esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Contact Heart Rate Detection Based on Hand Vein Transillumination Imaging
## 3 Results and Discussion
### Qualitative Index  
This section uses the Bland-Altman method to verify whether the non-contact heart rate test results are consistent with the reference values, so as to confirm the effectiveness and accuracy of the method. The differences between the estimated values from the contact and non-contact methods are plotted with the average value of HR, and 100 pairs of measurement data of 10 participants are analyzed with the Bland-Altman method, as shown in Figure 6a,b.  
First, a scatter plot is drawn based on the true heart rate and the measured value obtained by the camera, and the image is used to analyze the correlation between the two. As shown in Figure 6a, the relationship between the true heart rate and the measured value obtained by the camera is approximately linear.  
Second, based on the number of data points outside the 95% consistency limit and the maximum difference within the agreement limit, and the degree of acceptability, consistency evaluation can be performed. It can be seen from Figure 6b that the 95% consistency is limited to -5.3-5.8 bpm, and 7.00% (7/100) of points are outside the 95% consistency limit. Within the consistency limit, the true heart rate is compared with the value measured by the camera. The maximum absolute value of the difference is 7 bpm, and the average error is 2.28 bpm. According to the pharmaceutical industry standard of the People's Republic of China (error <= 5 bpm), the error is acceptable. Thus, the value measured by the camera is considered to be in good agreement with the true heart rate. In addition, the average HR difference is 0.3 bpm. The closeness of the true heart rate to the measured value obtained by the camera is indicated by the blue solid line, which represents the average HR difference. Close to the line (orange dashed line) represents an average difference of 0, which also reflects the high consistency between the two datasets.  
Figure 6: Bland-Altman diagram. (**a**) The regression line between the true heart rate and the measured value of camera: the abscissa represents the true heart rate, and the ordinate represents the value measured by the camera; (**b**) the level of consistency between the true heart rate and the value measured by the camera: the abscissa represents the average difference between the true heart rate and the value measured by the camera, and the ordinate represents the difference between the true heart rate and the value measured by camera.
# Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT
## 3 System Models and Problem Formulation
### Mobility Prediction Model  
The Mobility Prediction Model is a core component of MACS, enabling the prediction of each client's future computational and communication state based on historical mobility data. This prediction allows MACS to evaluate whether the client will have sufficient resources at a future time point to contribute effectively to the training process. Specifically, the computational capacity and data rate at future time \(t+k\) are predicted as follows:  
\[f_{u}(t+k)=\text{PredictCPUFrequency}(\hat{M}_{u}(t+k)), \tag{5}\]  
\[r_{u}(t+k)=\text{PredictDataRate}(\hat{M}_{u}(t+k)), \tag{6}\]where \(\hat{M}_{u}(t+k)\) represents the predicted mobility state of client \(u\) at future time \(t+k\).
## 3 Wireless Communication Technologies
### Long-Range Communication Technologies
#### 3.2.4 Satellite Communication (SATCOM)  
SATCOM is used for sending electromagnetic signals from ground stations to space stations, or satellites, and vice versa. In SATCOM, various frequency bands are used by different satellites. C-Bands, which are still tied up with some systems, utilize an uplink band of 6 GHz and a downlink band of 4 GHz. X-Bands, on the other hand, which are normally used by the military and governmental systems, use 8 GHz for uplink and 7 GHz for downlink. The so-called Ku-Bands, operates on 14 GHz for uplink and 11-12 GHz for downlink. Moreover, these bands are also becoming saturated; therefore, Ka-Bands are evolving to be implemented with the time. Ka-Bands work on a 30 GHz uplink and a 20 GHz downlink. In [30], the authors studied key specifications such as satellite downlink, UAV uplink transmission power, and the image transmission rate. It was specified that satellite relay achieves a larger overlay range and provides a large-coverage image transmission with a high image-quality. Additionally, in [31], the authors highlighted the main problems behind applying SATCOM for live image and video transmissions with the help of micro and mini UAS. The two major issues are inadequate bandwidth and a high cost of data transmission.  
From the above discussion, it can be concluded that short-range communication technologies like Bluetooth, ZigBee, or Wi-Fi may be considered for medium-range FANET applications based on the range and throughput requirements. However, if the coverage area is large and these short-range technologies are not able to support the required throughput demands, long-range communication technologies such as WiMAX, LTE, 5G, and SATCOM may be far more appropriate.  
In summary, based on the transmission characteristics, we consider short-range communication technologies for our proposed scheme. There are valid reasons for choosing these technologies: they operate in the unlicensed spectrum; they do not require strict LOS; and they offer a reasonable data rate and coverage. Moreover, they can be easily integrated with small UAV. Moreover, due to the significant improvements in speed, power consumption, capacity, and coverage, Bluetooth 5 is the best option amongst the aforementioned short-range wireless technologies. However, in order to utilize the best features, Wi-Fi is integrated with Bluetooth 5 in our proposed approach.
# Photonics-Based Microwave Image-Reject Mixer
## 3 Photonic IRM Based on Phase-Cancellation Techniques
### Photonic IRM Based on 90\({}^{\circ}\) Optical Hybrid
For example, beating between RF and image signals will generate mixing spurs of the 2 nd-order harmonic downconverted IF signal at \(2f_{\mathrm{IF}}\), which is very close to the optimum IF signal at \(f_{\mathrm{IF}}\). Thus, this beating is difficult to remove through filtering. For wideband applications, mixing spurs at \(2f_{\mathrm{IF}}\) can overlap with the desired IF signal at \(f_{\mathrm{IF}}\). To deal with this problem, in one of our recent works, a photonic IRM based on a 90\({}^{\circ}\) optical hybrid and two balanced photodetectors (BPDs) was proposed and demonstrated [30], which could suppress mixing spurs of RF and image signals. The RF signal (containing the image) and electrical LO signal are both converted into the optical domain and then injected into the signal and LO ports of a 90\({}^{\circ}\) optical hybrid, respectively. The 90\({}^{\circ}\) optical hybrid introduces a 180\({}^{\circ}\) phase difference into the optical LO for two in-phase and the quadrature optical outputs. Through optical to electrical conversion, the IF electrical outputs for two in-phase optical outputs (or two quadrature optical outputs) at \(f_{\mathrm{IF}}\) are out of phase, while the other mixing spurs are in-phase. Thus, by injecting two in-phase and two quadrature optical outputs into two BPDs, respectively, at the output of each BPD the mixing spurs will be suppressed. In addition, the two IF outputs, from the two BPDs, are quadrature because the 90\({}^{\circ}\) optical hybrid also introduces a quadrature phase of for the optical LO for the in-phase and the quadrature optical outputs. The two IF outputs  
Figure 7: Schematic diagram of a typical photonic IRM using a 90\({}^{\circ}\) optical hybrid [25]. are then combined by a low-frequency electrical hybrid to introduce an additional 90\({}^{\circ}\) phase difference, through which the unwanted signals in the \(I\) and \(Q\) paths, downconverted from the image, are out of phase, and those downconverted from the wanted RF signals are in phase. In this way, the wanted IF signals will be enhanced, while the downconverted image is eliminated. Thus, a photonics-based microwave IRM with mixing spurs largely suppressed can be achieved. A good image rejection can also be achieved. Multi-octave frequency downconversion can also be achieved, since the mixing spurs included those at 2\(f_{\text{IF}}\), and are almost fully suppressed. For RF signals that have an instantaneous wide bandwidth (i.e., 2 GHz [30]), the proposed photonic-based microwave IRM can realize a clean downconverted IF signal with all mixing spurs suppressed, as shown in Figure 8a. As a comparison, the corresponding mixing results using a different scheme are shown in Figure 8b [25]. In addition, an alternative photonics-based microwave IRM system [30] showed superior performance as compared with the scheme represented in Figure 8b [25].
# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
## 5 Evaluation and Results
### Mission 1  
The dataset for Mission 1, as illustrated in Figure 3, consists of 60.6% authentic signals and 39.4% attack signals. The attack samples are further classified into 11.5% simplistic attacks, 10.3% intermediate attacks, and 17.6% sophisticated attacks. Figure 4 shows the DELTA_TOW curve over time.  
The feature importance parameter provided by our tree-based ML algorithms provides insight into the relevant features that strongly contribute to detecting GPS spoofing attacks regarding this dataset. As demonstrated in Table 9 and Figure 5, the features PD, CP, DO, TCD, PRN, and C/N0 are widely the most important for the three models, having the highest scores. Meanwhile, the remaining features have a low contribution score. Decision tree and random forest rely on PD as the feature with the highest score, followed by CP and then DO, TCD, PRN, and finally C/N0. The rest of the features converge to zero and can be neglected. The XGBoost model, on the other hand, depends first on the PRN feature, followed by PD, DO, CP, TCD, and C/N0 as the strongest features.  
For this mission, the decision tree model achieved an average accuracy of 99.943%, a precision of 99.930%, a recall of 99.927%, and an F1-score of 99.929%. On the other hand, the random forest model demonstrated even stronger results, with an average accuracy of 99.979%, a precision of 99.977%, a recall of 99.973%, and an F1-score of 99.975%. Meanwhile, XGBoost recorded the lowest performance among the three models, with an accuracy value of 99.918%, a precision of 99.902%, a recall of 99.895%, and an F1-score of 99.898%. Despite this, the model remained stable and reliable.  
Figure 4: Continuous DELTA_TOW values in Mission 1.  
Figure 3: Mission 1 class distribution.  
Figure 5: Feature importance ranking from most to least significant—Mission 1.
## 6 Numerical Examples  
According to the results derived in the preceding sections, the predictions from the proposed model may automatically, accurately fit any given data for both the stress-softening effect and the strain recovery effect simply by choosing three shape functions \(h=f(\tau)\), \(h=g_{s}(\tau)\), \(h=h(T)\) that accurately fit these data. As indicated before, it may be straightforward to present these three shape functions by choosing usual interpolating functions. However, they may be given by other forms of functions. As illustrative examples, in this section the simple forms of shape functions given in Sections 4 and 5.2 will be used to fit test data in literature.  
In what follows, test data will be fitted by using the shape function Equation (36) for the initial elastic curve, the shape function (37) for the monotone loading curve and the shape function (34) for the unloading curve, as well as the strain-temperature function Equation (57) for the strain recovery curve.  
Below, the model predictions will be compared with two sets of data in literature, separately.
## 2 Materials and Methods
### Path-Tracking Method Design
#### 2.2.3 Design of Fuzzy Controller  
(1) Membership Function  
The lateral deviation \(d\) and the heading deviation \(\theta\) of the test prototype and the desired path were taken as the input variables of the fuzzy controller. The domain of \(d\) was [\(-1.2\) m \(1.2\) m], and the domain of \(\theta\) was [\(-90^{\circ}\) \(90^{\circ}\)]. The look-ahead distance \(LD\) was regarded as the output variable, and the domain was [\(1\) m \(2.2\) m]. The positive and negative signs of each variable were defined as follows: when the vehicle is located on the left forward side of the desired path, \(d\) is positive, and when the vehicle is located on the right forward side of the desired path, \(d\) is negative. The heading deviation \(\theta\) is positive when rotated counterclockwise and negative when rotated clockwise; the steering angle is positive when the front wheels turns left and negative when the vehicle turns right. Comprehensively considering the precision of system control and the flexibility of optimization, in the fuzzy controller, the input and output variables were quantified into 5 fuzzy subsets. Different from a traditional fuzzy controller, we adopted non-uniform quantization scale instead of a uniform quantization scale in membership function. That is, a large-scale quantitative level is used to obtain good control stability when vehicle deviation is large. When the deviation is small and the vehicle is close to the desired path, the high resolution (small scale) quantitative level is used to realize the more precise adjustment of the vehicle position and posture. A triangle membership function is used for each input and output variable. The basic information of each input and output variable is shown in Figure 6.  
(2) Control Rules  
Based on the experience of experts and drivers, the control rules should be in accordance with following principles. When the deviation is very large, a small look-ahead distance should be selected to quickly converge to the desired path to improve the efficiency of path tracking. In contrast, when the deviation is small, the look-ahead distance should be appropriately increased to prevent oscillation during path tracking, thus increasing the stationarity of the prototype vehicle. Through repeated simulation and debugging, 25 control rules were determined, as shown in Table 1. In Table 1, the upper left and lower right corners show that the vehicle is located in a position with large lateral deviation \(d\) and heading deviation \(\theta\), so smaller look-ahead distance scales NB and NS are used; nearby, the middle and secondary diagonals (the line from the lower left to the upper right corner in the table) are the locations where the deviation of the vehicle (\(d\), \(\theta\)) is small, and the greater look-ahead distances PB and PS are used.  
Figure 6: Membership functions.
## 3 Results  
### Hydrology and Tree Mortality Trends  
Using a relative reference point [10], groundwater table depth (GWT) increased from \(-\)4.3 cm below the ground surface to 1.2 cm above ground between 2009 and 2014. From 2015 onward, GWT consistently ranged between 3.2 cm and 6.9 cm above the land surface, indicating a sustained rise over time (R\({}^{2}\) = 0.30; \(p\) < 0.05; Figure 2a). A weak but detectable relationship was observed between rising groundwater table depth (GWT) and increasing sea level rise (SLR) over the 2009-2019 period (R\({}^{2}\) = 0.11, \(p\) < 0.01), suggesting that even at a distance of approximately 20 km from the Croatian Sound and 40 km from the Atlantic Ocean, changes in sea level may influence local hydrology. During the same period, total tree mortality exhibited a pronounced and consistent upward trend (Figure 2c), increasing from nearly 0% at the beginning of the study to over 45% by 2019. The increase in mortality corresponds with a steady decline in leaf area index (LAI) observed throughout the study period (Figure 2d).  
The linear regression also showed an excellent fit (R\({}^{2}\) = 0.93, Figure 2c), reflecting the accelerating rate of cumulative mortality in the study area. This rapid increase aligns temporally with both the rise in GWT and SLR, suggesting a potential linkage between prolonged hydrological stress and wetland forest decline.
# Line Scan Spatial Speckle Contrast Imaging and Its Application in Blood Flow Imaging
## 3 Results and Discussion
### Comparison of LS-TSCI and LS-SSCI  
After the optimum exposure time was determined, we compared the performances of LS-TSCI and LS-SSCI. Figure 5a,b shows the temporal speckle contrast image and spatial speckle contrast image of chick embryo (day 3.5, NO. 2) using LS-TSCI and LS-SSCI at line exposure durations of 4 ms, respectively. The results demonstrate that LS-TSCI and LS-SSCI were essentially identical, but due to the poor temporal resolution, LS-TSCI is sensitive to motion artifacts due to heartbeats, particularly around the heart, dorsal aorta, and artery (shown in Figure 5a). Though spatial sampling in LS-SSCI reduces the spatial resolution, LS-SSCI maintains high temporal resolution, and is less affected by motion artifacts. Consequently, LS-SSCI can provide better image quality for the in vivo imaging blood flow of chick embryos than LS-TSCI.  
Figure 4: **(a–h)** Temporal laser speckle contrast image of a chick embryo (day 3) obtained with the SI-LSCI at different exposure durations 0.1, 0.2, 0.5, 1, 2, 4, 6, and 10 ms, respectively. (i) Relative noise in the speckle contrast image \(\sigma_{K}\) /K at different exposure times.
# A Fiber-Optic Six-Axis Force Sensor Based on a 3-UPU-Compliant Parallel Mechanism
## 5 Experiments and Results Analysis
### Results and Analysis  
#### 5.3.1 Accuracy and Hysteresis  
In order to study the measurement accuracy of the sensor, the above calibration Equation (31) is used to measure the loading force in the individual direction. Forces \(F_{X}\), \(F_{y}\) and \(F_{z}\) were applied with a step size of 6 N within the range of \(-60\) N to 60 N, and the torques \(T_{X}\), \(T_{y}\) and \(T_{z}\) were applied with a step size of 0.25 N\(\cdot\)m within the range of \(-2.5\) N\(\cdot\)m to 2.5 N\(\cdot\)m. And then, the same load was applied in the opposite direction for all measurement directions. The loading methods for all forces and torques are shown in Figure 16. Figure 21 illustrates the measurement results. To study the hysteresis of the sensor, the measured values of the corresponding directional force are also shown in Figure 19 when the load is applied in the opposite direction. The hysteresis \(H_{S}\) can be expressed as:  
\[H_{S}=\frac{\max\lvert F_{F}-F_{R}\rvert}{F_{mr}}\times 100\% \tag{32}\]  
where \(F_{F}\) is the measured force of forward loading (increasing loading force), \(F_{R}\) is the measured force of reverse loading (decreasing loading force).  
Figure 21: Measurement results of the loading force in individual direction.  
According to Figure 21, the maximum hysteresis occurs when the torque is applied in the X-direction, with a value of 2.78%.  
The corresponding residual error analysis result is shown in Figure 22. In Figure 22, all values of Pearson' \(r\) and determination coefficient \(R^{2}\) are greater than 0.99, and all \(p\)-values are smaller than 0.01, which shows the validation of the experiment.  
The error matrix \(E_{r}\) computed by substituting the data in Figure 21 into Equation (26) is as follows:  
\[E_{r}=\begin{bmatrix}E_{Fx}&E_{Fy}&E_{Fz}&E_{Tx}&E_{Ty}&E_{Tz}\end{bmatrix}= \begin{bmatrix}0.0452&0.0012&0.0125&0.0069&0.0148&0.0122\\ 0.0141&0.0365&0.0085&0.0295&0.0106&0.0054\\ 0.0117&0.0035&0.0263&0.0251&0.0011&0.0121\\ 0.0012&0.0326&0.0159&0.0253&0.0294&0.0116\\ 0.0166&0.0023&0.0072&0.0094&0.0264&0.0075\\ 0.0235&0.0152&0.0325&0.0154&0.0035&0.0183\end{bmatrix} \tag{33}\]  
The elements on the diagonals constitute the type-I errors and are equivalent to the maximum relative errors between the real forces acting in the corresponding directions and the measured force. The remaining elements constitute the type-II errors and are equivalent to the coupled outputs in other directions resulting from forces in individual dimensions. According to the matrix \(E_{r}\), type-I errors are 4.52%FS, 3.65%FS, 2.63%FS, 2.53%FS, 2.64%FS and 1.83%FS in the \(F_{x},F_{y},F_{z},T_{x},T_{y}\), and \(T_{z}\) principal measurement directions, respectively. The maximum type-II error is 3.26%FS, corresponding to the coupled torque output in the \(M_{x}\) direction when the load is applied in the \(F_{y}\) direction. The maximum values of type-I errors and type-II errors are both smaller than that of [8].
# Design and Experiments of Autonomous Path Tracking Based on Dead Reckoning
## 2 Autonomous Driving Based on Dead Reckoning
### Pure Pursuit Control with Bicycle Model  
Based on the rear wheel position calculated using the above differential drive model, the vehicle can be further simplified as a bicycle model, as shown in Figure 3.  
\[\tan(\delta)=\frac{B}{R} \tag{10}\]  
\[v_{c}=R\omega \tag{11}\]  
\[\dot{\theta}=\omega=\frac{v_{c}\tan(\delta)}{B} \tag{12}\]  
With the vehicle's real-time position based on the above bicycle model, the pure pursuit model can be used for tracking a given reference path. The pure pursuit establishes a geometric model between the moving vehicle and the reference path, as can be seen in Figure 4. The reference path (green) is a predefined path upon which all the points in the path are known, and the vehicle trajectory (orange) is the predicted trajectory based on the current steering angle and speed. The objective of the pure pursuit is calculating the target steering angle and vehicle speed in real time based on the geometric relationship.  
From Figure 4, the following formula is obtained:  
\[180^{\lx@math@degree}=\gamma_{1}+\gamma_{2}+\gamma_{3}=\gamma_{1}+\left(90^{ \lx@math@degree}-\alpha\right)+\left(90^{\lx@math@degree}-\alpha\right) \tag{13}\]  
which yields \(\gamma_{1}=2\alpha\), according to the law of sines:  
\[\frac{l_{d}}{\sin(\gamma_{1})}=\frac{R}{\sin(\gamma_{2})} \tag{14}\]  
Based on the result of Equation (14), the following formulas are obtained step by step:  
\[\frac{l_{d}}{\sin(2\alpha)}=\frac{R}{\sin(90^{\lx@math@degree}-\alpha)} \tag{15}\]  
\[\frac{l_{d}}{2\sin(\alpha)\cos(\alpha)}=\frac{R}{\cos(\alpha)} \tag{16}\]  
\[R=\frac{l_{d}}{2\sin(\alpha)} \tag{17}\]  
Figure 4: Pure pursuit model.  
Figure 3: Bicycle model.  
\[\delta=\arctan\biggl{(}\frac{2\text{B}\text{sin}(\alpha)}{l_{d}}\biggr{)} \tag{18}\]  
The angle is the target steering angle, which is required in the PID controller.