esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Health Problems Using Deep Learning Images and Bio-Signals
## 4 Experimentation and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{(a) Control group (normal body temperature)} \\ \hline
**N** & **Pulse Variability** & **Mean BPM** & **SDNN** & **RMSSD** \\ \hline
1 & 44.68 & 77.90 & 58.65 & 38.00 \\
2 & 39.78 & 74.50 & 55.90 & 42.10 \\
3 & 35.23 & 77.00 & 73.80 & 44.10 \\
4 & 34.25 & 76.00 & 66.00 & 4  
Table 2 compares the detection performance (mean average precision) and frames per second (FPS) of faster R-CNN, a deep learning model used in the proposed system, with YOLO and SSD, which are similar models, in order to compare the performance of the detection models. Table 3 shows the functions of the existing thermal imaging system and our proposed system. In most of the thermal image access management systems, a face recognition algorithm is applied to the general image to find a face, and the body temperature is derived by mapping it with the thermal image in the corresponding area. The face recognition algorithm of the proposed system and the existing thermal imaging system both detect the face area; however, the thermal imaging system detects faces in the input real color image, whereas the proposed system is trained to detect faces directly in the thermal image. Deep learning is applied to the thermal image to determine if a person is recognized and if the individual has a fever. Existing thermal imaging systems can recognize faces and measure body temperature, but it is difficult to accurately predict the health status of a measurer because it does not have a separate function for measuring other bio-signals. Our proposed study uses the PPG sensor to additionally acquire the person's heart rate and HRV data and compare and analyze the correlation with body temperature, so it is possible to predict the health status of the subject and make more accurate discrimination. Due to the fact that the proposed system detects faces from thermal images with different textures, the detection accuracy may be lower than that of existing thermal imaging systems; however, it has been confirmed that detecting the shape of the face and whether a mask is worn is not a problem. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Detection Model** & **mAP (\%)** & **Inference Speed (FPS)** \\ \hline Faster R-CNN (proposed system) & 76.4 & 5 \\ YOLO & 73.7 & 81 \\ SSD300 & 74.3 & 46 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative result of detection model performance of the proposed system. Figure 6: Heart rate distribution and health status analysis. (**a**) Control group (normal body temperature); (**b**) experimental group (fever body temperature).
# Measurement-Device-Independent Two-Party Cryptography with Error Estimation
## 3 Weak String Erasure
### Protocol  
In the previous protocol, there is a no error estimation process because the measurement results of the BB84 protocol are directly related to the final key. We redesign the WSE protocol by using the independence of key and measurement results of the MDI-QKD and PM-QKD protocols, adding a error estimation process to improve the security of the protocol.  
The specific agreement is as follows:  
1. [PERSON] chooses a string \(x^{n}\in_{R}\{0,1\}^{n}\) and bases the specifying string \(\theta_{A}^{n}\in_{R}\{+,\times\}^{n}\) randomly. She encodes each bit \(x_{i}\) in the basis given by \(\theta_{A_{i}}\) (as \(H^{\theta_{A_{i}}}|x_{i}\rangle\)) and sends it to the third party [PERSON];
2. Similarly to [PERSON], [PERSON] chooses a string \(y^{n}\in_{R}\{0,1\}^{n}\) and bases specifying string \(\theta_{B}^{n}\in_{R}\{+,\times\}^{n}\) randomly. He encodes each bit \(y_{i}\) in the basis given by \(\theta_{B_{i}}\) (as \(H^{\theta_{B_{i}}}|y_{i}\rangle\)), and sends it to the third party [PERSON];
3. [PERSON] performs a Bell measurement, and announces the outcome;
4. [PERSON] selects a subset of the measurement outcome as the error estimator (about \(m\) qubits) and sends a subset of the measurement outcome \(I_{check}\) to [PERSON]. [PERSON] sends \(\theta_{B_{check}}\) and a subset of the measurement outcome \(y_{check}\) (\(y_{check},\theta_{B_{check}},\theta_{B_{check}}=\{y_{i},\theta_{B_{i}}|i\in I _{check}\}\)) to [PERSON]. Then, they initiate error estimation process and compute: \[Q_{u}=\frac{\mathrm{num}(x_{i}\
eq y_{i}|\theta_{B_{i}}=\theta_{A_{i}})}{ \mathrm{num}(\theta_{B_{i}}=\theta_{A_{i}})};\]
5. If \(Q_{u}>e_{r}\), the communication is terminated, otherwise, the process continues;
6. [PERSON] sends the remaining bases \(\theta_{A}^{n-m}\) to [PERSON] and outputs the remaining string \(x^{n-m}\);
7. [PERSON] computes \(I:=\{i\in[n]|i\
otin I_{check}\wedge\theta_{A_{i}}=\theta_{B_{i}}\}\) and outputs \((I,z^{|I|}):=(I,y_{I})\).
## 4 Discussion  
Skin Contact Capacitive Imaging is a promising, novel imaging technique based on the dielectric constant measurement principle, which has been used not only for skin water content measurements, but also for solvent penetration measurements [6], skin texture/microrelief and hair water content measurements [7; 8]. With Skin Contact Capacitive Imaging, we have studied the water content of different facial skin sites. Although the water content results available in the literature for different face areas are not always consistent, our results agree well with [PERSON]'s study [36]. Apart from normal skin measurements, Skin Contact Capacitive Imaging can also be used for measuring diseased skin. Combined with the advances of Artificial Intelligence, which has already been used for skin classification [9] and decision supporting in radiotherapy [37], it is possible to develop a skin capacitive image classification system to identify different types of skin diseases, such as neoplastic lesions [38]. With the 50 \(\upmu\)m spatial resolution, it is also possible to detect skin diseases at an early stage.  
High-Resolution Ultrasound Imaging is a state-of-the-art ultrasound technology, which allows us to measure the thickness of the different skin layers, such as the stratum comeum, epidermis and dermis. Estimating the thickness of the stratum comeum, the outmost skin layer, is very useful for skin cosmetic studies, and is not possible with any other techniques. The skin histology information can reflect skin conditions, such as damaged skin or diseased skin. Our thickness results agree generally well with literature studies [39; 40]. We have also developed two new ultrasound image classification techniques based on image features, rather than the whole images. The first is based on skin ultrasound image luminosity values from the red, green or blue channels as well as the histogram values. The second is based on skin image texture, where 10 skin image textural features were used. Finally, we evaluated the feasibility of training standard Machine Learning classifications to identify the different facial sites based on the pre-processed High-Resolution Ultrasound images. We consider our result a moderate success, with lots of room to improve accuracy by having a more systematic measurement setup.
# Face Recognition and Tracking Framework for Human-Robot Interaction
## 6 Limitations, and Future Work  
The study conducted has a complex setting that contains two workstations (WS1 and WS2) synchronized together using the ROS operating system. In addition, extracting face features during the experiments is a challenging task due to the illumination conditions, extreme deviation in head pose angles, and occlusion. However, the aforementioned performance evaluation showed the effectiveness of the proposed framework in recognizing the subject's identity in a multi-person environment.  
Few subjects caused a wrong identification during the experiments due to the lack of the registration process and good face feature embedding, which lead to the re-registration of the mentioned subjects.  
The advantage of our framework is that it depends on lightweight CNNs for all face recognition stages, including face detection, alignment and feature extraction, to meet  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**ID**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} \\ \cline{3-6}  & & **Tracking** & **No Tracking** & **Tracking** & **No Tracking** \\ \hline
7 & 25 & 0.89 & 0.53 & 0.92 & 0.63 \\ \hline
8 & 29 & 0.98 & 0.83 & 0.98 & 0.79 \\ \hline
9 & 32 & 0.99 & 0.68 & 0.98 & 0.60 \\ \hline
10 & 36 & 0.95 & 0.76 & 0.97 & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Method** & **Average Time (ms)** \\ \hline Detection & 3.2 \\ Alignment & 1.4 \\ Tracking & 0.8 \\ Recognition (Embedding Inference) & 1.3 \\ Identification (Similarity) & 0.08 \\ Visualization \& Delays & 7.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average execution time of individual methods used in the proposed framework.  
the real-time requirements in HRI systems. Furthermore, the developed framework can simultaneously recognize the faces of the cooperating subjects in various poses, face expressions, illumination, and other outdoor-related factors. Although two of the subjects were wearing face masks for the whole experiment, our model succeeded to recognize their identity with reasonable confidence.  
Future work would involve a new study with a large number of subjects with different human-robot interaction scenarios to effectively assess the performance of the framework and overcome the limited number of subjects in the RoSA study. In addition future work would involve designing an end-to-end trainable convolutional network framework for all the face recognition stages.
## 2 Antenna Design
### Radiation Modes
Varying the length from 8.49 mm to 4.49 mm drastically shifted the resonant frequency from approximately 5.7 GHz to 9.0 GHz, pushing it out of the required band for Wi-Fi/WLAN, with a huge effect on the lower band. Moreover, we have examined the antenna structure, in which the monopole strip current is concentrated on the meander line, which further reduces the impact from the bending and provided stability in the design. Therefore, in our proposed antenna, we observed that the S\({}_{11}\) changes with length, as well as how it shifted the frequency and optimized the dimensions to minimize this shift. The optimized design parameters are listed as above in Table 2.  
Figure 3: Distribution of current density with radiation modes at (**a**) lower band, and (**b**) upper band.
# Combining Dielectric and Hyperspectral Data for Apple Core Browning Detection
## 4 Experiment
### Experimental Results
#### 4.3.2 Impact of CCA Target Dimension  
The target dimension after CCA dimensionality reduction has an impact on the classification performance of the classifier for the reduced data, and [PERSON] et al. [42] have indicated that there is an optimal balance between dimension and classifier performance. To avoid redundant data and information loss, we conducted experiments to determine the optimal number of target dimensions. Figure 6 illustrates the influence of target data dimensions on the sensitivity, specificity, and accuracy of different classification models. From this figure, it can be observed that after the target dimension reached 35, the sensitivity, specificity, and accuracy of the classification models tended to stabilize. Excessive dimensions contained redundant data information that did not contribute to the performance improvement of the classification models. Therefore, we chose to reduce the number of hyperspectral features and dielectric signal features to 35 each, as it provided a good balance between avoiding redundancy and preserving classification model performance.  
Figure 6: _Cont._
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis comparing global connectivity measures (\(\varphi\), \(\Delta\)L, \(\Delta\)C) measures was performed using nonparametric bootstrap procedures with 1000 random permutations for each frequency and condition [38; 39]. Independent permutation tests required corrections for multiple comparisons. Bonferroni correction was applied to the significance level (\(p=\alpha/3=0.017\); \(\alpha=0.05\)). Statistical analysis of local SWP measures (inter-group design) was carried out. An essential role in information processing in the network is played by high BC/stronger nodes through which the shortest paths pass. The strength and BC of the nodes were estimated using statistics based on non-parametric permutation clusters [39]. This analysis was performed for the hubs defined by nodes with maximum strength/BC selectively crossing the threshold criteria (one s.d. relative to the strength (BC) of a group node). Significant clusters were identified by applying a critical value for the maximum cluster statistic. By controlling the false alarm rate with multiple comparison corrections, the repeated clusters significantly exceed the threshold criteria and determine an ordered order in whose histograms the nodes have indices corresponding to the sensors. The medians of their distributions are sensitive to hemispheric differences. Controlling for multiple selected thresholds required a Bonferroni correction (\(p=\alpha/2=0.025\)). Given the exploratory nature of this initial investigation and building upon the research group's prior hypotheses, we adjusted for multiple comparisons to mitigate the risk of Type I errors while minimizing the risk of Type II errors by avoiding overly stringent significance thresholds. The nonparametric size effect (analog of Cohen's d, [40; 41; 42]) was applied to assess the sample size and sufficient power to detect meaningful differences, particularly given the small subgroups. The nonparametric size effect (Q) was small: Q = 0.1; medium: Q = 0.3; large: Q = 0.5. Nonparametric statistics were performed in MATLAB. The selective criteria were also applied to the links and edges with BC and strength (Str) presented in the figures below.  
The degree of participation (in percent) of each measure in the comparisons was performed on an individual-level analysis for each pair of children. Single-level global and local characteristics were nonparametrically compared between two subjects in each subgroup. The number of network measures for performers in a given subgroup that differed significantly from those of other performers was displayed compared to the total number of performers in the given subgroup (in percentage) for each network.
## 2 Materials and Methods
### Headspace Solid-Phase Microextraction and GC-MS (HS-SPME/GC-MS)  
HS-SPME was conducted using a manual SPME holder and polydimethylsiloxane/divinylbenzene (PDMS/DVB) fibre purchased from Supelco Co. (Bellefonte, PA, USA). The fibre was conditioned using the instructions by Supelco Co. Samples of the herbal extracts and their mixtures were placed separately in 5 mL glass vials immediately after extraction (2 mL) and closed hermetically with PTFE/silicone septa. The vials were kept in a water bath at 60 \({}^{\circ}\)C during equilibration (15 min) and HS-SPME (45 min) and were partially submerged so that the liquid phase of the sample was below the water level. The extraction was conducted under constant stirring (1000 rpm) with a magnetic stirrer. An SPME fibre was withdrawn into the needle, removed from the vial, and inserted into the injector (250 \({}^{\circ}\)C) of a gas chromatograph with a mass spectrometer (GC-MS). The extracted volatiles were thermally desorbed directly to the GC column after 6 min. All extractions (HS-SPME) were performed in duplicate (\(n\) = 2 replicates).  
GC-MS analyses were performed on a gas chromatograph model 7890A (Agilent Technologies, Palo Alto, CA, USA) and equipped with an HP-5 MS capillary column (5% phenyl-methylpolysiloxane, Agilent J and W; 30 m \(\times\) 0.25 mm i.d., coating thickness 0.25 \(\upmu\)m) and a mass selective detector (MSD) model 5977E (Agilent Technologies, Palo Alto, CA, USA). The carrier gas was helium (He 1.0 mL min\({}^{-1}\)). The oven temperature was set at 70 \({}^{\circ}\)C for 2 min; then, the temperature was increased from 70 to 200 \({}^{\circ}\)C (3 \({}^{\circ}\)C min\({}^{-1}\)) and held at 200 \({}^{\circ}\)C for 15 min. The MSD (EI mode) was used at 70 eV with a 30-300 amu mass range. The compounds' identification was based on the retention indices (RIs) determined relative to \(n\)-alkanes' (C\({}_{9}\)-C\({}_{25}\)) retention times and their comparison with data in the literature (National Institute of Standards and Technology), as well as by their mass spectra compared with the spectra from Wiley 9 (Wiley, New York, NY, USA) and NIST 17 (D-Gaithersburg) mass spectral libraries. The results are expressed as a percentage composition as mean values \(\pm\) standard deviation (\(n\) = 2 replicates).
## 3 Experimental Results and Analyses
### Evaluation Metrics  
For each image, the intersection over union (IoU) between the bounding box of the detected crack and ground truth can be calculated as: \(I_{oU}=\frac{A_{u}}{A_{u}}\), where\(I_{oU}\) is the intersection over union, \(A_{o}\) is the area of overlap, and \(A_{u}\) is the area of union.  
When the IoU of the predicted bounding box and ground truth is greater than a certain threshold value (e.g., 0.5), it is considered to be a true positive; otherwise, it is a false positive. A false negative is obtained by missing a crack. Then we can calculate the precision and recall. Finally, average precision (AP) which is equivalent to the area under the precision-recall curve [28] can be computed. In addition, in order to measure the efficiency of the algorithm, the offline detection time for one picture is also an important evaluation index. We compare the detection time (i.e., the offline time) and analyze the accuracy performance of several models.
## 2 Establishment of the Seasonally Frozen Ground-Pipeline System Model
### Conceptual Model Design and Simulation Parameters
#### 2.3.2 Model Parameters  
Considering the multiphysical field coupling process of frozen soil, the mechanical parameters of the frozen soil (such as elastic modulus, Poisson's ratio, and strength parameters) are set as functions of the soil temperature \(T\) in the following form:  
\[\begin{array}{ll}E=a_{1}+b_{1}|T|^{m},&\
u=a_{2}+b_{2}|T|\\ C=a_{3}+b_{3}|T|,&\varphi=a_{4}+b_{4}|T|\end{array} \tag{13}\]  
In the equation, both \(a_{i}\) and \(b_{i}\) are experimental constants. The values of other physical and mechanical parameters in the model are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & **Mixed** & \multirow{2}{*}{**Silty Clay**} & **Gravelly** & \multirow{2}{*}{**Steel**} & **Water** & **Ice** \\  & **Fill Soil** & & & **Sand** & & \\ \hline \(\rho(\mathrm{kg/m^{3}})\) & 1820 & 1800 & 1700 & 7800 & 1000 & 918 \\ \(\lambda(\mathrm{W/(m\cdot^{\circ}C)})\) & 1.474 & 1.125 & 1.38 & 66.6 & 0.63 & 2.31 \\ \(C(\mathrm{kJ/(kg\cdot^{\circ}C)})\) & 0.92 & 0.84 & 0.89 & 0.460 & 4.2 & 2.1 \\ \(T_{f}(^{\circ}C)\) & \(-0.16\) & \(-0.54\) & \(-0.18\) & / & / & / \\ B & 0.61 & 0.56 & 0.65 & / & / & / \\ \(a(\mathrm{kPa^{-1}})\) & 0.05 & 2.59 & 0.66 & / & / & / \\ \(m\) & 0.5 & 0.22 & 0.14 & / & / & / \\ \(l\) & 0.5 & 0.5 & 0.5 & / & / & / \\ \(\theta_{s}\) & 0.5 & 0.5 & 0.42 & / & / & / \\ \(\theta_{f}\) & 0.231 & 0.02 & 0.05 & / & / & / \\ \(k_{s}(\mathrm{m/s})\) & \(10^{-5}\) & \(10^{-8}\) & \(9.62\times 10^{-5}\) & / & / & / \\ \(a_{1}\) & 61 & 28 & 28 & / & / & / \\ \(b_{1}\) & 53 & 26 & 26 & / & / & / \\ \(a_{2}\) & 0.35 & 0.40 & 0.38 & / & / & / \\ \(b_{2}\) & \(-0.007\) & \(-0.008\) & \(-0.006\) & / & / & / \\ \(a_{3}\) & 0.03 & 0.15 & 0.018 & / & / & / \\ \(b_{3}\) & 0.094 & 0.090 & 0.006 & / & / & / \\ \(a_{4}\) & 23 & 22 & 31 & / & / & / \\ \(b_{4}\) & 9.5 & 8.0 & 2.0 & / & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters.  
Figure 3: Structural cross-sectional diagram.  
Complementing the material parameters, the specification of boundary and initial conditions plays an equally crucial role in simulation outcomes.
## 2 Results
### Specific Energy Consumption  
Figures 7 and 8 illustrate the specific energy consumption expressed in (1) kilojoules per gram of fresh weight and (2) kilojoules required for the removal of 1 g of water. Specific energy consumptions increased as the moisture content decreased. A steep increase was observed at the final drying stage. Figure 9 shows the final specific energy consumptions of CD, VMD, and CPD-VMFD. CD consumed more energy than VMD did. However, the high final specific energy of CD at 50 \({}^{\circ}\)C could be reduced with the application of VMFD, reducing specific energy consumptions from 81.28 kJ/g fresh weight (fw) to 58.70 kJ/g fw and from 113.99 kJ/g water to 87.59 kJ/g water. The final specific energy consumption also reduced as hot air temperature and microwave power increased in CD and VMD, respectively. High drying intensities corresponded to a short drying duration, thereby reducing the specific energy consumption. The specific energy consumption can be considered as the heat energy input which affects the chemical changes in the raw material during drying for a certain time at different drying parameters. The highest energy input distributed over a long time period at the lowest temperature (40 \({}^{\circ}\)C) was beneficial for antioxidant activity and TPC (Table 1). On the other hand, decreasing the energy input and drying time by increasing temperature to 50 and 60 \({}^{\circ}\)C increased the content of phytosterols (Table 3). This enhancing effect consisting of decreasing the energy input and drying time was confirmed by the highest content of phytosterols achieved for VMD at 9 W/g. Decreasing the energy input and drying time was also favorable for the concentration of volatile compounds in the case of CD samples (Table 2).  
Figure 6: Chemical structure of \(\alpha\)-linolenic acid.  
Figure 7: Specific energy consumption of _S. crispus_ leaves per gram of fresh weight dried using CD, VMD, and CPD-VMFD.
## 5 Pre-Identification Method for Continuous Low Thrust Parameters
### Pre-Identification Method of Continuous Tangential Thrust Parameters  
The arc segment effect of continuous thrust action is given by the curve integral of Equation (18):  
\[\mathrm{d}v=\frac{1}{2}\sqrt{\frac{\mu}{a^{3}}}\mathrm{d}a \tag{18}\]  
\[\Delta v=\int_{a_{0}}^{a_{\mathrm{f}}}\frac{1}{2}\sqrt{\frac{\mu}{a^{3}}} \mathrm{d}a=\sqrt{\mu}\bigg{(}\frac{1}{\sqrt{a_{0}}}-\frac{1}{\sqrt{a_{\mathrm{ f}}}}\bigg{)} \tag{19}\]  
where \(\Delta v\) represents the cumulative amount of change in satellite velocity due to tangential thrust action, and does not represent the direction and magnitude of the actual satellite velocity. \(a_{0}\) is the initial semi-major axis of the satellite when the tangential thrust begins to act. \(a_{\mathrm{f}}\) is the final semi-major axis after the tangential thrust has been acting for a period of time. Let the time of action of the thrust be \(\Delta t\), then  
\[a_{\mathrm{T}}=\frac{\Delta v}{\Delta t}=\frac{\sqrt{\mu}\Big{(}\frac{1}{\sqrt {a_{0}}}-\frac{1}{\sqrt{a_{\mathrm{f}}}}\Big{)}}{\Delta t} \tag{20}\]  
From Equation (20), the tangential acceleration can be solved by the initial semi-major axis, the final semi-major axis and the thrust action time of the tangential thrust action arc segment.