esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Robustness Evaluation
#### 2.2.3 Robustness Benchmarks  
Currently, some studies have evaluated robustness benchmarks for object detection, most of which focus on robustness evaluation for 3D object detection. One study [36] established three corruption robustness benchmarks, including KITTI-C, nuScenes-C, and Waymo-C, and proposed 27 types of corruption for 3D object detection to test a lot of models. Another study [37] designed 16 real-world corruptions according to the real damages in camera sensors and image pipeline and chose some representative 2D object detectors to evaluate robustness. Moreover, an easy-to-use benchmark [38] to assess the robustness of object detectors was proposed. There are many studies about adversarial robustness. For example, both white- and black-box adversarial attacks [39] for object detectors were proposed to evaluate robustness.  
In summary, comprehensive evaluations of robustness benchmarks for object detection remain limited, and most of these studies are based on general object detectors without thoroughly analyzing factors causing corruption in real complex traffic environments. Additionally, they often overlook the importance of real-time performance in traffic-related tasks. Accordingly, our goal is to comprehensively analyze potential types of corruption in real traffic environments and to conduct a detailed robustness benchmark evaluation of representative real-time vehicle detection models, providing guidance for future directions in vehicle detection development.
# Remote Sensing Image Enhancement Based on Non-Local Means Filter in NSCT Domain
## 2 Theoretical Analysis
### Non-Local Means Filter  
Non-local means filter (NLM filter) is a very effective denoising method [26; 27]. This algorithm makes full use of the redundant information in the image. It can eliminate the noise in the image and keep the details of the image to their maximum extent. In this paper, we assume that the input image is \(y\), and the estimated value of the denoised image is \(x\); \(I\) is the image domain, and \(i\) is an arbitrary pixel in the image domain. The gray value of \(i\) can be obtained by weighing the average of all pixels in the image. The corresponding formula is expressed as follows:  
\[x(i)=\frac{\sum\limits_{i\in I}w(i,j)y(j)}{Z(i)} \tag{3}\]  
\[Z(i)=\sum\limits_{j\in I}w(i,j) \tag{4}\]  
where \(w\) presents the similar weight of the pixels \(i\) and \(j\). The value of this weight depends on \(y(N_{i})\) and \(y(N_{j})\). The formula for similar weight can be expressed as follows:  
\[w(i,j)=\exp\biggl{(}-\frac{d(i,j)}{h^{2}}\biggr{)} \tag{5}\]  
\[d(i,j)=\left\|y(N_{i})-y\bigl{(}N_{j}\bigr{)}\right\|^{2} \tag{6}\]  
where \(y(N_{i})\) is the gray vector value of the \((2k+1)\)\(\times\)\((2k+1)\) window; \(h\) is a smoothing parameter; \(N_{i}\) and \(N_{j}\) represent square neighborhoods of the fixed size, same size centered on pixels \(i\) and \(j\), respectively. In this paper, we set \(h\) = 5, \(k\) = 2, \(w\) = 10.
## 1 Introduction  
An individual's facial expressions (FEs) or countenance convey their psychological reactions and intentions in response to a social or personal event. These expressions convey non-verbal stealth messages. With technological advancements, human behavior can be understood through facial expression recognition (FER) [1]. To express emotions, humans use facial expressions as their primary nonverbal communication method [2]. Biometric authentication and nonverbal communication applications have recently drawn considerable attention to facial recognition. The movie industry has also conducted studies that predict emotions experienced during scenes. These works sought to identify a person's mood or emotion by analyzing facial expressions.  
With landmarks in 2D and 3D [3], facial appearances [4], geometry [5], and 2D/3D spatiotemporal representations [6], facial models can be developed. Review papers provide comprehensive reviews [6, 7, 8]. It is also possible to categorize approaches based on images, deep learning, and model-based approaches. Engineered topographies such as HOGs [9], local binary pattern histograms (LBPHs) [10], and Gabor filters [11] are used in many image-based approaches.  
The most recent works in this field focus on hand-engineered features, but various techniques have been developed [12, 13, 14, 15]. In today's image processing and computer vision applications, deep neural networks are the best choice due to the variety and size of datasets [16; 17; 18]. Conventional deep networks can easily handle spatial images [18]. Traditional feature extraction and classification schemes are computationally complex and difficult to use for achieving high recognition rates. A DNN based on convolutional coatings and residuals is proposed in this paper for recognizing facial emotions [19; 20]. By learning the subtle features of each expression, the proposed model can distinguish them [21; 22]. The proposed model presents a facial emotion classification report, along with the confusion matrix derived from the image dataset.  
Afterwards, Section 2 presents a literature review with a deeper look at facial expression recognition based on deep learning. DBN and spatiotemporal CNN are integrated into Section 3 of the future model methodology. Section 4 grants the two sub-sections, the first sections present the three datasets, and the second section presents the untried outcomes and analysis. In the last section, we present the conclusion of the paper.
# Robust and Accurate Wi-Fi Fingerprint Location Recognition Method Based on Deep Neural Network
## 3 Theoretical Frameworks  
### Overview  
Figure 1 shows the architecture of Wi-Fi fingerprint location recognition method based on a DNN. The method has two phases: an offline training phase and online positioning phase. During the offline phase, the raw \(RSS\) of different Wi-Fi APs on the selected fingerprint points in the area of interest is recorded. We then trained the DNN and extracted features from the noisy \(RSS\) to construct an offline feature-weighted fingerprint database. During the online phase, the real-time AP information observed on unknown points is transferred to the feature fingerprint database trained in the offline phase to estimate the floor or location of the current user. We chose constrained Kalman filtering and hidden Markov model (HMM) to optimize the results of dynamic and static positioning, respectively, in order to reduce the number of outliers and obtain more accurate positioning results with regard to different motion modes of users.
## 4 Results and Discussion  
### Contribution of Traffic CO Predictors  
As shown in Table 4, the traffic CO predictors make different contributions to the traffic CO values that resulted from this study. The statistical analysis based on the Chi-square method shows that the parameters that contributed the most are the number of heavy vehicles (F = 32.784) and the number of vehicles (F = 18.277). Conversely, the findings indicated that the other traffic CO predictors (number of motorbikes, DSM, wind speed and temperature) did not make a significant contribution (Table 4).
## 2 Concepts and Methods
### De-Speckled Image HMS Noise Behavioral Modeling
The results of this are shown in Figure 5 and listed in Table 2. As can be deduced, the shape, the distribution and the peaks of the HMS presence within the texture after de-speckling filter implementation are completely different. The minimum presence level of HMS is 0.14 dB, which is observed when the Local sigma has been implemented, and the maximum presence value is 0.62 dB for the Gamma de-speckling filter implementation. Figure 4: Resulting PSD modeling (**a**) comparison (**b**) difference with the reference SAR image. Figure 3: SAR images with different de-speckled filters and their relative PSD modeling (**a**) Frost; (**b**) Frost PSD; (**c**) Gamma; (**d**) Gamma PSD; (**e**) Kuan; (**f**) Kuan PSD; (**g**) Lee; (**h**) Lee PSD; (**i**) Lee sigma; (**j**) Lee sigma PSD; (**k**) Local adaptive median; (**l**) Local adaptive median PSD; (**m**) Local sigma; (**n**) Local sigma PSD. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Column** & **De-Speckling Filter** & **Value** \\ \hline
1 & Local sigma & 0.14 dB \\
2 & Kuan & 0.14 dB \\
3 & Local adaptive median & 0.18 dB \\
4 & Lee sigma & 0.31 dB \\
5 & Frost & 0.36 dB \\
6 & Lee & 0.60 dB \\
7 & Gamma & 0.62 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Residue HMS presence evaluation after de-speckling implementation. Figure 5: PSD of the residue HMS after the de-speckling procedure (**a**) Frost; (**b**) Gamma; (**c**) Kuan; (**d**) Lee; (**e**) Lee sigma; (**f**) Local adaptive median; (**g**) Local sigma; (**h**) Comparison of the residue HMS. From Figure 5, it can be deduced that the Kuan and Local sigma filters have the least amount of residue HMS in the texture after de-speckling, while according to Table 1, their performance in HMS suppression was just moderate. It also verifies that spatial de-speckling filter implementation not only affects the texture consistency after HMS removal, but also changes the HMS distribution and statistics based on amplitude, intensity, and phase. In other words, there has always been residue HMS with non-specific patterns within the texture after de-speckling.
# A Compact Monopole Wideband Antenna Based on DGS
## 3 Simulation Results and Analysis
### Surface Current Distribution  
The antenna shows distinct characteristics due to the L-slot and semi-circular slot defects in the ground plane. Analyzing the surface current distribution at frequencies of 2.5 GHz, 7.5 GHz, 12.5 GHz, and 17.5 GHz, Figure 9a-d offers insights into the antenna's performance. At low frequency (2.5 GHz), the surface current is mainly concentrated at the edges of the patch, indicating strong coupling with the ground. The L-slot has minimal impact on current distribution, with most current remaining within the patch. At mid frequency (7.5 GHz), as frequency increases, the surface current patterns become more complex. The current spreads more uniformly across the patch while maintaining significant edge currents. The L-slot enhances impedance matching and bandwidth, while the semi-circular slot improves radiation characteristics. Near center frequency (12.5 GHz); at this frequency, the current distribution is well-balanced, with strong currents in the patch and around the slots. Ground plane defects enhance radiation efficiency, resulting in a low S\({}_{11}\) parameter (\(-\)48.59 dB) and low VSWR (1.01). This reflects optimal antenna performance and effective energy radiation. At high frequency (17.5 GHz), the current distribution indicates operation in a higher order mode, with currents concentrated towards the center of the patch. The slots enable a unique distribution that supports higher frequency operation, achieving a peak gain of about 7.4 dB. This shift in distribution affects radiation patterns and overall gain. Overall, the surface current distribution analysis reveals key insights into the patch antenna's performance. The L-slot and semi-circular slot effectively optimize current flow, enhancing bandwidth and gain. As frequency increases, the current distribution becomes more complex, leading to improved radiation efficiency at the target frequency of 11.5 GHz. This analysis underscores the importance of ground plane modifications in achieving desired antenna characteristics.  
Figure 9: Surface current distribution at different frequencies.
# Prediction Method of Blast Wave Impact on Crew Module for Liquid Rocket Explosion on Launch Pad
## 5 Influence Analysis of Key Parameters in Simulation
### Influence of Grid Sizeon Simulation Results  
The accuracy of numerical results is highly sensitive to the grid size [36]. Therefore, it is necessary to analyze the convergence of grid size in numerical simulation to determine the appropriate grid size. In order to study the influence of grid size on numerical calculation results, seven kinds of grid sizes [37] are selected that range from 8 cm to 20 cm. In addition, the gradient grid is used to divide the air computing domain into seven parts, with grid sizes from 8 cm to 15 cm. A total of 3,241,252 grids are divided in the gradient grid model. Grid number is between the number of grid size of 8 cm and 10 cm, and the calculation time was also between them, as is shown in Table 10.  
As can be seen from Figure 5, the effect of grid size on numerical calculation results depends on the scaled distance. When the scaled distance is less than 0.60 m\(\cdot\)kg\({}^{-1/3}\), grid size has great influence on the calculation accuracy. For example, when scaled distance is 0.53878 m\(\cdot\)kg\({}^{-1/3}\), peak overpressure of the model with grid size of 8 m is 5.22 MPa, while that of the model with grid size of 20 cm is 2.39 MPa. The former is 2.18 times the later. With the increase of scaled distance \(R\), the influence of grid size on the results decreases gradually. What is more, for the same scaled distance, as the grid size decreases, the peak overpressure increases graduallyand finally tends to be stable. This is because the smaller the mesh, the better it responds to pressure changes. When the scaled distance is greater than 0.60 m\(\cdot\)kg\({}^{-1/3}\), the peak overpressure of models with different grid sizes tends to be consistent.  
Therefore, the grid size greatly influences the calculation results of the model. The gradient grid can improve the computational efficiency under the premise of accurately reflecting the propagation law of blast wave. The gradient grid will be used in the numerical simulation.
## 2 Classical Topologies
### Input Impedance Behavior  
As concluded before, the PS and PP topologies presented a convenient behavior in relation to the reduction of the coupling coefficient. However, the use of parallel compensation in the primary makes the use of voltage sources to supply the circuit unfeasible. In applications subject to biocompatibility restrictions, sources that oscillate in the order of MHz are not easily implemented by means of conventional current inverters. Therefore, a proposal that appears feasible is the use of Class-E amplifiers. The major drawback of using amplifiers as current sources is that they must be designed considering the load impedance.  
Figure 6: Input current as a function of \(\kappa\). (**a**) SS. (**b**) SP. (**c**) PS. (**d**) PP.  
Thus, the evaluation of \(Z_{1}\) becomes essential, as the parametric variations of the circuit can compromise the impedance matching between the amplifier and the wireless transfer circuit. Figures 7 and 8 show, respectively, the magnitude and angle of the input impedance as a function of frequency. Figures 9 and 10 show, respectively, the impedance modulus and angle as a function of the coupling coefficient.  
Through Figure 7, it is notable that, at frequencies different from nominal, the impedance modulus tends to increase for the SS and SP compensations and to decrease for PS and PP. In Figure 8, one can conclude that, for the SS and SP topologies, the load tends to be inductive for frequencies lower than nominal and capacitive for higher. On the other hand, Figure 8c,d show that, for the PS and PP topologies, for frequencies lower than the design frequency, \(Z_{1}\) presents an inductive behavior and, for higher frequencies, a capacitive behavior.  
With respect to the behaviors of the topologies in relation to variations in the coupling coefficient, the data suggest that topologies exhibiting parallel resonance in the primary, as depicted in Figure 9c,d, exhibit an increase in their impedances under conditions where the coupling coefficient, \(\kappa\), is diminished. Given that reductions in the coupling coefficient can be anticipated due to commonplace operational misalignment and offsets, this characteristic behavior confers a marked advantage to these particular topologies.  
In Figure 10, it is noteworthy to highlight the SS topology's consistent null angle throughout the entire range of \(\kappa\). This conclusively demonstrates that fluctuations in mutual inductance do not introduce imaginary components to the equivalent impedance. Further, based on the observations from Figure 10b-d, it can be inferred that coil misalignments or displacements manifest as discernible inductive or capacitive segments in the input current.  
Figure 8: Angle of \(Z_{1}\) as a function of frequency. (**a**) SS. (**b**) SP. (**c**) PS. (**d**) PP.  
Figure 9: \(|Z_{1}|\) as a function of \(\kappa\). (**a**) SS. (**b**) SP. (**c**) PS. (**d**) PP.
## 3 Results and Discussion
### The Dependency of SIP Parameters on Pore Fluid Conductivity  
[PERSON] and [PERSON] reported a strong relationship between pore fluid salinity and chargeability [15]. In particular, they specified that increasing salinity and decreasing chargeability correlate with the 150 \(\upmu\)m grain size graphite if the sample contains 5% graphite. However, [PERSON] et al. found the chargeability to be independent for measurements varying the pore fluid in a conductivity range between 0.1 and 10 S/m [33]. To better evaluate the pore fluid dependency of our SIP signatures (and the derived petrophysical model), we conducted SIP measurements in the lab, varying the pore fluid conductivity at different intervals between 0.03 and 0.23 mS/m on ground samples as well as commercial graphite with well-defined grain size.  
Figure 9a shows that the chargeability (M) of commercial graphite samples and site-specific rock samples (volumetric content is 2.01% in all samples; values are presented in  
Figure 8: Cole-Cole parameters in terms of (**a**) chargeability and (**b**) normalized chargeability versus the volume percentage covered by a plug within the measurement cell.  
Table 5) decreases exponentially when increasing the pore fluid conductivity (\(\sigma_{f}\)) in a range from 0.005 S/m to 0.25 S/m. Here we observe the highest chargeability values (M = 0.7) for commercial graphite samples (grain size \(<\) 0.07 mm), which is more than two times higher than the value observed for the ground samples d3 (M = 0.3 for). Figure 9b shows a clear linear relationship between the normalized chargeability (M\({}_{\rm n}\)) and \(\sigma_{f}\). Additionally, we observe in Figure 9c that the relaxation time (\(\tau\)) decreases exponentially when increasing \(\sigma_{f}\) from 0.005 S/m to 0.25 S/m.  
Similar to [PERSON] and [PERSON] and [PERSON] et al., our results demonstrate the dependency on fluid conductivity of SIP signatures in graphite measurements [13; 15]. For our experiments, we applied a solution in the conductivity range between 0.005 and 0.25 S/m, corresponding to the pore fluid conductivity we measured at sites with no visible graphite on the surface and graphite outcrop. Furthermore, we conducted measurements with increasing pore fluid salinity on samples collected on the field and found the same behavior: a decrease in chargeability with increasing salinity as well as an increase in the normalized chargeability with increasing the fluid conductivity (Table 5). However, in the case of the grain size d3 and fluid conductivity above 0.08 S/m, the chargeability remains constant. [PERSON] et al. found that in the case of big particles (d > 60 \(\mu\)m), where the volume diffusion dominates, the relaxation time has negligible sensitivity to the changes in the pore fluid conductivity [28]. However, it is sensitive to the reaction current. We observe a decrease in relaxation time with increasing fluid conductivity. The decrease is larger at low salinity (<0.05 S/m) with almost constant relaxation time values at high salinity (>0.05 S/m).
## 3 Results
### Content and Predictive Validity
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Kinematic Variables** & **Mean \(\pm\) SD** & **Min–Max** & **CV\%\%** & **CV\%** & **SWC\%\%** & **SWC\%\%** & **SE** & **Sensitivity** & **ICC (Cl95\%)** \\ \hline
**Velocity [m/s]** & **10.03 \(\pm\) 0.45\({}^{\mathrm{v}}\)** & **8.57** & **10.87** & **4.47** & **0.78** & **0.09** & **0.22** & **0.06** & **GOOD** & **0.98 (0.95–0.99) \\ Trial 1 & 10.05 \(\pm\) 0.45 & 8.56 & 10.76 & & & & & & & \\ Trial 2 & 10.00 \(\pm\) 0.45 & 8.57 & 10.87 & & & & & & & \\ **Glide time [ms]** & **433 \(\pm\) 27\({}^{\mathrm{v}}\)** & 377 & **503** & **6.15** & **3.72** & **5** & **13** & **14** & **MARGINAL** & **0.69 (0.37–0.85) \\ Trial 1 & 437 \(\pm\) 28 & 385 & 500 & & & & & & & \\ Trial 2 & 437 \(\pm\) 28 & 377 & 503 & & & & & & & \\ **Double support time [ms]** & **82 \(\pm\) 12\({}^{\mathrm{v}}\)** & 55 & **115** & **15.06** & **9.79** & **2** & **6** & **8** & **MARGINAL** & **0.57 (0.14–0.79) \\ Trial 1 & 84 \(\pm\) 13 & 58 & 115 & & & & & & & \\ Trial 2 & 80 \(\pm\) 16 & 55 & 114 & & & & & & & \\ **Glide length [m]** & **4.35 \(\pm\) 0.32\({}^{\mathrm{v}}\)** & **3.56** & **4.98** & **7.24** & **3.79** & **0.06** & **0.15** & **0.14** & **GOOD** & **0.78 (0.53–0.89) \\ Trial 1 & 4.39 \(\pm\) 0.34 & 3.52 & 4.92 & & & & & & & \\ Trial 2 & 4.31 \(\pm\) 0.35 & 3.55 & 4.98 & & & & & & & \\ **Double support length [m]** & **0.84 \(\pm\) 0.15\({}^{\mathrm{v}}\)** & **0.54** & **1.55** & **18.05** & **9.92** & **0.03** & **0.08** & **0.09** & **MARGINAL** & **0.64 (0.26–0.83) \\ Trial 1 & 0.85 \(\pm\) 0.14 & 0.57 & 1.17 & & & & & & & \\ Trial 2 & 0.83 \(\pm\) 0.21 & 0.54 & 1.55 & & & & & & & \\ **Step length [m]** & **3.51 \(\pm\) 0.25** & **2.90** & **4.00** & **7.06** & **3.28** & **0.05** & **0.12** & **0.11** & **GOOD** & **0.79 (0.57–0.90) \\ Trial 1 & 3.52 \(\pm\) 0.28 & 2.81 & 4.11 & & & & & & & \\ Trial 2 & 3.50 \(\pm\) 0.26 & 2.89 & 4.00 & & & & & & & \\ **Step frequency [step/s]** & **2.89 \(\pm\) 0.21** & **2.51** & **4.23** & **7.41** & **4.52** & **0.04** & **0.11** & **0.23** & **MARGINAL** & **0.26 (–0.48–0.62) \\ Trial 1 & 2.85 \(\pm\) 0.18 & 2.38 & 3.16 & & & & & & & \\ Trial 2 & 2.94 \(\pm\) 0.36 & 2.51 & 4.23 & & & & & & & \\ \hline \hline \end{tabular} Legend: ms = milliseconds; SD = standard deviation; Min = minimum value; Max = maximum value; CV%(HS) = between-subject coefficient of variation; CV%(HS) = within-subject coefficient of variation; ICC = intra-class correlation coefficient; SWC = smallest worthwhile change; TE = typical error of measurement; Cl95% = 95% confidence interval; \(\mathrm{V}=\) significantly different from the acceleration phase, \(p<0.01\).  
\end{table} 
Table 3: Descriptive and reliability parameters for the kinematic variables during the steady-state phase.