esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Perceptual Consistency  
In previous self-supervised depth estimation networks such as Monodepth, the reconstruction work is supervised by minimizing the difference between the input image and its reconstruction image on a pixel level. However, in the low texture and color fluctuation areas, the ignorance of high-level semantic information affects the photometric consistency.  
For the low-texture areas, we present the reasons in detail by calculating the gradient through the reconstruction loss [22]. Assuming that \(\mathcal{L}\) is the direct loss of the original image and the generated image at the pixel level, the gradient of \(\mathcal{L}\) on \(d_{ij}\) is calculated as follows:\[\frac{\partial\mathcal{L}}{\partial d_{ij}^{l}}=\frac{\partial\ell\left(I_{ij}^{l},I_{ij+d_{ij}^{l}}^{r}\right)}{\partial I_{ij+d_{ij}^{l}}^{r}}\cdot\frac{ \partial I_{ij+d_{ij}^{l}}^{r}}{\partial\left(ij+d_{ij}^{l}\right)}\cdot\frac{ \partial\left(ij+d_{ij}^{l}\right)}{\partial d_{ij}^{l}}. \tag{5}\]  
The gradient of reconstruction loss mainly depends on the \(\frac{\partial I_{ij+d_{ij}^{l}}^{r}}{\partial\left(ij+d_{ij}^{l}\right)}\). In the low texture region, the gradient is close to zero so that the value of the entire equation closes to zero, resulting in the disappearance of the image gradient. However, high-level semantic features such as contours in the low-texture regions enhance the feature representation, greatly increase the image gradient, and improve the prediction capability of the model.  
As for the color fluctuation regions, it is unreliable to use RGB values to evaluate the image reconstruction effect; however, the semantic features such as shapes and sizes of the objects in this region will not be affected by external factors; therefore, using high-level semantic features to evaluate the effect of image reconstruction will produce more reliable results.  
Therefore, the perceptual loss is utilized to solve the above problems by ensuring multi-scale feature consistency. In this section, we describe our approaches, including feature extraction, feature aggregation, and computation of perceptual loss.
## 5 Numerical Example for the MDOF System  
Consider a three-degree-of-freedom model for numerical studies, where we are going to suppress the two lowest natural modes. So, in this case, \(n=3,\;m=2\). The masses, spring constants, damping coefficients, and gains are as follows:  
\[m_{1}=m_{2}=m_{3}=1\;\text{kg},\;k_{1}=k_{2}=k_{3}=100\;\text{N}/ \text{m}\] \[c_{1}=c_{2}=c_{3}=0.1\;\text{Ns}/\text{m},\;g_{1}=g_{2}=0.1\]  
The damping factor of the PIPO controller is set to \(\zeta_{f}=0.3\). Figure 13 shows magnitude and phase plots for this problem, where the peak amplitudes decrease as expected. Therefore, it can be said that the proposed SIMO PIPO controller is effective, even for the MIMO system. Figure 14 shows the uncontrolled and controlled impulse responses. Using logarithmic decrement, the damping factor of the uncontrolled system was calculated as 0.007, and the damping factor of the closed-loop controlled system was calculated as 0.068. Figure 15 shows the uncontrolled and controlled impulse responses, and that the MIMO PIPO controller can suppress vibrations effectively.  
Figure 13: The bode diagram of the closed-loop system. (**a**) Magnitude; (**b**) Phase.
# Metal Release and Cell Viability of 316L Stainless Steel Sputter-Coated with N-Doped a-C:H Coatings
###### Abstract  
Hydrogenated amorphous carbon (a-C:H) has been considered a promising biocompatible coating to protect metallic alloys against corrosion for medical applications, namely orthodontics. However, there is still no optimal solution for this biomedical field; hence, the investigation remains open. In this work, the effect of a nonmetallic doping element (N) on sputter-deposited a-C:H coatings was studied concerning both salivary corrosion and cytotoxicity behavior. After a 30-day corrosion test in an acidic modified Fusayama-Meyer artificial saliva, metal release from both coated and uncoated 316L stainless steel (SS) substrates was quantified. Tests on the corrosion extracts were then performed by using monocultures of macrophages and fibroblasts, and their coculture; and cell viability was evaluated via the MTT test. Results show an overall inhibition of the SS corrosion, which enhanced the in vitro biocompatibility with a minimal effect on the coatings' microstructure. Among all the coatings tested, the undoped a-C:H coating performed the best, whereas an increase in N doping led to poorer protection against metal dissolution and a subsequent slightly lower biocompatibility. The findings corroborate that selecting the nonmetallic element N for doping C-based coatings is not a good choice for this biomedical field, even at low contents up to 10 at.%.  
cytotoxicity; corrosion; DLC coatings; hydrogenated amorphous carbon (a-C:H); metal release; nitrogen doping; orthodontics; protective coatings; sputtering +
Footnote †: journal: Applied sciences
# Time-Frequency Analysis of Daily Activities for Fall Detection
## 2 Materials and Methods
### Data Analysis  
The most common and simple method for fall detection uses a triaxial accelerometer to detect a fall event [4; 26]. In these algorithms, the magnitude of acceleration is computed following Equation (1):  
\[\text{Acc}=\sqrt{\left(\text{A}_{\text{x}}\right)^{2}+\left(\text{A}_{\text{ y}}\right)^{2}+\left(\text{A}_{\text{z}}\right)^{2}} \tag{1}\]  
where \(\text{A}_{\text{x}}\), \(\text{A}_{\text{y}}\), \(\text{A}_{\text{z}}\) are the acceleration (g) in the x, y, z axes, respectively. If Acc surpasses a predetermined critical threshold, the activity is designated as a fall event. While simple, sensitivity and specificity of these fall algorithms range between 80-95%. To achieve high detection rates, the critical threshold is lowered. However, this results in increased false positives. Clearly, there is a trade-off between high sensitivity (i.e., correctly detecting a fall event) and high specificity (i.e., false positives). We analyze collected data to characterize the range of peak accelerations for each activity using Equation (1).  
In addition to examining the time-domain signal, acceleration data is also analyzed in the frequency domain to characterize spectral information of various daily activities  
Figure 1: Body and sensor orientation.  
including falls. For this, the power spectral density (PSD) of the acceleration signal is computed for each activity using:  
\[\text{PSD}=\sum\frac{\left|\text{FFT(A)}\right|^{2}}{\text{Length(A)}} \tag{2}\]
## 5 MPPT Algorithms
### Benchmark Algorithms  
To ensure a fair and consistent comparison across all MPPT algorithms evaluated in this study, a common benchmarking strategy was adopted. Table 13 summarizes the key implementation parameters defined for each method, including initial duty cycle, perturbation step size, input variables, and control structure. This standardized setup ensures that observed performance differences are driven by algorithmic behavior rather than configuration disparities.  
Additionally, for the hybrid algorithms GA-P&O and GA-Inc, a small exponential moving average (EMA) filter was applied to the input measurements of panel voltage and current (\(V_{pv}\) and \(I_{pv}\)) in order to mitigate the effect of high-frequency measurement noise. The filtered signal is computed using Equation (16) [35; 36]:  
\[x_{f}(k)=\alpha\cdot x(k)+(1-\alpha)\cdot x_{f}(k-1), \tag{16}\]  
where \(x\) represents the measured signals \(V_{pv}\) and \(I_{pv}\), and \(\alpha=0.01\) is the smoothing coefficient. This filtering strategy enhances the stability of the GA-based controllers by providing cleaner input data during operation.
## 3 Results
### Flexural Modulus  
The results of the three-point bending test are summarized in Table 3, and corresponding boxplots are provided in Figure 4. The mean flexural moduli ranged between 1960 MPa and 2762 MPa for the untreated groups. After autoclaving, the mean flexural modulus was lower for each resin/printer combination, ranging from 1205 MPa to 2466 MPa and from 1337 MPa to 2454 MPa for cycles 1 and 2, respectively. However, in the majority of groups, these data failed significance.  
A qualitative difference was noted in group 1 (ANOVA, \(p\) = 0.035). The post hoc test revealed a significantly lower flexural modulus following cycle 2 autoclaving compared to the untreated group (\(t\)-test, \(p\) = 0.045). In contrast, no differences were seen following cycle 1 autoclaving compared to the untreated control (\(t\)-test, \(p\) = 0.085). However, there were no significant differences in flexural modulus between the two autoclaving protocols (\(p\) > 0.999).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & \multicolumn{2}{c}{**Flexural Modulus**} & \multicolumn{2}{c}{**ANOVA**} & \multicolumn{2}{c}{**Bonferroni-Corrected Post Hoc Test**} \\  & & **E\({}_{\text{f}}\) [MPa]** & & & & **(Students-t, If p\({}_{\text{A}}\) \(<\) 0.05)** & \\ \cline{3-8}
**Group \({}^{\$}\)** & **Subgroup \({}^{\$}\)** & **M \(\pm\) SD** & **F (2,12)** & **PF** & **Comparison** & **t (8)** & **Pt** \\ \hline \multirow{3}{*}{0} & untreated & 1960 \(\pm\) 405 & & & - & - & - \\  & cycle 1 & 1738 \(\pm\) 494 & 0.322 & 0.731 & - & - & - \\  & cycle 2 & 1812 \(\pm\) 261 & & & - & - & - \\ \hline \multirow{3}{*}{1} & untreated & 2762 \(\pm\) 88 & & & untreated & & 2.671 & 0.085 \\  & cycle 1 & 2466 \(\pm\) 203 & & 4.492 & 0.035 \({}^{\star}\) & & untreated & \\  & cycle 1 & 2466 \(\pm\) 203 & & & & vs. cycle 2 & & \\  & cycle 2 & 2454 \(\pm\) 179 & & & & cycle 1 vs. & 0.089 & \textgreater{}0.999 \\ \hline \multirow{3}{*}{2} & untreated & 1710 \(\pm\) 194 & & & - & - & - \\  & cycle 1 & 1205 \(\pm\) 204 & 1.810 & 0.206 & - & - & - \\  & cycle 2 & 1337 \(\pm\) 613 & & & - & - & - \\ \hline \multirow{3}{*}{3} & untreated & 2280 \(\pm\) 1108 & & & - & - & - \\  & cycle 1 & 2050 \(\pm\) 542 & 0.181 & 0.837 & - & - & - \\  & cycle 2 & 2372 \(\pm\) 551 & & & - & - & - \\ \hline \multirow{3}{*}{4} & untreated & 2654 \(\pm\) 338 & & & - & - & - \\  & cycle 1 & 2440 \(\pm\) 564 & 0.580 & 0.575 & - & - & - \\ \cline{1-1}  & cycle 2 & 2367 \(\pm\) 536 & & & - & - & - \\ \hline \end{tabular} \({}^{\star}\)_p \(<\) 0.05; \({}^{\$}\)_n_ = 15 for each of the 5 groups; \({}^{\$}\)_n_ = 5 for each of the 15 subgroups; \({}^{\$}\)_p_ \(p\)-value from ANOVA; \({}^{\$}\)_p_ \(p\)-value from Student’s \(t\)-test.  
\end{table}
Table 3: Results of the three-point bending test and statistical analysis.  
Figure 4: Boxplots: flexural moduli of the different groups and subgroups. The x represents the mean, the circles represent data points not covered by the boxplots.
## 1 Introduction
### Contributions  
The contributions of this paper are to:  
* Design a fast convolution filter-bank (FC-FB) based non-orthogonally multiplexed cognitive radio (NOMCR) system.
* Devise a neuromorphic receiver structure which exploits the spectral redundancy features in the superimposed PU and SU signals to jointly equalize the channel and extract the desired SU signal under interference limited non-orthogonal spectrum sharing scenarios.  
The paper focusses on communication in a cyclostationary interference limited spectrum sharing environment, where the PU and SU transmissions are non-orthogonally superimposed. In this work, the cyclostationarity characteristics of the superimposed PU and SU signals are taken into account, as well as the non-linear prediction capability of the de-noising neural network. These two features are utilized to design a neuromorphic FRESH receiver, which is a combination of an adaptive cyclostationary interference canceller and a channel equalizer. The proposed technique is capable of achieving good BER performance as well as better signal-to-interference and noise-ratio (SINR) compared to the conventional recursive least squares (RLS) based FRESH receiver.
## 3 Results
### Objective 2: Land Cover Classification  
Objective 2 was to _establish effective data classification methods to quantify land cover from both before and after planned disturbances_ and was achieved by testing the various segmentation, sampling, and classification algorithms for the prescribed prairie burn (PWA) and selective timber harvest (Volz) datasets. The same segmentation parameters were used for both the PWA and Volz datasets--17 spectral detail, 10 spatial detail, and 80 minimum segment size. Then, classification schemas were established for both the NDBM treatment and land cover type. A total of 65 samples were collected for each of the four classes in the PWA prescribed prairie burn datasets (Figure 10), while the Volz selective timber harvest datasets required 80 samples for each of the three classes to produce similar quality land cover classifications (Figure 11). A support vector machine (SVM) classification algorithm was used to classify each of the datasets, and an accuracy assessment of 250 equalized stratified random (ESR) points to produce confusion matrices for the resulting classifications was conducted (Tables 14 and 15).
## 5 Pre-Test and Pre-Test Data Analysis
### Discussion of Pre-Test  
The proposed VRET system has integrated system and bio-data collection for real-time data analytics compared to previous VRET research. In the [PERSON] et al. VRET pilot study, anxiety and habituation during the VRET sessions were assessed using ratings on subjective units of a distress scale as well as the heart rate [20]. [PERSON] et al. used subjective discomfort scores and heart rate to measure subjects' physiological response and sense of presence [21]. Additionally, in [PERSON] et al.'s fear of driving in tunnels research, questionnaires measured the outcome of the experiment and no real-time data was collected [22]. Most pervious VRET literature uses heart rate for real-time data tracking. Temperature, respiration, and skin conductance are for real-time data tracking in our VRET system. These preliminary design and development efforts are for the construction of a self-adaptive VRET system that will automatically adjust the levels of exposure according to real-time data tracking. The proposed future system will be tested for improvements to the immersive experience and to better ensure subjects' safety.  
Figure 12: Shewhart control chart of skin conductance amplitude data.  
Figure 13: Shewhart control chart of standardized heart rate data.  
Compared to previous VRET research which only demonstrates the development of VRET without verification, this research has uniquely combined VRET design and development with the verification of its efficacy through pre-test. According to the results of Mann-Whitney U test, there is a significant difference between the overall satisfaction medians of high anxiety groups and low anxiety groups. Moreover, the results of ANOVA analysis show that bio-data and level are positively correlated except for Level 1. Therefore, through pre-test data analysis, VR is demonstrated to provide the subject with a sense of authenticity and can indeed cause anxiety similar to actual driving.  
The problem of high level 1 bio-data is also emerging within pre-test data analysis. For this problem, follow-up studies may consider adding a ten minutes of VR experience before the experiment starts to assist the subjects to adapt to the VR environment and to improve the VRET system for official clinical trials.
## 4 Experiments
### Comparison Methods  
We first established baseline performance with the milestone VPR models NetVLAD [20] and GeM [22]. For more recent and robust learning-based models, we experimented with Conv-AP [43], Patch-VPR [24], TransVPR [49], CosPlace [31], MixVPR [21], and EigenPlaces [9]. Finally, we compared our proposed CriSALAD model with AnyLoc [18], Crica-VPR [19], and DiNO-SALAD [23], three recent VPR models that leverage visual foundation models like DiNOv2. To ensure a fair comparison, we re-trained all models with the same training dataset, i.e., GSV-Cities [43], except for CosPlace and EigenPlaces, which were trained on a different large-scale dataset (SF-XL [31]), and AnyLoc, which did not require re-training. For the training batch size, CricaVPR followed the original paper by using a training batch size of 288, while the other methods maintained consistency with our current training batch size of 192. Moreover, we disabled the principal component analysis (PCA) dimension reduction step in all models and used the output dimension of the global aggregator directly.
## 3 Motion Mapping
### Locomotion Mapping
#### 3.5.1 Body Rotation  
The vector pointing from Hip_Left (point 17) to Hip_Right (point 21), \(\vec{V}_{\text{17-21}}\), is introduced as the reference for the rotations of the human body. The corresponding vectors, denoted by \(\vec{V}_{\text{c17-21}}\) for the current locomotion loop and \(\vec{V}_{\text{p17-21}}\) for the previous one, are obtained for every pair of adjacent locomotion loops. Then, \(\vec{V}_{\text{c17-21}}\) and \(\vec{V}_{\text{p17-21}}\) are projected onto the horizontal plane (the \(x\)-\(z\) plane in the Kinect frame) to eliminate the errors caused by vertical deviations. The projections are denoted by \(\vec{V}^{\prime}_{\text{c17-21}}\) and \(\vec{V}^{\prime}_{\text{p17-21}}\), respectively, and the angle between these two vectors, \(|\omega|\), is calculated as  
\[|\omega|=\arccos(\frac{\vec{V}^{\prime}_{\text{c17-21}}\cdot\vec{V}^{\prime}_{ \text{p17-21}}}{|\vec{V}^{\prime}_{\text{c17-21}}||\vec{V}^{\prime}_{\text{p17- 21}}|}), \tag{24}\]  
where \(\vec{V}^{\prime}_{\text{c17-21}}\) and \(\vec{V}^{\prime}_{\text{p17-21}}\) are collinear with the \(y\)-axes of the corresponding human base frames, as shown in Figure 9.  
To determine whether the robot should turn clockwise or counterclockwise, the vector representing the direction of the lower torso on the horizontal plane in the previous locomotion loop, \(\vec{N}^{\prime}_{\text{pLT}}\), is introduced as follows:  
\[\vec{N}^{\prime}_{\text{pLT}}=\vec{V}_{\text{up}}\times\vec{V}^{\prime}_{ \text{p17-21}}, \tag{25}\]  
where \(\vec{V}_{\text{up}}\) is a vertically upward vector. \(\vec{N}^{\prime}_{\text{pLT}}\) is also collinear with the \(x\)-axis of the previous human base frame, as seen from Figure 9. Then, the angle \(\theta_{\omega}\) is introduced as follows:  
\[\theta_{\omega}=\arccos(\frac{\vec{V}^{\prime}_{\text{c17-21}}\cdot\vec{N}^{ \prime}_{\text{pLT}}}{|\vec{V}^{\prime}_{\text{c17-21}}||\vec{N}^{\prime}_{ \text{pLT}}|}). \tag{26}\]  
Thus, \(\omega\) can be obtained as follows:  
\[\omega=\begin{cases}|\omega|,&\text{if }\theta_{\omega}\leq\frac{\pi}{2},\\ -|\omega|,&\text{if }\theta_{\omega}>\frac{\pi}{2}.\end{cases} \tag{27}\]  
A positive value means that the human demonstrator is turning counterclockwise, whereas a negative value means that the demonstrator is turning clockwise. If \(|\omega|\) meets or exceeds a threshold value \(\omega_{0}\), the robot will rotate with angle \(\omega\).
# Modeling Palletized Products: The Case of Semi-Filled Bottles under Top-Load Conditions
## 3 Experiments  
### Experimental Evidence  
During the experimental tests, five 1.5 L water bottles (h = 310 mm, \(\phi\) = 87 mm) were considered as specimens. Compressive tests were realized on an Instron 8032 servo-hydraulic universal testing machine with a 2517-005 load cell with \(\pm\)250 kN dynamic and \(\pm\)500 kN static range at a 0.25 mm/s ramp accounting for different displacements. The output data given by the testing machine were force, time, and displacement. Tests were run along the bottle axis (vertical direction), with a behaviour as shown in Figure 5. Force-displacement diagrams were also measured and displayed in Figure 6 where results from five tests are reported. These trends show an initial proportional zone (a), the bottleneck completely dented (b) and, lastly, the buckling phenomenon (c).  
It can be noted that an exceptional event occurred at 8-10 mm of displacement, equal to around 2.5-3.0% of axial deformation and 0.5 kN, able to split the graph in two different regions, one with linear and the other with nonlinear behaviors. It corresponds to the indentation of the bottle, with the cap collapsing inside the neck (Figure 7a). Once the elastic response zone was passed, various phenomena became visible, increasing the load. In particular, over 2 kN, buckling happened in the specimens due to the geometry of the bottle: the tapered shape in the midways transversal plane prompted this behavior, whereas axial compression was left aside. At the end of the test, in the absence of a load, a residual deformation was also present (Figure 7b).  
Figure 5: Predominance of buckling on single-bottle compression tests: initial part of the test with a proportional response (**a**), as soon as the bottleneck is completely dented (**b**), and near to the 3% strain region when buckling takes place (**c**).
# Deep Learning Model with Sequential Features for Malware Classification
## 3 Malware Classification Method Based on Sequence Features and Deep Learning
### Feature Pre-Processing  
After the malware features (opcode and bytecode features) were extracted, we checked whether this data had missing values, treated the missing values as 0 uniformly, and then performed standard normalization on the malware feature data. Data normalization reduced the variance of the features to a smaller interval, reduced the impact of the difference in the size of different feature values, and improved the convergence rate of the model. Current normalization methods are commonly used to normalize the values to (0,1) and (\(-1,1\)). The normalization method used in this study was maximum-minimum normalization, which scales the values to the interval (0,1), as shown in Equation (5).  
\[x^{\prime}=\frac{x-M_{min}}{M_{max}-M_{min}} \tag{5}\]  
where \(x^{\prime}\) is the scaled value, \(M_{min}\) is the smallest value in the feature dimension, and \(M_{max}\) is the largest value in the feature dimension.  
In the process of malicious code feature extraction, there are many zero values. This method can retain the zeros in the features and can handle the data values with small variances in the features.
## 3 Algorithms Used in Correlation Mining Analyses
### Machine Learning-Based Algorithms
#### 3.3.3 Random Forest Algorithm  
Random forest is a supervised learning algorithm that randomly constructs the forest algorithm and includes a group of decision trees. As a type of ensemble learning algorithm, it combines the predictions of multiple decision trees to improve accuracy and robustness. Each decision tree in the random forest is trained on a random subset of training data and a random subset of features. The final output of the random forest is determined by aggregating the predictions of all the individual decision trees. This approach allows for the algorithm's accuracy and significantly reduces the risk of overfitting, where the model performs well on training data but poorly on test data [58; 59; 60].  
As illustrated in Figure 5(c), random forest is an ensemble method that combines multiple decision trees to improve prediction accuracy. The same features and targets, as mentioned in previous algorithms, were utilized as independent and dependent variables, respectively. Random forest introduces additional randomness during tree growth by considering only a random subset of features at each split. The input data format remains the same as for decision trees. Random forest aggregates predictions from individual trees to make a more accurate overall prediction.