esa-sceva/satcom-chunk-collection huggingface dataset:
# Pedestrian Localization in a Video Sequence Using Motion Detection and Active Shape Models
## 3 Experimental Set-Up
### Active Shape Model (ASM)
#### 3.3.2 Principal Component Analysis (PCA)  
A set of \(L\) reference points (landmarks) represents the shape of the object. They are used to train the ASM model, which is the human contour detection method. Figure 8 shows a set of \(M\) misaligned shapes of the same image which belongs to the training set. Although each shape \(m\) in the training set is in two-dimensional space, the shape can be modeled with a reduced number of parameters using the PCA technique. Suppose we have \(M\) shapes in the training set that are represented by \(\mathbf{X}_{m}\), for \(m=1,\ldots,M\). The PCA algorithm works as follows:  
1. [wide=0 pt,wide=0 pt,wide=0 pt,wide=0 pt]
2. Calculates the mean of the \(M\) sample shapes in the training set (Equation (12)). \[\bar{x}=\frac{1}{M}\sum_{m=1}^{M}\mathbf{x}_{m}\] (12)  
Figure 8: Representation of the aligned shapes of the training set.  
2. Computes the covariance matrix \((S)\) of the training set (Equation (13)). \[S=\frac{1}{M}\sum_{i=1}^{M}(\mathbf{x}_{m}-\bar{x})(\mathbf{x}_{m}-\bar{x})^{T}\] (13)
3. Build the matrix of eigenvectors (Equation (14)). \[\Phi=\frac{1}{M}\sum_{m=1}^{M}[\phi_{1}\mid\phi_{2}\mid\ldots\mid\phi_{q}]\] (14) where \(\Phi_{j}\), \(j=1,\ldots,q\) represents the eigenvectors of \(S\) corresponding to the largest \(q\) eigenvalues.
4. Given \(\Phi\) and \(\bar{x}\), each form can be updated as described by Equation (15). \[\mathbf{x}_{m}\approx\bar{x}+\Phi b_{m}\] (15) where: \[b_{m}=\Phi^{T}(\mathbf{x}_{m}-\bar{x})\] (16)
5. Calculate \(q\) such that the sum of the largest eigenvalues is greater than 98% of the sum of all eigenvalues.  
To generate fair shapes, the distribution of \(\mathbf{b}\) is evaluated. To constrain acceptable values, hard conditions can be applied to each element \(b_{m}\) or a constraint to \(\mathbf{b}\), which can be reflected in a hyper-ellipsoid.  
The PCA algorithm reduces the amount of data by simplifying the initial image to a set of eigenvalues and eigenvector while preserving enough information to perform the training stage. The matrix of eigenvectors \(\Phi\) (Equation (14)) contains the principal components of the training subset, which are used to update the model parameters during the fitting stage explained in the following section.
## 3 Results and Discussion
### Mechanical Properties of Wax3D  
The results of the tensile strength test are shown in Table 9. It has been indicated that the Wax3D has tensile strength and Young's modulus considerably lower compared to general 3D printing plastics such as PLA and ABS. For example, [PERSON] et al. [22] reported the UTS for PLA and ABS plastics to be close to 55 and 42 MPa, respectively. As for Young's moduli, the values were found to be 1.94 (PLA) and 1.49 GPa (ABS). By comparing the data, we can see that the commonly used FDM plastics exceed the values of the wax filament by around 20 times in tensile strength and 10 times in modulus of elasticity. This means that both advantages and drawbacks can be found when working with the Wax3D filament. For instance, low elasticity is advantageous for avoiding ceramic shell cracking. On the other hand, the challenge exists when manufacturing parts with thin sections. Nonetheless, it should be noted that the strength of popular IC wax such as B140 is inferior to Wax3D, having a UTS value equal to 0.98 MPa [23]. The tested Wax3D specimens representing almost brittle failure mode are shown in Figure 5, and the average nominal strain at break was found to be 11.42%.
# Transforming Group Codes in Mealy Finite State Machines with Composite State Codes
## 6 Experimental Results
The smaller the value of this integral characteristic is, the higher the quality of the circuit (and the better the balance between the spatial and temporal characteristics). Using Vivado reports, we created Table 11, including values of the area-time products obtained for the circuits of the benchmark FSMs. As follows from Table 11, the proposed method made it possible to obtain LUT-based FSM circuits with smaller values for the area-time products compared with their \(U_{2}\)-based counterparts. The \(U_{2}\)-based FSMs were inferior in relation to the equivalent \(U_{3}\)-based FSMs (the difference was 12.19%). Also, our method provided significantly better results than those for the other methods studied. The improvement was 189.39% in relation to MB-based FSMs, 216.87% in relation to OH-based FSMs and 104.06% in relation to JEDI-based FSMs. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**BM** & **MB** & **OH** & **JEDI** & \(\mathbf{U_{2}}\) & \(\mathbf{U_{3}}\) & **L + R** \\ \hline ex1 & 70 & 74 & 53 & 40 & 38 & 16 \\ kirkman & 42 & 58 & 39 & 31 & 30 & 18 \\ \hline planet & 131 & 131 & 88 & 77 & 72 & 14 \\ planet1 & 131 & 131 & 88 & 77 & 72 & 14 \\ \hline pma & 94 & 94 & 86 & 72 & 69 & 14 \\ s1 & 65 & 99 & 61 & 55 & 52 & 14 \\ s1488 & 124 & 131 & 108 & 87 & 84 & 15 \\ s1494 & 126 & 132 & 110 & 85 & 82 & 15 \\ s1a & 49 & 81 & 43 & 43 & 40 & 15 \\ s510 & 48 & 48 & 32 & 26 & 28 & 27 \\ s820 & 88 & 82 & 68 & 48 & 46 & 25 \\ s832 & 80 & 79 & 62 & 50 & 52 & 25 \\ sand & 132 & 132 & 114 & 91 & 87 & 18 \\ styr & 93 & 120 & 81 & 71 & 69 & 16 \\ tma & 45 & 39 & 39 & 31 & 29 & 13 \\ \hline Total & 1318 & 1431 & 1072 & 884 & 850 & \\ \hline Percentage, \% & 15,506 & 16,835 & 12,612 & 10,400 & 10,000 & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Results of experiments (LUT count). An analysis of the results of the experiments carried out allowed us to draw the following conclusion regarding the equivalent \(U_{2}\)- and \(U_{3}\)-based FSMs. If condition (28) holds, then our method is always an improvement over the method based on composite state codes [11]. Under this condition, our method provides a shorter cycle time and requires fewer LUTs than the method based on CSCs. So, mixed composite state codes can be viewed as a good alternative to maximum binary CSCs.
## 5 Simulation Results
### Hover Flight  
The suspended load is subjected to a disturbance of 30 degrees in both directions. This is carried out by directly changing the joint state in the Gazebo simulation or by enforcing force disturbances on the load, but the first yielded better and more accurate state changes. The time history of the load swing angles is shown in Figure 16, while the time history of the quadrotor position in the forward and lateral directions is shown in Figure 17, without including the ASC. It is observed that the quadrotor tracking controller can dampen the oscillations of the suspended load due to the moments generated on the quadrotor from the motion of the suspended load. The tracking controller implemented in PX4 was able to return the quadrotor to its initial position. This indicated that the system is stable with this controller. The ISH determined for this flight maneuver is 0.6752.  
In the second maneuver, the ASC is implemented as described in the previous section with the following parameters \(K_{gx}=K_{gy}=0.6L\) and n. After including the ASC in the control loop, it is observed that the oscillations history of the suspended load was improved in both directions, as shown in Figure 18. The deviation of the quadrotor from the hover is smaller compared with the uncontrolled case, as shown in Figure 19. The ISH determined for this maneuver is 0.4271. Therefore, the reduction is 36.7%.  
Figure 17: Time history of the quadrotor position during hover without the ASC.  
Figure 18: Time history of the load swing angle during hover with the ASC.
# A Review of Photothermal Detection Techniques for Gas Sensing Applications
## 2 First Experimental Verification of PTS Gas Sensors
### Homodyne Hydrazine Detector with Folded Jamin Interferometer  
One of the first attempts to use a modified Jamin interferometer [21] configuration to detect PT-induced RI changes was presented by [PERSON] et al. in 1999 [22]. The schematic of the sensor is presented in Figure 3.  
The proof-of-concept experiment used a single-mode He-Ne laser with an output power of 1 mW as the _probe_ laser. The beam was split into two independent arms using two  
Figure 3: Schematic diagram of the unfolded Jamin interferometer PT ammonia detector. A single-mode He-Ne laser is used to form the interferometer. A 7 W modulated CO\({}_{2}\) laser beam is used as the _pump_ source. Laser beam and gas interaction path length is 38 cm. Reprinted with permission from [22].  
Figure 2: Schematic of the Mach-Zehnder interferometer-type photothermal gas sensor. BS—beam splitter, Ge—germanium window, M—mirror, ZnSe—zinc selenide window, E—beam polarization vector direction, M-Ge—germanium mirror. Reprinted with permission from [18] C The Optical Society.  
45\({}^{\circ}\) angle. The etalons were appropriately coated to achieve a nearly even split ratio, forming an unfolded Jamin interferometer with a total optical path \(\sim\)50 cm. Beams exiting the interferometer were focused onto two independent photodiodes. In this experiment, a static phase difference between the signal and the reference beam was maintained (at zero) by driving a piezoelectric transducer on which one of the etalons was mounted. This allowed for compensating any thermal drifts of the sensor (similarly to work by [PERSON]). The core of the sensor was enclosed in plexiglass housing and the gas was delivered to the sensor via an inlet near the center of the interferometer. In this experiment, the detected gas sample was ammonia (NH\({}_{3}\)). The gas particles were excited by an auxiliary, high-power (7 W), tunable CO\({}_{2}\) laser-_pump_ laser. The emission of the _pump_ laser was co-aligned with one of the arms of the interferometer, forming a 38 cm-long gas-laser interaction length. The laser was capable of reaching a strong NH\({}_{3}\) transition centered at 9.22 \(\upmu\)m and was modulated at 1.2 kHz with a sinewave signal. By exciting the gas particles in one arm of the interferometer, a change in the refractive index was obtained. This imbalance was easily detected as a phase shift between both beams forming the interferometer. The signal analysis was simplified by using a lock-in amplifier (LIA) which was referenced by the 1.2 kHz modulation delivered to the _pump_ laser. This NH\({}_{3}\) PTS detector was capable of reaching a detection limit of 250 ppt and 33 ppt for 1 s and 100 s integration time, respectively. Worth noting is the fact that the sensor had a linear response of over 5 orders of magnitude of the analyte concentration. Although reaching promising results, the proposed sensor configuration is rather bulky, requires precise alignment and reaching the detection limit is possible only in lab environment using a high-power excitation laser.
## 1 Introduction  
A considerable amount of literature has been published on the effects of anterior cruciate ligament (ACL) rupture on gait biomechanics. The authors describe different functional dynamics over time after surgery [1; 2; 3]. In their study, [PERSON] et al. observed decreased amplitudes of knee flexion-extension-adduction and hip adduction in the affected limb 1.5 years after ACL reconstruction. At later periods (within 3.5 years), the authors found no differences from the contralateral limb,although they noted decreased amplitudes of knee flexion and knee and hip adduction 8.5 years after the ACL reconstruction [4]. [PERSON] et al. studied changes in knee joint kinematics before and six months after ACL reconstruction. The study showed a gradual functional improvement in the postoperative period--a significant increase in mean walking speed and in the movement amplitudes of both hips and knees, as well as improved range of ankle motion in the operated limb [5]. A study by [PERSON] et al. also demonstrated improvement in some gait parameters over a period from two to eight years after ACL reconstruction. The authors noted an increasing angle of knee flexion and decreasing amplitudes of rotation and abduction of the operated limb [6].  
On the other hand, some studies found no favorable functional changes [7]. For instance, [PERSON] et al. examined patients long after (approximately five years) ACL reconstruction. The study showed a decrease in the knee flexion angle on the affected side compared to control, both in walking and in running. In addition, maximum muscle strength asymmetry during running was greater for the unaffected limb [8]. Another study, by [PERSON] et al., identified a relationship between the quadriceps strength asymmetry and the asymmetries in the lower extremity biomechanics during walking in patients after ACL reconstruction. The isometric quadriceps strength of the injured leg was significantly lower, and the flexion-extension amplitudes in the operated knee were smaller that those in the uninjured leg [9].  
Despite the available literature on the effects of anterior cruciate ligament rupture on gait parameters, the dynamics of evolving changes is still poorly studied, and the available data are contradictory. This hampers rehabilitation and prediction of long-term outcomes.  
The objective of the study was to evaluate the clinical, functional and biomechanical symptoms in patients with anterior cruciate ligament rupture before ACL reconstruction, 2-3 months after reconstruction, and in the long term.
###### Abstract
These techniques utilize the target model's gradients to produce samples that assist an adversary train a replica model with improved performance. Furthermore, we explore data-free model extraction attacks [25], which aim to extract a model without needing access to the original training dataset. This approach raises significant privacy concerns, as it potentially enables an adversary to obtain a functional replica of a model solely through querying, without requiring any data from the model owner. Through extensive experiments and analysis, we aim to provide valuable insights into the effectiveness of these sample selection and data-free strategies for model extraction attacks. Our findings will contribute to a better understanding of the vulnerabilities present in image classification frameworks and contribute to the creation of robust defense mech  
Figure 1: Black-box adversarial model extraction attack. The surrogate model is refined using feedback from queries to more closely mirror the victim model. anisms to protect intellectual property and preserve privacy. Our key contributions are outlined as follows: (i) To the best of the author's knowledge, this work presents the first attempt to leverage DQN extensions to model extraction attacks, which has not been explored in the existing literature. (ii) The development of an open-source framework that integrates popular DQN extensions such as Prioritized Experience Replay, Double DQN, and Dueling DQN. We illustrate how these can be used to enhance attack methodologies against neural networks. (iii) The conduct of comparative experiments that benchmark the effectiveness of these methods against traditional approaches, providing a clear demonstration of their relative performance and advancing the understanding of effective attack strategies in the realm of AI security. (iv) By evaluating different techniques for synthetic data generation, such as FGSM and LinfPGD, and comparing their effectiveness in the context of model extraction, our work identifies best practices and guidelines for practitioners. This helps in refining the approach to generating synthetic data, which is crucial for training robust adversarial models. This paper is organized as follows: Section 2 provides an overview of related research in adversarial machine learning, underscoring the progress and challenges of current attack methodologies. Section 3 outlines the process used to develop the model extraction methodology. It provides a comprehensive explanation of how active learning and reinforcement learning strategies were combined, along with techniques for selecting samples to create synthetic data. Section 4 presents the experimental framework and the outcomes of applying our approach to image classification models. Discusses the implications of our findings. Finally, Section 5 suggests avenues for future investigation, and wraps up the paper.
## 3 Application Results and Discussion
### Model Validity
Furthermore, \(\pm 1.96\)\(S_{e}\) yields 95% confidence band around predicted \(P_{i}\) as follows:  
\[\frac{t_{obs}\times t_{pre}}{t_{obs}^{2}} \tag{13}\]2  
The outputs of this analysis, such as the uncertainty band width and mean absolute deviation (MAD), are given in \[\frac{t_{obs}\times t_{pre}}{t_{obs}^{2}} \tag{13}\]3 According to this table, the positive mean prediction error indicates that the predicted values calculated by all these methods are higher than the experimental values. It is seen that RF and CHAID methods for CS yielded the minimum (33.065% and 33.240) bandwidth \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Model** & \(K\) & \(K^{\prime}\) & \(m\) & \(n\) & \(R_{m}\) \\ \hline \multirow{4}{*}{**CS**} & RF & 0.995 & 0.990 & \(-0.071\) & \(-0.071\) & 0.691 \\  & M5 rule & 0.978 & 0.955 & \(-0.452\) & \(-0.444\) & 0.303 \\  & M5p & 0.971 & 0.984 & \(-0.254\) & \(-0.261\) & 0.436 \\  & CHAID & 0.986 & 0.983 & \(-0.238\) & \(-0.240\) & 0.552 \\ \hline \multirow{4}{*}{**TS**} & RF & 1.035 & 0.961 & \(-0.020\) & \(-0.019\) & 0.834 \\  & M5 rule & 1.038 & 0.927 & \(-0.358\) & \(-0.328\) & 0.354 \\  & M5p & 1.014 & 0.956 & \(-0.282\) & \(-0.266\) & 0.413 \\  & CHAID & 1.044 & 0.936 & \(-0.181\) & \(-0.164\) & 0.508 \\ \hline \multirow{4}{*}{**FS**} & RF & 0.984 & 1.006 & \(-0.060\) & \(-0.061\) & 0.716 \\  & M5 rule & 0.914 & 1.043 & \(-0.278\) & \(-0.356\) & 0.400 \\ \cline{1-1}  & M5p & 0.934 & 1.018 & \(-0.355\) & \(-0.402\) & 0.353 \\ \cline{1-1}  & CHAID & 0.910 & 1.043 & \(-0.324\) & \(-0.380\) & 0.370 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistical measures of explained variance (EV) for all proposed models. uncertainties, respectively. Moreover, in other developed models, RF had the lowest uncertainty and satisfied the bandwidth criteria.
## 2 Results
### Comprehensive Review of Molecular Dynamics Simulations
Additionally, the solvent-accessible surface area (SASA) measurements show that apigenin, luteolin, and omaveloxolone are more stable in the solvent compared to the Cpd16 compound, which experienced significant fluctuations from 100 ns to 400 ns (Figure 1d). The average SASA values for apigenin, luteolin, omaveloxolone, and Cpd16 are 123.09 nm\({}^{2}\), 122.54 nm\({}^{2}\), 122.68 nm\({}^{2}\), and 123.32 nm\({}^{2}\), respectively. This suggests that apigenin and luteolin tend to form more stable and compact complexes with 4 IQK, reducing surface fluctuations and enhancing their interactions with the solvent. The MM-GBSA method delivers reliable calculations of binding free energy between a receptor and ligand by accounting for various energy components, including van der Waals interactions, electrostatic forces, and polar solvation energies [26]. The calculated binding free energies for apigenin, luteolin, omaveloxolone, and the Cpd16 compound are \(-\)67.039 kJ/mol, \(-\)71.136 kJ/mol, \(-\)50.269 kJ/mol, and \(-\)52.379 kJ/mol, respectively (Table 2). More negative binding energies indicate a stronger affinity towards the receptor protein, 4 IQK, which aligns with its lower RMSD values. Consequently, MD simulation investigations reveal that apigenin and luteolin have binding free energies comparable to those of the Cpd16 compound and omaveloxolone, forming strong, enduring, and energetically favorable interactions within the ligand binding site of the 4 IQK receptor molecule. Luteolin demonstrated the strongest binding, driven by its significant van der Waals contributions (\(-\)112.472 kJ/mol) and electrostatic interactions (\(-\)30.230 kJ/mol), closely followed by apigenin with van der Waals energy of \(-\)103.984 kJ/mol and electrostatic energy of \(-\)17.229 kJ/mol. These interactions ensure stability and specificity within the receptor pocket, counterbalancing the polar solvation penalties. Additionally, the favorable desolvation contributions highlight the strong hydrophobic interaction network established by both ligands, further enhancing their binding affinity. Notably, luteolin's slightly higher polar solvation penalty (83.023 kJ/mol) compared to apigenin (65.496 kJ/mol) is effectively countered by its robust van der Waals and electrostatic contributions, underscoring its superior overall binding efficiency. The nonpolar solvation energy also contributes positively, reflecting the stable hydrophobic environment around these ligands within the Keap1 pocket. These findings indicate that both apigenin and luteolin not only achieve strong binding but also exhibit interaction profiles that support their biological relevance and therapeutic potential. Apigenin, in particular, shows significant interaction with the Keap1 binding pocket suggesting that both apigenin and luteolin may serve as promising candidates for further drug development targeting the 4 IQK receptor. These insights into the energy contributions provide a deeper understanding of the hydrophobic interaction network.
# A New Driver Model Based on Driver Response
###### Abstract  
In this paper, a new microscopic traffic model based on forward and rearward driver response is proposed. Driver response is characterized using the distance and time headways. Existing models such as the Intelligent Driver (ID) model characterize traffic flow based on a constant acceleration exponent. This exponent reflects uniform driver behaviour during different conditions which is unrealistic. Driver response is slow with a large distance headway and quick with a short time headway. Conversely, it is quick with a small distance headway and slow with a long time headway. Thus, a new microscopic traffic model is proposed which incorporates driver response. Results are given that show the proposed model provides better traffic stability than the ID model as this stability is based on traffic physics. Further, for effective utilization of road infrastructure, shorter time and longer distance headways are preferred. The performance of the ID and proposed models was evaluated over an 800 m circular road with a string of 15 vehicles for 120 s. These models are numerically discretized using the Euler scheme. The results obtained show that traffic queue dissemination with the proposed model is more realistic than with the ID model and the changes in density with the proposed model are smaller. This is because traffic dissemination with the proposed model is based on traffic parameters rather than a constant exponent.  
microscopic traffic flow; driver response; acceleration exponent; intelligent driver model 2022
## 3 Result and Discussions
The hydrogen flowing into the discharging part shows a difference of less than 4% in the flow velocity due to the change in the inlet temperature after the measurement position X = 140 mm in the valve, indicating that the flow of hydrogen flowing into the discharging part has stabilized and the distribution of pressure and flow velocity is constant. The flow inside the check valve showed a similar trend for all hydrogen-inlet temperature conditions according to the hydrogen charging protocol. In this case, the flow rate increases proportionally as the differential pressure (\(\Delta\)P) increases, and the flow velocity increases as the hydrogen-inlet temperature (T\({}_{\text{in}}\)) increases under the same conditions as the differential pressure, so the change in hydrogen-inlet temperature seems to be the main factor affecting the flow-rate change. Through the correlation analysis of pressure and velocity changes with the initial hydrogen-inlet temperature change, it is found that the velocity increases as the hydrogen  
Figure 3: Pressure and velocity distribution results of check valve. (**a**) Results of pressure distribution; (**b**) Results of velocity distribution. inlet temperature increases in the on-board condition, but the effect on the pressure change of the check valve is insignificant.