esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimentation on the Case Study
### Experiments and Equipment  
To assess the thermal efficiency of the students' accommodation, a suite of current technological equipment is used. The tools used within the process were meticulously selected to ensure the precision and reliability of data collection, paramount for a comprehensive analysis and the subsequent implementation of energy-efficient solutions. The equipment includes devices ranging from Unmanned Aerial Systems (UAS) with thermal imaging capabilities to ground-based Terrestrial Laser Scanners (TLS) and high-accuracy GPS receivers. Each piece of equipment plays an important role in capturing detailed thermal profiles and structural details of the buildings.  
This equipment not only shows technical robustness, but also underlines the project's commitment to technical excellence in sustainable energy consumption and the creation of a green campus ethos.  
In addition, the use of RGB cameras on UAS for building surveying offers a balance of detailed visual information, cost-effectiveness and versatility, making them indispensable tools in the field of remote sensing and aerial surveying. They capture images in the colour spectrum that are visible to the human eye, providing detailed and intuitive visual information, identification and assessment of the real-life condition of various structural elements, materials, and surfaces in buildings and surrounding infrastructure. In addition to RGB equipment, thermal cameras mounted on UAS offer a non-invasive, efficient and effective method for detecting thermal anomalies, facilitating the assessment, maintenance, and optimisation of building performance. These systems are instrumental in identifying heat losses and inefficiencies in buildings, pinpointing areas where insulation may be lacking, or where there are gaps in the building envelope. Furthermore, thermal imaging can reveal the presence of moisture (e.g., infiltrations or leaks) within a building's structure,as water has a different thermal signature than surrounding materials. Thermal cameras can help locate moisture intrusion, thus preventing mould growth and structural damage. By detecting temperature variations, thermal cameras can help identify structural problems that might not be visible to the naked eye (e.g., faulty insulation, thermal bridging, or hidden structural defects).
# On-Board Wind Scatterometry
## 3 Wind Scatterometry Processing Chain and On-Board Processing Modifications  
The well-developed off-line ground-based processing is composed of pre-processing and wind inversion [34]. During pre-processing, noises in observations are subtracted and effects due to observing geometry and antenna pattern are eliminated before normalized radar cross sections  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Indexes** & \\ \hline
**Weight** & 15 kg \\
**Size** & \(425\times 254\times 240\) mm \\
**Power** & 280 W \\
**Temperature** & 50 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Hardware scale of the specific on-board processor (SOBP).  
Figure 2: Illustration of system simulating the on-board environment and processing routine for method validation.  
(NRCS) are calculated. In wind inversion, NRCS obtained within a wind retrieving unit, i.e., a wind vector cell (WVC), are grouped. A WVC is usually a square on the ocean surface. With a GMF, the wind field is inverted for each WVC by using the maximum likelihood estimation (MLE) method. GMFs are semi-empirical models that map NRCS to winds of the ocean surface, considering their observing geometry from scatterometers operating in different frequencies and polarizations. The whole procedure of on-board scatterometer data processing inherits procedures from this chain, with specific procedures and NRCS and wind products being formation modified to provide RT products. In this section, differences in product formation are first described for on-board procedures, followed by modified algorithms in processors generating them.
# Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network
## 5 Conclusions  
In this study, an artificial neural network was employed to establish a prediction model for the rigidity of the positioning stage. The dynamic vibration signal was measured during the movement of the positioning stage, which was processed and fed into the ANN models to predict the linear guide preload degradation. Based on the results of the comprehensive study, the following conclusions can be drawn:  
1. The vibration characteristics of the positioning stage structure in the static state and in motion are closely related to the linear guide preload. This shows that the vibration level of the high preload positioning stage is higher than that of the low preload positioning stage. The variation in the linear guide preload was determined and affected by the change in the interference between the rolling balls and the raceway. Basically, interference can be reduced by wearing out the rolling interface, which further causes a preload loss in linear guides with service time. This concept provides the basic knowledge for monitoring the degradation of a linear guide preload.  
Figure 17: Application scenarios of predictive model with graphical user interface for monitoring rigidity decay of feeding mechanisms in milling machine: (**a**) X axis linear guide; (**b**) Y axis linear guide; and (**c**) Z axis linear guide.  
Figure 18: Data processing workflow of the preload prediction system.  
2. The ANN predictive models were shown to have good prediction performance in all training datasets collected from experiments, with an accuracy of 96.5%. This implies that the ANN models perform well in predicting the preload conditions of linear guides with consistent sliding blocks. In the verification dataset, the average percentage prediction error of the stage rigidity is approximately 8.54-11.23%, which is slightly higher than that in the training phase. This can be ascribed to the fact that the stage with an unevenly distributed preload in the sliding blocks easily induces vibration with more fluctuation, which ultimately affects the prediction accuracy.
3. The results of this study can provide a reference basis for the future development of a positioning stage preload intelligent monitoring system. The monitoring system output display was established using GUI from an SQL Server. In the current study, a monitoring system has been designed so that the prediction results from the ANN and the rest of the process are easily interpreted by the user to maintain the positioning stage in optimum performance. Future work in this field would entail incorporating online monitoring concepts to obtain real-time prediction values, that are updated and displayed quickly.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Robust 3D Joint Inversion of Gravity and Magnetic Data: A High-Performance Computing Approach
## Appendix A Linear Operators for Gravimetric and Magnetic Direct Modeling  
The operators \(A^{(1)}\) and \(A^{(2)}\) are calculated using analytical solutions for straight prisms with the dimensions described by the limits \(x_{1}\leq x\leq x_{2},y_{1}\leq y\leq y_{2},z_{1}\leq z\leq z_{2}\) following the development of [77,78] for gravimetry and magnetometry, respectively.  
For the number of data \(m=1,2,\dots,\ M\) and the number of prisms \(n=1,2,\ \dots,\ N\) the matrices are defined by:  
\[A^{(1)}(m,n)=\gamma\sum\limits_{i=1}^{2}\sum\limits_{j=1}^{2}\sum\limits_{k=1} ^{2}\mu_{ijk}\left[z_{k}\text{arctan}\frac{\Delta x_{i}\Delta y_{j}}{\Delta z_{ k}r_{ijk}}-\Delta x_{i}\ln\left(r_{ijk}+\Delta y_{j}\right)-\Delta y_{j}\ln \left(r_{ijk}+\Delta x_{i}\right)\right]\]  
\[A^{(2)}(m,n)=C_{m}\ \sum\limits_{i=1}^{2}\sum\limits_{j=1}^{2}\sum\limits_{k=1} ^{2}\mu_{ijk}-\left[\alpha^{2}\text{arctan}\left(\frac{\Delta y_{j}\Delta z_{ k}}{\Delta x_{i}r_{ijk}}\right)-\beta^{2}\text{arctan}\left(\frac{\Delta x_{i} \Delta z_{k}}{\Delta y_{i}r_{ijk}}\right)-\gamma^{2}\text{arctan}\left(\frac {\Delta x_{i}\Delta y_{j}}{\Delta z_{k}r_{ijk}}\right)\right.\]  
\[\left.+2\alpha\beta\ln\left(r_{ijk}+\Delta z_{k}\right)+2\alpha\gamma\ln\left( r_{ijk}+\Delta y_{j}\right)+2\beta\gamma\ln\left(r_{ijk}+\Delta x_{i}\right)\right]\]  
where:  
\[\Delta x_{i}=x_{0}-x_{i},\ \ \Delta y_{j}=y_{0}-y_{j},\ \ \Delta z_{k}=z_{0}-z_{k}\]  
\[r_{ijk}=\sqrt{\Delta x_{i}^{2}+\Delta y_{j}^{2}+\Delta z_{k}^{2}}\]  
\[\mu_{ijk}=(-1)^{i}(-1)^{j}(-1)^{k}\]  
And \(\alpha,\beta,\ \gamma\) are directional cosines of the magnetic field.
# UAV Detection Using Thrust Engine Electromagnetic Spectra
## 4 Conclusions  
In this study, a hypothesis was validated that, by measuring the spectrum of engine control signals, it is possible to detect UAV-specific traces in the spectrogram. In the Introduction section, it was shown that electromagnetic interference signatures are expected to increase, due to the emergence of electric motors.  
Thus, in this study, we showed that it is possible to detect a signature of an electromagnetic spectrum of multi-rotor UAVs. The interference frequency range is expected to be from 1 to 100 kHz. For this task, a precision tunable variable inductance magnetic antenna design was proposed.  
The resonant frequency of the designed tunable antenna is changed by varying the inductance using a motorized actuator. The obtained results show a change in the spectrogram based on the thrust of the engine and the different types of UAVs.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Harmonics** & **1** & **2** & **3** \\ \hline The lower frequencies of the harmonic signature, Hz & 42,468 & 88,793 & 135,465 \\ \hline The upper frequencies of the harmonic signature, Hz & 49,977 & 96,533 & 142,859 \\ \hline The width of the harmonic signature, Hz & 7509 & 7740 & 7394 \\ \hline The center frequencies of the harmonic signature, Hz & 46,280 & 92,575 & 13,8931 \\ \hline The amplitude of center frequency, dBm & \(-66.1\) & \(-52.6\) & \(-80.6\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main frequencies of the harmonics of the DJI drone’s spectrogram.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Harmonics** & **1** & **2** & **3** & **4** \\ \hline The lower frequencies of the harmonic signature, Hz & 19,392 & 39,413 & 59,612 & 79,733 \\ \hline The upper frequencies of the harmonic signature, Hz & 20,791 & 40,878 & 60,998 & 81,198 \\ \hline The width of the harmonic signature, Hz & 1399 & 1465 & 1386 & 1465 \\ \hline The center frequencies of the harmonic signature, Hz & 20,090 & 40,110 & 60,270 & 80,221 \\ \hline The amplitude of center  
**Author Contributions:** Conceptualization, T.J. and V.U.; methodology, A.S., V.A. and D.P.; formal analysis, A.K., T.S. and D.P. investigation, T.J.; writing--original draft preparation, V.A. and T.S.; writing--review and editing, D.P. and A.K.; visualization, T.J.; supervision, A.S. and D.P.; funding acquisition, A.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing not applicable.  
**Acknowledgments:** The authors would like to thank the editors and reviewers for many constructive suggestions and comments that helped improve the quality of the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Preparation of Nanoparticle-Loaded Microbubbles via an Electrohydrodynamic Atomization Process
## 4 Conclusions  
In this study, nanoparticle-loaded microbubbles were prepared by coaxial electrohydrodynamic atomization technology for the first time. The results show that the loading of nanoparticles has great impact on the stability of microbubbles. A proper quantity of nanoparticles increases the stability and reduces the size of microbubbles. A nanoparticle concentration of 1 mg/mL is suggested to produce microbubbles with the best performance in terms of size and stability. Investigations on the effects of gas/liquid flow rate ratios and applied voltages suggest that a 50/10 gas flow rate ratio and 11 kV are the optimal conditions to produce the most stable and uniform microbubbles. Under these conditions, the size of nanoparticle-loaded microbubbles ranged from 120-200 \(\upmu\)m.  
Figure 11: (**a**) Variation of average bubble size with voltage. (**b**) Variation of normalized bubble number over time with different voltages. (\(Q_{l}\) = 10 mL/h, \(Q_{g}\) = 50 mL/h, \(C_{NP}\) = 1 mg/mL).  
**Author Contributions:** Conceptualization, W.-C.Y. and J.-C.W.; methodology, X.-B.N., Y.W. and X.R.; validation, R.W., W.-C.Y. and J.-C.W.; formal analysis, R.W.; investigation, X.-B.N.; data curation, X.-B.N.; writing--original draft preparation, X.-B.N., Y.W. and X.R.; writing--review and editing, R.W., W.-C.Y. and J.-C.W.; visualization, Y.W.; supervision, W.-C.Y., R.W. and J.-C.W.; project administration, W.-C.Y. and J.-C.W.; funding acquisition, W.-C.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (21808088), the Natural Science Foundation of Jiangsu Province (BK20180868), the startup funding for high-level talent of Jiangsu University of China (18 JDG022), Innovation and Entrepreneurship Program of Jiangsu Province (2020) and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX21_1676).  
Not applicable.  
The authors declare no conflict of interest.
# Optimization of HS-SPME-GC-MS for the Determination of Volatile Flavor Compounds in Ningxiang Park
## 3 Results and Discussion
### Influencing Factors on GC-MS Analysis
#### 3.1.4 Effect of Equilibration Conditions on VFCs  
To enrich and achieve dynamic equilibrium in the upper part of the headspace bottle, which is advantageous to the extraction of volatile compounds by the extraction head in the headspace, the VFCs of pork should be balanced under specific conditions before extraction. The rate at which the volatile chemicals achieve equilibrium during this procedure will depend on the equilibration temperature and equilibration period.  
According to the findings (Figure 2a,b), the equilibrium conditions had a slight impact on the number of compounds that were detected (\(p>0.05\)). However, the difference between the conditions was highly significant (\(p<0.01\)) for the total amount of substances (Figure 2c,d). The total number of compounds discovered increased overall as the equilibrium duration and temperature increased, and the equilibrium amount of substances detected increased (Figures S4 and S5). The overall amount of aldehydes produced significantly increased (\(p<0.05\)) as the reaction duration and equilibrium temperature increased. Additionally, as the equilibrium temperature increased, so did the production of unidentified compounds. The low-temperature oxidation of lipids produces many aldehydes [50]; hence, more aldehydes are produced when the equilibrium is brief (less than 30 min) or when the equilibrium temperature is low. The production of aldehydes rapidly increased as temperature and time increased, possibly because the Maillard reaction became stronger. The initial Maillard process was suppressed by fatty aldehydes created by lipid oxidation.  
However, reactive aldehydes will encourage the late reaction, leading to the production of additional novel, sulfur-containing carnivorous compounds with alkyl chains [46]. In the middle and late phases of the Maillard reaction, aldehyde compounds are involved, and while numerous additional reactive aldehydes are formed, thiophene, furan, and other substances are also produced in greater quantities. The results also illustrate the relationship between lipid oxidation and Maillard reactions; however, equilibrium conditions have different effects on various substances by affecting the reaction between flavor precursors, and aldehydes are the most affected.  
Equilibrium can enhance the stability and effectiveness of the extraction process. Aldehydes, alcohols, nitrogen-containing chemicals, and hydrocarbons are primarily impacted by the equilibrium temperature. The heat-treated samples continued to react in the headspace vial during equilibration to reach dynamic equilibrium. The species of the compounds in the headspace vial do not change much in this state, but the interactions between them will cause alterations in the relative contents of the substances. In this experiment, the aldehydes dramatically increased as the equilibrium time and temperature increased, whereas the alcohols, nitrogen-containing compounds, and hydrocarbons significantly declined.
# An Improved Jacobi-Based Detector for Massive MIMO Systems
## 3 Proposed Algorithm for MIMO Detection
### Convergence Proof
```
Input: (1) \(\mathbf{H}\), channel matrix (2) \(\mathbf{y}\), received signal matrix (3) \(K\), number of iterations Output: detected signal \(\mathbf{\tilde{x}}^{(K)}\)//Initialization
1:\(\mathbf{A}=\mathbf{H}^{\mathrm{H}}\mathbf{H}+\sigma^{2}\mathbf{I}_{N_{t}}, \mathbf{b}=\mathbf{H}^{H}\mathbf{y}\);
2:\(\mathbf{\tilde{x}}^{(0)}=\left(\mathbf{D}^{-1}-\mathbf{D}^{-1}\mathbf{E} \mathbf{D}^{-1}\right)\mathbf{b}\);
3:\(\mathbf{r}^{(0)}=\mathbf{b}-\mathbf{A}\mathbf{\tilde{x}}^{(0)}\); //Iterative Process(repeat K times)
4:for\(k=1\) to \(K\) (iteration times) do
5: if \(k=1\); the value of \(\mathbf{r}^{(0)}\) is obtained in the initialization
6: else;\(\mathbf{r}^{(k-1)}=a_{1}\mathbf{r}_{1}+a_{2}\mathbf{r}_{2}\), the value of \(\mathbf{r}_{1},\mathbf{r}_{2}\) is obtained in the previous iteration
7: end if
8:\(\mathbf{p}^{(k-1)}=\mathbf{A}\mathbf{r}^{(k-1)}\);
9:\(\alpha_{k-1}=\frac{\left[\mathbf{r}^{(k-1)},\mathbf{r}^{(k-1)}\right]}{\left[ \mathbf{p}^{(k-1)},\mathbf{r}^{(k-1)}\right]}\);
10:\(\mathbf{\tilde{x}}^{(k)}=\mathbf{\tilde{x}}^{(k-1)}+\alpha_{k-1}\mathbf{r}^{( k-1)}+\mathbf{D}^{-1}\left(\mathbf{r}^{(k-1)}-\alpha_{k-1}\mathbf{p}^{(k-1)}\right)\);
11:\(\mathbf{\tilde{x}}_{1}=\mathbf{\tilde{x}}^{(k-1)},\mathbf{\tilde{x}}_{2}= \mathbf{\tilde{x}}^{(k)}\);
12:\(\mathbf{r}_{1}=\mathbf{r}^{(k-1)},\mathbf{r}_{2}=\mathbf{b}-\mathbf{A} \mathbf{\tilde{x}}_{2}\);
13:\(a_{1}=-\frac{\left(\mathbf{r}_{1}-\mathbf{r}_{2}\right)^{H}\mathbf{r}_{2}}{ \left\|\mathbf{r}_{1}-\mathbf{r}_{2}\right\|_{2}^{2}},a_{2}=1-a_{1}\);
14:\(\mathbf{\tilde{x}}^{(k)}=a_{1}\mathbf{\tilde{x}}_{1}+a_{2}\mathbf{\tilde{x}}_ {2}\);
15:endfor
```  
**Algorithm 2**Proposed improved Jacobi-based algorithm with \(m=2\)  
In addition, the spectral radius of the Jacobi iterative matrix is \(\rho\left(\mathbf{I}-\mathbf{D}^{-1}\mathbf{A}\right)\). Comparing Label (18) with the spectral radius of the conventional Jacobi algorithm, we obtain the following inequality:  
\[\rho\left(\left(\mathbf{I}-\alpha_{k-1}\mathbf{A}\right)\left(\mathbf{I}- \mathbf{D}^{-1}\mathbf{A}\right)\right)<\rho\left(\mathbf{I}-\mathbf{D}^{-1} \mathbf{A}\right). \tag{19}\]  
It is worth pointing out that the smaller spectral radius of the iterative matrix, the faster the convergence of the scheme. Therefore, we can infer that the convergence velocity of the proposed algorithm is faster than the conventional Jacobi algorithm. For the second part, we focus on the convergence of the whole correction method, that is, prove the convergence of the whole correction method [21]. Supposing that the residuals value correspond to \(\mathbf{\tilde{x}}_{1},\mathbf{\tilde{x}}_{2}\) is \(\mathbf{r}_{1}=\mathbf{b}-\mathbf{A}\mathbf{\tilde{x}}_{1},\mathbf{r}_{2}= \mathbf{b}-\mathbf{A}\mathbf{\tilde{x}}_{2}\); then, the residual of the correction solution is \(\mathbf{r}=\mathbf{b}-\mathbf{A}\mathbf{\tilde{x}}\). Assume the residual value \(\mathbf{r}_{1},\mathbf{r}_{2},\mathbf{r}\) as the space vector which takes the origin of the coordinate as the starting point, and the \(\mathbf{r
## 3 Coherent Pointing Estimation using Notch and Boresight Pattern
### Acquisition Strategies  
A challenge for the proposed technique is the demand to acquire two different coherent SAR datasets at the same time using only one satellite. Two techniques are proposed to achieve this requirement:  
* doubling of the PRF (pulse repetition frequency): The two datasets are interleaved sampled by doubling the PRF. Even pulses are used to generate one SAR image; odd numbers are used for the other image. This approach shrinks the effective swath width to 50% to still satisfy the azimuth sampling requirement. The experimental data presented in Section 5 have been acquired using this approach.
* digital beam forming (DBF): Some upcoming SAR missions employ DBF to generate their receive beams, e.g., Tandem-L. The DBF concept also allows to generate the two input SAR dataset by means of digital signal processing without affecting the effective swath width. This solution will be the preferred approach in the frame of Tandem-L.  
The proposed pointing retrieval technique can be summarized as follows. Two coherent datasets of the same scene have to be acquired using either of the two proposed methods. After generating the difference dataset and optionally converting it to \(\gamma\)-values, the derivation of the range profile is to be performed in complex domain. The pixel lines along azimuth have to be consolidated to points of the range profile. Besides the commonly used mean operator which is the basis for the conventional method described in Section 2 other operations like forming the median can be beneficial. The generated measurement profile is compared to the predicted profile from the antenna model and the offset between both profiles is retrieved using the above-discussed correlation method. Alternatively, the profiles could be matched by detecting the minimum power level in both profiles or applying a polynomial fitting.