esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The exponential proliferation of unmanned aerial vehicles (UAVs) in critical applications, from autonomous delivery systems to infrastructure monitoring, demands fundamentally secure and resilient motor control architectures [1, 2]. As these cyber-physical systems evolve toward greater autonomy, their low-level actuator interfaces exhibit increased vulnerability to sophisticated timing-based attacks that can bypass traditional safeguards and compromise flight safety [3, 4].
# NMR Analysis of Pulegone in Food Products
## 2 Results and Discussion
### Method Development and Optimization
#### 2.1.1 Influence of the Solvent on the Pulegone Signals
For example, the doublet of the triplet was observed at a MeOD/CDCl\({}_{3}\) ratio of 1:4 (_v/v_) at \(\delta_{\rm H}\) at = 2.73 ppm, at a ratio of 1:1 (_v/v_) at \(\delta_{\rm H}\) = 2.74 ppm, and at a ratio of 4:1 (_v/v_) at \(\delta_{\rm H}\) = 2.75 ppm. Furthermore, cyclohexane-\(d_{12}\) was tested as a solvent. The halogenated alkane chloroform is harmful to the environment, has proven target organ toxicity, and is possibly carcinogenic to humans (IARC Group 2B) [25; 26]. Thus, substitution with a less harmful solvent is sensible [27]. For economic reasons, it is feasible to use nondeuterated cyclohexane as a solvent because cyclohexane solely manifests in the spectrum as a singlet at 1.40 ppm, which can be excluded from the spectrum through solvent suppression. However, no enhanced separation was observed for the CDCl\({}_{3}\)/MeOD mixture. Additionally, the time required to adjust the solvent suppression and adapt the spectrum to this suppression is greater than the benefit, which is why cyclohexane was not used. Xylene-\(d_{10}\) is used as a collecting agent for steam distillation. Subsequently, the distillate is then diluted in conjunction with the xylene-\(d_{10}\) in CDCl\({}_{3}\). As a result, the  
Figure 2: Comparison of \({}^{1}\)H NMR spectrum of pure pulegone in a solvent mixture consisting of methanol-\(d_{4}\) (MeOD) and chloroform-\(d_{1}\) (CDCl\({}_{3}\)) at mixing ratios of 1:4, 1:1, and 4:1 (_v/v_) (blue line, green line, red line, from top to bottom). The inset shows an enlargement of the range \(\delta_{\rm H}\) = 1.85–2.05 ppm for a better view of the differences between the spectra. proportion of xylene-\(d_{10}\) in CDCl\({}_{3}\) varies with the volume of the distillate. Most mixing ratios include 1:20 (\(v\)/\(v\)) and 1:50 (\(v\)/\(v\)) dilutions. Accordingly, the occurrence of signal shifts from pulegone at varying mixing ratios was investigated. With different mixing ratios of xylene-\(d_{10}\) and CDCl\({}_{3}\), slight signal shifts are evident in the spectrum: dt is located at \(\delta_{\rm H}\) = 2.69 ppm at a mixing ratio of 1:20 (\(v\)/\(v\)) and at \(\delta_{\rm H}\) = 2.70 ppm at a mixing ratio of 1:50 (\(v\)/\(v\)). The signal from the proton at position 8 exhibits a chemical shift of \(\delta_{\rm H}\) = 1.76 ppm at a xylene-\(d_{10}\)/CDCl\({}_{3}\) ratio of 1:20 (\(v\)/\(v\)) and a chemical shift of \(\delta_{H}\) = 1.77 ppm at a xylene-\(d_{10}\)/CDCl\({}_{3}\)-ratio of 1:50 (\(v\)/\(v\)).
## 2 Materials and Methods
### Data Processing
#### 2.3.1 Kinematic and Kinetic Data  
The body's center of mass (CoM) trajectory, angular displacement, and torque of the ankle and hip joints were calculated using the Plug-in Gait dynamic pipeline (Vicon, Oxford Metrics, Yarnton, UK). Trunk flexion angle was defined as the angle between the thorax and the laboratory coordinate system. Postural sway (body oscillation) during the SLS phase of the perturbed leg was calculated to examine postural stability. To quantify postural sway, the standard deviations (SD) of CoM acceleration (SDCoMAcel) in the AP, ML, and vertical directions were computed. Kinematic and kinetic data were Butterworth low-pass filtered at 6 Hz and 25 Hz, respectively [24].
# Flavour Generation during Lactic Acid Fermentation of Brassica Vegetables--Literature Review
## 6 Changes in Flavour during the Fermentation of _Brassica_ Vegetables  
LA fermentation significantly modifies the aroma and sensory perception of fermented products. Compared to unfermented foodstuffs, LA fermentation leads to the diminishing of some compounds, forming new ones, while others remain unchanged, which finally leads to the unique flavour of fermented food. Flavour can be formed based on the composition of non-volatile and volatile compounds derived from the main nutrients in the fermenting matrix, including proteins, carbohydrates and fatty acids. In the case of Brassica vegetables, the important group of compounds generated during fermentation are derivatives of GLS and sulfur compounds, which are responsible for the unique taste and aroma of fermented products. Non-volatile fraction includes mainly amino acids and non-volatile acids, such as citric and malic acids, while volatile fraction refers to a wide range of chemicals, such as alcohols, aldehydes, acids, ketones, terpen, hydrocarbons, esters, sulfur-containing compounds and others. The aroma compounds are formed in several metabolic pathways through fatty acid oxidation, Strecker degradation, glycosides hydrolysis and oxidation, as well as miscellaneous reactions caused by bacteria activity and through the change in environmental conditions [24]. Flavour compounds generated during fermentation include some end-products of primary metabolism (e.g., from the metabolism of carbohydrates) and in the majority the secondary metabolites, for which their physiological role for bacteria is often unknown and their formation is only partly understood [50]. Notably, the formation of flavour compounds is dependent on LAB species. For instance, branched-chain aldehydes formed from branched-chain amino acids could be formed only by _Lactococcus lactis_ and not by _Lactobacillus_ and _Leuconostoc_ strains [51]. The most obvious change in the fermented Brassica vegetables is an increased sourness, which is caused by LA formation. Since the majority of LA is produced from sugars, the sweetness of vegetables will likely be reduced. Importantly, lowering the pH during the progress of fermentation and a higher content of LA may affect the activity of plant enzymes that generate compounds responsible for flavour or their precursors.  
In the following sections, the complexity of the formation of aroma-active compounds from different metabolic pathways occurring during the fermentation of Brassica vegetables will be presented.
## 4 Case Study
### SRRI Calculation  
The Pacific Earthquake Engineering Research (PEER) Centre methodology, Ref. [31] was utilized to calculate the repair cost ratio (RCR) and recovery time (RT). The seismic scenario consisted of 100 input motions (from the PEER NGA database, [[http://peer.berkeley.edu/nga/](http://peer.berkeley.edu/nga/)]([http://peer.berkeley.edu/nga/](http://peer.berkeley.edu/nga/)), accessed on 15 August 2022) in order to reproduce typical California seismicity, as previously applied in [41]. Peak ground acceleration (PGA) was used as the reference intensity measure [31]. The losses were assessed by considering the Caltrans Comparative Bridge Costs database [42] and utilizing the LLRCAT methodology described in [33; 35].  
The results in terms of RCR (in %) and RT (in crew working days (CWD)) are shown in Figures 4 and 5 for the two bridge configurations (B1 and B2). Figure 4 shows that at lower intensities, RCR is low and similar, while for PGA > 0.22 g, B1 has greater values than B2, and for PGA > 0.7 g, B1 reaches values of 1, meaning that the repair cost is the same as the complete reconstruction of the bridge.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristic** & \\ \hline Length (m) & 90.00 \\ Width (m) & 11.90 \\ Depth (m) & 1.83 \\ E (MPa) & \(2.80\times 10^{5}\) \\ G (MPa) & \(1.15\times 10^{5}\) \\ Area (m\({}^{2}\)) & 5.72 \\ I\({}_{\text{transv}}\) (m\({}^{4}\)) & 2.81 \\ I\({}_{\text{veirt}}\) (m\({}^{4}\)) & 53.9 \\ Weight (kN/m) & 130.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Deck characteristics.  
Figure 3: (a) Benchmark bridges (vertical view). (b) Benchmark bridges (plan view).  
Figure 5 shows the repair time that is necessary to return the individual bridges to their original functionalities. RT depends on the characteristics of the bridges, especially the adopted isolators. It is worth noting that the rate of RT is similar for many values of PGA. For the ranges 0.218 g-0.275 g and 0.402 g-0.427 g, B2 shows greater values of RT, with the maximum difference between 0.238 g and 0.275 g with RT values of 52.4 and 18.0, respectively, for Bridge 2 and Bridge 1. For these ranges of intensities, the sliders (model B1) seem to work better than rubber bearings. On the other hand, at the highest intensities (PGA > 0.684 g), the rubber bearings seem to perform better than the sliders. In particular, model B1 presents greater values than model B2, with maximum values of 171 CWD (corresponding to almost those times those associated with B2 (64.8 CWD)).
# Pose Estimation of Primitive-Shaped Objects from a Depth Image Using Superquadric Representation
## 3 Methodology
### Preprocessing  
First, in the preprocessing stage, a depth map \(\mathcal{D}_{t}\) at current frame \(t\) is transformed into a metric vertex map \(\mathcal{V}_{t}(\mathbf{u})=\mathbf{K}^{-1}\dot{\mathbf{u}}\mathcal{D}_{t}( \mathbf{u})\), with the known camera intrinsic matrix \(\mathbf{K}\), a depth map pixel \(\mathbf{u}=(x,y)^{\mathrm{T}}\) in the image domain \(\mathbf{u}\in\Omega\subset\mathbb{R}^{2}\) and its homogeneous representation \(\dot{\mathbf{u}}\). The vertex map \(\mathcal{V}_{t}(\mathbf{u})\) is then smoothed by applying a median filter. The normal map of the current frame \(\mathbb{N}_{t}\) is simply generated from the vertex map \(\mathcal{V}_{t}\) by a cross-product of neighborhood pixels [29].  
Figure 3: An overview of our proposed method.  
We assume that the z-axis of all the objects are horizontal to the normal vector of the floor plane at the initial depth frame \(\mathcal{D}_{0}\). The preprocessing of plane estimation to the initial depth map is applied to extract the normal vector of the floor plane \(\mathbf{n}_{p}\). We apply RANSAC-based plane parameter estimation to estimate the vector \(\mathbf{n}_{p}\).
## 5 Numerical Results  
In this section, we numerically elaborate on the proposed multi-layer VR delivery scheme. To this aim, we first compare the proposed multicast grouping scheme with the ones reported in the literature. Then, by utilizing the density of NR BS deployment as the main metric of interest, we report our main performance evaluation results. The antenna parameters utilized in our calculations are provided in Table 2. The default system parameters utilized in this section are shown in Table 3.
## 1 Introduction
### Vision Contribution  
Another aspect that must be considered for auditory distance perception in an AAR scenario is the influence of the visual environment. Indeed, in most cases, users of an AAR scenario see their own environment while it is enhanced with virtual sound sources. The geometry of the room is not necessarily totally known and implemented in AAR applications, and several studies have suggested that the visual context influences distance perception of the sound sources through mechanisms related to multisensory integration [13; 14]. Divergent visual context was also demonstrated to have an effect on the externalization of virtual sound sources [2].  
It has long been argued that vision could calibrate auditory distance perception [15; 16]. [PERSON] et al. [13] demonstrated that a priori visual information about an environment can be beneficial to the accuracy of auditory distance perception of real sound sources.  
The authors hypothesized that the representation of the visual space can serve as a spatial reference into which distance cues are integrated to create an auditory distance judgment. Consequently, the reference space conditioned by visual information scales auditory distance perception and can compress or expand the auditory distance responses, depending on the congruence of the acoustic information with visual spatial cues. Thishypothesis about calibration of space perception has been tested with stimulation of a single or multiple sensory modalities. Previous studies have notably demonstrated that distance perception responses can be calibrated by information collected by the same or a different sensory modality. For instance, [PERSON] et al. [17] demonstrated how sound reverberation conditions affect visual distance perception, with highly reverberant rooms implying an overestimation of visual distance perception.  
Given these results about spatial calibration of a sensory modality, the specific impact of visual spatial boundaries on the perception of virtual sound sources is addressed.
# Proxy Signature-Based Management Model of Sharing Energy Storage in Blockchain Environment
## 6 Conclusions  
A management method of SES based on a proxy signature mechanism in the blockchain environment is proposed in this paper. Several conclusions are drawn as follows:  
1. Based on budget cost, profit expectation, management ability and market rules, the owners of idle energy storage resources can select a suitable management model, such as a self-operation model or principal-agent model, to gain more profits in the SES market.  
2. Blockchains can meet the demands of SES well. The fairness, efficiency and credibility in the sharing process can be achieved by blockchains and basic technologies.  
3. The proxy signature mechanism can achieve the delegation of digital signature power under the premise of security and reliability, which is suitable for the principal-agent model in the blockchain environment.  
4. Two typical application scenarios for the principal-agent model with the proxy signature mechanism on an SES blockchain, the digital signature process of an SES bidding message and the consensus process based on PBFT are simulated and compared. Some characteristics such as the online decision support, the agent for the off-line or faulty delegation nodes and the enhanced efficiency of consensus processes are represented, providing the potential to improve the flexibility of the SES market and its blockchain.  
Future work will focus on the SES market mechanism considering the principal-agent model and the application of the proxy signature mechanism on the data sharing and transaction blockchain of SES.  
Conceptualization, L.D., L.Y. and Z.L.; methodology, Y.W. and W.Q.; software, Y.W., W.Q. and Y.P.; validation, W.Z., W.Q. and Y.P.; formal analysis, Y.W.; investigation, H.N.; resources, L.D., H.N. and Z.L.; data curation, Y.P.; writing--original draft preparation, Y.W. and W.Q.; writing--review and editing, L.D., L.Y. and Z.L.; visualization, W.Q.; supervision, Y.W. and W.Z.; project administration, Y.W., L.D. and H.N.; funding acquisition, L.D., H.N. and Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Project of the State Grid Qinghai Electric Power Company.  
This work was supported by the Science and Technology Project of the State Grid Qinghai Electric Power Company.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Whole Transcriptome Sequencing  
Whole transcriptome sequencing of 10 MCL tumor RNA samples as well as 8 MCL tumor samples, one reactive tonsil naive B-cell sample, and one reactive tonsil centrocyte sample were performed, respectively, at Macrogen and Novogene as follows: An Agilent 2100 Bioanalyzer was used to evaluate the quantity and integrity of total RNA samples at Macrogen. A Qubit 2.0 Fluorometer (Life Technologies, Waltham, MA, USA), Agilent 2100 Bioanalyzer, and qPCR were used to evaluate the quantity and integrity of RNA samples delivered to Novogene for WTS. The RIN and DV200 scores of the total RNA samples sequenced are available in Table S2. The NGS libraries were prepared using Illumina TruSeq Stranded Total RNA with Ribo-Zero Human/Mouse/Rat Kit. First, ribosomal RNA biomolecules were removed from total RNA samples with the rRNA Removal Kit. Then, RNA was fragmented with a fragmentation buffer, and converted to cDNA with random hexamers as primers. Second-strand cDNA synthesis was performed by DNA Polymerase I in the presence of buffer solution, dNTPs, and RrazH. Having repaired ends of cDNAs, adenine nucleotides--and then, sequencing adaptors--were attached to cDNA fragments. After size selection and PCR enrichment steps, cDNA libraries were prepared. Paired-end 100 bp (Macrogene) or 150 bp (Novogene) NGS reads were obtained through sequencing with the Illumina HiSeq platform. Around 80 million total (i.e., 40 million paired-end) NGS reads were obtained per sequenced sample. Whole transcriptome sequencing of 17 total RNA samples (14 MCL cases, one naive B-cell [Control-02], one centrocyte [Control-05],and one memory B-cell [Control-03]) was performed at Novogene using the NEBnext Ultra Directional RNA Library Prep Kit according to the manufacturer's recommendations, which follows the same procedure as the Illumina TruSeq Stranded Total RNA with Ribo-Zero Human/Mouse/Rat Kit. As the amounts of B-cell subset RNAs were, by themselves, not enough for WTS apart from Control-01 and Control-04, they were combined to obtain enough RNA samples. Control-02 is a combination of the total RNAs of naive B-cells isolated from the reactive tonsils of control cases 2, 3, 4, and 5. Control-03 sample is a combination of reactive tonsil memory B-cells of control cases 1, 6, and 7. Control-05 represented a combination of total RNA samples from centrocytes from control cases 3, 4, and 5 (Table S3).
# Hub Airport End-Around Taxiway Construction Planning Development: A Review
## 3 End-Around Taxiways (EATs) around the World
### More Land Occupation and Higher Construction Costs  
Figure 7 illustrates the land occupations of EATs in eight airports, primarily referring to the area beyond the physical ends of runways. The global variations in EAT land occupation are primarily due to differences in planning schemes, which are influenced by factors like height disparities between taxiways and runways, as well as the types of aircraft used. Notably, the land occupation resulting from the inward shift of runway entrances due to EAT construction has been overlooked. In comparison to runway crossings, EATs significantly increase land requirements. Newly constructed airports can allocate adequate land for EATs through comprehensive planning, but operational airports often face a scarcity of space for significant lateral expansion, posing both a drawback and a challenge for EATs. In addition to land usage, the construction costs of EATs are considerable. Besides land acquisition and pavement expenses, aircraft taxiing on EATs must adhere to obstacle clearance standards. In practice, this often necessitates reducing the EAT's elevation, which increases earthwork and drainage complexities. Some airports have installed stormwater lift stations to divert rainfall from the area between the EAT and the runway [61]. In  
Figure 6: EAT taxiing length and layout (m).  
areas with high groundwater, dewatering systems may be crucial, significantly inflating project budgets.
## 2 Materials and Methods
### Inverse Distance Weight Interpolation Method  
The inverse distance weighting (IDW) method and the Kriging interpolation method are two commonly used spatial interpolation methods in geographic information systems and spatial analysis. The inverse distance interpolation method does not require strict assumptions about the spatial distribution characteristics of the data. It only focuses on the distance relationship between the known points and the points to be interpolated. Whether the data distribution is uniform or non-uniform, this method can be directly used for interpolation. For example, in the environmental monitoring of mountainous  
Figure 5: The legend of the Air Quality Index (AQI) levels and colors.  
areas with complex terrains and irregularly distributed data collection points, the inverse distance interpolation method can perform interpolation calculations without considering the complex distribution rules of the data. In contrast, the Kriging interpolation method requires the data to have a certain degree of spatial autocorrelation. It needs to calculate the variogram to analyze this correlation. If the data does not show obvious spatial autocorrelation, the effect of Kriging interpolation may not be satisfactory.  
In practice, it is often difficult to obtain detailed information across an entire plane. Therefore, we collect data from some specific points and then use interpolation analysis to calculate the data for the entire region. Within a specific area, there are differences in the distances between any two points. The interpolation value is determined by the distance from the monitoring point. Specifically, the closer a point is to the monitoring point, the more similar the interpolated value is to the actual value in spatial simulation. Conversely, the farther the distance, the greater the difference [12].  
The application of inverse distance weight interpolation requires that the points be uniformly distributed and dense enough to capture the regional value change trends. Additionally, this method can effectively handle missing data and outliers. For these reasons, this paper employs the inverse distance weight interpolation (IDW) [31]. The calculation formula is as follows:  
\[Z(\mathrm{x})=\frac{\sum_{i=1}^{n}\omega(i)\cdot Z(i)}{\sum_{i=1}^{n}\omega( i)} \tag{2}\]  
Among them, Z(x) represents the value of the unknown position, Z(i) represents the value of the known position i, and \(\omega\)(i) represents the ratio of the reciprocal of the distance between the known position i and the unknown position to the reciprocal of the total distance. By calculating the weighted average of all known positions, an interpolation estimate of unknown positions can be obtained.  
This study's spatial distribution of pollutant concentrations was interpolated and analyzed using GIS software.