esa-sceva/satcom-chunk-collection huggingface dataset:
# Unobtrusive Respiratory Flow Monitoring Using a Thermopile Array: A Feasibility Study
## 1 Introduction
In the data used, both face and torso are included in the field of view, as specified in a previous work where the same dataset was used [21]. The respiration signal detected is a combination of motion-related breathing (due to shoulder motion) and thermal changes due to respiration flow. The proposed approach eliminates the need of ROI selection based on facial features. However, an unconstrained combination of motion and flow-related information is not desirable and should not be trusted in environments where apneas and, in particular, obstructive apneas are common. In this paper, we present an unobtrusive respiration monitoring technique that uses a thermopile array and an ROI detection method that does not require facial landmarks' visibility. To our knowledge, this is the first work that aims to use thermopile arrays to detect respiratory flow. In this study, we assess the accuracy of the breathing signal detection in the optimal stationary condition on healthy adults in a laboratory environment as a proof-of-concept. The technique has been tested on multiple healthy subjects. Moreover, to prove the feasibility of detecting respiration with a thermopile array for typical respiration conditions, oral/nasal breathing and different Respiration Rates (RRs) have been analyzed. Furthermore, several other factors like distance from the sensor and orientation have been considered to mimic real-world applications. The rest of the paper is organized as follows, Section 2 introduces the background and the main challenges and contains the processing steps and the experimental setup. The results and discussion will be presented respectively in Section 3 and Section 4. Finally, Section 5 provides the conclusions.
## 3 Results and Discussion
### Morphological Characterizations
\begin{table}
\begin{tabular}{l c c c c} \hline
**Deposition Method** & **RMS (nm)** & **Ssk** & **K** & **h \(\pm\) SE (nm)** \\
**Control Sample** & 3.54 & 1.18 & 6.47 & 1032 \(\pm\) 24.5 \\ \hline RF-MS & 2.40 & \(-\)0.03 & 0.10 & 1366 \(\pm\) 21.6 \\ CdTe/glass & 4.93 & 2.66 & 11.13 & 553 \(\pm\) 13.4 \\ CdTe/Au/Ti/Si & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Morphological parameters of the CdTe thin films deposited by VTE, RF-MS, and ECD, as statistically evaluated by AFM analysis. RMS represents the route mean square roughness and Ssk and K are the corresponding Skewness and Excess Kurtosis coefficients, respectively. The thickness of the deposited CdTe films (h) is indicated (SE denotes the standard error) as well, being directly estimated by SEM for all samples and subsequently calculated from the optical transmission spectra only for the VTE- and RF-MS-deposited CdTe thin films, respectively (see Section 3.3). Figure 4: Atomic Force Microscope (AFM) top-view (**a,c,e**) and corresponding three-dimensional (3D) surface profile (**b,d,e**) of the CdTe films surfaces deposited by VTE (**a,b**) and RF-MS (**c,d**) on a pristine optical glass substrate and by ECD (**e,f**) on an Si substrate previously coated by a thin Au layer. In particular, the excellent smoothness of the sputtered CdTe film is confirmed by an almost null value of the Ssk coefficient (\(-0.08\)), where the slightly negative Skewness may indicate the subtle presence of height values below the average, represented by pit defects in the film. Notably, the corresponding K coefficient (0.10) is almost zero, demonstrating a normal distribution of the heights on the surface of the sputtered CdTe thin film. In contrast, the evaporated and electrodeposited CdTe films exhibit larger positive Ssk coefficients (1.18 and 2.66, respectively), demonstrating a flat surface with the presence of a few extreme heights considerably above the average, as suggested by the larger K coefficient values (6.47 and 11.13, respectively). This effect is more pronounced for the CdTe thin film prepared by ECD, as can be directly seen in Figure 4e.f. The obtained results are in agreement with other reports, countering unevenness issues in evaporated films potentially caused by typically high and less-controllable deposition rates associated with VTE processes [37] or growth abnormalities in the electrodeposited films (which are mostly associated with the ECD procedure complexity in terms of applied potential, chemical species unbalance in the electrolytic solution, process cleanness, or bath temperature stability during film growth [39]).
## 4 Analysis of Results
### Detecting PNS Attacks  
Despite the creativeness of [PERSON]'s PNS attack, her detectability is based on the decoy state protocol's ability to differentiate between subtle changes in the signal and decoy states. In lieu of comparing photon number dependent yields \(Y_{n}^{signal}\), \(Y_{n}^{decoy}\), \(Y_{n}^{expected}\) which can be statically bounded or directly measurable using expensive Photon Number Resolving (PNR) detectors [62], this work utilizes the efficiency based security condition which provides a direct measurement in a cost-conscience QKD system implementation [8].  
\[\eta^{signal}=\eta^{decoy} \tag{5}\]  
where \(\eta^{signal}\) is the signal state efficiency and \(\eta^{decoy}\) is the decoy state efficiency. The efficiency based decoy state security method directly compares the signal and decoy state efficiencies from readily available measurements instead of requiring advanced technologies. The signal (and decoy) state efficiency is defined as  
\[\eta^{signal}=\frac{-\ln\left|1+Y_{0}-Q_{\mu}\right|}{\mu} \tag{6}\]  
where \(Y_{0}\) is the system's measured dark count rate defined as  
\[Y_{0}=\frac{\text{Number of vacuum state detections}}{\text{Number of vacuum state pulses sent}} \tag{7}\]  
and \(Q_{\mu}\) is the measured signal state gain defined as  
\[Q_{\mu}=\frac{\text{Number of signal state detections}}{\text{Number of signal state pulses sent}} \tag{8}\]  
and \(\mu\) is the signal state's prescribed MPN (typically 0.5). This method also allows the QKD system to assure the quantum channel is free from unwanted attacks without a priori knowledge such as a well-characterized quantum channel as required in prior art.
## 5 Case Study and Analysis
### Analysis and Comparison of Results
The results depicted in Figure 10a demonstrate that though the combined inertial control with BP training parameters can achieve the similar effect of frequency control as the combined inertial control with optimal parameters, the latter responds more quickly than the former. It is evident from the results that obtaining the optimal control parameters is crucial for achieving optimal frequency regulation using combined inertial control. The rapid and accurate determination of these parameters is necessary to fully utilize the frequency regulation capability of the combined inertial control and achieve optimal control effects under its control strategy. Figure 9: Combined inertial control optimal parameter prediction results. (**a**) \(K_{\text{pf}}\) (**b**) \(K_{\text{df}}\). Figure 10b shows that as the amount of load disturbance increases, the nadir of the system frequency keeps decreasing. Although the method presented in this paper has tried to minimize the maximum deviation of frequency as much as possible, the test has shown that it is not enough to rely on the WT primary frequency regulation alone. It is still necessary to cooperate with other types of turbines as much as possible to achieve the optimal control of frequency.
# Lightweight Robust Image Classifier Using Non-Overlapping Image Compression Filters
## 4 Experimental Results
### Comparison Results with Other Methods  
We compared our method with two defense methods based on diffusion models. We followed the basic setup and used the PyTorch implementation of [PERSON] et al. ([[https://github.com/wzekai99/DM-Improves-AT](https://github.com/wzekai99/DM-Improves-AT)]([https://github.com/wzekai99/DM-Improves-AT](https://github.com/wzekai99/DM-Improves-AT)), accessed on 23 September 2024) and DiffPure ([[https://github.com/NVlabs/DiffPure](https://github.com/NVlabs/DiffPure)]([https://github.com/NVlabs/DiffPure](https://github.com/NVlabs/DiffPure)), accessed on 23 September 2024). The classification model employed was WideResNet [53]. Due to limitations in device performance and graphics card memory size, we only used 1M to generate denoised images. We trained a WRN-28-10 model on CIFAR10 using 1M generated data. We used the SGD optimizer with Nesterov momentum, where the momentum factor and weight decay were set to 0.9 and \(5\times 10^{-4}\), respectively. We used a cyclic learning rate schedule with cost annexing, where the initial learning rate was set to 0.1. The dropout rate was set to 0.3. We considered two metrics for evaluation: standard accuracy and robust accuracy. The standard accuracy measures the performance of the defense methods on clean data and is evaluated on the entire test set. The robust accuracy measures the performance on adversarial instances generated by three different attacks.  
Table 8 shows the standard accuracy and robust accuracy of the two diffusion model-based defense methods and our proposed method on the CIFAR10 (\(l_{\infty},\epsilon=8/255\)) test set. It is evident that our method performed better in both standard accuracy and robust accuracy when using the WRN-28-10 architecture. The results demonstrate the effectiveness of using the non-overlapping method to preprocess images in enhancing the robustness of the model.  
Figure 5: The attack success rates of Square attacks against five different defense models on the CIFAR10 test dataset. The attack factor \(\epsilon\) ranges from 0.1 to 0.9.
## 7 Security and Privacy Challenges and Limitations in AMRSys
### Network Security and Proxy System  
When remote access to the robot is invoked; any standard data IPSec security mechanism can be adopted. However, remotely connecting directly to the robot is not safe, because any cyber-attacks could lead to dysfunction or malfunction of the robot itself. Therefore, for any form of remote access, a proxy system should be incorporated to act as a firewall and safeguard the robotic system. To offload the computation power of the robot, network intrusion detection and network prevention mechanisms should be incorporated within the proxy firewall system. Having said that, a system intrusion detection and prevention system should also monitor any abnormal activities within the robotic system, to protect it from any local attacks and attacks that could not be detected by the proxy firewall.
## 5 Parameter Analysis
### The Influence of Stiffness of Tie-Bar  
Because the stiffness of tie-bars in the previous developed finite element models of SC composite beams was assumed to be 40 kN/mm, the influence of the stiffness of the tie-bar on the minimum shear connection ratio should be investigated. The stiffness of tie-bars varied from 50% to 150% of the initial stiffness. For four SC composite beams with different shear span versus depth ratios, the changes in minimum shear connection ratio are shown in Figure 14a. It is shown that the influence of the stiffness of the tie-bars on the minimum shear connection ratio of the SC composite beams could be negligible. Therefore, the previous investigation in this paper was accurate and reasonable.  
Figure 13: Behavior of SC composite beam (\(\gamma\) = 1.30–1.60). (**a**) Tension of steel plate versus displacement; (**b**) institution of shear slip of tie-bars when tensile steel plate was yield.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\gamma\) & \(F_{y,s}\) **(kN)** & \(F_{u,s}\) **(kN)** & \(\mu\) \\ \hline
1.40 & 2056.0 & 2249.4 & 1.52 \\
1.45 & 2071.7 & 2252.0 & 1.65 \\
1.50 & 2087.5 & 2262.3 & 1.74 \\
1.55 & 2117.0 & 2269.3 & 1.83 \\
1.60 & 2161.1 & 2272.1 & 1.91 \\ \hline \hline \end{tabular} Note: \(F_{y,s}\) is the tension of tensile steel plate at yield displacement, \(F_{u,s}\) is the tension of tensile steel plate at ultimate displacement.  
\end{table}
Table 3: Tension of tensile steel plate in SC composite beam under cyclic loadings.
## 3 Results and Discussion
### Effect of Jet Direction
#### 3.2.2 Effect of Elastic Cylinder Size on Bubble Jet Direction
Under the same dimensionless distance, the larger the dimensionless radius of the cylinder, the more obvious the difference in size between the two parts of the bubble; that is, the more obvious the deformation of the bubble after expansion. Secondly, the larger dimensionless size also means that the contact area between the bubble and the cylindrical boundary is larger, so during the bubble collapse process, the area of interaction between the elastic cylindrical boundary and the bubble is larger. The bubble attached to the boundary of the elastic cylinder will produce a larger curvature than that away from the boundary. During the collapse process, a composite velocity away from the elastic cylinder will be generated. When the velocity away from the elastic cylinder is greater than the velocity toward the wall, the bubble cloud will be carried away from the wall of the elastic cylinder, thus generating the jet away from the wall of the elastic cylinder defined in this section. In region II (blue shading), the inverted triangle symbols are almost all distributed in this region. When the conditions in this region are met, the bubble will generate a jet, and the direction of the jet is toward the boundary of the elastic cylinder. Figure 13: Schematic of bubbles surrounding the elastic cylinder. (**a**) bubble around thin cylinder; (**b**) bubble around wide cylinder. To summarize the jet direction in Figure 12, the relationship between the jet direction and \(\gamma\) and \(\gamma_{rad}\) is as follows:  
\[\left\{ \begin{array}{ll}\gamma>3&\text{no jets},\\ 3>\gamma\geqslant 0.13\gamma_{rad}+0.37&\text{jet toward the wall}\\ \gamma<0.13\gamma_{rad}+0.37&\text{jet away from the wall}\end{array} \right. \tag{3}\]
## 3 Evaluation of the Fit of Estimated Distributions  
Accurate estimated parameter values are essential for ensuring reliability. When selecting a statistical distribution for data analysis, its goodness of fit must be evaluated. A goodness-of-fit test was conducted by comparing the error between the actual and estimated values. Various methods can be employed to assess the goodness of fit of the chosen distribution, including the correlation coefficient analysis, the Kolmogorov-Smirnov test (K-S test), the mean difference (MD) test, and the likelihood value (LKV) test [9; 10; 11; 12; 13].  
The correlation coefficient gauges the relationship between two variables, ranging from \(-1\) for a perfect negative correlation to 1 for a perfect positive correlation. The coefficient of determination R\({}^{2}\) was used to assess the goodness of fit without factoring in correlations. The R\({}^{2}\) values ranged from 0 for no relationship to 1 for a perfect relationship. Equation (6) expresses the coefficient of determination, where \(\hat{y}\) represents the \(i\)-th estimate, \(\overline{y}\) represents the average value of the observed value, and \(y_{i}\) represents the \(i\)-th observation value.  
\[R^{2}=\frac{\sum(\hat{y}_{i}-\overline{y})^{2}}{\sum(y_{i}-\overline{y})^{2}} \tag{6}\]  
The K-S test calculates the maximum difference between the predicted values of the selected distribution and the observed data, with smaller values indicating a better fit. The K-S value can be calculated using Equation (7), where \(D\) represents the statistics of the K-S test, \(F_{n}(x)\) represents the empirical distribution function, and \(F(x)\) represents the cumulative density function.  
\[D=sup|F_{n}(x)-F(x)| \tag{7}\]  
The \(MD\) test measures the average difference between the predicted and observed data and is less sensitive to outliers than the K-S test. Both the K-S and \(MD\) tests are valuable for assessing the goodness of fit. The \(MD\) value can be calculated as follows:  
\[MD=\frac{1}{n}\sum_{x=1}^{n}|F_{n}(x)-F(x)| \tag{8}\]  
The LKV test involves applying the estimated distribution parameters to the likelihood function and calculating the values for each data point that were summed to calculate the LKV. However, the LKV test may be biased when the amount of observed data is limited. Equation (9) can be used for calculating the LKV. Here, \(L(x)\) represents the likelihood function.  
\[LKV\ =\ \sum_{i=1}^{n}L(x) \tag{9}\]  
Following the assessment of the goodness of fit, the number of parameters in the chosen distribution was determined using the Akaike information criterion (AIC) and Bayesian information criterion (BIC). Lower AIC and BIC values signify a better fit. The AIC accounts only for the number of parameters; this approach may become less accurate when the number of samples is large. To address this issue, the BIC was employed because it simultaneously considers the number of parameters and samples. Equations (10) and (11) represent the \(AIC\) and \(BIC\), respectively, where p signifies the number of parameters used in the model.  
\[AIC=-2\ ln(L(x))+2\ p \tag{10}\]  
\[BIC=-2\ ln(L(x))+p\ ln(n) \tag{11}\]
## 6 Conclusions  
In this paper, privacy preserving data collection framework is proposed for WSNs. The network and threat model of framework states that there exists multiple sinks and each sink has different level of privacy requirements.  
In order to meet the requirements of network and threat model, we propose a method called I\(k\)-ACM (Iterative \(k\)-Anonymization Clustering Method). Proposed I\(k\)-ACM reduces energy consumption while fulfilling the required different privacy levels of sinks.  
I\(k\)-ACM Method uses encryption operations with generalization operations in order to have one common anonymized output. This common output enables us to multicast the same message to all sinks. Multicasting of this output enables WSN to reduce amount of energy consumed for transmitting it to the sinks. In WSN, each local region has one group head node. They gather event data from sensors of their local region, anonymize it and send it to the all sinks. According to the positions of group head nodes and their distances to sinks, multicasting can be better alternative for some of the group head nodes. Each group head node decides whether multicasting is appropriate for itself or not. If it decreases energy consumption, group head node uses it. Multicasting method degrades the quality of data gathered by some sinks. Here, there is a trade-off between data loss and energy consumption. WSN designers has to decide about the trade-off between energy saving and information loss. We analyze this trade-off for different sized WSN topologies and for different sink locations. Our analyses show that, in a WSN having two sinks, it is possible to save 32% of energy, however the loss of data utility received by one of the sink increases to 0.90 from 0.66 in some topologies.
# Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization
## 5 Deep Learning Approaches
### Radar Based Loop Closure
[122] presented a sophisticated loop closure verification scheme for spinning radar data by employing two logistic regression classifiers. Although this method is not trained via deep learning, it is still relevant for state-of-the-art research. The process involves extracting hand-crafted CFEAR [36] features from a Cartesian Bird's Eye View (BEV) radar scan as global descriptors. These global descriptors are then utilized to identify loop closure candidates. An alignment measurement between the two scans is applied, extracting various features from the joined point clouds, including joint and separate entropy, radar peak detections, and a measure of overlap. These features serve as input for training a logistic classifier, reflecting the similarity of the two point clouds based on their overlap. Finally, this similarity, combined with the point clouds' similarity in odometry and global descriptor distance, is integrated into a logistic regression classifier scheme to determine a verified loop closure candidate. This comprehensive approach enhances the robustness of loop closure verification for spinning radar data. The focus of radar based ego-localization can be categorized into two categories: methods that depend on training a model to generate unique local place descriptors, and methods that want to predict a global pose \(T\in SE(3)\) from a single scan. The models generating a local descriptor are often trained using a NetVLAD layer in an End2 End approach. To some extent, the same modules were integrated from the odometry methods into the place recognition methods, including CNNs and LSTMs for spatial and temporal feature extraction. Similar and dissimilar places are extracted from the training data to force a small distance metric of feature descriptors of similar places, and large differences between places that are physically far apart. This leads to a large dependency on the chosen similar and dissimilar distance criteria, as well as on the investigated dataset. The other main method to perform localization is by predicting the ego-pose \(T\in SE(3)\) directly from a local scan. These methods are far less researched in comparison to utilizing a BOW approach, because they do not rely on the usage of any map, and are thus very susceptible to over-fitting. However, these methods are more prevalent when adding an extra sensor to the system.
## 2 Materials and Methods
### Dispersion Analysis  
Different methodologies can be employed to evaluate the stability of nanolubricants, which is intended as the avoidance of any phenomenon concerning nanoparticles and the alteration of their uniformity of distribution in the mass of the liquid hosting them (e.g., precipitation, floating, aggregation). This investigation aimed to assess the comparative stability of the nanosuspension by employing sedimentation photography, FTIR, and UV-Vis spectrophotometry [20; 43; 44; 45]. Sedimentation photography is an invaluable technique for observing the settling characteristics of nanoparticles in a suspension. The experiment entailed obtaining images of the nanolubricant at precise time intervals subsequent to its preparation. The purpose of these photographs was to observe the sedimentation of SiO\({}_{2}\) nanoparticles that were coated with the KH570-silane coupling agent in the lubricant. The variations in the dispersion quality and settling rate were analysed with time, yielding useful insights into the stability of the nanolubricant. FTIR is another vital tool employed in assessing the stability of nanolubricants. FTIR spectroscopy provides valuable information regarding the chemical composition and structural changes within the nanolubricant. By analysing the infrared absorption spectra of the nanolubricant, any alterations in molecular bonds or functional groups can be detected. These changes can be indicative of interactions between the nanoparticles and the lubricant matrix [25; 46]. The instrument used for FTIR analysis was a Perkin Elmer Spectrum 100, enabling precise measurements of infrared spectra. UV-Vis spectrophotometry is a precise method used to measure the concentration and dispersion of nanoparticles in a liquid media. Due to the high viscosity of the used lubricants, they were first diluted with a ratio of 1:10 using hexane as the solvent to facilitate the UV analysis. The nanolubricants' absorption spectra were measured at specified wavelengths, which were determined through a comprehensive scanning process. This approach ensured precise determination of nanoparticle concentration and distribution within the lubricant. Fluctuations in the absorbance spectrum could indicate changes in the dispersion of nanoparticles, which, in turn, affects the stability of the nanolubricants [47; 48]. The instrument used was an Agilent Cary 100 UV-Visible Spectrophotometer, as shown in Figure 3.