esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Extraction, Enrichment and GC-MS Analysis of the Volatile Compounds in Tea  
Headspace solid-phase microextraction (HS-SPME)-gas chromatography-mass spectrometry (GC-MS) was used to extract, enrich and characterize the volatile components in tea. The collected tea samples were ground with liquid nitrogen and quickly passed through a 60-mesh (0.250 mm) filter to collect the powder. Then, 2 g of the powder was added to a 20 mL headspace flask, sealed and heated in a 50 \({}^{\circ}\)C hot water bath for 45 min in order to allow the volatile compounds in the tea to fully evaporate. Meanwhile, an SPME needle (50/30 \(\upmu\)m, DVB/CAR/PDMS, Bellefonte, PA, USA) which had been pre-aged for 1 h was inserted into the headspace flask for absorption for 45 min. It was then immediately withdrawn and inserted into the GC-MS inlet for desorption for 5 min. Three replicates were conducted for each sample.  
The GC-MS analysis was conducted utilizing an Agilent Technologies 7890A-5975C instrument (Agilent, Palo Alto, CA, USA) equipped with a DB-WAxETR column (60 m \(\times\) 0.32 mm \(\times\) 0.25 \(\upmu\)m), using helium (purity \(>\) 99.999%) as the carrier gas at a flow rate of 1 mL/min. The temperature program commenced at 35 \({}^{\circ}\)C and was maintained for 5 min before gradually it was gradually increased to 150 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min, followed by a 10 \({}^{\circ}\)C/min increase to 240 \({}^{\circ}\)C and a subsequent 2 min hold. The ionization energy in EI mode was 70 eV, and the emission current was 34.6 \(\upmu\)A. The ion source temperature was set at 230 \({}^{\circ}\)C, while the quadrupole and interface temperatures were set at 150 \({}^{\circ}\)C and 240 \({}^{\circ}\)C, respectively. The mass scan range was 45-500 \(\upmu\)m.  
LGC Standards (Charleston, SC, USA) provides a product named o2 si smart solution. A which is a mixed standard solution of n-alkanes ranging from C10 to C20 and consisting of even-numbered carbon atoms. This solution was used to measure the concentrations of the n-alkane samples as per the previously established method [23]. Utilizing the external standard method, the results from the n-alkane samples and the mixed standard solution were compared and converted to determine their concentrations.  
Upon the completion of the sample testing process, volatile compounds were identified through a comparison with mass spectra from the National Institute of Standards and Technology Mass Spectrometry Library (NIST 8.0) in combination with retention indexes, similarity matches and the relative abundances of fragment ions. The concentrations of the compounds in the samples were determined by comparing them with the known concentrations of n-alkane standard solutions.
# A Survey of the Applications of Text Mining for the Food Domain
###### Abstract  
In the food domain, text mining techniques are extensively employed to derive valuable insights from large volumes of text data, facilitating applications such as aiding food recalls, offering personalized recipes, and reinforcing food safety regulation. To provide researchers and practitioners with a comprehensive understanding of the latest technology and application scenarios of text mining in the food domain, the pertinent literature is reviewed and analyzed. Initially, the fundamental concepts, principles, and primary tasks of text mining, encompassing text categorization, sentiment analysis, and entity recognition, are elucidated. Subsequently, an analysis of diverse types of data sources within the food domain and the characteristics of text data mining is conducted, spanning social media, reviews, recipe websites, and food safety reports. Furthermore, the applications of text mining in the food domain are scrutinized from the perspective of various scenarios, including leveraging consumer food reviews and feedback to enhance product quality, providing personalized recipe recommendations based on user preferences and dietary requirements, and employing text mining for food safety and fraud monitoring. Lastly, the opportunities and challenges associated with the adoption of text mining techniques in the food domain are summarized and evaluated. In conclusion, text mining holds considerable potential for application in the food domain, thereby propelling the advancement of the food industry and upholding food safety standards.  
text mining; food quality control; recipe recommendation; food safety regulation +
Footnote †: journal: _Algorithms_
###### Abstract  
In recent years, low-cost and lightweight RGB and depth (RGB-D) sensors, such as Microsoft Kinect, have made available rich image and depth data, making them very popular in the field of simultaneous localization and mapping (SLAM), which has been increasingly used in robotics, self-driving vehicles, and augmented reality. The RGB-D SLAM constructs 3D environmental models of natural landscapes while simultaneously estimating camera poses. However, in highly variable illumination and motion blur environments, long-distance tracking can result in large cumulative errors and scale shifts. To address this problem in actual applications, in this study, we propose a novel multithreaded RGB-D SLAM framework that incorporates a highly accurate prior terrestrial Light Detection and Ranging (LiDAR) point cloud, which can mitigate cumulative errors and improve the system's robustness in large-scale and challenging scenarios. First, we employed deep learning to achieve system automatic initialization and motion recovery when tracking is lost. Next, we used terrestrial LiDAR point cloud to obtain prior data of the landscape, and then we applied the point-to-surface inductively coupled plasma (ICP) iterative algorithm to realize accurate camera pose control from the previously obtained LiDAR point cloud data, and finally expanded its control range in the local map construction. Furthermore, an innovative double window segment-based map optimization method is proposed to ensure consistency, better real-time performance, and high accuracy of map construction. The proposed method was tested for long-distance tracking and closed-loop in two different large indoor scenarios. The experimental results indicated that the standard deviation of the 3D map construction is 10 cm in a mapping distance of 100 m, compared with the LiDAR ground truth. Further, the relative cumulative error of the camera in closed-loop experiments is 0.09%, which is twice less than that of the typical SLAM algorithm (3.4%). Therefore, the proposed method was demonstrated to be more robust than the ORB-SLAM2 algorithm in complex indoor environments.  
SLAM; LiDAR; RGB-D camera; deep learning +
Footnote †: journal: Applied  
14 July 2019; Accepted: 6 August 2019; Published: 9 August 2019
## 5 Simulation Results
### FDI Attack Results
For the LFC of AC/DC system with virtual inertia provided by ESS, there are eight system zeros (\(-\)0.0622, 0.5846, 0.5908, 0.6008, 0.6661, 0.9332, 0.9779, 0.9920) of real values and correspondingly 8 unitary vectors. To compare the three LFC system models under zero-dynamics attacks, first we let \(\mathbf{f}_{0}(i)\) in (35) be the injection on the AC power flow measurement with a value of 0.5 p.u. and solve (35) for all the LFC systems. Figure 9 depicts the state trajectory of frequency under the resulted zero-dynamics attacks on the normal AC system and the AC/DC system with emulated inertia. We can observe that the zero-dynamics attack is able to drive the state of frequency in the LFC of the system interconnected by AC/HVDC lines and equipped with ESS to outside the safe set; see Figure 9b where the MFD can reach a certain value to mislead wrong system operations of load shedding. Besides, we can notice that different from the stationary univariate/multivariate attack, the false data injections in the zero-dynamics attack are \"dynamic\" (time-variant) and coordinated to remain stealthy to an arbitrary anomaly detector. To be noted, in the zero-dynamics attack scenario, the operator believes that there are no load changes as the system outputs are \"made\" zero by attacks, while the system states of frequencies in both areas have been driven to unsafe sets. This implies that the zero-dynamics attack can cause severe damages to the system frequency stability. Figure 9: State trajectory under zero-dynamics attacks on wide-area measurements with \(\mathbf{f}_{0}(i)\) in (35) being the injection on the AC power flow measurement with a value of 0.5 p.u.. (**a**) normal AC system; (**b**) AC/DC system with virtual inertia. Similarly, Figure 10 shows the state trajectory of the AC/DC system and AC/DC system with virtual inertia under zero-dynamics attacks where \(\mathbf{f}_{0}(i)\) in (35) is the injection on the DC power flow measurement with a value of 0.5 p.u. (the normal AC system is not attacked in this scenario as it does not have DC power flow measurement). The zero-dynamics attack can still result in large (frequency) state deviations especially in the context of LFC in a hybrid AC/DC system with virtual inertia. Notably, the impact index MFD would be made more large under such zero-dynamics attack if one increases the initial attack value of \(\mathbf{f}_{0}(i)\) in (35). To conclude, the stealthy zero-dynamics attack can be very impactful to the LFC systems when the loads are constant during a specific time period and the null-space of \(\mathbf{P}(z_{0})\) has multiple dimensions. Besides, the LFC model considering the added elements of HVDC link and ESS for virtual inertia can be more vulnerable to such attacks when the three LFC system models have the same initial attack value of \(\mathbf{f}_{0}(i)\) in (35).
## 3 Results and Discussion
### Cost and Environmental Impact Analyses
#### Comparative Interpretations  
Combining the cost and the environmental impacts related to gasoline and mineral powder, Table 6 depict the results connected to the removal of 1 kg asphalt binder, after that, Figure 6 shows the cumulative percentage distribution for these two approaches. For the case study, treating 1 kg of bituminous binder merely with gasoline required CNY 71.20, caused 362.90 MJ of energy consumption and 25.80 kg equivalent CO\({}_{2}\) emission. On the other hand, using mineral powder to adsorb asphalt reduced the cost by 80%, energy consumption by 97% and equivalent CO\({}_{2}\) emission by 93%. However, the accessible asphalt binder adsorbed by mineral powder accounts for a small part in the dissolved solution. After adsorbing 1 kg asphalt binder with mineral powder, approximately 25 kg asphalt binder should be dissolved in the gasoline solvent.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Cost (CNY)** & \
\begin{tabular}{c} **Energy** \\ **Consumption (MJ)** \\ \end{tabular} &
\begin{tabular}{c} **Equivalent CO\({}_{2}\)** \\ **EMISSION (kg)** \\ \end{tabular} \\ \hline Gasoline & 71.20 & 362.90 & 25.80 \\ Mineral powder & 14.10 & 10.02 & 1.85 \\ Reducing percentage & 80\% & 97\% & 93\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Cost and environmental impacts of cleaning 1 kg asphalt with 2 approaches.  
Figure 6: Cumulative percentage distribution for the investigated approaches according to cost and environmental impacts.
## 3 The Improved Path Planning Method
### Analysis of the 3D Simulation Environment  
In order to verify the performance of the CDTD3-RRT* path planning method in 3D complex obstacle environments, a 3D space simulation environment was designed, with size of 100 (cm\({}^{3}\)). The environment is shown in Figure 10a, Figure 10b is locally enlarged, and multiple obstacles of different sizes and shapes were set up in the environment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Index** & **Complex Obstacle** & **Maze Space** \\ \hline \multirow{2}{*}{CDTD3-RRT*} & Mean (cm) & 136.6 & 244.32 \\  & Variance & 2.59 & 20.51 \\ \hline \multirow{2}{*}{APF-IRRT*} & Mean (cm) & 171.13 & 312.15 \\  & Variance & 200.33 & 316.21 \\ \hline \multirow{2}{*}{APS-RRT} & Mean (cm) & 167.32 & 315.18 \\  & Variance & 191.35 & 322.69 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the path planning in the 2D simulation environment.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Index** & **Complex Obstacle** & **Maze Space** & **Narrow Space** \\ \hline \multirow{2}{*}{CDTD3} & Success rate & 91.3\% & 87.3\% & 95.6\% \\  & Reduction rate & 11.4\% & 14.2\% & 19.14\% \\ \hline \multirow{2}{*}{TD3} & Success rate & 88.2\% & 85.5\% & 98.7\% \\  & Reduction rate & 7.4\% & 10.2\% & 13.59\% \\ \hline \multirow{2}{*}{DDPG} & Success rate & 76.2\% & 79.5\% & 86.8\% \\  & Reduction rate & 5.5\% & 73.2\% & 9.44\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimization success rate and path reduction rate of the 2D simulation environment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Index** & **Complex Obstacle** & **Maze Space** \\ \hline \multirow{3}{*}{CDTD3-RRT*} & Mean (cm) & 136.6 & 244.32 \\  & Variance & 2.59 & 20.51 \\  & Reduction rate & 11.4\% & 14.2\% \\ \hline \multirow{3}{*}{CDTD3-RRT} & Mean (cm) & 154.9 & 258.92 \\  & Variance & 8.49 & 51.56 \\ \cline{1-1}  & Reduction rate & 10.8\% & 19.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of CDTD3-RRT* and CDTD3-RRT in the 2D simulation environment.  
RRT* was first used for 5000 iterations with the starting position at (6, 6, 6) and the target position at (98, 96, 95). DRL were used to carry out 50 simulation experiments on the DRL-RRT*, each experiment was conducted under different random seeds, and each experiment had 400 iterations. The length of each point-to-point task was 400 steps.  
The simulation results are shown in Table 5. The average path lengths for 50 repeated experiments were 186.61 cm, 191.23 cm, and 197.29 cm, respectively. Figure 11 shows the relationship between path length and algorithms. The random policy was added to the CDTD3 to further optimize the performance of the algorithm. In the 3D environment, CDTD3 consistently discovered shorter paths across multiple experiments, leading to reduced path length and higher success rates for D-P2P tasks.
## 4 Conclusions and Perspectives  
A granulated geopolymer (GP) was prepared from an alkali-activated metakaolin powder and subsequently exposed to copper (Cu\({}^{2+}\))-containing solutions to determine the immobilization potential of the GP with(out) pH adjustment. The Cu\({}^{2+}\) removal efficiency depended mainly on the initial Cu:GP ratio and on the pH and reached >99.9% under alkaline conditions and up to 91% under acidic conditions, which is equivalent to Cu\({}^{2+}\) removal capacities of 193 mg/g and 560 mg/g, respectively. A substantial fraction of Cu\({}^{2+}\) immobilization was due to chemical incorporation into the GP matrix, but a large amount of Cu\({}^{2+}\) was also precipitated in the form of gerhardtite (low pH) or tenorite and spertinite (high pH). The resistance of the Cu\({}^{2+}\)-bearing GPs to leaching and ion exchange was experimentally verified, yielding a small Cu\({}^{2+}\) release of less than 2.4%. Accordingly, geopolymers bear a high potential to consistently immobilize aqueous Cu\({}^{2+}\). Further work should (i) make use of the herein described novel Cu-GPs and explore their performance and durability in corrosive settings at field scale and (ii) explore the links between crystal structure and Cu-binding environments in Cu\({}^{2+}\)-substituted geopolymers using e.g., solid-state nuclear magnetic resonance (\({}^{29}\)Si and \({}^{27}\)Al MAS-NMR) or chemical-state X-ray photoelectron spectroscopic analysis (XPS), both to gain insights into the resistance of Cu-bearing geopolymers to natural and man-made corrosion settings.  
Conceptualization, C.G. and A.B.; methodology, A.B.; software, A.B.; validation, N.G., C.G. and A.B.; formal analysis, N.G.; investigation, A.B.; resources, C.G. and M.D.; data curation, N.G. and M.P.; writing--original draft preparation, N.G., M.P. and A.B.; writing--review and editing, N.G., M.P., C.G., M.D. and A.B.; visualization, A.B.; supervision, N.G. and A.B.; project administration, M.D.; funding acquisition, C.G. All authors have read and agreed to the published version of the manuscript.  
This research was partly funded by the NAWI Graz Geocenter (Graz University of Technology) and supported further by the TU Graz Open Access Publishing Fund. The authors gratefully acknowledge the financial support of the Ministry of Science, Technological Development and Innovation of the Republic of Serbia, grant numbers 451-03-47/2023-01/200125 and 451-03-47/2023-01/200134. Open Access Funding by the Graz University of Technology.  
Not applicable.  
All raw data will be made available upon request to the corresponding author.  
We appreciate the technical support by [PERSON], [PERSON] and [PERSON] from the Institute of Applied Geosciences (Graz University of Technology). Open Access Funding by the Graz University of Technology.  
The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.
# \(L_{1}\) Adaptive Control for a Vertical Rotor Orientation System
## 6 Experimental Results for the Rotor Orientation System
### Experimental Results  
The experimental results for the orientation control system are shown in Figure 9. To clearly show the performance of the adaptive orientation control system, a result of the nonadaptive orientation control, that \(\mathbf{u}_{\text{ad}}=\mathbf{0}\) and only \(\mathbf{u}_{\text{m}}\) activates, is also illustrated, as a contrast. \(\gamma_{d0}\) is the average angular displacement without control, which is about 0.8\({}^{\circ}\) of the adaptive control and 0.7\({}^{\circ}\) of the nonadaptive control. The control system starts to actuate at \(t=4\) s.  
Figure 8: Block diagram of the orientation experiment: (**a**) gap sensor; (**b**) electromagnet; (**c**) rotor; (**d**) gap calculation; (**e**) angle calculation; (**f**) controller; (**g**) control current calculation; (**h**) power amplifier.  
It can be seen from Figure 5 that, after the controller activates, the average angular displacement of the rotor from the vertical direction reduces to less than \(\sim\)50% of the adaptive controller and about \(\sim\)60% of the nonadaptive controller. The adaptive controller has a better performance in vibration reduction.  
Furthermore, by comparing Figure 9 with Figure 6, it can be seen that the trend of the angular displacement is basically the same.
# A Neural Network Model for Estimating the Heart Rate Response to Constant Intensity Exercises
## 2 Materials and Methods  
### Raw HR Measurements  
The data collection [18] took place in the Department of Physical Activity and Sport of the Technical University of Madrid, Spain, under the approval of the local ethics committee. A written informed consent was signed by the participant before data collection. The present research meets the highest ethical standards and has been performed in accordance with the guidelines of the Helsinki Declaration of 1975, as revised in 2013 [19].  
A healthy male (age 33, height 1.83 m and weight 82 kg) served as the test subject. His HR parameters were: \(hr_{max}=185\) beats per minute (bpm) and \(hr_{min}=40\) bpm; the former is defined as the highest HR value achieved in an all-out-effort to the point of exhaustion, whereas the latter refers to the HR at absolute rest [1]. The participant was a high-level professional runner, so he was able to keep the exercise intensity at constant levels and, thus, provide raw HR data of exceptional accuracy, as required by the study. He performed four running bouts of constant intensity exercise [9, 10], ref. [18] followed by a passive recovery period, during which he laid horizontally and still on the floor. The experiment was carried out on a tartan track. Care was taken to ensure that the data recording environment was free of any additional electromagnetic signals, such as high-tension power lines, engines producing electromagnetic fields, or mobile phones.  
The data recording protocol resulted in four sets of HR data, hereafter named as set (I), (II), (III), and (IV), respectively. Each set corresponds to the recorded HR response (beat-to-beat intervals) to exercises of constant intensity followed by a recovery period. The velocity of each exercise was measured by a Garmin Forerunner 201 GPS system and a Polar S625x speed-distance heart rate monitor. The latter provided measurements of the beat-to-beat time interval as well.  
Table 1 presents the exercise intensity and the duration of each exercise/recovery set, whereas Figure 1 shows, as an example, the HR time series recorded during the second exercise and its subsequent recovery, i.e., set (II). In Figure 1, oscillations in the HR time series data can be observed, as is expected for any un-edited physiological signals. These oscillations are more pronounced in the recovery period, a fact that may be attributed to factors more apparent during recovery such as environmental or mental distractions. It should be noted here that the HR time series used in this study (as shown in Figure 1) underwent minimum editing, in order to preserve all information included in the raw data. Occasional errant data points that sporadically appeared due to technical or physiological reasons, such as missed/misread \(R\) or \(T\) waves, or abnormal ectopic beats, were excluded when their values were larger than 10 standard deviations from the local mean HR values [18].
## 4 Methods  
### Spatial Autocorrelation and Uniformity Analysis  
After the preprocessing stage, consisting of radiometric correction of the Level 1R Hyperion data and co-registration of the whole dataset to WGS84 UTM Zone 11 North system, the relative uniformity and spatial autocorrelation of the calibration site were examined. The Coefficient of Variation (CV) and Local Indicators of Spatial Association (LISA), specifically local indices of the Moran (\(I\)) [(81)] and the Getis Ord statistics (\(Gi\)) [(82)], were obtained for high spatial resolution data of Frenchman Flat. These indicators have been used by [PERSON] et al. [(44)], [PERSON] et al. [(83; 84)] and [PERSON] et al. [(85)] for the identification and characterization of suitable areas for the absolute calibration of satellite sensors.  
The CV was derived for each band considering a 5 \(\times\) 5-pixels window [(84)], and \(Gi\) and \(I\) were calculated under the Queen's rule. The following thresholds were established to select the most homogeneous areas across the playa: CV \(\leqslant\) 2%, \(Gi\)\(\geqslant\) 3.2 and \(I\)\(\geqslant\) 3.5. The threshold limits were empirically defined, testing different values of the above-mentioned indices, as follows: (1) selecting and applying the values for CV, \(I\) and \(Gi\); (2) vectorizing the obtained images; (3) intersecting the layers corresponding to each index; and (4) visually evaluating the extension of the resulting polygons, according to the information provided in [PERSON] et al. [(71)]. The previous steps were repeated until areas with the characteristics of the specific LED-Based Spectral Surface Monitoring (LSpec) calibration site were delineated. In order to reduce the influence of registration errors, caused by differences in spatial resolution [(72; 58)], these criteria were applied for the identification of the most homogeneous areas, from which the samples for the cross-calibration of FASat-C were extracted.