esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System and Security Model
### Security Model  
In this part, we give the definition of the security model to prove that our scheme is an IND-CPA secure CLAP-PRE scheme under the \(k\)-multilinear decisional Diffie-Hellman (\(k\)-MDDH) assumption in the random oracle model.  
* SETUP: The challenger generates the system's public parameters and gives them to the adversary;
* On \(\mathcal{O}_{pk}(ID_{i})\), return \(H_{1}(ID_{i})\) and public parameters of \(ID_{i}\);
- On \(\mathcal{O}_{sk}(ID_{i})\), return the decryption keys of \(ID_{i}\);
- On \(\mathcal{O}_{pa}(ID_{i})\), return the delegation path of \(ID_{i}\);
- On \(\mathcal{O}_{rk}(ID_{i})\), return the re-encryption keys of delegation path generated by \(ID_{i}\). At the end of this phase, the adversary submits two equal length messages \(M_{0}\), \(M_{1}\) and an identity \(ID\). The adversary \(\mathcal{A}\) is restricted to choices of \(ID\) such that the decryption keys of \(ID\) has not been queried on \(\mathcal{O}_{sk}(ID_{i})\), and the adversary \(\mathcal{A}\) cannot translate the ciphertext from \(ID\) to \(ID^{\prime}\), for which \(\mathcal{A}\) holds the decryption keys by using re-encryption keys extracted during this phase;
* CHALLENGE: The challenger randomly choose a value \(\mu\in\{0,1\}\) and returns the ciphertext of \(M_{\mu}\) under the delegator's public key \(ID\);
* GUESS: \(\mathcal{A}\) makes queries as in the FIND phase with the same restrictions. At the end of this phase, the adversary submits a guess, \(\mu^{\prime}\in\{0,1\}\), of \(\mu\). If \(\mu^{\prime}=\mu\), the \(\mathcal{A}\) wins the game. \(\mathcal{A}\)'s advantage in the above game, \(Adv^{IND\_CLAPPRE-CPA}_{\mathcal{A}}\), is defined as \(|Pr[\mu^{\prime}=\mu]-1/2|\). We say that the certificateless autonomous path proxy re-encryption scheme is IND-CLAPPRE-CPA-secure if for all probabilistic polynomial time algorithms \(\mathcal{A}\), \(Adv^{IND\_CLAPPRE-CPA}_{\mathcal{A}}\leq v(\lambda)\).
# Simulation of Electromagnetic Generator as Biomechanical Energy Harvester
###### Abstract  
Portable electronic devices are dependent on batteries as the ultimate source of power. Irrefutably, batteries only have a limited operating period as they need to be regularly replaced or recharged. In many situations, the power grid infrastructure is not easily accessible to recharge the batteries and the recharging duration is also not convenient for the user to wait. Enhancement of a reliable electronic system by preventing power interruptions in remote areas is essential. Similarly, modern medical instruments and implant devices need reliable, almost maintenance-free power to ensure they are able to operate in all situations without any power interruptions. In this paper, the small-sized electromagnetic generator was designed to produce higher power by utilizing the knee angle transition involved during the walking phase as the input rotary force. The proposed generator design was investigated through COMSOL Multiphysics simulation. The achieved output RMS power was in the range of 3.31 W to 14.95 W based on the RPM range between 360 RPM to 800 RPM.  
gait cycle; steps; biomechanical energy harvester; knee angle; RPM; generated power +
Footnote †: journal: Applied Sciences
# Accuracy of Dynamic Modulus Models of Asphalt Mixtures Containing Reclaimed Asphalt (RA)
###### Abstract  
The dynamic modulus (\(|E^{*}|\)) is a fundamental mechanical parameter for studying the performance of hot mix asphalt and simulating its viscoelastic behaviour under different loading and thermal conditions. It is a primary tool to replicate road surface behaviour under vehicle traffic loading and temperature variations. Though, laboratory testing to determine this parameter is time-consuming and costly. Several predictive models have been developed to estimate the dynamic modulus, ranging from rheological to empirical regression models. This research was dedicated to studying two predictive models for determining the master curve of the dynamic modulus of hot mix asphalt used in a regular pavement binder course containing different reclaimed asphalt contents (0%, 30%, 40%, and 50%). Laboratory experiments were conducted to assess their accuracy. The results show that Witczak's sigmoid function provided the best accuracy for the master curves, while the Generalized Huet-Sayegh (2S2P1D) model showed less accurate predictions, particularly at the range of low and high frequencies.  
dynamic modulus; asphalt mixture; Witczak model; sigmoid function; 2S2P1D; generalized Huet-Sayegh model; prediction models; reclaimed asphalt; master curve
## 8 Conclusions  
Image reconstruction from projections has wide applications, particularly in medical imaging. Emission and transmission tomography and X-ray CT all fall into this category. Three types of reconstruction methods are available: Fourier methods, algebraic methods and likelihood based reconstruction methods. Our attention in this paper is on the penalized likelihood approaches.  
In this paper we present and discuss several important simultaneous MPL reconstruction algorithms, where the non-negativity constraint is enforced. The EM algorithm is limited to maximum likelihood reconstruction problems in emission tomography and is difficult to extend to other imaging modalities and probability models for the likelihood. One variation of EM, called the alternating minimization, is developed for transmission tomography. Another variation of EM, called the OT algorithm, is suitable for any imaging modalities and probability models, but its derivation is often cumbersome as the option for the surrogate function is flexible. The OT algorithm based on the separable parabola surrogate is relatively easy to implement to different tomographic imaging. The MI algorithm, on the other hand, is easy to derive and to implement as its line search step is cheap to compute. Its convergence speed, according to the simulation study, is similar to the separable parabola surrogate algorithm. The FS-JOR and FS-SOR algorithms first apply the Fisher information matrix to obtain a quadratic approximation to the objective function, and then optimize it using JOR or SOR schemes. Implementation of ICA-FS reverses the order of FS and SOR in FS-SOR. For both FS-SOR and ICA-FS, their convergence speeds are usually superior, but their potential problem is that both involves column retrieval of \(A\), which may not be pre-generated and stored.  
For some of the algorithms covered in this paper, their corresponding block-iterative algorithms have been developed. Block-iterative algorithms can usually achieve faster convergence than their simultaneous counterpart. However, discussions of the block-iterative algorithms are not included in this paper.
## 3 Results and Discussion
### Influence of Soil Characteristics on Plant Uptake
Clearly, the plant uptake model and the model parameters applied in this study are not necessarily valid for different plant species and soils. For example, in the case of soils subject to seasonal variations in the oxidation-reduction conditions, it will be essential to take into consideration also the redox potential [61], which may influence the mobility of the element and therefore its transferability to plants. However, this approach is very simple and can be easily adjusted in different environmental contexts where the tungsten uptake by plants needs to be monitored.
## 2 Methodology of Multi-Scale Subset and Subvoxel Shifting in DVSP
### X-ray Microtomography System and In Situ Loading Setup  
The schematic of the X-ray microtomography system and a custom-made in situ loading setup is illustrated in Figure 2. The X-ray microtomography system mainly consists of a FXE 225 kV micro focus X-ray source from YXLON (Hamburg, Germany), an array detector (XRD 0822 AP14, Varex Imaging Corporation, Salt Lake City, UT, USA) of dimensions 1024 \(\times\) 1024 pixels from PerkinElmer (Waltham, MA, USA), a turntable and its mechanical control unit, and a scanning control workstation [39]. The custom-made in situ loading setup is mounted on the turntable by using bolts, and rotates 360\({}^{\circ}\) with the turntable. In the loading experiment, the specimen is placed on the stage in the Poly(methyl methacrylate) (PMMA) chamber of the setup, and the mechanical loading is controlled by an electric motor. While the specimen is rotating step by step over 360\({}^{\circ}\), a set of 720 radiograph projections are captured. When each projection is captured, the turntable does not move. It takes about 25 min for one 360\({}^{\circ}\) scan. After that, a Feldkamp cone-beam algorithm is used to reconstruct a sequence of 2D 16-bit slice gray images [43]. Based on these slice images, a 3D image is obtained.
## 7 Methodology and Experimentation
### Reinforcement Learning Agent  
The training method used for the RL agent was divided into two phases. The first was to train an agent using a set randomly selected from 20 random walk environments. A different selection was made for each iteration in the training phase. The environments were the same used in Section 5, each composed of 25 devices, a 60 m by 60 m grid with centrally located Wi-Fi access covering a 30 m radius. The Job \(J\) set contains jobs with [15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25] Kb data input and [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25] Kb data output and computing requirements in the range of [80; 81; 19; 20; 21; 22; 23; 24; 25] mega float-point operations.  
For the RL agent, we used a convolutional neural network (CNN) with five layers of 512 neurons, based on previous experience [9] where this architecture demonstrated a good balance between computational efficiency and learning capacity. The learning rate was set to \(1\times 10^{-5}\), chosen to ensure a stable and gradual learning progress. We utilized 32 Gym environments to parallel and speed up the training process. The lambda and gamma functions were both set to 1.0 to give equal importance to all states in the value function estimation. The model was trained for \(5\times 10^{7}\) steps with a batch size of 64, which were determined through experimental tuning to optimize the trade-off between training time and model performance.  
The other phase was related to the transfer learning phase due to the high probability of overfitting in RL [61; 62]. We trained a new environment using the previous model as a base, and with fewer steps, we achieved convergence in the new environment. We repeated these steps with different environments and collected the data. The new random waypoint environments with 25 devices and 1500 JOBs were new. We trained the latest models in \(1\times 10^{6}\) steps and a batch size of 64.
## 2 Materials and Methods
### Charge Carrier Mobility Characterization  
The characterization of the charge carrier mobility of the semiconductor channel was performed using the Dark Injection Space Charge Limited Current (DI SCLC) method, shown in Figure 2. In this configuration, the semiconductor is placed between a blocking electrode and an injecting electrode. A step function is then applied between the electrodes and the resulting transient current is measured. The carrier mobility is calculated using Equation (1), where t\({}_{\text{tr}}\) is the free carrier transient time, V the applied voltage, and d the thickness of the semiconductor film. \(\tau_{\text{DI}}\) corresponds to the time it takes for the current to peak and represents the time it takes for the first sheet of charge carriers to reach the counter electrode [21].  
\[\mu\ =\ \
icefrac{{d^{2}}}{{\
u_{tr}}}\text{ where }\tau_{DI}\ =\ 0.787\ t_{tr} \tag{1}\]  
Figure 2: Schematic of the Dark Injection Space Charge Limited Current measurement setup.  
The sample was prepared by depositing a thin film of C\({}_{60}\) on a silicon substrate coated with nickel acting as the blocking electrode. A thin film of PEDOT:PSS (\(\sim\)5 nm) was deposited on the C\({}_{60}\) film to enhance its conductivity, followed by a chromium layer acting as the charge injecting electrode.
# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 4 Classification of Personalized Video Summarization Techniques
### Based on Machine Learning
#### 4.6.4 Weakly Supervised  
Weakly supervised video summary methods use less expensive labels without using basic truth data. The labels they use are imperfect compared to tags that are complete in human annotations. However, they can lead to an effective training of summary models [79]. Compared to the supervised video summary approach, a weakly supervised summary approach needs a smaller set of training to carry out the video summary. [PERSON] et al. [70] suggested a personalized video summary approach with weak supervision. Weak supervision is carried out as semantic maps of reliability. Through predictions from pre-trained classifiers of actions/characteristics, semantic maps are obtained.  
The distribution of the papers reported in Table 1, according to the type of personalized summary method, is shown in Figure 9. It is evident from the distribution that in the largest number of papers, the proposed method of producing a personalized video summary is supervised, at a percentage of around 73%. Second, the percentage of unsupervised personalized video summarization methods is around 24%. In the last place, and with a percentage of around 3%, is the weakly supervised methods.
## 3 Results  
### Bench Test Measurements  
Table 1 lists the bench test measurement results of the PFT \({}^{13}\)C birdcage RF coil with CPSCs and the \({}^{13}\)C birdcage RF coil without CPSCs using a vector network analyzer. For all bench tests, with the exception of the unloaded Q-factor, the swine was loaded inside the 16-leg PFT \({}^{13}\)C birdcage RF coil with CPSCs and \({}^{13}\)C birdcage RF coil without CPSCs. The 3 dB quadrature hybrid coupler was connected to a 16-leg PFT \({}^{13}\)C birdcage RF coil during bench test measurements. Figure 3 demonstrates the entire configuration of the PFT \({}^{13}\)C birdcage RF coil system.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**16-Leg PFT \({}^{13}\)C Birdcage RF coil**} & \multicolumn{2}{c}{**16-Leg \({}^{13}\)C Birdcage RF coil**} \\  & \multicolumn{2}{c}{**with CPSCs**} & \multicolumn{2}{c}{**without CPSCs**} \\ \cline{2-5}  & **Loaded** & **Unloaded** & **Loaded** & **Unloaded** \\  & **Condition** & **Condition** & **Condition** & **Condition** \\ \hline Return Loss of Forward Reflection & & & & \\ Coefficient (S\({}_{11}\)) [dB] & \(-\)22.7 & \(-\)38 & \(-\)31 & \(-\)26 \\ Impedance Forward Reflection & & & & \\ Coefficient (S\({}_{11}\)) [\(\Omega\)] & 50 \(-\) j 2.3 & 50 \(+\) j 1.2 & 51 \(-\) j 2 & 51 \(-\) j 5 \\ Q-factor & 311.0 & 2906 & 345.0 & 3490 \\ Q-ratio & & 9.330 & & 10.11 \\ \hline \end{tabular}
\end{table}
Table 1: Network analyzer measurements of the 16-leg PFT \({}^{13}\)C birdcage RF coil using the vector network analyzer. The forward reflection coefficients are measured in the loaded condition where the swine is placed inside the 16-leg PFT \({}^{13}\)C birdcage RF coil.
## 3 Simulations and Experiments  
The analysis of the mechanism simulated on Solidworks(r) is divided into three types of motion; Figure 11 shows first the caudal fin in the vertical position, then the caudal fin is turned 90\({}^{\circ}\) to hold a horizontal position.  
The simulation followed the parameters listed in Table 2. Each experiment shows the displacement graph of the three limbs and the projection angle of the platform corresponding to the case of movement.  
* Experiment 1: The caudal fin starts in a neutral position for a vertical fluttering to swim like a tuna. Then, the caudal fin starts to flap from \(-30^{\circ}\) to \(+30^{\circ}\) for a total caudal amplitude of 60\({}^{\circ}\) (Figure 12).
* Experiment 2: The caudal fin starts in a neutral position for a vertical fluttering and then turns 90\({}^{\circ}\) along the body axis, positioning it for a horizontal fluttering to swim like a dolphin. Then, the caudal fin starts to flap from \(-30^{\circ}\) to \(+30^{\circ}\) for a total caudal amplitude of 60\({}^{\circ}\) (Figure 13).  
To verify the correctness of the forward dynamics model, a closed-loop proportional-derivative (PD) controller was implemented, as shown in the block diagram of Figure 14. It had the desired trajectory for the motion of the moving platform in three distinct scenarios: for a motion of \(30^{\circ}\) in roll only, then \(30^{\circ}\) in pitch only, and finally \(30^{\circ}\) in yaw only; the results are shown in Figure 15. The trajectory of the moving platform \(\mathbf{X}(t)\) was measured and compared to the desired trajectory \(\mathbf{X}_{d}(t)\). Furthermore, the tracking error \(\mathbf{e}(t)=\mathbf{X}_{d}(t)-X(t)\) was fed to a controller to determine the required wrench needed. The platform orientation is described by the roll-pitch-yaw angles (\(\Phi-\Theta-\Psi\)) trying to reach the desired trajectory (\(\Phi_{d}-\Theta_{d}-\Psi_{d}\)).  
The gravity compensation block was applied to attenuate the effect of gravity on the tracking performance, then a gravity compensation term was added to this wrench. This term is indeed the term \(\mathbf{G}(\mathbf{x})\) of Equation (24), derived from the dynamic formulation. The desired and real closed-loop motions were similar. The PD controller with gravity compensation was able to reduce tracking errors. Note that the controller used in this simulation was in its simplest form. However, the gravity compensation feedback is the key point to reach such a performance with a simple PD structure in control [25]. For this simulation, no disturbances of the hydrodynamical lateral forces were added.  
Figure 11: Simulation of the caudal fin (**a**) in a vertical position and (**b**) horizontal position.  
Figure 12: Motion study for Experiment 1: (**a**) neutral position, (**b**) fin position vertical at \(-30^{\circ}\), (**c**) limbs displacement at \(-30^{\circ}\), (**d**) fin position vertical at \(+30^{\circ}\), and (**e**) limbs displacement at \(+30^{\circ}\).
# An Overview of Indoor Positioning and Mapping Technology Standards
## 4 Our Research on IPMN Standards  
Although there are IPMN standards developed by different SDOs, the new indoor positioning, mapping, and navigation technologies, such as 5G, IoT, smart cities and digital twins, IPS is continuously evolving. Our work was focused on the indoor intelligent hybrid positioning and GIS technology with high availability and accuracy project, which began in the year of 2016 and it is supported by the National Key Research and Development Program of China. The goal of our research is to design a hybrid indoor positioning system architecture, develop IPMN standards, and integrate various indoor positioning technologies and GIS.
## 1 Introduction
Finally, we evaluated the applicability of vegetation phenological parameters under five selected methods using the Fluxnet2015 dataset above \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Continent** & **Latitude** & **Method 1** & **Method 2** & **Method 3** & **Method 4** & **Method 5** \\ \hline \multirow{4}{*}{Asia} & 30\({}^{\circ}\)\(-\)45\({}^{\circ}\)N & \(-\)0.32 & \(-\)0.45 & \(-\)0.28 & \(-\)0.40 & \(-\)0.30 \\  & 45\({}^{\circ}\)\(-\)60\({}^{\circ}\)N & \(-\)0.40 & \(-\)0.27 & \(-\)0.53 & \(-\)0.36 & \(-\)0.37 \\  & 60\({}^{\circ}\)\(-\)75\({}^{\circ}\)N & \(-\)0.27 & \(-\)0.16 & \(-\)0.32 & \(-\)0.25 & \(-\)0.37 \\  & 75\({}^{\circ}\)\(-\)90\({}^{\circ}\)N & \(-\)0.23 & \(-\)0.21 & \(-\)0.24 & \(-\)0.21 & \(-\)0.20 \\ \hline \multirow{4}{*}{Europe} & 30\({}^{\circ}\)\(-\)45\({}^{\circ}\)N & \(-\)0.54 & \(-\)0.54 & \(-\)0.52 & \(-\)0.51 & \(-\)0.64 \\  & 45\({}^{\circ}\)\(-\)60\({}^{\circ}\)N & \(-\)0.55 & \(-\)0.58 & \(-\)0.65 & \(-\)0.57 & \(-\)0.60 \\  & 60\({}^{\circ}\)\(-\)75\({}^{\circ}\)N & \(-\)0.54 & \(-\)0.40 & \(-\)0.68 & \(-\)0.51 & \(-\)0.61 \\  & 75\({}^{\circ}\)\(-\)90\({}^{\circ}\)N & 0.05 & \(-\)0.27 & \(-\)0.10 & \(-\)0.17 & 0.19 \\ \hline \multirow{4}{*}{North America} & 30\({}^{\circ}\)\(-\)45\({}^{\circ}\)N & 0.62 & 0.38 & 0.55 & 0.33 & 0.20 \\  & 45\({}^{\circ}\)\(-\)60\({}^{\circ}\)N & 0.27 & 0.34 & 0.20 & 0.30 & 0.29 \\ \cline{1-1}  & 60\({}^{\circ}\)\(-\)75\({}^{\circ}\)N & 0.06 & 0.11 & 0.06 & 0.10 & 0.01 \\ \cline{1-1}  & 75\({}^{\circ}\)\(-\)90\({}^{\circ}\)N & \(-\)0.14 & \(-\)0.28 & \(-\)0.17 & \(-\)0.18 & 0.13 \\ \hline \hline \end{tabular}  
* The values in the table represent the SOS average trend of all pixels in different regions under different extraction methods from 1982 to 2015.  
\end{table} 
Table 1: Trends of the SOS under different extraction methods at different latitudes. 30\({}^{\circ}\)N. The above results can provide reference for selecting the best method for vegetation phenology extraction in the Northern Hemisphere (above 30\({}^{\circ}\)N), and offer valuable guidance for more accurately evaluating the dynamic changes of vegetation phenology in this region.
## 4 Computer Simulation Results
### Evaluation Metrics
#### 4.4.3 ANOVA Analysis  
In this sub-section, we confirm the significance of the results achieved using the proposed speech enhancement approach over other methods using the analysis-of-variance (ANOVA) approach. The statistical tests were conducted at a 95% confidence interval. The differences between achieved ESTOI and PESQ using the proposed method and other approaches are considered to be significant if the probability value denoted by the \(p\)-value is less than 0.05 and if the \(F\)-value of the Fisher-Snedecor distribution (also referred to as the F-distribution) is higher than its critical value (\(F\)-critical). Table 10 presents the results of the statistical test at a 95% confidence interval with the value of \(F\)-critical at 4.10.  
From the results presented in Table 10, it is clear that the results are statistically significant, and that the proposed approach consistently outperforms prior state-of-the-art approaches in terms of speech intelligibility and quality (represented by ESTOI and PESQ metrics, respectively).
# Microstructured Phononic Crystal Isolates from Ultrasonic Mechanical Vibrations
## 5 Discussion
Secondly, uncertainties on the actual geometry must also be considered if a quantitative comparison between experiments and theoretical prediction is performed. Numerical results reported in Figure 6 are obtained on the geometry rebuilt starting from the SEM images of Figure 3, but uncertainties are present especially on the beams cross-sections. To quantify the effect of geometric imperfections, we compare the bandgap computed on the unit cell with \(a=200\) um with the bandgaps obtained on two unit cells obtained by reducing/increasing the beams cross-section of 0.5 um each side. The same [PERSON]'s modulus of 2.3 GPa employed before is considered here for the sake of simplicity. The bandgap of the \(a=200\) um unit cell covers the frequency range 577 kHz-3.53 MHz, by reducing the beams cross-section of 0.5 um each side, we obtain a bandgap in the range 539 kHz-3.45 MHz, while increasing the beams cross-section of the same quantity we obtain a 624 kHz-3.70 MHz bandgap. As expected, geometric dimensions are of fundamental importance in the quantitative agreement between experiments and numerical predictions. The bandgap opening frequency can indeed vary in a range of amplitude 85 kHz. A precise reconstruction of the geometry of the fabricated metastructures is indeed required. Finally, viscous properties of the resin have been neglected in this work for the sake of simplicity. They can influence the attenuation level obtained through the metastructures and also the bandgap opening and closing frequencies. Future work will address such aspects.