esa-sceva/satcom-chunk-collection huggingface dataset:
# Compensation Control Strategy for Orbital Pursuit-Evasion Problem with Imperfect Information
## 5 State Estimation Based on UKF  
Before applying the compensation control strategies given in the above section to the orbital pursuit-evasion problem, the spacecraft's state needs to be measured. However, noise is included in the measured data. To determine the opponent's state, we need to estimate the state of the spacecraft based on the observed data. Radar is a relative measurement sensor commonly used in space missions. We use radar as the measurement sensor. Besides, since the measurement equation is nonlinear, we need to adopt a nonlinear filtering method. Nonlinear filtering methods mainly include the extended Kalman filter (EKF), UKF, and particles filter (PF) [30]. Compared with PF, UKF has lower computational complexity [31]. Compared with EKF, UKF has better performance [31]. Thus, we use UKF to estimate the state. In this section, the spacecraft state estimation model is first established, and then the UKF toolkit [32] is used to estimate the spacecraft state.  
The spacecraft motion equation with environmental perturbation can be expressed as follows:  
\[\dot{\mathbf{X}}=\mathbf{A}\mathbf{X}+\mathbf{B}\mathbf{U}+\mathbf{B}\mathbf{w}, \tag{35}\]  
where \(\mathbf{w}=\left[\begin{array}{cc}w_{x}&w_{y}&w_{z}\end{array}\right]^{T}\) is the equivalent noise of environmental perturbation, which follows the normal distribution \(w_{x}\sim N(0,\sigma_{wx})\), \(w_{y}\sim N\big{(}0,\sigma_{wy}\big{)}\), \(w_{z}\sim N(0,\sigma_{wz})\), \(\sigma_{wx}=\sigma_{wy}=\sigma_{wz}=\sigma_{ww}\).  
We use radar as the measurement sensor. Taking relative distance \(\Phi\), elevation angle \(\psi\), and azimuth angle \(\varphi\) as measurement variables, the measurement equation can be expressed as follows:  
\[\mathbf{Z}=\left[\begin{array}{c}\varphi\\ \psi\\ \varphi\end{array}\right]=\left[\begin{array}{c}\sqrt{x^{2}+y^{2}+z^{2}}\\ \arctan\frac{y}{\sqrt{x^{2}+z^{2}}}\\ \arctan\frac{-z^{2}}{\pi}\end{array}\right]+\mathbf{V}, \tag{36}\]  
where \(\mathbf{V}\) is the measurement noise, which follows the normal distribution.  
Before UKF is used for state estimation, the continuous system needs to be discretized. We use the solution formula of the state equation to ensure that the continuous state equation and the discrete state equation have the same solution at the sampling point. The discretized system equation is as follows:  
\[\left\{\begin{array}{l}\mathbf{X}_{k+1}=\mathbf{\Phi}(t_{k+1}-t_{k})\mathbf{X}_{k}+\mathbf{ \Gamma}_{k}\mathbf{B}\mathbf{U}_{k}+\mathbf{\Gamma}_{k}\mathbf{B}\mathbf{w}_{k}\\ \mathbf{Z}_{k}=\mathbf{H}\mathbf{X}_{k}+\mathbf{V}_{k}\end{array}\right., \tag{37}\]  
where \(E\big{[}\mathbf{w}_{k}\mathbf{w}_{k}^{T}\big{]}=\mathbf{Q}_{k}\), \(E\big{[}\mathbf{V}_{k}\mathbf{V}_{k}^{T}\big{]}=\mathbf{R}_{k}\), \(E\big{[}\mathbf{w}_{i}\mathbf{V}_{j}^{T}\big{]}=\mathbf{0}\), \(\mathbf{\Gamma}_{k}=\int_{t_{k}}^{t_{k+1}}\mathbf{\Phi}(t_{k+1}-t)dt\).  
Finally, the UKF toolkit [32] is used to estimate the spacecraft state based on the established model.
# A Novel mHealth Approach for the Monitoring and Assisted Therapeutics of Obstructive Sleep Apnea
## 3 Materials and Methods
### EMG Sensor  
Surface electromyography is the process of obtaining and analyzing the electrical signals from muscle contractions, through electrodes placed in contact with the skin. There are several low-cost solutions available on the market; nevertheless, for this research we have selected the Grove EMG sensor, produced by _Seed Studio_.  
This sensor detects and collects the muscles' electrical signals; however, it does not present them in a direct way, which means that the sensor filters, amplifies, and rectifies the signal so that it can be read by the ADC of any microcontroller.  
The EMG device detects when muscle cells are activated electrically or neurologically. This activation creates a voltage difference in the muscles that is measured by two of the three electrodes that this sensor uses. The third electrode is used as the ground, and exists in order to reduce electromagnetic noise.  
In standby mode, this sensor has an output voltage of 1.5 V. When muscle activity is detected, the output signal increases to a maximum voltage of 3.3 V.  
The EMG signal is acquired using the differential amplification technique and is obtained with the help of a high input impedance instrumentation amplifier.  
The instrumentation amplifier performs the differential amplification by subtracting the voltages of both electrodes. In this way, the common noise signals between the VIN+ and VIN- electrode are eliminated.  
Since noise \"dirties\" the EMG signal, it must be filtered, even after differential amplification. This noise can have many origins, from sensor drift on the skin to temperature fluctuations, and a high-pass filter is needed to remove it.  
A high pass filter is used to remove the low frequencies from a particular signal, and the cut-off frequency is the frequency below which all frequencies are eliminated.  
To conclude, the signal, after being properly filtered and shaped to a suitable frequency band, must be amplified. In this case it is amplified with a zero-drift operational amplifier, which is a highly effective solution for applications requiring a high-precision signal.
Molecules2011162507-2518; doi:10.3390/molecules16032507  
_Article_  
Screening of Natural Organic Volatiles from _Prunus mahaleb_ L. Honey: Coumarin and Vomifoliol as Nonspecific Biomarkers  
**[PERSON] 1,*, [PERSON] 2 and [PERSON] 3**  
* 1 Faculty of Chemistry and Technology, University of Split, N. Tesle 10/V, 21000 Split, Croatia
* 2 Marko Marulic Polytechnic in Knin, P. Kresimira IV 30, 22300 Knin, Croatia
* 3 Department of Biotechnology, University of Rijeka, S. Krautzeka bb, 51000 Rijeka, Croatia
* 4 Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +385-21-329-434; Fax: +385-21-329-461.  
_Received: 18 February 2011; in revised form: 15 March 2011 / Accepted: 15 March 2011 / Published: 16 March 2011_  
**Abstract:** Headspace solid-phase microextraction (HS-SPME; PDMS/DVB fibre) and ultrasonic solvent extraction (USE; solvent A: pentane and diethyl ether (1:2 v/v), solvent B: dichloromethane) followed by gas chromatography and mass spectrometry (GC, GC-MS) were used for the analysis of _Prunus mahaleb_ L. honey samples. Screening was focused toward chemical composition of natural organic volatiles to determine if it is useful as a method of determining honey-sourcing. A total of 34 compounds were identified in the headspace and 49 in the extracts that included terpen, norisoprenoids and benzene derivatives, followed by minor percentages of aliphatic compounds and furan derivatives. High vomifoliol percentages (10.7%-24.2%) in both extracts (dominant in solvent B) and coumarin (0.3%-2.4%) from the extracts (more abundant in solvent A) and headspace (0.9%-1.8%) were considered characteristic for _P. mahaleb_ honey and highlighted as potential nonspecific biomarkers of the honey's botanical origin. In addition, comparison with _P. mahaleb_ flowers, leaves, bark and wood volatiles from our previous research revealed common compounds among norisoprenoids and benzene derivatives.  
**Keywords:**_Prunus mahaleb_ L. honey; headspace-solid-phase microextraction (HS-SPME); ultrasonic solvent extraction (USE); gas chromatography and mass spectrometry (GC, GC-MS); coumarin; vomifoliol
## 3 Results and Discussion
### Future Climate Projections  
#### 3.2.1 Temperature  
Having successfully validated the models relative to EOBS for temperature and HW characteristics, hereafter, only BC data are considered to evaluate climate change.  
Figure 6: (**a**) NDAYS annual average and (**b**) Tmax_sum HW average for EOBS, WRF CORDEX average, maximum and minimum (vertical bar) for HIST BC. NDAYS: Number of HW days.  
Figure 7 shows monthly differences relative to HIST of Tmax for (a) MED and (b) LONG, for WRF and average, maximum and minimum CORDEX models.  
For MED, all models simulate an annual average warming of 1.9 \({}^{\circ}\)C with the greatest Tmax increases from July to October. For LONG, changes relative to HIST are similar but stronger than those of MED with the models simulating an average warming of 3.7 \({}^{\circ}\)C with greatest Tmax increases also for the July-October period of 4.4 \({}^{\circ}\)C. Q-Q graphs for MED/LONG, relative to HIST, are shown in Figure 8.  
Figure 7: Monthly differences relative to HIST of Tmax for (a) MED and (b), for WRF and average, maximum and minimum CORDEX models. MED: Simulation for the 2046–2065 period where the WRF was forced by the MPI model considering the RCP8.5 scenario.  
Warming in MED relative to HIST is greatest between percentiles 50 and 80, and uncertainty (model dispersion) is larger for extreme low and high temperatures. WRF is very similar to the CORDEX ensemble average.  
Tmin (not shown) increases by 1.6 \({}^{\circ}\)C in MED and 3.2 \({}^{\circ}\)C in LONG, with the greatest increases observed from July to October. Together, Tmax and Tmin changes represent an increase of the average daily temperature range, relative to HIST, of 0.2 \({}^{\circ}\)C for MED and 0.5 \({}^{\circ}\)C for LONG.  
Figure 8: Quantile–quantile graph of Tmax for WRF HIST (horizontal axis) and WRF (**a**) MED and (**b**) LONG, and CORDEX average, maximum, and minimum (vertical axis). LONG: The same as MED but for the 2081–2100 period.  
Warming simulated by individual models is shown in Figure 9 for Tmax. Inter-model variability is about 0.2 \({}^{\circ}\)C and 0.5 \({}^{\circ}\)C (standard deviation) for annual and monthly conditions, respectively.  
[PERSON] et al. [32] evaluated temperature changes for Portugal and, while not comparable, they indicate similar warming for Portugal by the end of the century.
# Hop-by-Hop Multipath Overlay Routing for Optimizing Network Resource Allocation in WANs
## 3 System Design
It chooses the lowest-cost, non-congested node from its multipath next-hop set. Table 2 summarizes the mathematical symbols in this paper. The rest of the section is structured as follows. Section 3.1 illustrates the overlay node placement algorithm, Section 3.2 explains the next hop selection algorithm for hop-by-hop multipath routing in the overlay network, and Section 3.3 introduces the implementation of the hop-by-hop overlay multipath routing algorithm. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Node** & **Next Hop** & **Cost** & **Load** \\ \hline A & B & 2 & 0.2 \\  & D & 3 & 0.2 \\ \hline B & C & 2 & 0.9 \\  & D & 2 & 0.2 \\ \hline C & E & 2 & 0.5 \\ \hline D & C & 2 & 0.9 \\  & E & 3 & 0.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Routing table destined for node E in Figure 1. \begin{table}
\begin{tabular}{c l} \hline \hline
**Symbols** & **Definition** \\ \hline \(G\) & The underlay graph \\ \hline \(d_{n}\) & The hop-by-hop overlay path diversity of overlay node \(n\) \\ \hline \(D(x,y)\) & The end-to-end path diversity between node \(x\) and node \(y\) \\ \hline \(l(x,y)\) & The underlay link set of tunnel between node \(x\) and node \(y\) \\ \hline \(m\) & The number of overlay nodes \\ \hline \(\mathcal{N}\) & The location of overlay nodes \\ \hline \(Overlap(x,y)\) & The overlap of tunnel\((x,y)\) with node \(x\) and \(S_{n}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of the symbols. Figure 1: An example of hop-by-hop overlay multipath transmission.
## 3 Results
### Accuracy Assessment with Statistical Data  
The crop cultivation areas obtained from the MODIS data were summarized at the municipal level. The accuracy assessment was conducted by calculating the R\({}^{2}\) and RMSE using municipal statistical data. The area of wheat, corn, and rice detected from MODIS images using the PB-CB algorithm demonstrated a positive correlation with municipal statistics. The R\({}^{2}\) for wheat was 0.76, with an associated RMSE of 83.82 Kha; for corn, the R\({}^{2}\) was 0.72, accompanied by an RMSE of 72.83 Kha; and for rice, the R\({}^{2}\) was 0.70, with an RMSE of 69.64 Kha, while considering all data pairs for 2005, 2010, 2015, and 2020, as shown  
Figure 13: Crop spatial distribution map in North China using PB-CB in 2005 (a), 2010 (b), 2015 (c), 2020 (d).  
in Figure 14A. These results show excellent agreement between the area data derived from MODIS and the statistical data. To further analyze the results, the crop cultivation area for 2015 derived from the PB-CB algorithm was compared with county-level statistics and still showed a positive correlation. The R\({}^{2}\) reached 0.64 and RMSE of 14.60 Kha for wheat, R\({}^{2}\) attained 0.63 and RMSE of 14.01 Kha for corn, and R\({}^{2}\) reached 0.65 and RMSE of 12.92 Kha for rice (Figure 14B). This further confirms the feasibility of using the PB-CB model combined with NaE data for crop cultivation area extraction.
## 4 Results  
### General Results  
In the following statistical analysis, the logarithmic value of the geometric mean of each participant's responses over a type of stimulus was considered as a dependent variable (27 different stimuli: 9 distances \(\times\) 3 rendering methods). For both groups separately, a focus was placed on ensuring the normality of the dependent variables associated with a specific stimulus. A Jarque-Bera test indicated that in all cases for both groups, the null hypothesis \"the data were normally distributed\" was not rejected. Homoscedasticity was checked with [PERSON]'s tests, and no large deviations of the variance were detected across the conditions. Geometric means over all participants reports for each rendering methods are displayed in Figure 2.  
A repeated measures ANOVA (\(\alpha=0.05\)) was conducted on geometric mean of reported distances of each subject with RENDERING (three rendering methods) and DISTANCE (nine levels ranging from 1 to 7 m) as within-subjects factors and GROUP (two groups) as intersubject factor. The analysis revealed a main effect of DISTANCE (\(F(1,8)=580.8\), \(p<0.001\)) and a main effect of RENDERING (\(F(1,2)=68.0\), \(p<0.001\)), as well as a DISTANCE \(*\) RENDERING interaction (\(F(1,16)=11.7\); \(p<0.001\)) and a GROUP \(*\) DISTANCE interaction (\(F(1,8)=2.05\); \(p=0.039\)). No significant main effect of GROUP was revealed (\(F(1,1)=0.74\), \(p>0.05\)).
## 4 Results and Discussion
### Modeling Results
#### 4.2.2 The Results of the Classification of the Mechanical Strength Level  
The mechanical reference values of the three types of coniferous wood were initially categorized into mechanical strength classes, allowing for a rapid and preliminary classification of the specimen's mechanical properties during wood processing. The division results are shown in Table 6 below. PLS-DA, SVM, RF, DenseNet, and SE-DenseNet algorithms were used to construct the classification model. The number of PLS factors selected by PLS-DA was 3; SVM penalty coefficient C set at 2.0; sigmoid kernel function selected with kernel function coefficient gamma set at 0.0001; the number of RF classifiers set at 13; the number of layers in the SE-DenseNet model is 58; and dimension transformation rate \(\beta\) is 12. Table 7 displays a comparison of various methods and modeling results for various tree species. According to Table 7, in the traditional classification model, compared with PLS-DA and SVM, RF has better classification effect, but the results obtained by this method are often unstable. The DenseNet method can achieve end-to-end prediction without complex dimension reduction processing. After the introduction of the SE module, the SE-Densenet model established has obtained the optimal prediction performance. The test set accuracy of larch, hemlock, and mongolica were 0.8889, 0.8108, and 0.8571, respectively. The above results indicate that NIRS combined with deep learning can be used for the preliminary \begin{table}
\begin{tabular}{l l l} \hline \hline
**Range of Values** & **Grade 1: MPa** & **Grade 2: MPa** \\ \hline larch & 54.9–64.8 & 64.8–78.6 \\ hemlock & 40.2–52.3 & 52.3–59.8 \\ mongolica & 35.2–46.2 & 46.2–52.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mechanical strength level classification. Figure 15: RF classification results of three coniferous woods: (**a**) Sample distribution of training set; (**b**) Confusion matrix of the test set. identification of the mechanical strength grade of the species. Additionally, it has important reference value for rapid identification of mechanical properties in wood processing. Figure 16 depicts the SE-DenseNet model cross-validation set ROC curve and test set confusion matrix. The ROC curves could evaluate the quality of the models, and the area under the ROC curve (AUC) of the three needle-leaf materials reached 0.8, indicating that these models had good classification effects. As shown in Figure 16, the AUC of the cross-validation set ROC curve of the three coniferous woods were both above 0.8. The results of the confusion matrix of three conifers on the test set also show that SE-DenseNet has a good classification effect on the preliminary identification of mechanical grade.
## 5 Evaluation
### Autoencoder Model Training
#### 5.2.2 Comparison with Other Embedding Mechanisms  
As a comparison, we also build a convolutional restricted Boltzmann machine (CRBM), as presented in [31]. To incorporate it, we treat satellites' identifiers as their dimensions (D) and various features as the channel (C). Since the feature packing is different, we omit a comparison of reconstruction error but compare the effectiveness of representation next, based on the applications discussed in Section 4.3.
## 3 Human Activity Recognition Techniques
### Predicative Analytics and Hazard Identification  
Using historical and real-time data, predictive analytics aims to forecast future events or conditions. Human activity recognition contributes to this field in multiple ways. In healthcare monitoring and disease prediction, HAR can track movement patterns and detect anomalies to provide early warnings of diseases like Parkinson's, Alzheimer's, or cardiovascular conditions. Gait analysis can predict fall risks in elderly patients, enabling preventive interventions. For individuals with disabilities, HAR can predict assistance needs, optimizing caregiver responses. In industrial and office environments, HAR can analyze worker postures and movements to predict fatigue, musculoskeletal disorders, and potential productivity declines [81; 82]. Activity data can be studied to suggest er genomic adjustments or break schedules to prevent injuries. Furthermore, daily activity patterns extracted through HAR can be integrated into smart home and building management, automating lighting, heating, and security systems based on user behavior [83]. Finally, in sports and performance analytics, HAR tracks athlete movements to identify inefficiencies, predict performance trends, and suggest training modifications for injury prevention [84].  
HAR is widely used in safety-critical environments to detect hazardous conditions and prevent accidents. In hazardous environments such as construction sites, HAR systems can analyze deviations from standard workflows; detect risky behaviors like improper lifting, lack of safety gears, or proximity to heavy machinery; and trigger real-time alerts [85]. In chemical plants and warehouses, HAR monitors worker interactions with hazardous substances, ensuring compliance with safety protocols. It can predict exposure risks and suggest precautionary measures based on movement patterns. In transportation, HAR is used in driver monitoring systems to detect fatigue and analyze aggressive driving behaviors to prevent traffic violations [86]. In public spaces, HAR helps detect suspicious activity, helping security personnel identify potential threats or criminal behavior. In emergency response scenarios, HAR can detect panic behaviors or sudden movements that indicate distress.  
By combining HAR with predictive analytics, we gain the ability to identify and mitigate risks before they escalate. From healthcare and workplace safety to transportation and security, HAR-driven insights enable proactive interventions, ultimately improving efficiency, safety, and overall quality of life.
## 3 Results and Discussions  
### Resistivity  
Figure 1 shows the main effects plot (a) and interaction plot (b) of the parameters such as sputter power, sputter pressure, and annealing temperature. It shows that the resistivity of Cu-Cr-Zr thin film is effected more or less by all the three factors: sputter power, sputter pressure, and annealing temperature.  
As shown in Figure 1a, both the power and the pressure act as major effects, and when the temperature is in the range of 300-400 \({}^{\circ}\)C, there is little effect, but once exceeding 400 \({}^{\circ}\)C, the annealing temperature becomes the main effect factor of the resistance value.  
Further analysis shows that higher sputter power and lower sputter pressure will induce a lower resistivity; annealing in a certain range of temperature is advantageous in reducing the resistivity, while too high of an annealing temperature will lead to the opposite effect. When the effect of a factor depends on the quasi-level of another one, the interaction plot can be used to show the interaction between the two factors. The greater the slope difference between lines, the greater the degree of interaction. As shown in Figure 1b, the factors of sputtering power and sputtering pressure interact  
\begin{table}
\begin{tabular}{c c} \hline
**Percent Area Removed** & **Classification** \\ \hline
0\% & 5B \\ Less than 5\% & 4B \\
5–15\% & 3B \\
15–35\% & 2B \\
35–65\% & 1B \\ Greater than 65\% & 0B \\ \hline \end{tabular}
\end{table}
Table 2: Classification of adhesion test results.  
Figure 1: (a) Main effects plot and (b) interaction plot for the resistivity of Cu-Cr-Zr thin films affected by sputter power, sputter pressure, and annealing temperature.  
with each other, but the annealing temperature has little interaction with them. This is reasonable, as the factors of power and pressure work during sputtering deposition at the same time, while annealing is a separate factor that affects the film during post-processing. Thus, we fixed the optimal deposition conditions of the film at 150 W, 2 mTorr, and all the experiments below follow this condition.  
Figure 2a shows the resistivity of pure Cu thin films on different substrates with annealing temperatures of 300 \({}^{\circ}\)C, 350 \({}^{\circ}\)C, 400 \({}^{\circ}\)C, and 450 \({}^{\circ}\)C. Figure 2b shows the resistivity of Cu-Cr-Zr thin films on different substrates with annealing temperatures of 300 \({}^{\circ}\)C, 350 \({}^{\circ}\)C, 400 \({}^{\circ}\)C, and 450 \({}^{\circ}\)C. We learned that an annealing process at 300 \({}^{\circ}\)C leads to the best optimized resistivity for Cu-Cr-Zr, which is presumably caused by the reduction of the defect density of the Cu lattice and the increase in grain size to reduce electron scattering at grain boundaries. When the annealing temperature exceeds 400 \({}^{\circ}\)C, the resistivity rises sharply, which indicates that the electrical characteristics of the Cu alloy film deteriorate abruptly.
## 4 Overview: Signal Processing Methods
### Filters Applied
#### 4.2.2 60 Hz Notch Filter  
Typically, a notch filter is employed to eliminate noise generated by power lines, which normally falls within the 50 Hz to 60 Hz range. When recording raw or any biomedical  
Figure 12: PSD analysis of EMG signals captured from the forearm after average referencing.  
Figure 13: PSD analysis of EMG signals captured from the wrist after the application of a Band-pass filter with a range of 10 Hz to 450 Hz.  
signals, there is a chance of encountering power line noise in the room. If this noise falls within the frequency band of interest, such as the EMG range (which also starts from 50 to 60 Hz), a notch filter is utilized to specifically remove the power line noise without affecting the actual muscle activity in the EMG signal. In Figure 14, point 2 highlighted in red represents the removal of the 60 Hz signal.