esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Sensitivity Analysis  
The result of the FEM optimization depends on the right choice of the initial conditions. For this reason, a sensitivity analysis is performed by representing the evolution of the maximum of G and R curves (Figure 1) when each given parameter of the initial condition is changed. The sensitivity analysis is used together with the results of Table 1 to obtain the initial seed in the optimization. Figure 4 shows the sensitivity analysis results for the G curves for each elastic parameter.  
Figure 4: Sensitivity analysis for the real part of the elastic constants. Each curve shows the evolution of a resonance using the conductivity G. Each parameter is changed over a range \(\pm\)50% from the initial value.  
Figure 3: Experimental response at RT (symbols) plotted as R (blue) and G (black) peaks, together with the reconstructed spectra after parameter determination by iterative analysis (continuous lines), for: **(A)** planar and **(B)** thickness resonance modes of a thickness-poled, thin disk of BNBT6 (\(t\) = 1.91 mm and \(D\) = 10.60 mm); and for a shear resonance of a thickness-poled, thin plate of BNBT6 (9.53 \(\times\) 9.43 mm): **(C)** with \(t\) = 0.89, with strong coupling of modes and not fitted for parameters determination; and **(D)** with \(t\) = 0.83 mm, virtually uncoupled. The regression factor of the reconstructed to the experimental spectra (R\({}^{2}\)) is shown for each analyzed resonance.  
This sensitivity analysis for the R curves, together with the analysis for the dielectric and piezoelectric parameters for the G and R curves are given in the Supplementary Materials file (Figures S1-S3).  
The sensitivity is computed as the tangent of the angle in the sensitivity maps. According to this, the parameters can be divided into three groups. First, those parameters that provoke clear changes in the spectrum, high sensitivity ones: \(c_{11}^{E}\), \(c_{13}^{E}\), \(c_{33}^{E}\), \(c_{33}^{S}\), and \(e_{33}\). Second, those parameters that provoke some changes in the spectrum, medium sensitivity parameters: \(c_{12}^{E}\), \(e_{15}\), and \(c_{44}^{E}\). Finally, those parameters that practically do not change the spectrum, low sensitivity parameters: \(e_{31}\) and \(e_{11}^{S}\).  
We can summarize the sensitivity analysis in Table 3 showing the different influence of each parameter over each of the studied modes. Here we use four steps to characterize the different degrees of sensitivity (null, low, medium, high).
## 2 OBS Networks  
An OBS network consists of OBS capable nodes interconnected by optical fiber links supporting multiple WDM channels. In this meshed networks, nodes can be either edge nodes or core nodes (see Figures 1 and 3). Edge nodes are primarily responsible for assembling data packets into bursts, and scheduling the bursts for transmission on outgoing wavelength channels. This nodes, also commonly referred as edge routers, are to be located at the boundary of the OBS network, providing inter-working facilities between the client networks and the OBS network. Core nodes are mainly responsible for switching and forwarding bursts through output ports and for handling contentions. This nodes, also referred as core routers, compose the inner core of the OBS network, and comprise an optical switching fabric, a switch control unit, and processors for routing and signaling. Users, outside the OBS network, are typically electronic IP routers equipped with legacy networking cards through which packet traffic is forwarded (and received) to (and from) the OBS network.  
IP packets from client networks go through a burst assembly/disassembly process at the edge of the OBS network. The burst, the fundamental transmission and switching unit in OBS networks, is composed of _data payload_ and a separated _control packet_ (CP). The data payload contains the actual data packets after being aggregated and assembled into a bigger transport unit called _data burst_, or _burst_ for short. The CP carries all the relevant information of the corresponding burst in order to drive scheduling operations in the core network. The information carried by the CP can include, for example, the length of the burst and its class of service. In OBS networks the CP and the burst are transmitted separately on different channels (wavelengths) allowing for great network flexibility and scalability. In fact, while the CP undergoes OEO conversion at each intermediate core node, the burst is transmitted all-optically through the core. These features allow for two important savings [2]: first, with the assembly/disassembly process deployed at the edge, data needs to be buffered only at the edge nodes where electronic RAM is available, cheap and abundant; secondly, having data and control signals transmitted separately, costly OEO conversions are only required on a few control channels instead of a large number of data channels. The main functions carried out by OBS nodes are described bellow, after being contextualized in a IP over OBS layered architecture and followed by a discussion on adopted QoS perspective.
# Volatile Flavor Profile and Sensory Properties of Vegetable Soybean
## 2 Results and Discussion
### Volatile Compounds
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**RI**} & \multirow{2}{*}{**Compounds**} & \multirow{2}{*}{**Formula**} & \multicolumn{2}{c}{**Relative Contents (\%)**} \\ \cline{5-6}  & & & & **TW292** & **X3** & **SX6** \\ \hline
22 & 1202 & Dodecane & C\({}_{12}\)H\({}_{26}\) & 1.21 & 1.03 & 1.11 \\
23 & 1205 & Decanal & C\({}_{10}\)H\({}_{20}\)O & 1.58 & 1.92 & 1.27 \\
24 & 1220 & L-Cysteine & C\({}_{3}\)H\({}_{7}\)NO\({}_{2}\)S & 0.38 & 0.45 & 0.6 \\
25 & 1222 & (_E,E_)-2,4-Nonadienal & C\({}_{9}\)H\({}_{14}\)O & 0.2 & 0.7 & 1.52 \\
26 & 1228 & cis-3-Hexenyl isovalerate & C\({}_{11}\)H\({}_{20}\)O\({}_{2}\) & 1.02 & 1.95 & 2.41 \\
27 & 1269 & (_Z_)-2-Decenal & C\({}_{10}\)H\({}_{18}\)O & 2.28 & 7.95 & 8.84 \\
28 & 1295 & 3-Undecanone & C\({}_{11}\)H\({}_{22}\)O & 0.14 & ND & ND \\
29 & 1301 & Cyclohexanone,2,5,5-trimethyl-3-(1-methylethylidene)- & C\({}_{12}\)H\({}_{20}\)O & 0.52 & 0.56 & 0.82 \\
30 & 1314 & Undecanal & C\({}_{11}\)H\({}_{22}\)O & 0.26 & ND & ND \\
31 & 1325 & (_E,E_)-2,4-Decadienal & C\({}_{10}\)H\({}_{16}\)O & 1.73 & 1.19 & 2.38 \\
32 & 1357 & Methyl anthranilate & C\({}_{8}\)H\({}_{30}\)NO\({}_{2}\) & 2.47 & 0.72 & 0.41 \\
33 & 1372 & 2-Undecanal & C\({}_{11}\)H\({}_{20}\)O & 0.64 & 1.48 & 1.27 \\
34 & 1405 & Tetradecane & C\({}_{14}\)H\({}_{30}\) & 0.61 & 0.4 & 0.34 \\
35 & 1462 & (E)-6,10-dimethyl-5,9-Undecadien-2-one & C\({}_{13}\)H\({}_{22}\)O & 0.2 & 0.33 & 0.4 \\
36 & 1493 & 4-(2,6,6-trimethyl-1-cyclohex-1-yl)-3-Buten-2-one & C\({}_{13}\)H\({}_{20}\)O & 0.13 & 0.17 & 0.16 \\
37 & 1538 & 3-(2-pentenyl)-1,2,4-Cyclopentanetrione & C\({}_{10}\)H\({}_{12}\)O\({}_{3}\) & 0.2 & 0.25 & 0.34 \\
38 & 1576 & 2,4,6,10-tetramethylpetadecane & C\({}_{19}\)H\({}_{40}\) & 0.28 & 0.18 & 0.16 \\
39 & 1606 & Hexadecane & C\({}_{19}\)H\({}_{40}\) & 0.32 & 0.3 & 0.25 \\
40 & 1651 & Diethylene glycol adipate & C\({}_{10}\)H\({}_{18}\)O\({}_{6}\) & 0.24 & ND & ND \\
41 & 2000 & Hexadecanoic acid, ethyl ester & C\({}_{18}\)H\({}_{36}\)O\({}_{2}\) & ND & 0.14 & 0.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont_. In this study, low levels of ketones, esters, and other compounds were detected in vegetable soybeans and made a low contribution to flavor. The most abundant ketone was 3,5-octaleri-2-one. [PERSON] et al. reported that 3,5-octaleri-2-one makes a positive contribution to aroma [27]. On the basis of these results, the volatile composition of three major vegetable soybean varieties in China exhibited a large variability in type. However, 1-octen-3-ol, hexanal, (\(Z\))-2-heptenal, 2-octene, nonanal, (\(Z\))-2-decenal, and 3,5-octaleri-2-one were considered the main flavor compounds.
# Design of Micro-Phasor Measurement System to Identify Phasor of Distribution Transformers
## 4 Interface Design of the Portable Device  
To improve the process and efficiency of distribution transformers phasor identification, this paper developed an Android application (APP) that provides an easy way to identify phasor quickly. With this APP, the distribution transformer properties in the database of the DMMS were imported to the mobile device to enable staff to search for the information about a specific distribution transformer at the measuring site. The mobile device is then connected to the portable device such as smart phone or tablet with Bluetooth, which enables staff to give the information before measuring. When the portable device has measured voltage signal, these measurement results were returned to the mobile device automatically. Finally, with mobile network communication, data are retrieved from server and APP compares voltage angles between reference and measurement sides. The phasor of distribution transformer will be displayed in the APP that assists staff identify phasor quickly.
## 3 Results and Discussion
### Core-in-Wall Emulsions (CIWEs)
The amount of protein that was adsorbed at a unit area of the O/W interface reflected the overall results of protein adsorption at the O/W interface during the homogenization, some post-homogenization \"spreading\" of such proteins at the O/W interface, and those protein molecules from the bulk phase of the emulsion that became tightly bound, through stable protein-protein interactions, to protein molecules that were truly adsorbed at the O/W interface [30; 55]. Results (Table 2) indicated that, for a given W:C ratio, surface excess increased with WHPI concentration in the WS (\(p<0.05\)) and results of PSD analysis indicated very small among-CIWEs differences in d\({}_{3,2}\) (Table 1). It can thus be suggested that the increase in \(\Gamma\) with protein load in the CIWE could probably be attributed to the contribution of post-homogenization stable protein-protein interactions at the already established interfacially-adsorbed film, and, consequently, the formation of \"multi-layer films\" or \"advanced structures\" at the O/W interface [30; 55; 56]. \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**WHPI/Lactose (\%/\%)**} & \multicolumn{3}{c}{**d\({}_{3,2}\) (\(\mu\)m)**} \\ \cline{2-4}  & **75:25** & **50:50** & **25:75** \\ \hline
2.5/17.5 & 0.260 \({}^{\rm a,A}\) & 0.300 \({}^{\rm b,A}\) & 0.420 \({}^{\rm c,A}\) \\
5.0/15.0 & 0.300 \({}^{\rm a,B}\) & 0.320 \({}^{\rm b,B}\) & 0.435 \({}^{\rm c,A}\) \\
7.5/12.5 & 0.290 \({}^{\rm a,B}\) & 0.340 \({}^{\rm b,C}\) & 0.470 \({}^{\rm c,B}\) \\
10.0/10.0 & 0.345 \({}^{\rm a,C}\) & 0.360 \({}^{\rm a,D}\) & 0.460 \({}^{\rm b,B}\) \\ \hline \end{tabular} Wall-to-core ratio (%.%, \(w/w\)); \({}^{\rm a+C}\) for a given wall system, mean values in a given row that are followed by different letters, differ significantly (\(p<0.05\)); \({}^{\rm A-D}\) for a given column, mean values that are followed by different letters, differ significantly (\(p<0.05\)).  
\end{table} 
Table 1: The effect of wall composition and wall-to-core ratio on the mean particle sizes (d\({}_{3,2}\)) of the investigated CIWEs.
## 4 IETF 6 LoWPAN Working Group (IPv6)
### Research Challenges  
One of the key issues in connecting constrained networks with the Internet is fragmentation, caused by the 127 byte MTU of IEEE 802.15.4 versus the minimum MTU of 1280 bytes of links in IPv6 networks. Avoiding low-level fragmentation is important and requires knowledge about the MTU (_i.e._, via discovery), knowledge about other protocols (type of routing) and interaction with the applications (and the potential use of fragmentation at this layer, such as block transfers in CoAP as will be discussed later). A good understanding and evaluation of the impact of fragmentation and solutions to avoid it is needed. For example, when using route-over solutions, additional buffer space is needed for packet reassembly and fragmentation at each intermediate node, since the information needed for routing can only be found in the first fragment [34]. The need of this additional buffer space can be avoided, by providing a small buffer to store the first few bytes of each packet. As the delivery of the first fragment could be handled by looking at the destination address, subsequent fragments may be routed based on the datagram-tag which is stored in the small buffer we set aside. Different mechanisms may be considered for first fragments of a packet arriving late.  
Regarding compression, advanced compression techniques have been proposed, including the compression of next headers. Until now, a thorough evaluation of the performance of these mechanisms for various communication patterns, address sizes and setups is missing. Transport layer compression methods are currently defined for UDP only. The 6 LoWPAN working group is discussing generic next header protocol compression. Nevertheless, no compression mechanism is defined for TCP and ICMPv6 yet. Such a mechanism for TCP could be considered irrelevant for the majority of constrained devices, since they typically use UDP with an application-layer protocol such as CoAP that provides some of the missing features of TCP. However the use of CoAP on top of TCP has been explored [35].  
There are still a number of other issues to be investigated. Layer 3 route-over and layer 2 mesh-under routing protocols are supported on 6 LoWPAN networks. Mixing these protocols in a given 6 LoWPAN network might be a path worth exploring [23].  
Finally, the 6 LoWPAN working group has already published a proposed standard document relating to Neighbor Discovery. Yet, Secure Neighbor Discovery is still unexplored territory. IPv6-like security up to level of constrained devices is being researched. Despite several efforts, key distribution to constrained devices remains one of the biggest challenges so far.
# Functional Magnetic Resonance Imaging in Research on Dog Cognition: A Systematic Review
## 3 Results
### Research Articles on Canine fMRI Studies Using Auditory Stimuli  
Among the 13 articles on studied dog cognition using auditory stimuli, 7 articles (53.8%) represented an experimental randomized controlled trial, while 6 articles (46.2%) represented an experimental non-randomized trial. The dog sample size was moderately large, ranging from 10 dogs [35] to 40 dogs [36]. Two studies also included humans as participants, a total of 43 people [4; 37]. In total, 219 dogs were examined using fMRI and auditory stimuli. Of these, 209 dogs (95.4%) across twelve studies were assessed without anesthesia [4; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45], while 10 dogs (4.6%) in one study were examined under general anesthesia [46].  
[PERSON] et al. demonstrated that sound processing in dogs activates the perisylvian regions, whereas in humans, the same sounds activate the superior temporal sulcus and inferior frontal cortex [4]. However, [PERSON] et al. found that the sound in dogs also activates the medial geniculate nucleus, caudal colliculus, and temporal cortex [46], while pitch-specific sounds activate the olivary nucleus, lateral lemiscus, and internal capsule [35].  
Lexical processing in dogs is primarily thought to result in hemispheric bias and involve the mid ectosylvian gyrus and connectivity between auditory regions and the caudate nucleus [45]. [PERSON] et al., however, specified that dogs process humans' words in the parietotemporal cortex, left temporal cortex, amygdala, left caudate nucleus, and thalamus [44]. Adaptation to spoken words occurs in the temporal lobe, Sylvian sulci, and ectosylvian gyrus [43]. Additionally, voice-like sounds are also processed in the ectosylvian gyri, especially within the left auditory cortical region extending from the ectosylvian gyri [37].  
Independent of lexical processing, voice identity recognition occurs in the secondary auditory cortex located in the caudal ectosylvian gyrus, which is responsible for identifying novelty [42]. This region, along with the caudate nucleus, enables dogs to differentiate between the speech of their owners and that of a neutral speaker [41]. Furthermore, dogs can distinguish between familiar and unfamiliar language by activation in the secondary auditory cortex within the precruxiate, suprasylvian, and ectosylvian gyri [39]. Activation of the Sylvian gyrus and temporal pole also allows dogs to discern differences when speech is directed at infants or adults [38]. Additionally, neural activity patterns associated with tracking speech regularities are located in the ectosylvian and Sylvian gyri, as well as in the caudate nucleus and thalamus [40]. The caudate nucleus and amygdala process associations between sounds and appetitive stimuli [36], facilitating positive reinforcement when dogs are trained. The cognitive findings from canine fMRI studies using auditory stimuli are summarized in Table 3 and are detailed in the referenced articles.
# RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System
## 2 Materials and Methods
### RSIMeta: Metadata Management of Large-Scale Images with Heterogeneous Structures
#### 2.2.1 Distributed Spatial Index Based on Multi-Level Hilbert Grids
So, we would further compare Hilbert curve, Z-order curve and Gray curve to select the optimal SFC for large amounts of remote sensing images indexing. To quantify the locality-preserving property of a SFC, [PERSON] [36] proposed a description vector composed of the percentages of five segment types: jump, contiguity, reverse, forward, and still. If the distance of two points connected by a segment in SFC is equal to one in the origin multi-dimensional space, the segment connecting the points is a contiguity segment. Otherwise, if the distance is greater than one, it is a jump segment; If the distance is equal to zero, it is a still segment. If the direction from a point to another point doesn't change after mapped, the segment connecting the points is a forward segment, otherwise it's a reverse segment. Based on the above definition, jump and contiguity are the key factors for geospatial query performance of remote sensing images. The other three factors are important for applications dependent on the data access order. The jump and contiguity percentage comparisons of the Hilbert curve, Gray curve, and Z-order curve are shown as Figure 3.  
From Figure 3, we can find that the Hilbert curve and Gray curve are better than Z-order curve due to the lower jump and higher contiguity. Besides the above methods, [PERSON] et al. [37] measured the locality-preserving property with the number of clusters required by a query region of any arbitrary shape. With closed-form formulas and experiments, Hilbert curve was proven to be superior with better clustering; To compare Scan curve, Z-order curve, Gray curve and Hilbert curve, [PERSON] [38] constructed several test cases to evaluate the performance of file blocks fetching with different SFCs, and the final results showed that Hilbert curve outperformed other SFCs in all the test cases. Based on the above comparative analysis, Hilbert curve was adopted by RSIMeta due to its better locality-preserving property and recursive structure of multi-level squares. Based on Hilbert curve, we designed a distributed spatial index for remote sensing meta  
Figure 3: Jump and Contiguity percentages comparison of Hilbert curve, Gray curve and Z-order curve. data storage and retrieval, which can be used to select quickly images intersected with a very large geographical area from billions of remote sensing images.
# Pose Measurement for Unmanned Aerial Vehicle Based on Rigid Skeleton
## 2 System Framework
### Proposed Architecture  
The position and attitude measurement of UAVs is mainly used for formation flying and autonomous refueling in the air. During this process, the rear aircraft must sail at a speed slightly faster than that of the front aircraft. The two aircraft are constantly approaching, and the observation angle and relative distance between them may change remarkably over the course of flight. Thus, measuring the relative distance and attitude between the two aircraft is necessary to ensure safe navigation.  
This paper focuses on the online measurement of the position and attitude of short-range UAVs. The measurement system is shown in Figure 2. Two cameras are placed on the rear machine, video streams acquired by cameras are used as input. Our method solves the pose of the front UAV by each pair of images.  
The algorithm fuses 3D point cloud data with 2D information, such as image edges and feature points. Two cameras complete the acquisition of target images. Aircraft target positioning and tracking and 3D reconstruction are completed in the image. The set of points on the wing edge and plane are extracted according to the 3D point cloud and 2D  
Figure 1: Definition of aircraft attitude angle.  
Figure 2: Measuring system.  
images, and the straight-line equation of the wing edge space, the intersection line of the two wing planes, and the position of the front aircraft tail are subsequently calculated. Integrating the straight-line equation of the wing edge and the plane's intersection line realizes the calculation of the three attitude angles. In the next section, the specific implementation of the proposed method will be described in detail.
## 3 Phase Change Materials
### Classification of PCMs
#### 3.1.2 Inorganic Materials  
There are two kinds of inorganic compounds that can be used as PCMs: hydrated salts and metals. Hydrated salts have been immensely studied for their use as PCMs. Their general formula is AB-nH2O, and the phase change is in fact the dehydration reaction of the hydrated salt. Regarding metal category, they include low melting metals (eutectics) but these materials have not yet been seriously studied as PCMs [8]. The greatest properties of inorganic PCMs are the high thermal conductivity and the high energy efficiency (high enthalpy). In addition, other advantages are the low volume change during phase transition and that they are inflammable [14].  
The main drawback of inorganic PCMs is the incongruent melting which leads to phase segregation and, furthermore, to decreased performance after every charge-discharge circle. Another disadvantage is the poor nucleating properties that resulting in supercooling of the liquid phase before crystallization. Additionally, inorganic PCMs are widely corrosive, in some cases toxic, show limited compatibility with construction materials, and they are rather expensive [14; 15]. The advantages and disadvantages for both organic and inorganic PCMs are summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Material** & \
\begin{tabular}{c} **Melting** \\ **Temperature (°C)** \\ \end{tabular} &
\begin{tabular}{c} **Melting** \\ **Enthalpy (J/g)** \\ \end{tabular} & **Ref** \\ \hline Fatty acids & Capric Acid & 30.2 & 142.7 & [4] \\  & Capric Acid & 32 & 152.7 & [5] \\  & Lauric acid & 43.05 & 172.3 & [5] \\  & Myristic acid & 51.80 & 178.14 & [13] \\  & Palmitic acid & 60.42 & 233.24 & [13] \\  & Stearic acid & 54.29 & 188.28 & [5] \\ \hline Fatty acids esters & Butyli acetate & 19 & 140 & [16] \\  & Propyl palmitate & 19 & 186 & [17] \\  & Methyl palmitate & 29 & 205 & [8] \\  & Methyl eicosanate & 45 & 230 & [8] \\  & Methyl behenate & 52 & 234 & [8] \\ \hline Alcohols & 1-dodecanol & 26 & 200 & [17] \\  & Phenol & 41 & 120 & [8] \\  & Cethyl alcohol & 49.3 & 141 & [8] \\ \hline Polyethylene & PEG 400 & 3.2 & 91.4 & [13] \\ glycol (PEG) & PEG 600 & 22.2 & 108.4 & [13] \\  & PEG800 & 25.39 & 133.6 & [5] \\  & PEG 1000 & 34.89 & 143.62 & [13] \\  & PEG 2000 & 52.63 & 180.70 & [13] \\  & PEG 4000 & 48.95 & 183.10 & [13] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thermal properties of nonparaffins.