esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multicolumn{1}{c}{**Volatile Compounds**} & \multicolumn{6}{c}{**Transglutaminase (\%)**} & \multicolumn{1}{c}{\(p\)} & **SEM** \\ \cline{2-9}  & **KI** & **Control** & **0.25** & **0.50** & **1.0** & & \\ \hline
**Alcohols** & & & & & & & \\ Ethanol & 507 & 10.46 & 9.04 & 14.61 & 12.36 & 0.562 & 1.340 \\
1-propen-2-ol & 525 & 0.00 & 0.35 & 1.64 & 0.53 & 0.105 & 0.246 \\
**Aldehydes** & & & & & & & \\ Acetaldehyde & 623 & 6.99 & 9.09 & 5.27 & 5.45 & 0.312 & 0.802 \\ Pentanal & 742 & 5.39 & 4.09 & 9.13 & 6.02 & 0.363 & 0.976 \\
2-methyl-2-butrenal & 788 & 1.57 & 0.82 & 1.82 & 1.37 & 0.488 & 0.226 \\ Hexanal & 849 & 64.86 & 48.68 & 101.89 & 71.07 & 0.368 & 10.159 \\
2-hexanal & 902 & 0.25 & 0.25 & 0.33 & 0.29 & 0.941 & 0.047 \\
1-Heptanal & 955 & 4.89 & 3.75 & 7.02 & 5.53 & 0.418 & 0.714 \\
2-heptanal & 1019 & 0.26 & 0.16 & 0.09 & 0.51 & 0.328 & 0.080 \\ Benzaldehyde & 1026 & 28.38 & 30.08 & 34.03 & 23.13 & 0.266 & 2.122 \\ Octanal & 1051 & 8.09 & 6.69 & 11.10 & 8.30 & 0.474 & 1.040 \\
2-d-patadeni & 1076 & 0.90 & 1.09 & 1.28 & 0.78 & 0.905 & 0.226 \\
2-ocatrenal & 1133 & 1.60 & 1.72 & 1.91 & 1.33 & 0.818 & 0.197 \\ Nonanal & 1143 & 15.19 & 12.67 & 19.34 & 14.64 & 0.418 & 1.502 \\ Trans-2-nonenal & 1233 & 1.70 & 1.70 & 1.56 & 1.29 & 0.321 & 0.097 \\
2-t-nethyl-3-phenyl-propanol & 1305 & 0.26 & 0.37 & 0.41 & 0.45 & 0.773 & 0.061 \\
**Ketones** & & & & & & & \\
2-cyclopenten-1-one & 602 & 0.89 & 0.77 & 1.21 & 0.06 & 0.135 & 0.177 \\
2-pentanone & 746 & 1.61 & 1.25 & 1.76 & 1.12 & 0.789 & 0.229 \\
2-heptanone & 946 & 0.49 & 0.52 & 0.71 & 0.52 & 0.592 & 0.059 \\
2-octanedione & 1025 & 15.07 & 11.09 & 23.32 & 15.66 & 0.372 & 2.397 \\
6-methyl-5-hepten-2-one & 1050 & 1.03 & 0.41 & 0.57 & 0.32 & 0.473 & 0.181 \\
2-octanedione & 1023 & 0.22 & 0.03 & 0.3 Figure 2 shows the two main clusters between MTGase groups. The first cluster contains the control and 0.25% MTGase groups, while the second cluster includes the 0.5% and 1% MTGase groups. Two main clusters were formed in terms of free amino acids. Within these clusters, two more subclusters formed. Among the high number of amino acids found in the same cluster, glutamic acid showed a close correlation with valine; lysine with arginine; and cystine with serine (Figure 2). MTGase can cause changes in amino acids by catalyzing acyl transfer reactions, deamidation, and cross-linking (polymerization) between protein intra- or inter-chain glutamine and lysine peptide residues [4].
## 3 System Development: Methodologies and Analysis
### Observations and Forecast Data  
Near real-time rainfall and river stage/flow monitoring data from gauges located within and around the Vaisigano catchment (Figure 1) provided a basis for developing the observations-based elements of the system. Five (5) minute rainfall data are telemetered from each rain gauge every 10 min via dual mobile (primary) and satellite (backup) communications networks, which are accessible through a web-based telemetry management system (MNRE-NEON) administered by the Samoa Ministry of Natural Resources and Environment (MNRE) (Figure 2) [29]. Similarly, 5-min stage and flow data from each river gauge are telemetered via dual communications, although data packets are sent every 5 min. These data were used along with historical hindcast analysis, described in the sections below, to formulate empirical-based flood thresholds and predictive nowcasting tools capable of alerting flood monitoring and emergency responders of imminent flooding with up to 3-h lead time.  
Figure 2: Simplified flood decision support system (FDSS) schema showing key data components and information workflow. Real-time observations (or nowcast) and forecast data are integrated with a library of precomputed flood hazard and impacts data, which are accessible to local operators as decision-ready tools on a web-based portal. This relatively low-cost FDSS was developed between February 2020 and April 2021.  
The Weather Research and Forecasting (WRF) numerical weather prediction (NWP) 3 km rainfall model, available for Samoa through the Pacific Islands Ocean Observing System (PACIOOS), provided a means to enable the forecast-based elements of the system. Forecasts of hourly rainfall intensities up to 48 h in advance are updated every 24 h, with site-specific raw data (csv files) obtained via a THREDDS Data Server (TDS) [30]. Integration of these data with the real-time observation baselines using similar empirical relationships as those developed for the nowcasting tools enabled the development of automated and manual tools capable of providing forecast-based alerts with up to 48-h lead time.  
Real-time sea level data (1-min) from the Apia tide gauge accessible via the Climate and Oceans Support Program for the Pacific (COSPPac) enabled the tidal state and residual coinciding with a nowcast/forecast flood event to be monitored. This provides a proxy for understanding the effect this might have in exacerbating surface flooding in the coastal, lowest reaches of the catchment.
# Lateral Stability of a Mobile Robot Utilizing an Active Adjustable Suspension
## 7 Conclusions  
We presented a mathematical model of a mobile robot using a novel active adjustable suspension system on side slopes. The relationship between the active inputs of the robot and the lateral stability was thus obtained. The following takeaways can be extracted from this article.  
1. The ground clearance can be changed by the active suspension, and the robot is most stable at its lowest ground clearance. The height of the COG increases with the input of the active suspension (linear actuator length). When the linear actuator length varies from 170 to 220 mm, the height of the COG changes from 453 to 650 mm.
2. With an active suspension, the robot's configuration can enhance lateral stability by changing the linear actuator lengths on both sides. When the robot is at its lowest ground clearance, the lateral stability criteria increases by expending the linear actuator on the downhill side. Thus, the  
Figure 13: Basic field experiments illustration: (**a**) Slope; (**b**) Unilateral obstacle; (**c**) Low ground clearance; (**d**) High ground clearance; (**e**) Leveled attitude.  
robot can increase its lateral stability under different ground clearances. In addition, the robot can level itself when the angle of the side slope is less than 12.33\({}^{\circ}\), and leveled posture can be considered as an appropriate configuration to enable the robot and its tools to work smoothly. Thus, the robot can be applied to agriculture, especially in hilly and mountainous terrains.
3. The mathematical model represents the relationship between the input (linear actuator lengths on both sides) and the output (stability), providing a theoretical basis for the design and control for mobile robots.  
[PERSON], [PERSON] and [PERSON] conducted this study and proofread the manuscript together; [PERSON] conceived the idea, designed and analyzed the mobile robot with an active suspension, and wrote this manuscript; [PERSON] and [PERSON] contributed to the practical model for the mobile robot, and proofread the manuscript.  
This research was funded in part by the National Natural Science Foundation of China under grant numbers 51675027 and 51775016, and in part by the National Key Technology R&D Program of China under grant number 2016 YFD0700503-1.  
The authors declare no conflict of interest.
## 3 Experiments and Results
### Results of Temporal Analysis
The results are depicted for police services in Figure 7a, for health services in Figure 7b, and for transit services in Figure 7c. The OEs are clearly seen as located outside of the dashed lines, and the CI is represented as the gray area. This temporal summary representation can be very helpful to support the manager analysis of the near-periodic behavior and to systematically analyze the OE, hence supporting the resource allocation by emergency managers of each institution. In all the cases, the CI confirmed this behavior in the analyzed institutions. For STPI, Saturdays registered the maximum peak of reported emergencies, and Mondays registered the lowest number. These three institutions had a similar weekly  
Figure 6: Emergencies registered during 2014, grouped by temporal visual trends: (**a,b**) police, health, and transit services; (**c,d**) fire brigade; (**e,f**) risk management secretary (RMS), military, and municipal services. pattern. Figure 7d shows the characteristics of the LTPI, where 4 years were considered, from 2013 to 2016. The CI confirmed the behavior related with summer, forest fires and its yearly periodicity. An hourly representation of the police events was further scrutinized, as seen in Figure 8, to show an example of short-term analysis. In this case, we overlapped the 365 days of the year, and these were divided into 24 h periods. This allowed us to identify the time interval of the day in which the number of events reached their maximum and minimum. We represented separately the yearly average of emergencies attended during weekdays versus weekends. In all the cases, we see that from midnight to 06h00, there was a dramatic decrement in the number of emergencies; during daylight hours, the number of events started to gradually rise, and from 18h00 to midnight the events reached their peak values. Once again, we see that the nights of weekends clearly registered the maximum number of attended emergencies. We note that, in this case, there was an overlap between 8 and 16 h, indicating that these hours were equally intense for all the days of the week in terms of police events.
## 3 Results and Discussion
### VOC Analysis of Strawberry Juice and Wines  
#### 3.5.1 VOCs Analyzed by HS-SPME-GC-MS  
The aroma quality is the core factor affecting the preference for fruit wine, and VOCs are paramount components of the aroma structure of fruit wine [38]. A total of 42 VOCs were detected in the strawberry juice and wine samples by HS-SPME-GC-MS (Table S1), including 15 esters, 5 alcohols, 3 aldehyde-ketones, 7 acids, and 14 other compounds, and 38 VOCs were detected in strawberry juice and wines, respectively. While octanoic acid ethyl ester, ethyl-9-decenoate, and trans-2-pinanol were the main VOCs in strawberry juice, 3-methyl-1-butanol, octanoic acid ethyl ester, phenylethyl alcohol, hexanoic acid ethyl ester, and geranyl vinyl ether were the dominating VOCs in strawberry wine. After fermentation, the contents of alcohols, esters, and phenyl compounds in strawberry wine significantly increased, and the contents of terpen, organic acids, and other substances in cultivar BX were relatively high (Table 2). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cultivar** & **L*** & **a*** & **b*** & **E** \\ \hline BX & 17.73 \(\pm\) 0.62 b & 5.71 \(\pm\) 0.15 c & 0.01 \(\pm\) 0.05 b & 19.77 \(\pm\) 0.47 b \\ TCL & 15.32 \(\pm\) 0.21 c & 8.37 \(\pm\) 0.19 a & 0.45 \(\pm\) 0.04 a & 16.48 \(\pm\) 0.29 c \\ TZ & 20.62 \(\pm\) 0.42 a & 4.91 \(\pm\) 0.42 d & 0.44 \(\pm\) 0.03 a & 21.20 \(\pm\) 0.32 a \\ ZJ & 17.41 \(\pm\) 0.55 b & 6.91 \(\pm\) 0.19 b & 0.50 \(\pm\) 0.11 a & 18.95 \(\pm\) 0.79 b \\ \hline \hline \end{tabular} Mean \(\pm\) standard deviation (\(n\) = 3) followed by different letters within each row indicate significant differences (Duncan test, 5%). BX: Snow White; TCL: Sweet Charlie; TZ: Tongzhougongzhu; ZJ: Akihimé.  
\end{table} 
Table 1: L* (A), a* (B), and b* (C) values of different cultivars of strawberry wine.
# Deliquescence Behavior of Deep Eutectic Solvents
## 2 Results
### Deliquescence and Moisture Sorption  
All the DES mixtures were initially solid physical mixtures but, when the samples were deliquesed at ambient conditions in our laboratory, this occurred for all DESs across the entire composition range with the only exception of ChCl/succinic acid (20:80). The deliquescence process of a 45:55 molar mixture of betaine/xylitol was exemplary captured (see Figure 2) under a microscope at ambient conditions measured to 25 \({}^{\circ}\)C and 55% RH.  
Figure 1: Binary phase behavior of four different DESs: (**a**) ChCl/xylitol, (**b**) ChCl/succinic acid, (**c**) betaine/xylitol, and (**d**) betaine/urea. The red lines indicate the solid-liquid equilibrium line predicted by the simplified Schröder-Van Laar equation. The experimental data points (black dots) in diagram b were adapted from Reference [17] and diagram c was from a previous study in our lab [16].  
Figure 2: Optical light microscopy image of the deliquescence process of a betaine/xylitol 45:55 mixture at ambient conditions measured to 25 \({}^{\circ}\)C and 55% RH under polarized light. Remaining crystals show birefringence in the middle of the image.  
To study the deliquescence phase behaviour and to identify the eutonic composition, physical mixtures of the DES components at different molar ratios were stored in desicactors with various RHs. For comparison, the deliquescence of the individual components was also investigated. For all four DESs, a deliquescence lowering was observed compared with the individual components comprising the DESs (Figure 3). The deliquescense point of the eutonic composition was found to be lowered to between 11% and 22% RH for ChCl-based DESs and between 22% and 34% RH for betaine-based DESs. The eutonic compositions were between 0.7 and 0.8 mole fractions of ChCl for ChCl/xylitol and 0.8 for ChCl/succinic acid. For betaine/xylitol and betaine/urea, the eutonic compositions were 0.6 and between 0.4 and 0.6 mole fractions of betaine, respectively. The eutonic compositions were for all the DESs apart from the eutectic compositions seen in Table 1.  
Investigating the amount of sorbed water is important to understand the relationship between moisture sorption and the RH\({}_{0}\), and whether the systems at certain RH behaves as a DES or as an aqueous solution of DES components. The moisture sorption was investigated at a fixed RH of 72%. A relatively high RH was selected to be able to investigate a broad range of compositions. The amount of sorbed moisture was dependent on the composition seen in Figure 3 (on the right y-axis). Despite the lower RH\({}_{0}\) for the DES mixtures, the highest sorption of moisture at 72% RH was observed for the hydrogen bond acceptors alone (choline chloride and betaine) and not for the DES. The sorbed amount of moisture was equal to a weight change of 81% for betaine and 69% for ChCl. The amount of sorbed moisture decreased across the composition range from betaine and ChCl toward higher contents of the hydrogen bond donors (urea, xylitol, and succinic acid).
# Automated System-Level Malware Detection Using Machine Learning: A Comprehensive Review
## 3 Methodology  
This section contains the basic machine learning model for anomaly detection, which inspired us to provide a state of the art in the field of anomaly detection, including classification, combining techniques, and applications. Figure 9 depicts the basic architecture model for anomaly detection in automated system level.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**ML Classifier** & **Author** & **Description** \\ \hline Fuzzy Logic & [27] & A novel Knowledge-Based System (KBS) that integrates Fuzzy Logic (FL), particle filtering, and anomaly detection \\ \hline Genetic Algorithm & [28] & Network Anomaly Detection System by combining Genetic Algorithms with Fuzzy Logic \\ \hline Genetic Algorithm & [29] & Tsfresh Tool and Genetic Algorithm-based Anomaly Detection Algorithm Selection Service for IoT Stream Data \\ \hline Genetic Algorithm & [30] & Intrusion detection system based on a genetic algorithm with an updated starting population and selection operator \\ \hline Genetic Algorithm & [11] & Network Intrusion Detection Systems (NIDS) \\ \hline GAN & [31] & Better Anomaly Detection by GAN \\ \hline GAN & [32] & GAN-based Anomaly Detection for Imbalance Problems \\ \hline GAN & [33] & Adversarially Learnt Anomaly Detection \\ \hline HMM & [34] & An approach based on a hidden Markov model of privilege flows for identifying abnormalities \\ \hline HMM & [35] & Identifies anomalous network intrusions \\ \hline HMM & [36] & Anomaly Detection Techniques using the Hidden Markov Model \\ \hline Swarm Intelligence & [37] & SI-based IDS techniques to solve an issue and provide the best solution \\ \hline Swarm Intelligence & [38] & AntNag, the first Ant Colony Optimization technique for intrusion detection \\ \hline Swarm Intelligence & [39] & Ant-based Replication and MApping Protocol (ARMAP) can reduce the entropy of the system and efficiently propagate content \\ \hline Swarm Intelligence & [40] & An approach for efficient and transparent parallelization of a large class of swarm algorithms \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._The data repository is determined by the researchers. Different pre-processing techniques are applied to eliminate any noise from the raw data. Multiple-feature extraction is used from the pre-processed data. Feature selection from the primary stage is given to the classifiers, where machine learning plays a role in classifying anomalous conditions, which is conducted based on the training and test data. The efficiency and accuracy are evaluated based on the performance measures used for the evaluation [41; 42].  
Using this architecture, the study was conducted across different machine learning algorithms, and a critical review was conducted on their ability to reliably determine the anomalous and non-anomalous conditions.
## 3 Vehicle Model  
### Establishing Coordinate System  
Two coordinate systems based on ITTC (International Towing Tank Conference, 1932) are introduced to conveniently describe various motion parameters, as shown in Figure 1. One is the earth-fixed coordinate system [I]: (\(E\)-\(\xi\eta\zeta\)) with origin \(E\) fixed to the earth. The other is the body-fixed coordinate system [B]: (\(O\)-\(xyz\)) with origin \(O\) fixed to AUV, which is a moving reference frame [31].  
Figure 1: Coordinate system of AUV.  
The six degrees of freedom of AUV general motion can be summarized in Table 1. Due to the low speed motion of AUV, earth's motion could be neglected. Therefore, a point on the surface of the earth can be assumed to be fixed and applied as the center of the earth-fixed coordinate system [32]. In the earth-fixed coordinate, let \((x_{G},y_{G},z_{G})\) describe the AUV's center of gravity, and \((\phi,\theta,\psi)\) represent the orientation. In the body-fixed coordinate, let \(\mathrm{V}=[u,v,w,p,q,r]^{\mathrm{T}}\) be the linear velocity and angular velocity, and \(\tau=[X,Y,Z,K,M,N]^{\mathrm{T}}\) be the total forces and torque acting on AUV.
# The Impact of Inter-Modulation Components on Interferometric GNSS-Reflectometry
## 4 Conclusions  
This research has verified the contribution of the IM component in \(i\)GNSS-R waveforms and analysed the impact of this component on GNSS-R interferometric performance for ocean altimetry measurements.  
The IM component is a byproduct signal component of CASM in modernized GNSS signals to keep the power envelop of the composite signal constant, which is not officially documented and have been neglected in previous studies on \(i\)GNSS-R. This component in GPS L1 band is the product of the ranging components, i.e., the C/A, the P and the M codes, and is equivalent to a BOC(10, 10) signal. The existence of this IM signal increases the total transmitted power and hence the SNR of the \(i\)GNSS-R waveform. Moreover, the wide band and split-spectrum properties of this component concentrates more spectral energy farther away from the carrier, and thus improves the altimetric sensitivity. Considering the increase of both the SNR and the altimetric sensitivity, our analysis predicts that the final altimetric precision is 1.5\(\sim\)1.7 times better when including the IM component. In other words, the altimetric performance of the \(i\)GNSS-R has been underestimated in previous studies. This result should be considered for proper design and sizing of the PARIS instrument in future spaceborne GNSS-R altimetry missions, such as the PARIS IoD and GEROS-ISS.  
In this work, the GPS L1 band signal is considered as an example of application. It should be noted that the similar IM component also exists in other GNSS signals, which will be transmitting more than two services in each transmission band, such as GPS L2 (with L2C, P and M-code signal), Galileo E1 (with OS Data, OS Pilot and PRS signals), Galileo E6 (with CS Data, CS Pilot and PRS signals), BeiDou B1 (with B1C\({}_{\rm D}\), B1C\({}_{\rm P}\), B1A\({}_{\rm D}\), B1A\({}_{\rm P}\) and B1I signals) and BeiDou B3 (with B3C\({}_{\rm D}\), B3C\({}_{\rm P}\), B3A\({}_{\rm D}\) and B3A\({}_{\rm P}\) signals) [30; 31]. The auto-correlation characteristics of these signals should be further studied for the design and optimization of the PARIS altimeter, as well as the altimetric estimator.  
This work was supported in part by the European Space Agency (ESTEC RFP/IPL- PTE/FE/yc/1157/2015) and in part by the Spanish Ministry of Economy and Competitiveness (ESP2015-70014-C2-2-R).  
[PERSON] conceived the main idea of this letter. [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] contributed theoretical analysis of the IM signal; [PERSON] wrote the paper.  
The authors declare no conflict of interest.