) are represented with a bolded contour, while auxiliary equation blocks are represented with a normal contour. The final block is represented with a doubly-bolded contour:  
In this way, it is ensured that the behavior laws of the vehicle inside the tube are fulfilled under all the requirements and considering all the starting hypotheses, with which the physical phenomenon is completely characterized. Once the problem has been formulated, and the behavior equations and the input data are introduced into the software, the software finds the solution to the system of equations. Finally, the design parameters, such as the transported mass, the operating speed, and the length of the tube, are varied according to the simulation procedures. The results (the definitive results of the system of equations) are obtained in relation to the energy consumption of the transport operation.  
Figure 1: Flow diagram of the calculation process (algorithm). Source: Own elaboration.
## 4 Results
### Detection of Eruptive Deposits (SWIR, DInSAR, SAR)
#### 4.2.1 Using DInSAR Coherence and SWIR  
Detecting lava flows in non-vegetated regions is particularly efficient using the interferometric coherence ([PERSON] et al., 2017). Active lava flows will appear as highly incoherent areas (coherence values close to zero), contrasting with the coherent surface on which they are emplaced (coherence values typically \(>\)0.5). Moreover, because the summits of active volcanoes are usually vegetation-free, the coherence can also be utilized to monitor summital volcanic activity, as eruptive deposits will appear incoherent.  
Both applications are illustrated in Figure 6, which depicts the ongoing Erta Ale eruption and its precursory activity. Indeed, in the months preceding the eruption onset, intense summit activity is indicated from the coherence map analyzed in a \(2\times 2\) km box around the summit (Figure 6a black curve). In particular, from June 2016 onwards the northwestern lake becomes active (in addition to the permanently active southeastern lake), and in the following months the activity at both lakes progressively intensifies, leading to multiple lava overflows (e.g., small overflow on 20 August 2016 visible on coherence image in Figure 6(i), and large overflow on 19 January 2017 visible on Sentinel-2 image). On 28 January 2017, the S1 interferogram shows a strong deformation signal with a distinctive pattern characteristic of a dyke intrusion (Figures 6c and 6(ii)), which marks the onset of a \(>\)2-year eruption which is still ongoing today ([PERSON] et al., 2017). The eruptive fissure opened multiple new events located SE of the summit lava lakes (clearly identifiable from S1 coherence and amplitude images), which progressively focused on a single new eruptive vent from which lava flowed during the following months. During this time, the activity at the summit lava lakes significantly decreases (i.e., coherence analysis on \(2\times 2\) km extent, Figure 6a black curve), suggesting a possible drainage of the summit lava lakes. Both the coherence and the SWIR analyses computed on a large \(>\)20 km spatial scale (Figure 6a blue curve and Figure 6b respectively), depict the lava flow emplacement. The good agreement between the two highlights the fact that coherence can be used to track the active flow front, even when S2 images may not be usable due to cloud coverage. From this basic analysis, more elaborate parameters can be extracted offline, such as the lava flow front position through time, or the lava effusion rate, both key for hazard mitigation issues.  
The coherence threshold can be adapted to each volcano, in order to account for specific environments (i.e., how incoherent is the surrounding land surface). More elaborate strategies can also be implemented to enhance the decorrelation sensibility to volcano-related processes, such as using NDVI masks (computed from S2 images) to exclude vegetated areas from the analysis. Nevertheless, in certain climatic settings coherence-based detection is simply not the best suited, and intensity-based detection can prove more useful.
# Detection of Underwater Targets Using Polarization Laser Assisted Echo Detection Technique
## 5 Conclusions  
Through the above test data, the results show that in the air tile DOP > iron DOP > plastic DOP, tile in the three target materials can reach a maximum polarization of 1.00, iron and plastic average polarization of 0.56 and 0.48. In the water at 3 m, the polarization of different materials varies greatly, with iron, tile and plastic polarization of 0.97, 0.80 and 0.46. However, with the increase in distance, all objects are close to the polarization of about 0.36. This phenomenon may be due to the experiments used in the pool being relatively narrow; the distance gradually increased, the laser by the body of water and the wall of the pool many times the reflection level scattering, causing the final recession effect to be relatively close. In addition, at a closer distance, the main factor causing the change in polarization to the polarization characteristics of the target reflection, so at a closer distance, the polarization of the reflected light varies greatly.  
For underwater target detection, polarization detection can obtain rich information and less interference. Compared with other polarization detection experiments, we measured that the polarization of different materials at different depths will change after laser reflection, taking into account the influence of material properties, materials and clutter, but there are more factors that cause the change of polarization, in some fixed conditions, it is possible to distinguish different targets by polarization. Due to the limitations of the test conditions, only a few characteristics of the distance can be tested during the in-water test, and the database composed of data samples is not yet able to accurately distinguish between different targets. To distinguish the targets more accurately, a large number of data tests on the target samples are needed to establish and improve the target polarization parameter database, and at the same time, repeated tests should be conducted in different experimental environments to verify the correctness of the test results.  
Conceptualization, Y.Z.; Methodology, Q.F.; Validation, J.L.; Investigation, C.D.; Data curation, J.D.; Writing--original draft, Q.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Changchun University of Science and Technology, China. [PERSON], National Natural Science Foundation of China, grant number: 61890960, 61890963, JJKH20220744 KJ, 62127813.  
Not applicable.  
The study did not report any data.  
The authors declare no conflict of interest.
# Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3 GPP Standards
## 4 4G and NSA-Based 5G V2X Services
### System Support
#### 4.1.1 Enhancements Support  
The 3 GPP discusses the enhancements to support V2X services aiming to meet the increasing services requirements of V2X applications. The enhancements support discussed in the document (_TR 22.886 Rel-15_) provides various use-cases for eV2X services such as cooperative perception, lane change, information sharing, cooperative driving, remote driving, platooning, video-sharing, and composition for enhanced V2X scenarios. However, the majority of use cases are backed/sustained by the identification of basic use cases (from _TR 22.885_) and their potential service requirements. Hence, the old and newly identified use cases make use of 4G and 5G NR communication technologies to meet the communication and other supports for eV2X services. Moreover, the specified potential requirements are used for communicating warning messages to the neighboring vehicles through EPS enhancements confirming the use-cases evolution as a necessary step for emerging eV2X applications. To help the readers with an easy understanding through an abstract view, the 3 GPP reported a mapping between use-cases to form use-case groups. These use case groups further help in the system support for 5G V2X services discussed in Section 5.
# Mobile LiDAR for Scalable Monitoring of Mechanically Stabilized Earth Walls with Smooth Panels
## 1 Introduction
Since most MSE walls are located at the vicinity of traffic flow, collecting the needed data subjects field workers to dangerous conditions. Therefore, a practical, scalable monitoring strategy has to address such safety concerns. The monitoring strategy should also be capable of deriving serviceability measures for planar and non-planar MSE walls--i.e., an MSE wall facade has a set of individual planar faces as shown in Figure 2a or a curved facade, which could be considered as piece-wise planar, as shown in Figure 2b. To date, current research has only addressed planar MSE walls (e.g., [PERSON] et al. [5])--Figure 2a. To deal with the aforementioned MSE wall monitoring challenges, this research aims to achieve the following objectives:  
1. Develop a monitoring strategy that delivers both standard (global) and newly-developed (local) serviceability measures. 2. Provide a reliable, target-less, scalable monitoring strategy using mobile LiDAR mapping system (MLS). 3. Introduce a methodology that can tackle MSE walls comprised of smooth precast concrete panels along either planar or piece-wise planar facades. Figure 2: An illustration of **(a)** MSE wall with multi-face planar facade and **(b)** MSE wall with piece-wise planar facade (image sourced from Pinnacle Design/Build Group, Inc. [9]). This research work starts by providing a coverage of existing strategies for MSE wall monitoring, while emphasizing those employing LiDAR point clouds, in Section 2. The used MLS in this study is introduced in Section 3, which also covers the data acquisition for the case study conducted to illustrate the comparative performance of MLS and TLS. The proposed methodology is then presented in Section 4. Experimental results from the data processing/reduction strategy for the case study are discussed in Section 5. Lastly, Section 6 presents the conclusions and suggestions for future research direction.
# A High-Resolution, Wide-Swath SAR Imaging System Based on Tandem SAR Satellites
## 3 Signal Model and Imaging Algorithm  
According to the system introduced above, Sat-1 works in traditional scanning mode; its received data can be processed by existing algorithms [1; 2; 3]. This section mainly focuses on the modeling and imaging for Sat-2.
# The Fusion of MRI and CT Medical Images Using Variational Mode Decomposition
###### Abstract  
In medical image processing, magnetic resonance imaging (MRI) and computed tomography (CT) modalities are widely used to extract soft and hard tissue information, respectively. However, with the help of a single modality, it is very challenging to extract the required pathological features to identify suspicious tissue details. Several medical image fusion methods have attempted to combine complementary information from MRI and CT to address the issue mentioned earlier over the past few decades. However, existing methods have their advantages and drawbacks. In this work, we propose a new multimodal medical image fusion approach based on variational mode decomposition (VMD) and local energy maxima (LEM). With the help of VMD, we decompose source images into several intrinsic mode functions (IMFs) to effectively extract edge details by avoiding boundary distortions. LEM is employed to carefully combine the IMFs based on the local information, which plays a crucial role in the fused image quality by preserving the appropriate spatial information. The proposed method's performance is evaluated using various subjective and objective measures. The experimental analysis shows that the proposed method gives promising results compared to other existing and well-received fusion methods.  
MRI; CT; Image fusion; intrinsic mode functions (IMFs); LEM; VMD +
Footnote †: journal: Medical Image Fusion
# Face Spoofing Detection with Stacking Ensembles in Work Time Registration System
## Appendix A
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Combination** & **Small** & **Spoofing** & **Large** & **CelebA** & **Rank** \\ \hline B,S,C,N,X,E,V (voting) & 86.19 \(\pm\) 4.47 & 88.13 \(\pm\) 4.98 & 93.64 \(\pm\) 1.44 & 96.60 \(\pm\) 0.53 & 123.25 \\ \hline B,S,N (voting) & 89.29 \(\pm\) 3.01 & 87.68 \(\pm\) 2.54 & 96.49 \(\pm\) 0.40 & 91.81 \(\pm\) 0.62 & 123.5 \\ \hline C,V (stacking) & 77.92 \(\pm\) 4.11 & 91.38 \(\pm\) 2.38 & 95.25 \(\pm\) 0.47 & 98.32 \(\pm\) 0.21 & 124.5 \\ \hline S,X,E,V (stacking) & 86.82 \(\pm\) 5.89 & 92.63 \(\pm\) 3.50 & 92.21 \(\pm\) 0.79 & 94.16 \(\pm\) 0.43 & 126.25 \\ \hline B,S,X,E (stacking) & 87.86 \(\pm\) 5.54 & 92.51 \(\pm\) 3.45 & 92.28 \(\pm\) 0.80 & 94.07 \(\pm\) 0.49 & 126.5 \\ \hline B,S,N,X,E (voting) & 87.64 \(\pm\) 4.31 & 87.92 \(\pm\) 2.93 & 94.17 \(\pm\) 0.99 & 94.84 \(\pm\) 0.85 & 129.75 \\ \hline C,N,V (voting) & 78.65 \(\pm\) 4.17 & 89.24 \(\pm\) 3.18 & 96.00 \(\pm\) 0.31 & 96.70 \(\pm\) 0.26 & 130.5 \\ \hline S,X,E (stacking) & 86.49 \(\pm\) 6.13 & 92.55 \(\pm\) 3.47 & 92.18 \(\pm\) 0.73 & 94.06 \(\pm\) 0.49 & 130.75 \\ \hline C,N,E,V (voting) & 84.45 \(\pm\) 4.68 & 92.69 \(\pm\) 2.30 & 91.44 \(\pm\) 2.14 & 95.68 \(\pm\) 0.37 & 132 \\ \hline S,C,X,E (voting) & 83.66 \(\pm\) 5.49 & 92.02 \(\pm\) 2.55 & 92.18 \(\pm\) 1.37 & 95.88 \(\pm\) 0.39 & 132.5 \\ \hline B,S,N,E,V (voting) & 87.94 \(\pm\) 3.73 & 88.66 \(\pm\) 3.36 & 92.69 \(\pm\) 1.36 & 94.37 \(\pm\) 0.59 & 134 \\ \hline S,N,X,E (voting) & 85.98 \(\pm\) 5.16 & 91.57 \(\pm\) 2.88 & 91.76 \(\pm\) 1.60 & 94.88 \(\pm\) 0.51 & 134.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: All obtained accuracies [%] for different stacking and voting ensemble combinations for five runs of a 5-fold cross-validation, part 3.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 3 Mobility with UAV Technology
### Handover Concept
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**HO Procedure**} & \multicolumn{1}{c}{**Description**} \\ \hline Source—inter evolved Node BS (eNB) HO & This occurs when the user leaves the coverage area of eNB and enters another area covered by another eNB (within E-UTRAN). \\ User—inter eNB HO & This occurs when the user enters a coverage area managed by eNB to one that is managed by another eNB (within E-UTRAN). \\ Source—Inter RAT HO & This occurs when the user leaves the E-UTRAN cell. \\ User—Inter RAT HO & This occurs when the user enters the E-UTRN cell. \\ Source—intra eNB HO & This occurs from one sector to another when the user leaves the sector. \\ User—intra eNB HO & This occurs from one sector to another when the user enters the sector. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: HO procedure list. HO is that it saves money by establishing a connection to a neighboring BS with lower communication power. It further preserves device battery life by regulating the transmitted power [48; 50].
# A Blockchain-Based Secure Inter-Hospital EMR Sharing System
## 4 Security Analysis
### Computation Cost  
Table 2 shows the computation costs of the proposed scheme.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Phase** & **Item** & **Proof** & **Issuer** & **Holder** & **Verification** \\ \hline Initial Treatment Authentication & \((c_{HPA},Sig_{HPA})\) & Hospital A & Patient & \((EMR,Cert_{HPA})\overset{?}{=}V_{PK_{HPA}}(Sig_{HPA})\) \\ Inter-Hospital Authentication and & \((c_{HPA2},Sig_{HPA2})\) & Hospital A & Hospital B & \(EMR\overset{?}{=}V_{PK_{HPA}}(Sig_{HPA2})\) \\ Communication Phase & \((c_{HPB2},Sig_{HPB})\) & Hospital B & Patient & \((EMR,Cert_{HPB})\overset{?}{=}V_{PK_{HPA}}(Sig_{HPB})\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Non-repudiation of the proposed scheme.  
The computation costs of our proposed system for the blockchain center, hospital A, hospital B, and the patient in each phase are analyzed in Table 2. We found the highest computation cost in the inter-hospital authentication and communication phase; for example, hospital A needs five multiplication operations, one comparison operation, four hash function operations, two symmetric encryption operations, and one signature operation. Hospital B needs ten multiplication operations, three comparison operations, eight hash function operations, four symmetric encryption operations, and two signature operations. The patient needs five multiplication operations, two comparison operations, two symmetric encryption operations, four hash function operations, and one signature operation. Thus, the computation cost is acceptable in our proposed system.