esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Visible and Thermal Imaging Co-Registration and Processing  
The spatial RMSE of the optical co-registration was \(0.66\pm 0.25\) pixels, thus indicating that the accuracy in the coordinate transformation from visible to IR imagery at the specific distance of one meter was less than one pixel.  
The percentage value of the correctly identified landmark on the total amount of considered frames and the confidence value in correctly classifying a face are reported in Table 4 for each subject. These parameters were returned by the software OpenFace [40]. The confidence value ranged from 0 (total misclassification of face) to 1 (correct face classification), and it was the result of a landmark detection validation process. In detail, to avoid tracking drift over time, it was necessary to determine if landmark detection succeeded during video processing. The landmark detection validation was performed transforming the area surrounded by the landmarks to a pre-defined reference shape. The vectorized resulting image was then used as a feature vector for a classifier which acts as  
Figure 5: Flow chart of the applied machine learning approach: the thermal features are used as predictors whilst the Electrocardiogram(ECG)-derived Stress Index (SI) is considered as the regression output. Support Vector Regression with Radial Basis Function kernel (SVR-RBF) was used as regressor. A leave-one-subject-out cross-validation was employed to test the generalization of the regression. The result of the regression (SICross) was then used to perform a two-level classification of the driver’s stress (i.e., STRESS versus NO STRESS). Since the two classes were not balanced, a bootstrap procedure was implemented. Receiver Operating Characteristic (ROC) analysis was executed to investigate the performance of the classifier.  
the validator (i.e., input of the classifier). To train the classifier on the vectorized reference warp, positive and negative landmark detection examples were considered. The positive samples were ground truth landmark labels, whereas the negative samples were generated from the ground truth labels, applying offset and scale transformations. The classifier employed in OpenFace is SVM [49].  
On average, 94.66% of the video frames were correctly processed, whereas the confidence index for face classification was 0.90%.  
To notice, concerning subjects 3 and 5, the average success and confidence scores were lower with respect to the other subjects, given the scarce lighting conditions of the acquisitions (Table 4). However, in general, for all the subjects, only the frames with high confidence and success scores were considered for further analysis (i.e., success index \(>\) 90%, confidence value \(>\) 0.8). This ensures there was no impact on the ROIs' identification and, consequently, on their features' estimation.  
Finally, the average execution time of the developed algorithm was 0.09 s/frames with MATLAB 2016b(r) (64-bit Windows 7 Pro, Service Pack 1; Intel (R) Core (TM) i5 CPU; 8.00 GB RAM).
## 5 Experiment
### Results Analysis  
#### 5.2.1 Time Interval  
In the edge-terminal training process, we set every time interval \(T\) to upload the local model to the edge server for edge aggregation. This approach ensures that each device completes the training within the specified time and avoids certain devices converging early or lagging behind due to differences in computational power or data volume, thus maintaining consistency and fairness. However, the time \(T\) should be set within a reasonable margin, neither too long nor too short, in order to ensure that all equipment can complete the training within the specified time and achieve the desired results.  
We conducted four sets of experiments using CNN models under the BHFL architecture. We used the MNIST dataset with the number of training rounds set to 30, and we used the BHFL algorithm when \(T\) is equal to 300 s, 400 s, 500 s, and 600 s to conduct experiments and observe the convergence speed and accuracy. As shown in Figure 4, when \(T=400\) s, BHFL shows significant accuracy at earlier rounds, presents the highest accuracy in the entire dataset, and maintains a more stable accuracy at later rounds. Compared with other time settings, the accuracy of BHFL under other time settings fluctuates more, and the final result is not as good as that of \(T=400\) s. Therefore, our experiments are conducted under the time interval \(T=400\) s setting.
###### Abstract  
In this study, we applied the 1988-2017 monthly average sea ice concentration data from the Met Office Hadley Centre and the 1988-2017 monthly average reanalysis data from the National Centers for Environmental Prediction/Department of Energy (NCEP/DOE) Reanalysis II to analyze the relationship between the winter precipitation in the Barents and Kara Seas (BKS) and the previous autumn eastern Siberian Sea ice anomalies. Through the correlation analysis, we found that the correlation between eastern Siberian Sea ice and the BKS winter precipitation was strongest in September and weakest in November. The results indicated that, when the eastern Siberian Sea ice extent decreased in September-October, a significant positive geopotential height anomaly would occur in the coming winter (December-February) in the Norwegian-Barents region. This result in turn caused anomalies in the northward meridional wind. Consequently, the anomalous water vapor from the mid-latitude Atlantic to the Arctic passed through the Greenland Sea before finally reaching the BKS. The meridional wind also caused the temperature in said seas to increase and the BKS ice to melt, leading to an increase of winter precipitation. We also found that the increase of the Siberian high (SH) in winter was related to the decrease of autumn East Siberian Sea ice extent and the increase of the winter BKS precipitation anomaly. Further research still needs to be refined for this issue in future studies.  
autumn eastern Siberian Sea ice; BKS winter precipitation; water vapor transport; sea ice melt +
Footnote †: journal: Applied Sciences
# Numerical Study on Aerodynamic Noise Reduction of Pantograph
## 3 Computational Methods
### Fowcs Williams-Hawkings Equation  
Considering the high computational cost of noise propagation, the far-field noise evaluation is performed using the Ffowcs Williams-Hawkings (FW-H) equation [22]. In this work, the pantograph surface is defined as the FW-H surface. Since the flow is low subsonic, the contribution of the Lighthill stress tensor is small, and the volume integral of the FW-H equation is omitted. Thus, the sound pressure at the far-field can be written as the sum of thickness term and loading term:  
\[p^{\prime}(\mathbf{x},t)={p^{\prime}}_{T}(\mathbf{x},t)+{p^{\prime}}_{L}(\mathbf{x},t) \tag{6}\]  
where  
\[4\pi{p^{\prime}}_{T}(\mathbf{x},t)=\int_{f=0}\bigg{[}\frac{\rho_{t}U_{t}n_{i}}{r( 1-M_{t})^{2}}+\frac{\rho_{t}U_{t}\frac{n_{i}}{T}M_{i}}{r(1-M_{t})^{3}}\bigg{]} _{\tau=\tau_{e}}dS+\int_{f=0}\bigg{[}\frac{\rho_{t}U_{t}\rho_{t}\left(M_{r}-M ^{2}\right)}{r^{2}(1-M_{r})^{3}}\bigg{]}_{\tau=\tau_{e}}dS \tag{7}\]  
\[4\pi{p^{\prime}}_{L}(\mathbf{x},t)=\int_{f=0}\bigg{[}\frac{\dot{L}_{t}\frac{\dot{ T}_{t}}{r}}{\alpha r(1-M_{t})^{2}}+\frac{L_{t}\frac{\dot{T}_{t}}{r}\frac{\dot{T}_{t}}{r}M _{i}}{\alpha r(1-M_{t})^{3}}\bigg{]}_{\tau=\tau_{e}}dS+\int_{f=0}\bigg{[}\frac {L_{t}\frac{\dot{T}_{t}}{r}\left(M_{r}-M^{2}\right)}{r^{2}(1-M_{r})^{3}}+\frac {L_{t}\frac{\dot{T}_{t}}{r}-L_{t}M_{i}}{r^{2}(1-M_{r})^{2}}\bigg{]}_{\tau=\tau _{e}}dS \tag{8}\]  
The subscript \(a\) denotes the quantities of the surrounding medium. The surface \(f=0\) corresponds to the source surface. \(r\) and \(r_{i}\) represent the magnitude and the direction of the vector from the source surface to the observer \(\mathbf{x}\), respectively. The quantities \(U_{i}\), \(M_{i}\), \(M_{r}\), and \(L_{i}\) are given by  
\[\left\{\begin{array}{l}U_{i}=\biggl{(}1-\frac{\rho}{\rho_{a}}\biggr{)}v_{i}+ \frac{\rho u_{i}}{\rho_{a}}\\ M_{i}=\frac{\dot{v}_{i}}{\dot{c}_{a}}\\ M_{r}=M_{i}\frac{\dot{r}_{i}}{r}\\ L_{i}=\bigl{(}p^{\prime}\delta_{ij}-\sigma_{ij}\bigr{)}n_{j}+\rho u_{i}u_{n} \end{array}\right. \tag{9}\]  
In order to take into account that the sound reflects on the ground, an additional observer is arranged in the mirror position. The sound pressure at the two observer points is calculated by Equations (6)-(8), and the obtained time-domain signals are superimposed to obtain the sound pressure with considering the ground effect.
## 4 Proposed Detection Scheme
### Probability of Erroneous Estimation  
In order to measure how well the estimator performs, the probability of erroneous estimation \(P_{e}=\text{Prob}\{\hat{\epsilon}_{i}\
eq\epsilon_{i}\}\) is derived in the AWGN channel. From (16), the probability density function (PDF) of \(|\hat{\Omega}_{d}(m)|\) under hypothesis \(H_{1}\), or equivalently when \(m=\epsilon_{i}\), can be given by  
\[p(y|H_{1})=\frac{y}{\sigma_{1}^{2}}e^{-(y^{2}+\mu^{2})/2\sigma_{1}^{2}}I_{0} \left(\frac{y\mu}{\sigma_{1}^{2}}\right), \tag{19}\]  
where \(\mu=B_{l}E_{X}N_{t}\), \(\sigma_{1}^{2}=N_{t}(\sigma_{1}^{2}+\sigma_{Z}^{2})/2\), and \(I_{0}(\cdot)\) stands for the modified Bessel function of the first kind and zero order. Under hypothesis \(H_{0}\) (\(m\
eq\epsilon_{i}\)), it can be seen from (17) that \(|\hat{\Omega}_{d}(m)|\) has the Rayleigh distribution  
\[p(y|H_{0})=\frac{y}{\sigma_{0}^{2}}e^{-y^{2}/2\sigma_{0}^{2}}, \tag{20}\]  
where \(\sigma_{0}^{2}=N_{t}(E_{X}^{2}+\sigma_{I}^{2}+\sigma_{Z}^{2})/2\). For equally likely IFOs, the probability of a correct estimation is expressed as  
\[P_{c}=\int_{0}^{\infty}p(y|H_{1})\left[\int_{0}^{y}p(x|H_{0})dx\right]^{2M}dy. \tag{21}\]  
Substituting (19) and (20) to (21) yields  
\[P_{c}=\int_{0}^{\infty}\frac{y}{\sigma_{1}^{2}}e^{-(y^{2}+\mu^{2})/2\sigma_{1 }^{2}}I_{0}\left(\frac{y\mu}{\sigma_{1}^{2}}\right)\left[1-e^{-y^{2}/2\sigma_ {0}^{2}}\right]^{2M}dy. \tag{22}\]  
Using the binomial expansion, (22) can be further derived as  
\[P_{c}=e^{-\mu^{2}/2\sigma_{1}^{2}}\sum_{m=0}^{2M}(-1)^{m}{2M\choose m}\int_{0 }^{\infty}\frac{y}{\sigma_{1}^{2}}e^{-y^{2}(1/\sigma_{1}^{2}+m/\sigma_{0}^{2} )/2}I_{0}\left(\frac{y\mu}{\sigma_{1}^{2}}\right)dy, \tag{23}\]  
where the integral has a closed-form solution [24]. After some mathematical manipulations, a closed-form expression for the probability of correct estimation can be obtained as  
\[P_{c}=\sum_{m=0}^{2M}(-1)^{m}{2M\choose m}\frac{\sigma_{0}^{2}}{\sigma_{0}^{2} +m\sigma_{1}^{2}}e^{-m\mu^{2}/2(\sigma_{0}^{2}+m\sigma_{1}^{2})}. \tag{24}\]  
Denoting \(\gamma_{z}=E_{X}/\sigma_{Z}^{2}\) and \(\gamma_{i}=E_{X}/\sigma_{1}^{2}\) as the SNR and signal-to-ICI ratio, respectively, we have from (24)  
\[\frac{\sigma_{0}^{2}}{\sigma_{0}^{2}+m\sigma_{1}^{2}}=\frac{\tilde{\gamma}+1}{ (1+m)\tilde{\gamma}+1} \tag{25}\]\[\frac{\mu^{2}}{\sigma_{0}^{2}+m\sigma_{1}^{2}}=\frac{2N_{t}}{(1+m)\hat{\gamma}+1}, \tag{26}\]  
where  
\[\hat{\gamma}=\frac{2}{\gamma_{i}}+\frac{2}{\gamma_{i}\gamma_{z}}+\frac{1}{ \gamma_{i}^{2}}+\frac{2}{\gamma_{z}}+\frac{1}{\gamma_{z}^{2}}. \tag{27}\]  
Using (25) and (26), the probability of erroneous estimation can thus be given by  
\[P_{e}=1-\sum_{m=0}^{2M}(-1)^{m}\binom{2M}{m}\frac{\bar{\gamma}+1}{(1+m)\hat{ \gamma}+1}e^{-mN_{t}/((1+m)\hat{\gamma}+1)}. \tag{28}\]
# Bounded Attitude Control with Active Disturbance Rejection Capabilities for Multirotor UAVs
## 5 Simulation Results
### Quaternion and Angular Velocity Evolution  
In this subsection, a comparison between the proposed control law (16) and control law (17) is highlighted. It is worth mentioning that the error quaternion is computed using (6), which is depicted here for clarity; that is,  
\[\mathbf{\ddot{q}}=\left(\mathbf{q}^{d}\right)^{-1}\odot\mathbf{q}=\left( \mathbf{\ddot{q}}_{0}\quad\mathbf{\ddot{q}}_{v}^{T}\right)^{T}\]  
Figure 3 depicts the evolution of the quadrotor attitude when the two control laws are applied. In the left column, the results for the proposed control law are shown, whereas the right column portrays the results using (17). In both cases, the first subplot of each column shows the time evolution for the scalar component of the desired and current quaternion. Moreover, the vector component of the quaternion error corresponding to each control law is shown in the second subplot of each column. Note that when the current quaternion \(\mathbf{q}\) reaches the desired one \(\mathbf{q}^{d}\), the vector components of \(\mathbf{\ddot{q}}\) have to become \(\mathbf{\ddot{q}}_{v}=(0\;0\;0)^{T}\) due to the norm constraint of the unit quaternions. In contrast with the compared control law, with the proposed control, the vector component of the quaternion error converges to zero and its magnitude at \(0.5\;\text{s}\) is approximately \(4.1\times 10^{-3}\), which is a desirable response time behavior for this kind of quadrotor.  
Figure 4 illustrates the first component of the angular velocity and the angular velocity tracking error vector of the proposed (left column) and the compared control (right column). Note that with the proposed control law, the angular velocity of the micro UAV achieves the desired value in approximately \(0.5\;\text{s}\) and the angular velocity error remains in the zero neighborhood despite the external disturbances (except at t = \(7.5\;\text{s}\), when enough control conditions are violated).  
Figure 3: Quaternion and quaternion error.
## 4 Experimental Results and Analysis
### Analysis of Algorithm Results  
To further verify the effectiveness of the DP-CDPC algorithm, this paper compares it with several other algorithms including the K-means, DBSCAN, SNNDPC, CDPC, ADPC, and DP-ADPC algorithms. The parameters for the DP-CDPC and CDPC algorithms are set based on the parameters that yield better clustering results in Section 4.2, the parameters' truncation percentage \(p\) of DP-ADPC and ADPC can be set according to the literature [29]. The parameters of the K-means and SNNDPC algorithms can be obtained from the actual number of categories in each dataset. The parameters of the DBSCAN algorithm are determined by the standard of the maximum \(AR1\). Table 4 shows an overview of the parameter settings for each algorithm, including \(eps\) and \(mpts\) of the DBSCAN algorithm, \(p\) and \(\varepsilon\) of the DP-ADPC algorithm, \(b_{1}\), \(b_{2}\) and \(\varepsilon\) of the DP-CDPC algorithm on each dataset.
###### Abstract  
The present article investigates in laboratory conditions the effect of cyclic freezing and thawing (CFT) on characteristics of the neat bitumen and bitumens modified with different polymers, as well as stone mastic asphalt concretes prepared with the use of the neat and the modified bitumens. Maximal and minimal temperature in cycles of freezing and thawing (FT) is accepted equal to +20 \({}^{\circ}\)C and \(-18\pm 2\)\({}^{\circ}\)C respectively. Characteristics of low temperature resistance of bitumens (stiffness S and m-value) have been determined on bending beam rheometer at the temperatures of \(-24\)\({}^{\circ}\)C, \(-30\)\({}^{\circ}\)C, and \(-36\)\({}^{\circ}\)C. Strength at direct tension of the stone mastic asphalt concretes has been evaluated in the device TRAVIS (Infratest Ltd., Brackenheim, Germany) at the temperatures of 0 \({}^{\circ}\)C, \(-10\)\({}^{\circ}\)C, \(-20\)\({}^{\circ}\)C, and \(-30\)\({}^{\circ}\)C. Resistance of stone mastic asphalt concretes to rutting has been determined on a Hamburg wheel tracking machine. The characteristics of the bitumens have been determined in the initial condition and after 25 and 50 cycles of FT, and the characteristics of stone mastic asphalt concretes, in the initial condition and after 50 cycles of FT. The results obtained experimentally have shown that CFT impacts essentially on the investigated characteristics of bitumens and stone mastic asphalt concretes. Modification with polymers improves the mechanical characteristics of the bitumens and decreases the effect of CFT. It is also found out that when selecting a bitumen for specific climatic conditions it is necessary to take into account both the number of cycles of FT and the characteristic low temperature.  
bitumens; polymers; stone mastic asphalt concrete; freezing and thawing; stiffness; m-value; tensile strength; rutting resistance
## 3 Results and Discussion
### XRF Spectroscopy  
The spatial distribution of Ca, obtained through XRF mapping, was used as the marker for the presence of the soiling layer and for its spatial distribution. The analysis showed that the three cleaning methods displayed a different degree of effectiveness in removing the soiling layer (Figure 3a-c). In detail, the use of water in agarose (Figure 3a) appeared not to be effective in Ca removal after 180 min: the slight differences in Ca amounts among the application times confirm the mild action carried out on the soiling.  
Instead, when observing the XRF maps of the mock-ups cleaned by Tween 20 (3%) (Figure 3b) and Velvesil(tm) Plus (Figure 3c), we note that Ca is never detected in the areas of the cleaning tests. In particular, for what concerns the Velvesil(tm) Plus (Figure 3c), the edges of the cleaning test areas are less defined than those of the Tween 20 tests. The removal of soiling is observed at t\({}_{1}\) and no trend during the time was noticed.  
Figure 3: X-ray fluorescence (XRF) maps of the SSw area after the cleaning tests, respectively for water in agarose (**a**); Tween 20 (**b**); Velvesil(tm) Plus where Ca distribution highlights the cleaned area (from t\({}_{1}\) to t\({}_{4}\), from the left to the right) (**c**); and Si distribution revealed Velvesil(tm) Plus residues (**d**). The heat map colors represent the minimum (blue-violet) and the maximum (red) values.  
As regards exclusively the Velvesil(tm) Plus method, a not negligible quantity of Si was detected on the cleaned surface: its distribution allowed us to monitor the cleaning residue, as shown in Figure 3d. These results confirm what was previously supposed based on colorimetric observations: water in agarose seemed to interact with the soiling, but with a low removal power, whereas Tween 20 and Velvesil(tm) Plus were able to remove almost completely the soiling through their surfactant action. Moreover, by the XRF mapping, it was possible to highlight that the changes which occurred, were observed only in the area of the application of the cleaning test. It demonstrated that the interaction was limited to the areas where there was an effective contact between the cleaning products and the surface, and where the phenomena of absorption or binding were not detected. Nonetheless, it is worth noting that the selection of the cleaned area was more accurate in the case of the rigid gels (agar as support) with results more suitable for tiny and well-defined areas due to its stiffness. Contrarily, the silicone-based product seems to be unlikely to be accurately spread into the edges.
## 3 Results
### Analysis of Factors Affecting SOM Content
#### 3.3.2 Difference in SOM Content under Different Soil Textures  
Soil texture reflects intrinsic fertility characteristics and is an important influence factor on SOM content. The results of variance analysis showed significant differences in SOM content between different soil textures, so multiple comparisons were made, the results of which are shown in Figure 4. There are significant differences between loamy clay soils and sandy soils, and such differences were also found between loamy clay and loamy sand soils. The general trend was clay > loam > sandy, and similar research results were found in Taiyuan City and Jiangsu Province (both located in China) [14; 24]. Among them (Table 4), the SOM content of loamy clay soils was 15.32 g\(\cdot\)kg\({}^{-1}\), which was significantly higher than the others. The sandy soils had the lowest content, at only 3.07 g\(\cdot\)kg\({}^{-1}\), which may be due to the lapsed SOM induced by lighter texture and good aeration conditions. These results are similar to the studies in south-east areas of Chongqing City [25]. Sandy loam and sandy clay loam had the highest coefficient of variation, while the rest had moderate, with loamy sandy soil having the lowest coefficient of variation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Impact Factor** & **Sum of Squares** & **df** & **Mean Square** & **F** & **\(p\)-Value** \\ \hline Land use pattern & 2237.82 & 4 & 584.45 & 10.76 & 0.000 \\ Soil texture & 1281.42 & 8 & 160.18 & 2.70 & 0.007 \\ Parent material & 1162.17 & 7 & 166.02 & 2.78 & 0.008 \\ Soil type & 1625.06 & 10 & 162.51 & 2.78 & 0.003 \\ \hline \end{tabular}
\end{table}
Table 2: ANOVA results for each factor affecting SOM.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **N** & **Min/(g\(\cdot\)kg\({}^{-1}\))** & **Max/(g\(\cdot\)kg\({}^{-1}\))** & **Mean/(g\(\cdot\)kg\({}^{-1}\))** & **CV/(\%)** \\ \hline deluvium & 44 & 3.50 & 74.30 & 11.72 & 98.60 \\ diluvial deposit & 57 & 6.60 & 20.53 & 10.42 & 23.92 \\ river alluvium & 96 & 2.40 & 32.70 & 9.08 & 45.02 \\ Lacustrine & 11 & 4.89 & 74.90 & 17.63 & 112.18 \\ deposits & 12 & 1.20 & 32.42 & 11.75 & 79.91 \\ Loess parent material & 6 & 2.40 & 18.13 & 9.22 & 55.65 \\ Littoral sediment & 24 & 2.30 & 33.66 & 11.71 & 53.65 \\ Residual deposits & 7 & 4.83 & 17.56 & 10.16 & 47.65 \\ \hline \end{tabular}
\end{table}
Table 3: Analysis of the SOM content of different soil-forming parent materials.
# Design and Validation of a Camera-Based Safety System for Fenceless Robotic Work Cells
###### Abstract  
A two-dimensional (2-D) camera system with a real-time image processing-based safety technology is a cost-effective alternative that needs optimization of the cell layout, the number of cameras, and the camera's locations and orientations. A design optimization study was performed using the multi-criteria linear fractional programming method and considering the number of cameras, the resolution, as well as camera positions and orientations. A table-top experimental setup was designed and built to test the effectiveness of the optimized design using two cameras. The designs at optimal and nonoptimal parameters were compared using a deep learning algorithm, ResNet-152. To eliminate blind spots, a simple but novel 2-D image merging technique was proposed as an alternative to commonly employed stereo imaging methods. Verification experiments were conducted by using two camera resolutions with two graphic processors under varying illuminance. It was validated that high-speed entrances to the safety system were detected reliably and with a 0.1 s response time. Moreover, the system was proven to work effectively at a minimum illuminance of 120 lux, while commercial systems cannot be operated under 400 lux. After determining the most appropriate 2-D camera type, positions, and angles within the international standards, the most cost-effective solution set with a performance-to-price ratio up to 15 times higher than high-cost 3-D camera systems was proposed and validated.  
robotics; optimization; work-cell safety system; 2-D cameras +
Footnote †: journal: Applied Sciences  
15 November 2021  
1
# Assessment of Skin Deep Layer Biochemical Profile Using Spatially Offset Raman Spectroscopy
## 1 Introduction
We achieve that by employing the SORS configuration on a set of healthy samples and in depths where skin cancer lesions are expected to develop. We initially use optical phantoms to specify the optimum range of spatial offset while probing \"lesions\" in different depths and then employ the optimum offset range in healthy human skin of different anatomical areas to assess the sampling volume and study the different skin layers' signal contribution to the recorded Raman signal. The invasion depth of a skin tumor is frequently underestimated and inadequate excision and local recurrence is often the poor outcome of a surgical intervention. To that end, the preliminary results derived from this study aim to the development of in vivo skin cancer screening applications able to early detect the biochemical profile of cancer in deeper skin layers.