esa-sceva/satcom-chunk-collection huggingface dataset:
# Aromatic Characterization of Grasevina Wines from Slavonia and Podunavlje Sub-Regions
## 2 Materials and Methods  
### Chemicals  
Ester standards: Propyl acetate (99%), isobutyl acetate (97%), ethyl 2-methylbutyrate (99%), phenylethyl acetate (98%), trans-2 -hexen-1-ol (96%), cis-3-hexen-1-ol (98%), trans-3-hexen-1-ol (97%), ethyl propanoate (97%), butyl acetate (99%), hexanol (99%), Z-3-hexenyl acetate (98%), E-2-hexenyl acetate (98%), and ethyl dihydrocinamate were supplied from Sigma Aldrich Chemie GmbH (Steinheim, Germany). Ethyl leucate (98%) was supplied from TIC (Tokyo Chemical Industry; Tokyo, Japan). Ethyl butyrate (99%), hexyl acetate (99%), isoamyl acetate (99%), ethyl isovalerate (99%), ethyl decanoate (99%), octyl acetate (99%), ethyl hexanoate (99%), ethyl octanoate (99%), and ethylphenyl acetate (99%) were purchased from Acros Organics BV (Geel, Belgium). Ethyl isobutyrate (98%) and ethyl cinnamate (98%) were obtained from Alfa Aesar (Kandel, Germany). Absolute ethanol (99.8%, HPLC grade) was obtained from Honeywell (Riedel-de Haen, Germany). Milli-Q water was obtained using a Purelab Option-Q system (ELGA LabWater, High Wycombe, UK). Sodium chloride (purity 99%) was supplied from Chemlab (Zedelgem, Belgium). Internal standards for esters: ethyl butyrate-4,4,4 d\({}_{3}\) (99.8%), ethyl hexanoate d\({}_{11}\) (98.7%), ethyl octanoate d\({}_{15}\) (98.8%), and ethyl trans cinnamate d\({}_{5}\) (99.4%) were purchased from C/D/N Isotopes Inc. (Pointe-Claire, QC, Canada). Volatile phenols: methyl salicylate (99%), 4-ethyl guaicol (98%), guaiaicol (99%), and 2-methoxy-4-vinylphenol (4-vinyl guaiaicol) (98%) were purchased from Sigma Aldrich (Steienheim, Germany). 4-Vinyl phenol (10%) was purchased from BIOSYNTH (Carbosynth, Compton, UK). 4-Ethyl phenol (97%) was purchased from Acros Organics BV (Geel, Belgium). Terpenes and norisoprenoids: 3-carene (95%), \(\alpha\)-terpinene (95%), \(\gamma\)-terpinene (97%), 1,4-cineole (95%), eucalypt (99%), p-cymene (99.5%), \(\alpha\)-terpinolene (95%), \(\alpha\)-ionone (90%), \(\beta\)-ionone (96%), linalool (97%), cisergarniol (97%), rose oxide (99%), 4-terpineol (95%), trans-\(\beta\)-damascenone (98%), nerolidol (97%), and linalool oxide (cis and trans) (97%) were purchased from Sigma Aldrich (Steienheim, Germany). \(\alpha\)-Terpineol (97%), citronellol (95%), trans-geraniol (99%), and limonene (96%) were supplied by Thermoscientific, Acros Organics BV (Geel, Belgium). TDN (1,1,6 trimethyl-1,2-dihydronaphthalene (80%) was purchased from BIOSYNTH (Carbosynth, Compton, UK). Vitispirane 1 and 2 (98%) and hotrienol (97%) were purchased from Eptes Analytical (Vevey, Switzerland). Internal standards for quantification of terpen were as follows: geraniol d\({}_{6}\), linalool d\({}_{5}\), 2-octanol, and ethyl trans cinnamate d\({}_{5}\). The deuterated standards were purchased from EPTES Sarl (Vevey, Switzerland).
# The Influence of Sensor Size on Acoustic Emission Waveforms--A Numerical Study
## 1 Introduction
Therefore, in all cases the effect of sensor size, source orientation and frequency can be studied with a uniform frequency response of the sensor. Results show that in the case of surface waves and waves impinging under an angle to the surface, both the sensor size and the excitation frequency are parameters that crucially affect the waveform shape.
# Prediction of Structural Type for City-Scale Seismic Damage Simulation Based on Machine Learning
## 6 Conclusions  
An ML-based prediction method of the structural type of buildings was proposed, and the case study of Daxing and Touzhou downtowns in Beijing were investigated. Some conclusions are drawn as follows:  
1. The prediction result of the designed supervised ML solution for Tangshan with 230,683 buildings indicated that decision forest, artificial neural network and logistic regression models exhibited high prediction accuracy. Especially, the decision forest model has the best performance and is recommended to predict structural types.
2. The designed supervised ML solution could maintain high prediction accuracy for different building scales; however, it should be applied for cities similar to the sample city.
3. The designed semi-supervised ML solution was applicable to different cities, based on a sampling investigation. According to the prediction with different sampling fractions, the sampling fraction of 1% is recommended. Through multiple self-trainings, the semi-supervised ML solution achieved high accuracy for predicting structural types.
4. This study provided a smart and efficient method to predict structural type for a city-scale seismic damage simulation.  
Conceptualization, Z.X. and M.Z.; methodology, Y.W.; software, M.-z.Q.; validation, C.X.; resources, X.L.; writing--original draft preparation, Z.X.; writing--review and editing, M.-z.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Scientific Research Fund of Institute of Engineering Mechanics, China Earthquake Administration (Grant No. 2019 EEEVL0501), General Program of the National Natural Science Foundation of China (Grant No. 51978049) and Beijing Nova Program of Science and Technology (Grant No. Z191100001119115).  
The authors declare no conflict of interest.
# Listeners' Spectral Reallocation Preferences for Speech in Noise
## 5 General Discussion
### Limitations and Further Studies  
One major limitation of the listener-centric preferences paradigm adopted in the present study is its inability to distinguish between those signal characteristics that listeners are seeking to optimise when intelligibility is maximised, some of which are explored in Section 5.2 above. Studies like the present one, in which the existence of supra-intelligibility factors and the range of parameter variation over which intelligibility is at a plateau can be assessed, should be regarded as precursors to more detailed investigations, using, for example, pupillometry or dual-task paradigms, which target specific potential characteristics, such as cognitive effort.  
It is also not clear whether findings with a speech-shaped masker can be extrapolated to stationary maskers with different spectral profiles, or to non-stationary maskers, such as competing speech. In the latter case, it is possible that listeners will favour modifications that enhance cues that help in sound source segregation, such as those based on the fundamental frequency of the target and masker voices.  
Further, the available forms of manipulation were unidimensional, and therefore unrepresentative of both human speech production modifications and those implemented algorithmically. For example, [PERSON] et al. [86] demonstrated that shifts in fundamental frequency that are not accompanied by appropriate formant frequency changes have a negative impact on naturalness.  
While it is normal practice in speech perception studies, such as the present one, to use sentences of moderate linguistic predictability, embedded in maskers at relatively adverse noise levels, further studies will be needed to determine whether listeners' preferences show similar patterns for the high-context speech material and more benign SNRs, which are more typical of everyday speech communication.  
Finally, although allowing listeners to modify speech features to satisfy preferences may be beneficial due to the potential for customising applications to individual needs (see Section 5.5), it may be necessary to use larger sample sizes in future studies in order to provide a more fine-grained view of listeners' preferences without issues of data sparsity that could result from providing listeners with a wide spectrum of response possibilities.
## 4 Experimental Procedures
### Compressive Strength Recovery  
The compressive strength was measured in accordance with BS EN 12390-3 [44] and the evaluation was done on the average of three samples at 7, 28, and 56 days. The test was conducted in two sets of specimens, where the test was performed until failure for the ultimate compressive strength of non-pre-cracked specimens. A similar test procedure was performed on the pre-cracked specimens. For the healing efficiency, there is no standardized test method and recovery analysis for the self-healing concrete. Different researchers formulated different healing index assessment methods. Nevertheless, most of  
Figure 5: Bonding strength set-up and the schematic diagram of the sample.  
the healing analysis index is based on the properties' performance restored after healing in comparison to properties' performance of sample without damage aspect. With regard to this, the author adopted the same concepts of healing analysis, in which the strength recovery was evaluated with respect to the strength of 7-days. The strength recovery (%) was calculated using Equation (2).  
\[\text{SR}(\%)=\frac{\sigma_{\text{p,n}}-\sigma_{\text{p,7}}}{\sigma_{\text{u,n} }-\sigma_{\text{p,7}}}\times 100 \tag{2}\]  
where \(\sigma_{\text{p,n}}\) is the stress of pre-cracked samples at the age of n days, \(\sigma_{\text{p,7}}\) is the stress of pre-cracked samples at the age of 7 days and \(\sigma_{\text{u,n}}\) is the stress of un-cracked samples at the age of n days.
## 3 Theoretical Approaches
### Approach to Solubility Coefficient and Sorption Heat Estimation Using 'Hard-Spheres' Model  
A number of gas solubility correlations in liquids and polymers are known. For example, the boiling temperatures and the critical temperatures of gases [19], their Lennard-Jones potentials [12,20], and Van-der-Waals surface areas [21] could be used. The semi-empirical approaches to estimate the gas (vapor) solubility in the polymers are also known [22, 23, 24, 25]. The correlation approaches show, for instance, that the gas solubilities in liquids and polymers have a similar origin, and the thermodynamic contribution to selectivity can be pre-calculated [19,26].  
We suggest to extend the statistical thermodynamic approach used for the gas-polymer systems earlier [20] for the estimation of \(S\) and \(\Delta H_{S}\) of the lower hydrocarbons in polymers. The approach is based on the gas solubility analogy in liquids and polymers, setting the monomer unit as an 'active' item of a solvent [20,27].  
We defined thermodynamic (solubility coefficient) and kinetic (diffusion coefficient) contributions. To estimate the solubility contribution, we applied the previously used approach [15] for the calculation of the solubility coefficient of the C\({}_{1}\)-C\({}_{4}\) alkanes and the sorption heat (the 'hard-spheres' theory). For the main equations and some of the results see [27, 28, 29].  
The Henry constants calculated for 1 mol of solute is evaluated as follows [30]:  
\[lnK_{H}=\frac{\overline{G}_{i}}{RT}+\frac{\overline{G}_{c}}{RT}+ln\left(\frac {RT}{V_{1}}\right), \tag{4}\]  
where \(\overline{G}_{i}\) is the partial molar free energy of interaction of the solute and solvent molecules, \(\overline{G}_{c}\) is the partial molar free energy of the cavity creation, and \(V_{1}\) is the partial molar volume of the solvent (polymer).  
The solubility coefficient of the alkanes is linked to Henry's constant (\(K_{H}\)), as shown in Equation (5):  
\[S=\frac{V_{m}}{K_{H}V_{1}p\ast}, \tag{5}\]  
where \(V_{m}\) is the molar gas volume, and \(P\)\(\ast\) the dissolved gas partial pressure.  
It is linked with the sorption heat \(\Delta H_{s}\) via Equation (6):  
\[\Delta H_{S}=\left(\frac{\partial lnK_{H}}{\partial\frac{1}{RT}}\right)_{p}= \overline{H}_{c}+\overline{H}_{i}-RT+\alpha_{p}RT^{2}, \tag{6}\]  
where \(\overline{H}_{c}\) is the partial molar free enthalpy of cavity creation, \(\overline{H}_{i}\) is the partial molar free enthalpy of interaction of solute and solvent molecules, and \(\alpha_{p}\) is the coefficient of the thermal expansion of the solvent (polymer).  
Some results obtained using this approach are presented in Table 2 as examples.
## 3 The Proposed Method
### Extract the Silhouettes from Interactable Real Objects
#### 3.1.1 Animal DOLLS Dataset  
We aim to implement an AR scenario where a user plays with toys. A virtual human will appear in the AR space and interact with the real toys as a playmate. For convenience's sake, here we used a small set of animal dolls as interactable real objects. However, we believe the method described here can be applied to other types of objects, such as exhibits in a museum for an interactable AR curation.  
**Dataset creation:** We prepared seven animal dolls with different appearances. The animals we chose were a jaguar, zebra, giraffe, wallaby, panda, deer, and a fox (see Figure 2). From this set of dolls, we shoot an average of 25 videos lasting 45 seconds per animal. For each video, we varied the position of the doll, the background selection, the lighting condition, and the appearance of the animal in the video by walking around, dynamically zooming in/out, and rotating the camera. These choices improve the diversity in terms of positions and angles within the dataset and ensure the seamless interaction with deformable real objects. For annotation and labeling, we used the Interactive Video Object Segmentation model from [40] to extract the ground truth masks. Then, we extracted the frames to train the segmentation network. We sampled images at a rate of 5 frames per second to avoid duplicated images of the same position or appearance. Our dataset contains 37,990 images and each category of animals has 4856-6109 images. Our dataset is openly available at [[https://github.com/khseob0715/DollDataset](https://github.com/khseob0715/DollDataset)]([https://github.com/khseob0715/DollDataset](https://github.com/khseob0715/DollDataset)) (accessed on 18 March 2021).  
**Data augmentation:** We applied data augmentation techniques to our dataset before training our segmentation network. We added \(10^{4}\) synthesized images into the dataset by (1) replacing the background using the Describable Textures Dataset [41]; (2) rotating images in the range of 360 degrees, to improve the predictability when the mobile device rotates; (3) randomly changing the ratio of the doll area in the image, to mimic different zoom in/out conditions; (4) randomly flipping images on the horizontal and vertical axis. As thus, our network started training with the dataset that was robust for variation.  
Figure 2: Photo illustration of our dataset with seven animal dolls.
## 4 Discussion
### Biotic and Abiotic Factors versus Vegetation Monospecific Stands  
Although _Juncus maritimus_ was one of the four species common to all three salt marshes, it was rarely found in patches like the other three: _Artiplex portulacoides, Sporobolus maritimus_, and _Salicornia perennis_, but was rather characterized by many solitary specimens often found mixed with other species. As a result, this species was omitted from the smaller-scale study of monospecific stands, as there was no viable way of obtaining the intended environmental, physicochemical, and hydrodynamic variables in exclusive association with _Juncus maritimus_.  
Nevertheless, the analysis of the available monospecific stands of the other main species further highlighted the occurrence of a possible competition/tolerance trade-off. Even in a smaller scale of elevation ranges, the differences in species distribution and along the several measured variables were significantly distinct (Table 3). _A. portulacoides_, which does not develop aerenchyma [59], favored higher elevations and areas closer to creekbanks, likely as a response to better conditions of aeration and nitrogen availability at such sites, as was later evidenced by the typically higher redox values of sediment in which this species was found. As a better competitor, _A. portulacoides_ ultimately compelled the other main species into lower elevations with greater instability and more stressful conditions (Figure 7).  
Furthermore, the PCA analysis suggested that the inundation variables were within what would be predictable at the different elevation ranges [1], as the highest flood durations, frequencies, and water depths were effectively found at sampling points with lower altitudes (Figure 7). However, contrarily to previous assessments, lower redox values, although concurrent to lower elevations subjected to greater influence by the tides [30; 60; 61], did not necessarily match more waterlogged sediments (Figure 7) that are usually expected to have less oxygen available [20; 62].  
The observed greater waterlogging at upper elevations could either have resulted from better drainage at lower sites closer to the edge [63], especially at Mira's salt marsh where sediment was sandier and more permeable [64], or essentially portrayed the crucial influence exerted by vascular plants on their surroundings [8]. It has become clear that the redox potential can be effected by different root systems/anatomies and/or metabolic activities [65; 66; 67]. For instance, greater metabolic activity of grasses (such as _S. maritimus_) coupled with elevated evapotranspiration rates has been linked to sediment oxidation [68; 69] and thus may explain these observations.  
Lastly, although recognized as some of the major delimiting factors of vegetation establishment [1], pH and salinity did not appear to substantially influence species distribution where monospecific stands were more prominent, as most of the data variation stemmed from differences in topography and hydrodynamics (Figure 7).
## 4 Results
The differences between measurement and simulation are mainly due to manufacturing tolerances and the effect of surface roughness on electrodeposited metallic surfaces. For the same cut, the tolerance of the laser beam is only 2 \(\upmu\)m and for nonconsecutive cut is approximately 50 \(\upmu\)m. The tolerance in the alignment of the layers is the same as of nonconsecutive cuts. The measurements include the effect of the transitions from coaxial to microstrip, which can be estimated in approximately 1 dB of additional insertion losses, and possibly some mismatch. Discounting the effect of the coaxial to microstrip connectors, the measured insertion loss of the back-to-back prototype is smaller than 1 dB, and the measured return loss is greater that 14.5 dB between 9 and 20.66 GHz, which corresponds to a fractional bandwidth of 83%. In [16], the return loss was 11 dB and the insertion loss was 1.5 dB between 8 and 20.5 GHz, which corresponds to a fractional bandwidth of 87% for a back-to-back transition with the same waveguide characteristics. In comparison, the measured return and insertion losses of the new version of the transition are better than the previous one reported in [16]. Figure 8: Return loss comparison between back-to-back transition from microstrip to ESIW [11] and this work. Figure 9: Back-to-back manufactured prototype.
## 4 Method for UAV Electromagnetic Interference Suppression
### Anti-Electromagnetic Interference Method of UAV Data Link
#### 4.1.1 Anti-Jamming Technology to Improve Communication Reliability  
Literature [41] proposes a variable rate MIMO scheme that can be applied to UAVs to overcome the interference caused by scattering and obstacles and improve the reliability of communication. The literature [42] systematically analyzed the capacity of the airborne multiple-input multiple-output (MIMO) wireless communication system, and determined the maximum achievable capacity of the system, which further derived the airborne MIMO communication system in any given 3D transceiver A necessary and sufficient condition for reaching the upper limit of the capacity under the antenna array geometry. This research helps to select UAV communication system parameters more reasonably and design an airborne MIMO communication system that achieves the best performance in system capacity. The literature [41; 42] focuses on the performance evaluation of MIMO systems  
Figure 4: UAV power circuit and signal circuit [39].  
used in UAV communication. Literature [41] starts from the communication algorithm to improve the anti-interference ability. Literature [42] obtained the relationship between the UAV MIMO communication system and the 3D transceiver array antenna structure through a large amount of data fitting, and provided a basis for designing a communication system with more anti-interference ability.  
The research on anti-jamming technology to improve communication reliability mainly focuses on the physical layer and link layer (Table 4).  
Literature [48] studies the networking of drones. This paper compares a variety of different communication architectures used in UAV networking, and explores the self-organizing network capabilities of UAV ad hoc networks based on the IP protocol. Literature [49] puts forward a plan to integrate airborne networks with other heterogeneous wireless communication systems through field measurements, and studies the communication stability of the UAV data link system when integrated with other wireless communication systems.  
Literature [50] proposed a simulation model suitable for the analysis of air-ground hybrid system, and studied the influence of UAV height and maneuverability on communication range and channel capacity. Literature [51] analyzes the UAV-to-ground link of a small quadrotor UAV network based on 802.11a through a series of field experiments. The influence of the antenna azimuth on the received signal strength and UDP throughput performance of the UAV at different altitudes, yaw angles and distances is given.  
These studies have proposed different methods to improve the communication reliability of the UAV data link system from the perspectives of coding, self-organizing networks, and heterogeneous network integration, and provided a reference for the anti-electromagnetic interference scheme of the data link of the patrol drone.
# Transfer-Efficient Face Routing Using the Planar Graphs of Neighbors in High Density WSNs
## 5 Performance Evaluation
### Simulation Environment  
We simulated the performance of the proposed face routing using the network simulator Qualnet 4.0 (Scalable Network Technologies, US State) [29]. The radio range of the sensor node was 50\(\sim\)250 m, and 200\(\sim\)1500 nodes were randomly deployed in a space of 1000 \(\times\) 1000 m\({}^{2}\). Simulations were performed by increasing the radio range by 20 m intervals and increasing the number of deployed nodes by 100 units. In other words, for the simulation in Figure 11, we experimented with each case by increasing the radio range from 50 m to 250 m in 20 m increments in an environment with 500 deployed nodes (radio range: 50 m, 70 m, 90 m,..., 250 m). For the simulation shown in Figure 10, when the radio range was 100 m, experiments were carried out by increasing the number of nodes by 100 units from 200 to 1500 (deployed nodes: 200, 300, 400,..., 1500). The network model was a unit disk graph connected from the source to the destination, and GG was used as the planar graph. The network unit disk graph is an intersection graph, in which all nodes have the same radio range, and two nodes are connected if and only if they are within the unit-distance of each other. The simulation time was set to 100 s and each sensor node transmitted its position to its neighbors. All the results were the average of each simulation experiment repeated 100 times. In the simulation, Greedy Face Greedy Routing (GFG) [20] as legacy face routing using local information, Shortcut Based Face Routing (SBFR) [19] using all node information within 2-hop range, and TEFR using the planar graphs of neighbors were used. The overall simulation environments are given in Table 4.