esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion  
In order to determine the ideal conditions for the extraction of VOCs by HS-SPME, the effect of sample weight, temperature, and extraction time for each of the fibers was evaluated. The SPME fibers, PA, DVB/CAR/PDMS, and PDMS/DVB, were analyzed and individually compared according to the sum of the peak areas prepared in the chromatograms of the 19 design tests. The relative areas of the isolated volatile compounds for each of the SPME fibers are shown in Table 1.  
In the present study, it was observed that the fiber divinylbenzene/carboux/polydimethylsiloxane (DVB/CAR/PDMS) had a better efficiency due to the larger chromatographic area observed when the grumixama pulp was subjected to the conditions of 75 \({}^{\circ}\)C, 2.0 g, and an adsorption time of 20 min. On the other hand, studies on the optimization of extraction conditions in fruits belonging to the Myrtaceae family, such as acerola (_Malpighia emarginata_ D.C.) [17], cagaita (_Eugenia dysenterica_) [21], and cambui (_Myricaria floribunda_) [14] had a better efficiency for fiber polyacrylate (PA). For the pera do cerrado, a better performance was observed for the PDMS/DVB fiber [15]. The observed behaviors emphasized that the extracted fiber's efficiency could vary according to the matrix studied. Figure 1 shows chromatograms obtained from the studied grumixama sample, with the same conditions used with different fibers, differentiated by their colors: orange (DVB/CAR/PDMS fiber), red (PA fiber), and pink (PDMS/DVB fiber). For the conditions that presented a better efficiency due to the larger chromatographic area, a total of 14 volatile organic compounds (VOCs) were identified by the divinylbenzene/carboux/polydimethylsiloxane (DVB/CAR/PDMS), six by polyacrylate fibers (PA), and seven by polydimethylsiloxane/divinylbenzene (PDMS/DVB) through solid-phase microextraction in the headspace mode (SPME-HS). The fruits of grumixama are mainly composed of sesquiterpenenes. Evaluating each individually used fiber in the _Eugenia brasiliensis_, despite the significant difference in the number of compounds, made it possible to observe that the sum of compounds in sesquiterpene fractions (85.7%, 83.3%, and 85.7% of total VOCs ) was consistently higher than the sum of compounds in monoterrene fractions (14.3%, 16.7%, and 14.3%) for DVB/CAR/PDMS, PA, and PDMS/DVB, respectively (Table 2). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Assay**} & \multicolumn{3}{c}{**Factors**} & \multicolumn{3}{c}{**Response Variables**} \\ \cline{2-6}  & \multirow{2}{*}{**Sample Weight (g)**} & **Adsorption** & **Extraction** & & & \\  & & **Temperature (\({}^{\circ}\)C)** & **Time (min)** & & **DVB/CAR/PDMS** & **PDMS/DVB** \\ \hline  
\hline \hline \end{tabular} SPME fibers: polyacrylate (PA), polidimetilsiloxane/divinylbenzene (PDMS/DVB), and divinylbenzene/carboux/polydimethylsiloxane (DVB/CAR/PDMS). * Central points.  
\end{table} 
Table 1: Experimental design conditions and the total sum of the chromatographic peaks obtained for each fiber by the HS-SPME-GC-MS method. Figure 1: Chromatogram generated for grumixama fruits.
# Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications
## 4 Hybrid Algorithm for UAV Path Planning  
In this section, the proposed HPP algorithm is presented in detail. After summarizing our network model, we present the constraints of dynamic environments for optimization and the proposed algorithm.
## 2 State-of-the-Art Overview and Problem Formulation  
Here, an overview of both the handover schemes and the positioning techniques is given followed by the state-of-the-art and the problem formulation.
## 6 Numerical Results  
In this section, we evaluate the proposed JSCC-IA scheme and compare it with the conventional IA system in large-scale networks with limited antennas. The MIMO interference networks (4 \(\times\) 4, 1) [10], (4 \(\times\) 4, 1) [20] and (6 \(\times\) 6, 2) [18] are considered. According to the IA feasibility condition, when four antennas are used, the maximal numbers of users are seven and three for the cases of single and double DoFs, respectively. The JSCC-IA system partitions the users into two and four groups with five users in each cluster for the (4 \(\times\) 4, 1) [10] and (4 \(\times\) 4, 1) [20] networks, respectively. Similarly, the users in (6 \(\times\) 6, 2) [18] are divided into six groups with three users in each cluster. In the first subsection, the symmetric networks are considered, and the error performance, minimal received SINR and average received SINR are evaluated for the proposed and the conventional IA schemes. In the second subsection, we turn to more practical situations of asymmetric networks. The RGS algorithm is discussed in terms of BER and received SINR. In each figure below, the performances are averaged over 200 realizations of randomly generated channel coefficients. The Max-SINR algorithm is chosen to design the precoding and decoding matrices with the iterations of 1000. The Gold sequences are selected as the PN codes with the length of 63 in the following simulations.
## 3 Tracking Measurement Overview
### TDRS Measurements  
The TDRS system consists of a constellation of geosynchronous satellites orbiting around the Earth [1], each equipped with multiple antennas for communication and tracking. The TDRS satellites are strategically positioned to provide complete coverage of spacecraft in Low Earth Orbit (LEO). These satellites are operated by NASA and designed to provide continuous communications coverage between Earth and orbiting spacecraft, including the International Space Station, the Hubble Space Telescope, and various other satellites. At present, the TDRS orbit is obtained by the FDF through the utilization of range and Doppler tracking measurements obtained from TT&C and BRTS systems.  
Figure 1: User spacecraft’s one-way Doppler (OWD) tracking configuration with two TDRS satellites. Node #1 represents the position of the user satellite in space, whereas Node #2 denotes the location of the TDRS in space. Node #3 and Node #4 are identical ground stations used for transmitting and receiving signals in relation to Node #2. These ground stations are different for both TDRSs. By differencing two OWD measurements taken between the user spacecraft and two TDRS satellites, Differenced One-Way Doppler (DOWD) measurements are obtained.
## 3 Related Work
### Closed-Form Formulas for GDOP and WDGOP Calculation  
#### 3.2.1 Closed-Form Formulas for GDOP Calculation  
To achieve both high accuracy and low computational requirements, an efficient closed-form scheme for GDOP has been proposed in [7]. Because the closed-form method obtains the solution directly instead of utilizing an approximation method, the closed-form formulas for GDOP calculations are the most accurate among all. The GDOP formulas can reduce the computational complexity required for computing the matrix inversion. If exactly four measurements are available, the closed-form formula method can provide the best computational efficiency. The disadvantage is that it can be applied for four measurements only.
# Multi-Scale Flame Situation Detection Based on Pixel-Level Segmentation of Visual Images
## 3 Method
### Task Definition  
The flame situation detection method based on visual pixel-level segmentation aims to identify flame contours to extract flame information by determining whether each pixel in the fire image corresponds to a flame pixel.  
The input of the task is a fire image captured from the original video, denoted as \(I\in\mathbb{R}^{H_{I}\times W_{I}\times C_{I}}\), where \(H_{I}\), \(W_{I}\), and \(C_{I}\) represent the height, width, and number of channels of the image, respectively.  
The output of the flame segmentation method SeaFormer is a mask image \(Mask\in\{0,1\}^{H_{I}\times W_{I}}\), where 0 corresponds to background and 1 denotes flame. From the segmentation mask, the set of contour points of the flame objects \(C_{flame}\) is obtained. These points serve as the basis in calculating various flame parameters. The static flame parameters include the flame height, width, and area, while the dynamic flame parameters encompass the rates of change in the flame height, width, and area. These parameters serve as outputs provided by the Flame-SeaFormer model.
## 3 Flow Control Devices for Gust Load Alleviation
### Fluidic Actuators
#### 3.4.1 Surface Jet Blowing
For a 3D wing with jet slot only deployed near the wing tip, the effects of blowing have an extension to the whole wingspan. For the properties of blowing under unsteady actuations, a further experiment of the upstream blowing was conducted by [PERSON] et al. [93]. The time lag in lift responses corresponding to blowing actuation frequency has been observed due to the change in the circulation and the vorticity shedding. The time delay became more significant with increasing angles of attack because of more separated flow. The effects of the slot blowing on unsteady aerodynamic load control with a freestream velocity from 6.7 m/s to 22.2 m/s on NACA0018 aerofoil was experimentally evaluated by [PERSON] et al. [94]. The results showed that the lift oscillation due to the unsteady incoming flow can be effectively counteracted by dynamically adapting the slot blowing velocity. For the further understanding of the behaviour of the dynamic actuations, the responses of microjet blowing with periodic actuations are evaluated at by the authors in [92]. It was demonstrated that the microjet blowing is also effective under dynamic actuations. However, the load control effect is reduced with the increase in blowing frequency. Based on the load control capabilities of microjet blowing under steady and unsteady actuations, its capabilities for gust load alleviation was evaluated by the authors in Ref. [92] on the 2-D NACA0012 and 3-D BAH wing. The test cases show that normal microjet blowing is a promising approach for gust load alleviation with a fast frequency response characteristic. The results of gust load alleviation on both the 2-D airfoil and the 3-D BAH wing verify that microjet blowing is able to suppress the gust load disturbances. Due to the fast response characteristics, it is capable for timely adaptive gust load control. For the test cases of rigid BAH wing, a significant reduction in gust-induced lift and root bending moment coefficients has been achieved. Because of the alleviation in gust load, significant suppression of the gust-induced disturbances in the displacement and acceleration has been obtained in the case study on the elastic BAH wing. Due to the fast-response characteristics of microjet blowing, a near constant lift response under gust condition is obtained by adaptively adjusting the blowing momentum coefficients. Another gust load alleviation study using synthetic jets on the NACA0012 aerofoil was conducted by [PERSON], et al. [95]. It was demonstrated that synthetic jets also have the potential for gust load control.
## 4 Mechanisms by Which Sprouted Grains Improve Insulin Sensitivity
### Reduction of Postprandial Glycemic Response
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Mechanism Details** & **Metabolic Implications** & **References** \\ \hline Reduction of Postprandial Glycemic Response & \(\bullet\) Inhibition of \(\alpha\)-amylase and \(\alpha\)-glucosidase \(\rightarrow\) delayed carbohydrate hydrolysis \(\rightarrow\)\(\uparrow\) Insulin signaling & [22, 66, 67, 68] \\  & \(\downarrow\) glucose absorption & & \\ \hline \multirow{4}{*}{Anti-Inflammatory and Antioxidant Properties} & \(\bullet\)\(\uparrow\) Pro-inflammatory cytokines & & \\  & \(\bullet\)\(\uparrow\) Reactive Oxygen Species & \(\bullet\) Protection of insulin signaling & \\  & (ROS) scavenging via elevated & \(\downarrow\) Oxidative damage & [26, 69, 70, 71] \\  & polyphenols, fertile acid, and antioxidants & & \\ \hline \multirow{4}{*}{Prebiotic Effects on Gut Health} & \(\bullet\) Modulation of gut microbiota & \(\uparrow\) Metabolic homeostasis via the & \\  & [26, 69, 70, 71] composition & & \\  & \(\bullet\)\(\uparrow\) Intestine barrier integrity & & \\  & \(\bullet\)\(\uparrow\) Short-Chain Fatty Acids & & \\  & (SCFA) production & & by SCFAs & \\ \hline Modulation of Lipid Metabolism & \(\bullet\)\(\uparrow\) Expression of hepatic genes & & \\  & involved in lipid metabolism & \(\bullet\)\(\uparrow\) Improved lipid profiles & \\  & \(\bullet\)\(\uparrow\) Fecal lipid excretion & & \\ \hline \hline \end{tabular} A complete list of abbreviations is provided in the Abbreviations section. Symbols: \(\uparrow\) indicates increase; \(\downarrow\) indicates decrease; \(\rightarrow\) indicates the resulting effect.  
\end{table} 
Table 3: Mechanisms by which sprouted grains improve insulin sensitivity. Collectively, these findings suggest that sprouted grains not only slow glucose absorption but also enhance intracellular glucose utilization via improved insulin signaling. This dual mechanism of action--inhibition of starch-digesting enzymes and upregulation of insulin signaling pathways--underscores the therapeutic relevance of sprouted grains as a functional dietary strategy for managing IR and metabolic disorders associated with obesity.
# 3R Composites: Knockdown Effect Assessment and Repair Efficiency via Mechanical and NDE Testing
## 3 Results and Discussion
### Lap Strap  
#### 3.4.1 Knockdown Effect on Mechanical Properties  
3R lap strap specimens exhibited a knockdown effect of 3.3% compared to the conventional specimens (Figure 24). Additionally, the 3R specimens exhibited a load drop smaller than in the case of conventional specimens.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **9 J Impact** & \\ \hline
**Type of CFRP Specim** & **Strength (MPa)** & **Knockdown Effect** \\ \hline Conventional Resin (RTM6) & \(>216.00\) & \\
3R Resin & 212.43 \(\pm\) 12.18 & \(\approx-1.9\%\) \\ \hline
**Type of CFRP Specim** & **Max Load (kN)** & **Knockdown Effect** \\ \hline Conventional Resin (RTM6) & \(>105.00\) & \\
3R Resin & \(100.59\pm 4.75\) & \(\approx-3.0\%\) \\ \hline  & **15 J Impact** & \\ \hline
**Type of CFRP Specim** & **Strength (MPa)** & **Knockdown Effect** \\ \hline Conventional Resin (RTM6) & \(207.80\pm 11.74\) & \\
3R Resin & \(205.54\pm 9.27\) & \(-1.1\%\) \\ \hline
**Type of CFRP Specim** & **Max Load (kN)** & **Knockdown Effect** \\ \hline Conventional Resin (RTM6) & \(98.93\pm 4.91\) & \\
3R Resin & \(94.69\pm 7.06\) & \(-4.3\%\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Knockdown effect of compression after impact properties between conventional (RTM6) and 3R specimens.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **9 J Impact** & \\ \hline
**Type of CFRP Specim** & **Strength (MPa)** & **Repair Efficiency** \\ \hline
3R Initial & 212.43 \(\pm\) 12.18 & \\
3R Repaired & \(170.11\pm 12.32\) & 80.1\% \\ \hline
**Type of CFRP Specim** & **Max Load (kN)** & **Repair Efficiency** \\ \hline
3R Initial & \(100.58\pm 4.57\) & \\
3R Repaired & \(76.29\pm 4.94\) & 75.8\% \\ \hline  & **15 J Impact** & \\ \hline
**Type of CFRP Specim** & **Strength (MPa)** & **Repair Efficiency** \\ \hline
3R Initial & \(205.54\pm 9.25\) & \\
3R Repaired & \(153.76\pm 9.27\) & 74.8\% \\ \hline
**Type of CFRP Specim** & **Max Load (kN)** & **Repair Efficiency** \\ \hline
3R Initial & \(94.69\pm 7.06\) & \\
3R Repaired & \(58.28\pm 4.60\) & 61.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Repair efficiency values of compression after impact properties.
# Watermarking Protocols: A Short Guide for Beginners
## 3 Security Primitives  
The watermarking protocols documented in the literature exploit a wide variety of security primitives. However, the focus is on the primitives used in \"buyer and seller\" watermarking protocols based on the \"asymmetric fingerprinting\" protection scheme [7; 8; 20]. According to such a scheme, the buyer and seller can jointly protect digital content in such a way that only the buyer receives the final version of the content watermarked by using a fingerprint that unambiguously binds the buyer, the content, and the purchase transaction. As a consequence, if a copy of the protected content is illegally distributed and found in the market, the seller can run a proper dispute resolution protocol to prove the guilt of the buyer. Moreover, asymmetric fingerprinting schemes can also involve TTPs, which usually assist buyers in performing the most complex actions required by joint protections.  
Accordingly, the basic primitives employed in the asymmetric fingerprinting schemes are presented in the following. They are described also in relation to the goals that they make it possible to achieve. In fact, their knowledge can be considered sufficient to understand the basics to design modern watermarking protocols.
## 2 Nutritional Properties of Honey Corresponding to Its General and Specific Chemical Composition
### Specific Chemical Composition on Honey
#### 2.2.2 Volatile Compounds  
Monofloral honey acquire particular volatile compounds from the flower's nectar such as different aromas, fragrances or even pharmacological properties, which transform it into a highly demanded product by consumers [90].  
To date, the most relevant volatile aromatic compounds identified in different honey types are aldehydes, ketones, acids, alcohols, esters, linear hydrocarbons, and cyclic compounds [52], which exhibit a great importance in terms of contribution to the quality of aroma and fragrance found in honey [91]. These volatile compounds are taught to be related to the other substances present in the floral source [48], therefore, their chemical volatile composition is of great importance in indicating honey's botanical source [92].  
An increasing interest in volatile substances is underlined throughout studies that have described the volatile profile of different monofloral honey, trying to find a correlation between the presence of some volatile substances and the botanical origin of flowers and plants. [PERSON] et al. (2021) [93] indicate the presence of both isovaleric acid and 2-aminoacetophenone in the chemical composition of several chestnut honey samples from Turkey. Moreover, some authors have indicated the presence of methyl anthranilate and sinensal isomers only in citrus honey samples, although cis-linalo oxide, nonanol, and decanal were related to Acacia honey [60]. As for Honeydew, [PERSON] et al. (2018) [94] proposed 3-hydroxy-2-butanone, trans-2- pentanal, and 3-methylbutanol as indicators of Honeydew honeys, after analysing various samples of European honeydews. Another finding was reported by [PERSON] et al. (2016) [95] where the presence of several volatile compounds (3-hydroxy-2-butanone, 2,3-butanediol, 1-hydroxy 2-propanone and 1-(2-furanyl)-ethanone) was used to distinguish Honeydew honey from Blossom honey.  
It is important to mention that volatile compounds status can change according to some actions involved in honey storage and/or processing. In this regard, knowledge of reactions that may occur during the storage period is essential in order to avoid a negative impact on honey's sensory properties.
# Matchability and Uncertainty-Aware Iterative Disparity Refinement for Stereo Matching
## 3 Methods
### The Matchability and Uncertainty Decoder  
To reduce the susceptibility of the model to mismatched pixels, we propose to integrate a new MUD module into the iterative disparity refinement framework, which is trained to decode the matchability mask \(\hat{m}_{t}\) and the disparity uncertainty \(\hat{u}_{t}\) from the disparity field hidden state, thus enabling the model to be aware of the reliability of feature matching and estimated disparity. Notably, our method requires neither additional mask supervision nor additional dedicated training for the uncertainty prediction module.  
Figure 1: The architecture of the proposed matchability and uncertainty-aware iterative disparity refinement (MUHR) neural network, including feature extraction, disparity field initialization, iterative disparity refinement and upsampling.
## 4 Numerical Analysis and Discussion
### Analysis  
According to the numerical analysis in Section 4.1, when \(r_{1}=150\) mm, \(r_{2}=150\) mm, \(r_{3}=10\) mm, \(l=30\) mm and \(\alpha=35^{\circ}\), the lockable maximum load of the closing spring is \(F_{\rm ml}=109.414\) kN and the permissible stroke of the locking tab is \([L]=6.4059\) mm. However, the required working stroke of the locking tab \(L=8\) mm \(>[L]\), which means the above variable set cannot satisfy the requirement of the low-power holding mechanism. Thus, further optimization design is needed.  
According to the sensitivity analysis in Section 4.2, in the design space of the low-power holding mechanism, \([L]\) is more sensitive to \(l\) and \(\alpha\) rather than the other variables, \(r_{1}\), \(r_{2}\) and \(r_{3}\). However, \(F_{\rm ml}\) is also sensitive to \(l\). So \(\alpha\) is the better option to be adjusted comparing with \(l\) to maximize the effect on the lockable maximum load of the closing spring. Through further numerical simulation, the improved parameter set of the low-power holding mechanism is obtained, as listed in Table 6, with the permissible stroke of the locking tab \([L]=8.0031\) mm, which satisfies \([L]>L=8\) mm, and the lockable maximum load of the closing spring is \(F_{\rm ml}=71.44\) kN after the parameter adjustment.