esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this study, the best emulsion stability and microencapsulation efficiency were found in the NY (1:10) and KY (1:10) formulations. On the other hand, an increase in peroxide values was observed in both the oils and the microencapsulated samples during storage. The encapsulated samples showed better oxidative stability than the oils. The best oxidative stability in the encapsulated oils was observed in the NY (1:10) and KY (1:10) formulation ratios. Oleic acid was found to be the dominant fatty acid in both oils and microencapsulated powder products. The findings of the aroma analysis emphasize that various microencapsulation formulations (1:1, 1:4, and 1:10) affect the stability and concentration of key aroma components over time. In particular, compounds such as hexanal, D-limonene, \(\gamma\)-terpinene, and \(p\)-cymene exhibited significant concentration changes both during storage and across different wall material formulations. The initial presence of significant amounts of hexanal indicates that it plays an important role in the initial flavor profile of both NY and KY oils. However, its concentration decreased during storage, indicating oxidative degradation, contributing to the loss of certain aroma compounds. Conversely, microencapsulation enhanced the stability of D-limonene, with high retention rates observed even after 90 days. The significant presence of compounds such as \(\gamma\)-terpinene and \(p\)-cymene in microencapsulated powders suggests the existence of a mechanism activated during the microencapsulation process that facilitates their formation. Future research should focus on further elucidating the mechanical pathways underlying the retention and degradation of aroma compounds during microencapsulation and storage, and on discovering new wall materials that can enhance protective effects without compromising sensory qualities. By developing these techniques, the olive oil industry has the potential to significantly improve product quality, sustainability, and consumer satisfaction.  
E.E.K.: Formal analysis, investigation, methodology, and writing--original draft. S.K.: Supervision, conceptualization, methodology, writing--review and editing, and modeling. All authors have read and agreed to the published version of the manuscript.  
The authors thank the Scientific Research Projects (BAP) Coordination Unit of Gaziantep University for funding this research (Project Number: NTMYO.HZP.24.01).  
Figure 3: The scores and factor plane plots for fatty acids of oil and microencapsules of NY and KY.
# Integration of Polynomials Times Double Step Function in Quadrilateral Domains for XFEM Analysis
## 3 Materials and Methods
### Integration Algorithm for Single Discontinuity Problems
Let \(\hat{H}_{q^{+}}^{(s)}(\mathbf{x})\) and \(\hat{H}_{r^{+}}^{(s)}(\mathbf{x})\) be the equivalent polynomial functions for the discontinuities \(q\) and \(r\) evaluated into the regular \(2\times 2\) square element (called the _parent element_), with respect to the domain bounded by \(s\) (Figure 3). Combining them together, it is possible to find the equivalent polynomial \(\hat{H}_{i}(\mathbf{x})\):  
\[I_{i}=\int_{\Omega_{i}}p_{n}(\mathbf{x})\,\mathrm{d}\Omega=\int_{\Omega}\hat{H}_{ i}(\mathbf{x})p_{n}(\mathbf{x})\,\mathrm{d}\Omega \tag{16}\]  
With reference to Figure 3, the equivalent polynomial functions to perform the integration on each of the four areas in (16) are  
\[\hat{H}_{A}(\mathbf{x}) =\hat{H}_{q^{+}}(\mathbf{x})-\hat{H}_{B}(\mathbf{x}) \tag{17}\] \[\hat{H}_{B}(\mathbf{x}) =\hat{H}_{q^{+}}^{(s)}(\mathbf{x})-\hat{H}_{r^{+}}^{(s)}(\mathbf{x})\] (18) \[\hat{H}_{C}(\mathbf{x}) =\hat{H}_{r^{-}}(\mathbf{x})-\hat{H}_{B}(\mathbf{x})\] (19) \[\hat{H}_{D}(\mathbf{x}) =\hat{H}_{r^{+}}(\mathbf{x})-\hat{H}_{q^{+}}(\mathbf{x})+\hat{H}_{B}(\mathbf{ x}) \tag{20}\]  
with the equivalent polynomial functions \(\hat{H}_{A}(\mathbf{x})\ldots\hat{H}_{D}(\mathbf{x})\) representing linear combinations of equivalent polynomials for the single discontinuity lines \(q\) and \(r\), they will depend on the equations of the two discontinuities and, according to [13; 19], will have the same degree of \(p_{n}(\mathbf{x})\) and the algebraic polynomial form:  
\[\hat{H}_{i}(\mathbf{x})=\mathbf{c}\cdot\mathbf{m}(\mathbf{x}) \tag{21}\]  
Figure 3: Use of the auxiliary integration limit \(s\) to evaluate the equivalent polynomials \(\hat{H}_{i}(\mathbf{x})\). In the figure \(\hat{H}_{B}(\mathbf{x})=\hat{H}_{q^{+}}^{(s)}(\mathbf{x})-\hat{H}_{r^{+}}^{(s)}(\mathbf{x})\). (**a**) Integration domain evaluated by means of \(\hat{H}_{q^{+}}(\mathbf{x})\) with respect to the discontinuity \(q\) and the auxiliary integration limit \(s\). (**b**) Integration domain evaluated by means of \(\hat{H}_{r^{+}}(\mathbf{x})\) with respect to the discontinuity \(r\) and the auxiliary integration limit \(s\). where the vector \(\mathbf{m}(\mathbf{x})\) gather a monomial basis, i.e., \(\mathbf{m}(\mathbf{x})=(1,x,y,x^{2},...)\), and \(\mathbf{c}\) is a vector of coefficients [20]. Since \(\hat{H}_{i}(\mathbf{x})p_{n}(\mathbf{x})\) is a polynomial function that is continuous over the entire domain, \(\Omega\), it can be exactly integrated with a proper quadrature rule [20]. It has to be noted that the integrand in (16) has doubled its degree, compared to the one in (11), thus slightly increasing the computational effort. The main advantage of this approach is that it allows integration over the standard domain \(\Omega\) or its rectangular restriction defined by the line \(s\), rather than the non-standard partitioned subdomains \(\Omega_{A}\ldots\Omega_{D}\).
## 5 Simulation Study
### Impact of Varying Sensor Node Density  
Figure 8 illustrates the impact of sensor node density by varying the total number of sensor nodes in the network. As shown in Figure 8(a), the delivery ratios of both the two protocols vary slightly with the increase of sensor node density, which demonstrates that node density doesn't have a significant impact on data delivery ratios in both the CED and DG protocols. Meanwhile, we have also noticed that the CED protocol can always get higher data delivery ratio than the DG one with the increase of sensor node density. This is reasonable because the event delivery strategy is more aggressive in the CED protocol. Figure 8(b) depicts with higher sensor node density, the number of event copies in CED protocol rises while the duplicate event number decreases sharply in the DG protocol. This can be explained as following. In the CED protocol, more sensor nodes may enlarge the size of a community and make the total number of events in the community increase. Thus, more events can be delivered to a subscriber once the subscriber comes into contact with the community. Moreover, more events in the buffer queue of subscribers also make the number of re-forwarded events increase as a result. In the DG protocol, however, more sensors cannot meet the mobile subscribers in a timely way with more sensor nodes being deployed in the network. Thus events in these sensors are unlikely to be received by subscribers. This leads to the decrease of event copies in DG.  
As more copies can enlarge the opportunity to deliver the matched events to their subscribers, the average data delivery delay decreases in the CED protocol with the increase of sensor node density. In contrast, the data delivery delay decreases in the DG protocol with higher sensor node density, as shown in Figure 8(c).  
Figure 8: Impact of sensor node density.
## 3 Correlation Analysis of Seismic _IMs_  
### Initial Candidate Seismic _Ims_  
The appropriateness of selecting a seismic _IM_ is an important factor affecting the accuracy of structural seismic damage assessment. If too many seismic _IMs_ are selected, some of them may have a similar failure mechanism to that of engineering structures or strong correlations with other _IMs_, which may make it difficult to accurately evaluate the dynamic damage experienced by engineering structures. Moreover, selecting too few seismic _IMs_ may prevent a comprehensive and accurate evaluation of the correlation between ground motion and structural seismic damage. Therefore, to ensure accuracy and comprehensiveness, _IMs_ that can reflect the amplitude, spectral characteristics, and duration of ground motion should be selected from a comprehensive range of _IMs_. In this study, 29 seismic _IMs_, shown in Table 2, were selected as the initial candidate seismic _IMs_.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Intensity Measure** & **Description** & **Unit** & **Intensity Measure** & **Description** & **Unit** \\ \hline
1 & PGA & Peak ground acceleration & cm/s\({}^{2}\) & 16 & Arms & Housner root mean square & cm/s\({}^{2}\) \\
2 & PGV & Peak ground velocity & cm/s & 17 & ASI & Acceleration spectrum intensity & cm/s \\
3 & PGD & Peak ground displacement & cm & 18 & VSI & Velocity spectrum intensity & cm \\
4 & PGV/PGA & Peak ground velocity to peak & & 19 & DSI & Displacement spectrum & cm-s \\ ground acceleration ratio & s & 19 & DSI & intensity & cm/s \\
5 & PGD/PGV & Peak ground displacement to & & 20 & IAM & Modified Arias intensity & cm/s \\
6 & SI & Housner intensity & cm & 21 & I\({}_{\rm F}\) & Fajfar measure & / \\
7 & \(a_{\rm BMS}\) & Root mean square of acceleration & cm/s\({}^{2}\) & 22 & TP & Velocity pulse period & s \\
8 & \(v_{\rm RMS}\) & Root mean square of velocity & cm/s & 23 & VP & Velocity pulse amplitude & cm/s \\
9 & \(d_{\rm BMS}\) & & & & & & \\ \multicolumn{6}{c}{The interval of time between the} \\
10 & T\({}_{\rm d}\) & 5\% and 95\% thresholds of the & s & 25 & Sv\({}_{\rm max}\) & Peak velocity spectrum & cm/s \\  & & total Arias intensity & & & & \\
11 & I\({}_{\rm A}\) & Arias intensity & cm/s & 26 & Sd\({}_{\rm max}\) & Peak displacement spectrum & cm \\
12 & I\({}_{\rm C}\) & Characteristic intensity & / & 27 & Sa(T\({}_{\rm 1}\)) & Spectral acceleration at first & cm/s\({}^{2}\) \\
13 & SED & Specific energy density & cm\({}^{2}\)/s & 28 & Sv(T\({}_{\rm 1}\)) & Spectral velocity at first mode & cm/s \\
14 & CAV & Cumulative absolute velocity & cm/s & 29 & Sd(T\({}_{\rm 1}\)) & Spectral displacement at first & cm \\
15 & CAD & Cumulative absolute displacement & cm & & & mode period & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The initial candidate seismic _IMs_.  
Figure 5: Schematic diagram of the abutment model.
# A Bayesian Modeling Approach to Situated Design of Personalized Soundscaping Algorithms
## 2 Methods
### Factor Graphs and Message Passing-Based Inference
#### 2.3.1 Forney-Style Factor Graphs  
Factor graphs are a class of probabilistic graphical models. We focus on Forney-style factor graphs (FFG), introduced in [35], with notational conventions adopted from [36]. The interested reader may refer to [32; 36] for additional information on FFGs. FFGs visualize global factorizable functions as an undirected graph of nodes corresponding to the local functions, or factors, connected by edges or half-edges representing their mutual arguments. This factorized representation allows naturally for the visualization of conditional dependencies in generative probabilistic models.  
Here we will represent the factorizable probability density function \(p(x_{1},\;x_{2},\;x_{3},\;x_{4},\;x_{5},\;x_{6})\) using an FFG. Assume this function factorizes as  
\[p(x_{1},\;x_{2},\;x_{3},\;x_{4},\;x_{5},\;x_{6})=f_{a}(x_{1},\;x_{2})\;f_{b}( x_{2},\;x_{3},\;x_{4})\;f_{c}(x_{4},\;x_{5},\;x_{6})\;f_{d}(x_{5}), \tag{17}\]  
where the functions with alphabetical subscript denote the individual factors. The FFG, as shown in Figure 4, can be constructed from (17) following the three visualisation rules of [36].  
One of the most apparent constraints of these graphs specifying that edges can be connected to a maximum of two nodes, can easily be circumvented through the use of a so-called equality node and the introduction of two variable copies. Suppose a variable \(y\) is the argument of three factors. The introduction of an equality node function \(f=(y,\,y^{\prime},\,y^{\prime\prime})=\delta(y-y^{\prime})\delta(y-y^{\prime \prime})\), where \(\delta(\cdot)\) represents the Dirac delta function, allows for branching \(y\) into variable copies \(y^{\prime}\) and \(y^{\prime\prime}\). The equality node constrains the beliefs over \(y,\,y^{\prime}\) and \(y^{\prime\prime}\) to be equal.  
Figure 5 shows a (Forney-style) factor graph representation of the proposed generative Algonquin-based model. This representation simplifies the model as outlined by (8), (12)-(16). Furthermore Figure 6 shows the FFG of the Gaussian scale sum-based generative model, specified by (11)-(16). In these figures a single source model (the Gaussian mixture model) has been drawn to prevent clutter.  
Figure 4: Forney-style factor graph representation of (17).  
Figure 5: The Forney-style factor graph representation of the Algonquin-based generative model fully specified by (8), (12)–(16). The GMM composite node represents the Gaussian mixture node as specified in ([37], A.2). The Algonquin composite node has been summarized in Table 1. The dashed rectangular bounding boxes here denote plates, which represent repeating parts of the graph. Edges that are intersected by the plate boundaries are implicitly connected between plates using equality nodes.
# A Robust Hybrid Iterative Linear Detector for Massive MIMO Uplink Systems
## 4 Complexity Analysis  
It is well known that the complexity is greatly controlled by the number of performed mathematical operations, such as multiplications and divisions. In addition, large number of iterations contributes in elevating the computational complexity. Also, note that the selection of initial vector affect the computational complexity. Inverse of a stair matrix (\(\mathbf{S}^{-1}\)) requires a \(K\) real number of divisions as well as \(3(K-1)\) real number of multiplications. The JA method initialized based on a stair matrix requires \(n(4K^{2}-2K)\) real number of multiplications. The GS method requires \(4 nK^{2}\) real number of multiplications. Therefore, the proposed algorithm imposes \(K^{2}(1+4n)+K-3\) real number of multiplications. However, the proposed algorithm avails a small number of iterations which reduces the computational complexity. The number of multiplications is illustrated in Table 1.
## 2 Results and Discussion
### Optimization of Extractive Alkylation Conditions
#### 2.1.1 Reaction Time and Organic Solvent Volume  
The effects of reaction time and organic solvent volume on extractive alkylation were investigated individually. No significant differences in LODs for 4-MMP and 3-MHA were obtained for reaction times of 10, 25, and 40 min at room temperature (Figure A2). For 3-MH, the LOD decreased significantly by about a factor of two when reaction time was increased from 10 min to 40 min (\(p<0.05\)). Because the detection limit for 3-MH was below sensory threshold even without further optimization, we chose to use a 10 min reaction time for expedience.  
The volume of the pentane:diethyl ether organic solvent was found to have no significant impact on LOD when varied over 8 to 16 mL (Figure A2). This likely indicates that the PFB derivatives are sufficiently non-polar to partition nearly completely into the organic phase for the range of organic volumes used. A volume of 9 mL was selected for the optimized protocol for convenience, although it is possible that even less solvent could have been used.
# Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing
## 2 Background and Related Work
### Graph Embedding  
Graph embedding captures the neighbor structure of vertices to map the vertices or graph into the embedding space. The graph embedding technologies are mainly divided into dimension-reduction-based methods, random walk-based methods, matrix-factorization-based methods, and neural network-based methods [24]. With the development of deep learning, neural network-based models are proposed for the graph embedding issue. For example, a graph convolutional network (GCN) utilizes the convolutional kernels to extract the graph's structure information and maps the vertices into the embedding space. The convolutional kernels extract the local structure's features and compute the  
Figure 1: A general architecture of Vehicular Edge Computing.  
embedding vectors by reconstructing the graph. [PERSON] et al. [25] combined spatiotemporal graph convolution network and recurrent neural network to construct a hybrid model for embedding the traffic graph to predict the traffic speed. The structural deep network embedding model [26] defined two proximities for holding the local and global structures and computed the embedding vectors by using two autoencoders. The dynamic graph consists of the graphs in continuous-time intervals. Dynamic graph embedding maps the graphs into the embedding space based on similarity and temporality. [PERSON] et al. [27] utilized the autoencoder to map the graphs into the embedding space and input the embedding vectors to the recurrent neural network to capture the temporal information for computing the final embedding vectors.
## 3 Results and Discussion
### Factors Influencing the Quality of Classification Results
#### 3.1.3 ROI and LULC Class Definitions  
Factors such as the ROI creation, image quality, or pixel resolution influence, directly the initial classification (i.e., HRC). The variations of any LULC classification are also subject to the contrast of colors between different land cover classes. Since we are considering true-color images, the LULC classes and the ROIs selected to define them on the image may produce different results if the contrast or brightness of an image is modified. Especially in the case of roofs, where different materials, such as metal sheets, tiles, asphalt shingle, or concrete are used, the creation of ROIs may lead to a trade-off in terms of initial results obtained in the HRC. For example, common misclassifications were observed between clouds and white colored roofs; vegetation and green color roofs, streets; and grey color roofs, or rusty roofs and land undergoing an erosion process. This leads us to suggest that a pre-processing of the image quality could improve results.  
The initial approach which defined the LULC classes was based on the observation of the most prominent areas. As a consequence, unpaved streets and bare land were classified separately. Although this differentiation is necessary for specific topics dealing with the urban LULC, we suggest not to separate them in the MC used in the SCP plugin. Superimposing the street network in the post-edition not only adjusts the geometry of these areas in the image but also contributes to differentiating between paved and unpaved zones.  
Additional factors influencing the results are linked to the algorithm employed for the classification. In this study, the maximum likelihood algorithm was preferred, due to the better definition of the outcomes observed after each run of the plugin in comparison with the minimum distance algorithm. The yielding of better results using the maximum likelihood algorithm is supported by other studies based on Sentinel-2A images [38]. The fundaments behind this topic are not discussed here, since the theoretical background of these algorithms has been already considered by some authors [36; 39].
## 3 Results and Analysis
### Simulation Results
the operation frequency at the broadside direction shifts only \(11\) MHz, which makes the efficiency at the original operation frequency of 5.8 GHz 10% higher.
# Network Diffusion Algorithms and Simulators in IoT and Space IoT: A Systematic Review
## 4 Analysis
### Analysis of Research Question 3 and 5: Network Diffusion Algorithms and Performance Evaluation
#### 4.2.5 Other Models  
In addition to epidemic, cascading, and threshold models, there are other approaches to studying network diffusion in IoT configurations, but they are less commonly applied in IoT contexts.
## 2 Principle
### OFDM Modulation  
OFDM is a multi-carrier modulation technology and it uses multiple sub-carriers to convert the high-speed data streams to multiple sub-channels with relatively low rates for parallel transmission. Firstly, the data stream gets through the 4-QAM generator and OFDM modulator, as shown in Figure 1. Then, the OFDM signals are introduced into the system and the generated OFDM signal is defined as [24]:  
\[s(t)=\sum\
olimits_{t=-\infty}^{+\infty}\sum\
olimits_{k=1}^{N}c_{ki}s_{k}(t-iT_ {s}), \tag{5}\]  
where \(N\) represents the number of subcarriers, \(s_{k}\) is the subcarrier waveform, and \(c_{k}\) defines the information of \(k_{th}\) subcarriers, \(T_{s}\) is the symbol period. In this system, the OFDM signal is loaded into the LG mode through the LiNbO\({}_{3}\) modulator and the signal after modulation can be obtained by:  
\[E_{\text{OFDM}}(t)=E_{0}s(t)J_{1}(b)\cos(\omega)t+E_{0}s(t)J_{1}(b)\cos(\omega)t, \tag{6}\]  
where \(E_{0}\) and \(\omega\) are the amplitude and angular frequency of the LG mode, respectively.  
After MMF propagation, the OFDM signal is detected and demodulated at the receiver side. The photocurrent can be given by:  
\[I_{out~{}OFDM}(t)=\mu\Big{|}E_{out~{}OFDM}(t)\Big{|}^{2}, \tag{7}\]  
where \(\mu\) is the sensitivity of the PIN photodiode. In a word, the combination of LG mode and OFDM signals successfully builds the transmitter part of the optical communication system.
## 2 Methods and Materials
### Radial Basis Function (RBF) Networks  
In computational neural network modeling, Radial Basis Function (RBF) networks, introduced by [PERSON] and [PERSON] in 1988 [58], represent a distinctive type of feed-forward network with robust universal curve fitting approximation capabilities in a high dimensional space. Accordingly, the RBF networks are generally trained to map input vectors, \(x_{i}\in R^{i}\), into output vectors, \(y_{i}\in R^{0}\), where the sets \((x_{i},\ y_{i})\), \(1\leq i\leq n\), form the training pairs. Thus, learning is the same as determining a surface in the high dimensional space that delivers the best line of fit to the training dataset.  
In terms of the radial basis architecture shown in Figure 1, the RBF networks are characteristically made of three layers: one linear output layer, one hidden layer which utilizes a specific non-linear RBF transfer function, and an input layer. The neurons in the input layer have a linear function which primarily feeds and links the input signal vector to the hidden layer. The input layer outputs are obtained by computing the distance between hidden layer centers and the network inputs. Generally, the RBF networks can be expressed mathematically as [59]:  
\[y_{j}(\mathbf{x})=w_{jo}+\left(\sum_{i=1}^{n}w_{ji}\varphi_{j}(\mathbf{x})\right) \tag{15}\]  
\(i\)\(\in\){1,2,..., \(n\)},\(j\){1,2,..., \(j\)}  
The expression in Equation (15) can be simplified further by introducing an additional basis function, \(\phi_{0}=1\), and this yields (16):  
\[y_{j}(\mathbf{x})=\sum_{i=0}^{n}w_{ji}\phi_{j}(\mathbf{x}) \tag{16}\]In terms of Gaussian basis functions, \(\phi_{j}(\mathbf{x})\) is defined by (17):  
\[\phi_{j}(\mathbf{x})=\exp\left(-\frac{\left\|\mathbf{x}-\mathbf{\mu_{j}}\right\| ^{2}}{2\sigma_{j}^{2}}\right) \tag{17}\]  
where \(y_{j}\) = network output of the \(j\)th neuron, \(n\) = no. of hidden layer neurons, \(J\) = dimension of the output, \(\phi\) = radial basis function, \(W_{ji}\) = weight of the \(j\)th output and \(i\)th neuron, \(\sigma\) = spread parameter for the \(i\)th neuron, \(\mathbf{x}\) = input data vector, and \(\mathbf{\mu_{j}}\) = center vector of the \(i\)th neuron.  
In the literature [36; 37], the RBF network has been combined with particle swam (PS) optimization as a hybrid approach for optimal predictive analysis. Thus, in this paper, the RBF is introduced in combination with PS to enable us to benchmark the proposed hybrid RF-PS method.