esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensory Acceptance and Characterisation of Turmeric- and Black-Pepper-Enriched Ice Cream
###### Abstract  
This study investigated the sensory characteristics and overall acceptability of turmeric- and black-pepper-enriched ice creams. For this purpose, a control sample (C) and a series of flavoured ice creams were prepared as follows: samples with 0.5%, 1.0%, and 2.0% of turmeric powder (T0, T1, and T2), and samples with a mixture of turmeric (0.5%, 1.0%, and 2.0%), and 0.02% of black pepper (T0p, T1p, and T2p). Participants (_n_ = 103) were asked to rate the acceptability of the ice creams using a nine-point hedonic scale, the liking of attributes by a five-point scale, the evaluation of attributes intensity by a just-about-right (JAR) scale, and attribute characterisation through a CATA test. They were also asked about their purchase and consumption intention of prepared ice cream samples. The addition of turmeric powder significantly (_p_ < 0.05) affected the sensory properties of the ice cream, whereas no correlation was found between the addition of black pepper and consumers' ratings of the evaluated samples. According to the results, besides the control sample, the ice cream with 0.5% of turmeric powder and 0.02% black pepper (T0p) had the most desired attributes and the highest score for overall liking (6.94). In conclusion, these valuable spices could be used in the production of ice cream with potential functional properties.  
functional ice cream; turmeric powder; black pepper; consumer acceptance; JAR test; CATA questions; purchase intention Article
# Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
## 3 Area of Interest
### Data  
We created a coconut canopy density map _before_ the typhoon landfall on 31 October 2020, using Sentinel-2 images recorded between January 2020 and 15 October 2020. From this time period, we chose the 10 images from each location least effected by clouds, to build a density map without gaps. In that map we also count the number of intact coconut trees in each province, Figure 1. See details on the method in Section 4.1. We then computed a density map _after_ the typhoon with Sentinel-2 images recorded between 15 December  
Figure 1: Overview of estimated coconut tree counts per province in 2020 and Typhoon Goni’s path. Provinces with white boundaries represent our area of study.  
2020 and 15 April 2021. We use all available images from that period and predict a density for all cloud-free pixels.  
As reference labels for training, we used density maps derived from coconut trees identified with interactive photo-interpretation of images from Google Earth (GSD < 30 cm) from 2017. During annotation we manually marked the centre of each coconut tree crown in different areas in the Philippines. Contiguous areas of at least 300 ha represent the minimal areas for labelling, we refer to such base labelling areas as blocks. We created a density map with the annotated locations on a 0.625 m GSD grid and applied a squared kernel of size 20 \(\times\) 20 m, then we summed the densities into a 10 m GSD grid that matches the Sentinel-2 resolution. Using a grid 16 times denser than the 10 m of Sentinel-2, and a squared kernel, allows the model to focus on identifying the tree density rather than the specific (sub-pixel) tree location, thus reducing the impact of potential co-registration errors. That strategy already proved successful in our earlier work for oil palms [15], as well as olive trees and coconut trees [17]. Our training set consisted of an initial set of 37 blocks, plus additional 50 blocks which were selected using an active learning approach (see more details in Section 4.1), resulting in a total of 87 blocks for training and 31 additional blocks were set aside for validation.  
To validate our approach against actual, visually recognisable damages caused by the typhoon we relied on high-resolution images. For the state before the typhoon we used images from Google Earth (GSD < 30 cm), For after the typhoon we used Worldview-2\({}^{\copyright}\) images from Maxar Technologies (GSD 50 cm). We selected a variety of locations to inspect different levels of damage across the region of interest.
# The SSP-Tree: A Method for Distributed Processing of Range Monitoring Queries in Road Networks
###### Abstract  
This paper addresses the problem of processing range monitoring queries, each of which continuously retrieves moving objects that are currently located within a given query range. In particular, this paper focuses on processing range monitoring queries in the road network, where movements of the objects are constrained by a predefined set of paths. One of the most important challenges of processing range monitoring queries is how to minimize the wireless communication cost and the server computation cost, both of which are heavily dependent on the amount of location-update stream generated by moving objects. The traditional centralized methods for range monitoring queries assume that moving objects periodically send location-updates to the server. However, when the number of moving objects becomes increasingly large, such an assumption may no longer be acceptable because the amount of location-update stream becomes enormous. Recently, some distributed methods have been proposed, where moving objects utilize their available computational capabilities for sending location-updates to the server only when necessary. Unfortunately, the existing distributed methods only deal with the objects moving in Euclidean space, and thus they cannot be extended to processing range monitoring queries over the objects moving along the road network. In this paper, we propose the distributed method for processing range monitoring queries in the road network. To utilize the computational capabilities of moving objects, we introduce the concept of vicinity region. A vicinity region, assigned to each moving object \(o\), makes \(o\) monitor whether or not it should be included in the results of nearby queries. The proposed method includes (i) a new spatial index structure, called the Segment-based Space Partitioning tree (SSP-tree) whose role is to efficiently search the appropriate vicinity regions for moving objects based on their heterogeneous computational capabilities and (ii) the details of the communication strategy between the server and moving objects, which significantly reduce the wireless communication cost as well as the server computation cost. Through simulations, we verify the effectiveness for processing range monitoring queries over a large number of moving objects (up to 100,000) in the road network (modeled as an undirected graph).  
Keywords:spatial databases; location-based services; road networks; range monitoring queries; mobile devices; energy efficiency +
Footnote †: journal: Geo-Informational Journal of Geo-Information
## 4 Hybrid Action Space SAC-Based MLLM Task Offloading and Resource Allocation Scheme
### Continuous-Discrete Action Space-Based SAC Algorithm
To maintain stability, it is imperative to constrain the divergence between the new policy and the existing one. This is achieved through the following condition:  
\[\mathbb{E}\big{[}\rho\big{(}Q_{\ell}(a|s)\parallel Q_{\overline{\ell}}(a|s) \big{)}\big{]}<\chi, \tag{52}\]  
where \(\rho(\cdot)\) denotes the Kullback-Leibler divergence, \(\overline{\ell}\) represents the current hybrid policy, and \(\chi\) is the stability threshold. The updated policy is determined by optimizing the following objective:  
\[A=\{\mu,\
u\}, \tag{44}\]0  
subject to the constraints:  
\[A=\{\mu,\
u\}, \tag{44}\]1 \[A=\{\mu,\
u\}, \tag{44}\]2  
where \(\chi_{\mu}\) and \(\chi_{\
u}\) represent the stability thresholds for the discrete and continuous agents, respectively, and \(\overline{\partial}\) denotes the number of discrete choices. The structure of the hybrid action space AD-SAC algorithm is illustrated in \[A=\{\mu,\
u\}, \tag{44}\]3  
The proposed algorithm decomposes the hybrid action space into discrete and continuous components, allocating each to a distinct agent trained within its own SAC framework. Subsequently, the maximum a posteriori policy optimization algorithm [37] is utilized to integrate the decoupled policies from all agents. This approach effectively tackles the complexities associated with hybrid action spaces in the addressed problems, offering a robust and adaptable framework for hybrid action selection. Algorithm 1 provides a comprehensive overview of the AD-SAC algorithm. ```
1: Initialize all the networks, \(\phi^{u}\), \(\phi^{v}\), \(\bar{\phi}^{u}\), \(\bar{\phi}^{v}\), \(\theta^{u}\), \(\theta^{v}\), \(\theta^{u}\), \(\theta^{v}\) and \(\ell\). 2:repeat
3:for each time slot do
4: Obtain \(a(t)\), \(s(t+1)\), \(r(s(t),a(t))\) based on the policy \(\ell\) and current state \(s(t)\). 5: Save the sample \(\{s(t),a(t),s(t+1),r(s(t),a(t))\}\) to replay \(\mathcal{C}\). 6: Generate samples for unavailable actions and save them to replay \(\mathcal{C}\). 7:endfor
8:for each training epoch do
9: Update \(Q_{\mu}^{\phi}\) based on (45), \(Q_{\mu}^{\theta}\) based on (46), \(Q_{\mu}^{\theta}\) based on (47). 10: Update \(Q_{\
u}^{\phi}\) based on (47), \(Q_{\
u}^{\theta}\) based on (48), \(Q_{\
u}^{\theta}\) based on (50). 11: Soft update \(Q_{\mu}^{\phi}\) and \(Q_{\
u}^{\phi}\). 12: Update \(\ell\) based on (51), (52) and (53). 13:endfor
14:until convergence. ```  
**Algorithm 1** AD-SAC Algorithm for MLLM Task Offloading and Resource Allocation
# Graph-Level Label-Only Membership Inference Attack Against Graph Neural Networks
## 4 Graph-Level Label-Only Membership Inference Attack
### Attack Methodology
#### 4.2.2 Attack Overview  
Our attack process is shown in Figure 1; it consists of following three steps:  
1. **Shadow model generation.** The adversary splits the shadow dataset into training and testing sets and trains a shadow model on the training set to mimic the target model's behavior.  
(2) **Threshold selection**.  
1. The adversary generates a set of perturbed graphs for each shadow graph using Algorithm 1, as introduced in Section 4.2.3.
2. The adversary calculates robustness scores using the prediction labels via Equation (8), as introduced in Section 4.2.1.
3. The adversary labels the robustness scores of the shadow training data as \"member\" and those of the shadow testing data as \"non-member\" to construct the attack dataset, by which a threshold for the robustness score can be selected.
4. **Membership inference**.
5. The adversary perturbs the target graph to generate a set of perturbed graphs.
6. The adversary calculates the robustness score of the target graph.
7. The adversary compares the robustness score of the target graph with the selected threshold. If the score is above the threshold, the graph is a member; otherwise, it is a non-member.  
Figure 1: Illustration of GLO-MIA (step 1: shadow model generation; step 2: threshold selection; step 3: membership inference).
## 3 Results and Discussion
### Experiment Setup
#### 3.3.5 Microphone Characteristics  
One important aspect in audio analysis is the description of microphone characteristics, in particular the frequency range that the microphone can cover. Table 5 shows the frequency ranges of the microphones reported in articles that provided this information. As can be seen, the great majority did not report this information (85%). While the upper end of the spectrum clearly covers the regions of interest for beehive acoustics, of greater importance is the lower end of the spectrum. For example, lower frequencies, around 100 Hz, have been linked to fundamental frequencies of some bee species, such as _B. hortorum_, _B. argillaceus_, and _B. pascuorum_[81; 84]. Detection of such characteristics, with a microphone with a frequency resolution starting at higher than 100 Hz, for example, would not be possible. As such, accurate characterization of the used microphones should be detailed in future works to facilitate replication.
## 2 Theoretical Strength of Plate-to-RHS Connections
### Connection With Chord Face Stiffener  
For longitudinal plate-to-RHS connections stiffened by the chord face stiffener of sufficient width and thickness, the mechanism of chord face plastification or the distribution of yield lines is similar to that of RHS-to-RHS truss connections. According to the CIDECT Design Guide 3, if the chord face stiffener satisfies the minimum thickness in Equation (5), the chord face plastification strength \(N_{CP}\) of stiffened plate-to-RHS connections can be estimated using the same method as that of RHS-to-RHS connections, as follows.  
\[t_{s}\geq 0.5t\exp[3\theta^{*}]\text{ or }t_{s}\geq 0.5t\exp\left[3\left(\frac{b_{s }-t_{p}}{b-t}\right)\right] \tag{5}\]  
For AISC 360-10,  
\[N_{CP}=F_{yt}t^{2}\left[\frac{2h_{s}}{b-b_{s}}+4\sqrt{\frac{b}{b-b_{s}}} \right]Q_{f}\text{ for T- and Y-connections} \tag{6}\]  
\[N_{CP}=9.8F_{yt}t^{2}\left(\frac{b_{s}}{b}\right)\left(\frac{b}{2t}\right)^{0.5}Q_{f}\text{ for K-connection} \tag{7}\]  
\begin{table}
\begin{tabular}{l l l} \hline
**Joint Type** & **AISC 360-10** & **CIDECT Design Guide 3** \\ \hline Plate-to-RHS connection & T- and Y-connections & \(Q_{f}=\sqrt{1-U^{2}}\)1 [FOOTNOTE:1]Footnote 1: \(U=N_{u}/[A_{g}F_{y}]+M_{u}/[SF_{y}]\), where \(N_{u}\) and \(M_{u}=\) axial load and moment, respectively, and \(A_{g}\) and \(S=\) gross section area and elastic section modulus of chord member, respectively. 2\(n=N_{u}/N_{pl}\) +\(M_{u}/M_{pl}\), where \(N_{pl}\) and \(M_{pl}=\) axial yield strength and plastic moment strength of chord member, respectively. \\ \hline \end{tabular}
\end{table}
Table 2: Chord member stress functions \(Q_{f}\) for RHS truss T-, Y-, and K-connections.  
For CIDECT Design Guide 3,  
\[N_{CP}=F_{y}t^{2}\bigg{[}\frac{2h_{s}}{(b-b_{s})\sin\theta}+4\sqrt{\frac{b}{b-b_{s} }}\bigg{]}Q_{f}\text{ for T- and Y-connections} \tag{8}\]  
\[N_{CP}=14F_{y}t^{2}\bigg{(}\frac{b_{s}}{b}\bigg{)}\bigg{(}\frac{b}{2t}\bigg{)}^ {0.3}Q_{f}\text{ for K-connection} \tag{9}\]  
where \(h_{s}\), \(b_{s}\), and \(t_{s}\) = length, width, and thickness of the chord face stiffener, respectively (see Figure 2b). The chord member stress functions \(Q_{f}\) depending on the type of truss connections are presented in Table 2. Note that Equations (6) and (8) apply to the T- and Y-connections where one tensile or compression branch member is joined, whereas Equations (7) and (9) apply to the K-connections where both tensile and compression branch members are joined simultaneously.  
The connection strength \(N_{CP}\) in Equations (7) and (9) is the force acting in the transverse direction to the chord axis. Thus, the connection strength \(V_{CP}\) in the longitudinal direction is determined by substituting \(N_{CP}\) into Equation (4).
# Kinesthetic Learning Based on Fast Marching Square Method for Manipulation
## 4 Experimental Evaluation of FML Algorithm
### Experiments in a Real Indoor Environment
#### 4.2.2 Second Experiment: Environment with Static Objects  
In the second experiment, a static object (box) is added to the workspace and it is considered during data acquisition, as shown in Figure 24.  
Figure 23: Fast Marching Learning results for experiment 1.  
Figure 24: Data collected taking into account the obstacle. Learned data (blue) avoids the box (gray).  
This fact implies that taught paths indicate to the robot where the obstacle is located, encouraging newly generated paths to avoid the box. Figure 25a shows learned kinesthetic data (blue) taking into account the box in the data acquisition and the resulting planned path (green) when a new target is indicated. The initial point in this experiment is \(P_{0}=[30,130,110]\) cm and the endpoint is \(P_{f}=[40,78,87]\) cm with a position error of \(P_{error}=0\) cm. This endpoint has been placed behind the box to test the performance of the algorithm when dodging objects. Again, a large part of the resulting path coincides with taught data, so no auto-learning is performed (\(\kappa=0.7894\)). If we visualize the velocity field corresponding to this case, (Figure 25b), we can see how it generates maximum velocity lines (red) avoiding the box and including learned data (blue) as part of the maximum velocity regions. Figure 25c shows the generated path in the simulated environment, which correctly avoids the box without collisions.
## 4 Results and Discussion  
### Trapped Mode without Fluid  
The proposed FEM-PML method associated with the aeroacoustic Galbrun equation in determining the resonance frequencies of the trapped mode was used. To ensure the convergence of the mesh, the size of \(\
icefrac{{\lambda}}{{10}}\) was chosen. Various cavity lengths were selected in order to investigate its influence on the acoustic field under a trapped mode. For the calculation of the trapped mode, the cavity length was set from 1.0 to 2.8 with an interval of 1.8. The geometrical parameters of pressure, density, velocities, and angular frequency were normalized with duct height H, density \(\rho_{0}\), speed of sound \(c_{0}\), \(\rho_{0}c_{0}\), and \(c_{0}/H\), respectively.  
The distributions of the normalized acoustic pressure around the cavity at the first (\(f=0.394\)) and second (\(f=0.445\)) trapped modes are plotted in Figure 2. Resonance occurred at these two frequencies, and the amplitude of sound pressure reached the maximum near the cavity. The pressure distribution was symmetric about both the \(x\) and \(y\) axes for the first trapped mode. The central zone in the duct was controlled by negative pressure with two relative negative pressure centers. The positive pressure zone was located along the duct wall. For the second trapped mode, the pressure distribution was symmetric about the \(x\) axis, and antisymmetric about the \(y\) axis. Negative and positive pressure centers occurred in pairs along both the axial direction and the duct wall. This pressure distribution was naturally the result of different-order resonance.  
Figure 2: Resonances for the Neumann problem: (**a**) first trapped mode: \(f=0.394\); (**b**) second trapped mode: \(f=0.445\).  
Figure 3 shows the normalized acoustic pressure distribution of the embedded trapped mode (\(f=3.187\)) above the first cut-off frequency. Figure 3 shows that the acoustic pressure field was asymmetric about the \(y\) axis. In the trapped mode, both positive and negative pressure zones existed in the cavity even though the cavity length was relatively small.  
The evolution of the trapped-mode frequency with the length of the cavity at two different modes is illustrated in Figure 4. The \(x\) axis represents the length of the cavity, and the \(y\) axis is the normalized frequency of the trapped mode. The triangle is the first-order mode, and the star is the second-order mode.  
Figure 4 indicates that the increase in cavity length resulted in a decrease in trapped-mode frequency, and it was valid for both the first and second trapped-mode frequencies. This resulted from the small cavity length influencing the pressure distribution around a much smaller zone along the acoustic-wave spreading direction.
# Research on Passive Ranging Technology of Moving Ship Based on Vertical Hydrophone Array
## 3 Simulation
In the application, the range estimation result using only a single element may be inaccurate because the ocean environment is very complicated. Hence, the vertical line array composed of multiple hydrophones at different depths is used in the simulation, and further the data fusion method is adopted. The comparison results of the estimated ranges for each hydrophone and the fusion result are shown in Figure 8.  
The solid black lines represent the ranging results of each element. The solid red line is the 16-element fusion result, and the dotted blue line is the actual range with time. This figure indicates that the estimation results of individual elements have a large bias. In particular, the estimated CPA point is very different from the true point, which seriously affects the accuracy of the range estimation. The data fusion method assigns a small weight to inaccurate results to improve the estimation accuracy. After data fusion, the fusion curve is in better agreement with the actual curve. Figure 6: The simulation result. (**a**) Signal radiated from the source; (**b**) DEMON spectrum analysis.
## 3 Results and Discussion
### Fatty Acid Composition  
Fatty acid composition results are presented in Table 9. Total saturated fatty acids (SFAs) showed the highest value (0.71 g/100 g), followed by polyunsaturated fatty acids (PUFAs) (0.26 g/100 g), and total monounsaturated fatty acids (MUFAs) (0.18 g/100 g). The three predominant fatty acids found in the sample were palmitic acid, docosahexaenoicenoic acid (DHA), and oleic acid. Due to their nutritional value, fatty acids have important health benefits. Palmitic acid, the most common fatty acid in meat, fish, and fishery products, accounted for approximately 50-60% of total fats [49]. Furthermore, DHA is a known agent in treating primary and secondary heart disease and neurological and neuropsychological factors [50]. The Food and Agriculture Organization (FAO) of the United Nations and the World Health Organization (WHO) have given special consideration to fatty acids in food as essential nutrients, and their impact on early growth, development, and nutrition-related chronic diseases in humans.  
In processed seafood products, amino acids contribute to the taste of the product [51]. A sweet taste is caused by glycine, alanine, and trimethyl, whereas a bitter taste results from arginine [52]. The fatty acid content in the test seafood HMR product indicated its good nutritional value, thereby suggesting its potential to contribute to daily fatty acid requirements.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Amino Acid** & **g/100 g** & **\%** \\ \hline Alanine & 0.52 & 6.56 \\ Aspartate & 0.81 & 10.21 \\ Cysteine & 0.11 & 1.39 \\ Glutamine & 1.47 & 18.54 \\ Glycine & 0.45 & 5.67 \\ Proline & 0.33 & 4.16 \\ Serine & 0.38 & 4.79 \\ Tyrosine & 0.20 & 2.52 \\ \hline
**Total NE** & **4.27** & **53.84** \\ \hline Argin & 0.65 & 8.20 \\ Histidine & 0.15 & 1.89 \\ Isoleucine & 0.31 & 3.91 \\ Leucine & 0.63 & 7.94 \\ Lysine & 0.65 & 8.20 \\ Methionine & 0.19 & 2.40 \\ Phenylalanine & 0.32 & 4.04 \\ Threonine & 0.36 & 4.54 \\ Tryptophan & 0.07 & 0.88 \\ Valine & 0.33 & 4.16 \\ \hline
**Total E** & **3.66** & **46.16** \\ \hline
**Total amino acid** & **7.93** & **100** \\ \hline \hline \end{tabular}  
* NE: Non-essential; E Essential.  
\end{table}
Table 8: Amino acid analysis of test seafood HMR products consisting of a mixture of AMPS, CSM, and sauce.