esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Glottal Gap Segmentation and Facilitative Playbacks
### Image-Processing Methods  
The image-processing methods refer to a set of operations on an image, in order to get an enhanced image or to extract some useful information from it. It is a type of signal processing in which input is an image and output may be image or characteristics/features associated with that image. Let us denote a laryngeal image sequence as \(I(\mathbf{x},t)\), where \(\mathbf{x}=(x,y)\in\mathbb{R}^{2}\) represents the position of the pixels and \(t\) represents the time instants of the sequence. Hence, a single frame at instant \(t_{k}\), \(k=\{1,2,\ldots,N\}\), is denoted as \(I(\mathbf{x},t_{k})\). Therefore, the intensity of a given pixel \(\mathbf{x}_{ij}=(x_{i},y_{j})\), \(i=\{1,2,\ldots,n\}\) and \(j=\{1,2,\ldots,m\}\), at time \(t_{k}\) can be defined as \(I(\mathbf{x}_{ij},t_{k})\in\mathbb{R}\), which represents a pixel in gray-scale. For a color image sequence, the components can be represented by superscripts. For instance, in the RGB  
Figure 4: Graphical illustration of the procedure followed to extract glottal features from vocal folds motion during phonation. First row shows the coronal view of the vocal folds; second row represents the top view of the vocal folds obtained from the Laryngeal Osteofiboscopy (VS)or HSV recording; third row depicts the glottal segmentation; and fourth row illustrates the glottal area waveform (GAW) playback.  
space, the image sequence is denoted as \(I^{R,G,B}(\mathbf{x},t)\), where \(R\), \(G\), and \(B\) are the three components of the space. For single images that do not belong to an image sequence, the notation is simplified to \(I(\mathbf{x})\) or \(I(x,y)\) for gray-scale images and \(I^{R,G,B}(\mathbf{x})\) or \(I^{R,G,B}(x,y)\) for images in the RGB space. Figure 5 summarizes graphically the notation for a gray-scale laryngeal image sequence.  
Two of the most common tasks in image-processing are: image segmentation and motion estimation. The first one subdivides an image into its constituent regions or objects and the level of detail of each subdivision depends on the problem being solved [55]. The image segmentation methods can be classified, roughly speaking, into the following categories: thresholding [56], edge-based [57], region-based [58], classification-based [59], graph-based [60,61] and deformable models [62]. Meanwhile, the motion estimation algorithms precisely and faithfully model the motion in the scene which is typically represented using motion vectors, also known as vector displacements. The motion estimation techniques can be grouped into pixel based methods (direct) and feature based methods (indirect). The direct methods derive motion vectors for each pixel in the scene and can be categorized in Phase Correlation [63,64], Block Matching [65,66], Pel-Recursive [67,68], and Optical Flow (OF) [69]. On the other hand, the indirect methods use features matching between frames to compute the motion vectors [70].
# Effects of the Fruit Harvest Date and Shelf-Life Nexus of Apples on Different Quality Perspectives
###### Abstract  
Apples are considered one of the most extensively consumed fruits worldwide, prized for their superior nutritional profile, bioactive compounds, and sensory characteristics. The primary aim of this study was to evaluate the physical and sensory characteristics of two apple varieties, Braeburn and Golden Delicious, based on different harvest dates and during a 15-day shelf life period, using multiple quality assessment perspectives. The first part focused on key textural attributes and their changes over time. Golden Delicious fruit exhibited fewer alterations in texture, with only firmness showing significant changes during shelf life. In contrast, Braeburn fruit experienced alterations in multiple textural parameters, including fracturability, springiness, average peak load, and firmness, indicating a higher sensitivity to shelf life effects. Both apple varieties were significantly influenced by the harvest date. From a Kano model perspective, the quality attributes of apples were categorized as one-dimensional and attractive features, while oral processing requirements fell into three distinct categories: attractive, reverse, and indifferent. For the Braeburn variety, firmness was the dominant sensory attribute at the start of the shelf life at each harvest date, decreasing gradually over time. In contrast, the Golden Delicious apple maintained dominance in both firmness and juiciness across all harvest dates and shelf life testing days. Emotional responses during mastication were similarly distributed for all three harvest dates for the Golden Delicious variety, highlighting the stability of sensory experiences for this variety. These findings provide valuable insights into the complex interplay of textural, sensory, and emotional responses to apple varieties, offering practical implications for apple breeding and post-harvest management.  
Kano model; temporal dominance of sensations; emotion recognition; intrinsic quality; Golden Delicious variety; Braeburn variety +
Footnote †: journal: applied sciences
## 4 Results and Discussion
### Pressure Drop and Wear Rate of IPF Coupled Vibration  
The effects of IPF on pressure drop and wear rate were investigated at the flow rate of 3 m/s. The results showed that the pressure drop in the straight pipe segment hardly changed with IPF at the static state (Figure 8A). However, after the vibration was applied, the pressure drop increased with the increase of IPF. Compared with the non-vibration of the same IPF, the pressure drop with vibration coupling was always greater than that with the static pipe, which indicated that both vibration and IPF affected the pressure drop. The relationship between the curve of maximum wear rate and IPF was also explored. In the static state, the maximum wear rate was proportional to IPF along the path, and the maximum erosion rate increased gradually with the increase of particle flow (Figure 8B). However, after the vibration was applied, the change rate of the maximum wear rate increased greatly when the IPF was more than 8%, and the vibration condition was always greater than the corresponding static condition. This indicated that the vibration increased the flow resistance of the pipe. At the same angle section, the maximum wear rate increased with the increase of IPF, and the growth trend was similar (Figure 8C). At the same IPF, the maximum wear rate between different angles was not significantly different, indicating that the wear distribution was relatively uniform. With the increase of IPF, the ice crystals' flowability began to decline, and collisions between ice particles resulted in energy loss, resulting in a decrease in the frequency and intensity of interactions between particles and wall surfaces. However, the vibration acting on the particle reduced the carrying effect of the fluid, and the collision with the wall surface was more pronounced. The larger the IPF, the more significant the effect of vibration.  
Figure 8: The effect of IPF on pressure drop (**A**), and maximum wear rate at each angle section of elbow (**B**,**C**).  
Figure 7: The trajectory of particles at the elbow: (**A**) represents static state and (**B**) represents vibration.
## 2 Materials and Methods
### Single Cell Assembly and Testing  
A single cell (SUS316, CNL Energy Corp., Seoul, Republic of Korea) based on SUS316 was used to evaluate the performance and durability of the AEMWE. The single cell hardware included a SUS316 end plate and an all-in-one SUS316 current collector and flow field, as shown in Figure 3. Prior to single cell assembly, the AEM, anode, and cathode PTEs were soaked in 1.0 M KOH for 24 h at RT to convert \(\mathrm{Cl^{-}}\) to the \(\mathrm{OH^{-}}\) form. The membrane-electrode assembly was prepared by sandwiching the AEM between the anode and cathode PTEs. The single cell was assembled with a torque of 7.5 N\(\cdot\)m and incorporated into a custom-made AEMWE testing booth. The cell performance was evaluated using an SP-150 potentiostat with a VMP-3B current booster (Bio-Logic Science Instruments, Seyssinet-Pariset, France). Before evaluating the AEMWE, the single cell was preheated to 60 \({}^{\circ}\)C under a KOH flow rate of 30 mL \(\mathrm{min^{-1}}\), and the temperature of the 1.0 M KOH solution was gradually increased from RT to 60 \({}^{\circ}\)C. After this, the AEMWE evaluation was conducted under asymmetric (dry cathode) 1.0 M KOH fed conditions. Current-voltage (I-V) polarizations were obtained by conducting linear sweep voltammetry in the range from 1.35 to 2.00 V at a scan rate of 1 mV s\({}^{-1}\). PEIS measurements were conducted between 1.5 and 2.0 V (increasing by 0.1 V step\({}^{-1}\)), with frequencies ranging from 200 kHz to 0.1 Hz and an amplitude of 10 mV. The in situ durability evaluation of the AEMWE was conducted at a current of 500 mA \(\mathrm{cm^{-2}}\) for 100 h. After the in situ durability evaluation, the AEMs were delaminated from both electrodes to measure the IEC in order to confirm the degradation of the QA functional group by Hofmann's elimination and nucleophilic substitution under water electrolysis conditions.
# Efficient Path Planning Based on Dynamic Bridging Rapidly Exploring Random Tree
## 3 Problem Formulation
### Traditional Framework  
This section will briefly review the RRT-Connect path planning method. The RRT-Connect algorithm, a bidirectional version of the RRT algorithm, does not require pre-sampling and storage of the entire configuration space, but explores the entire state space through function iteration.  
The random tree growth process of RRT-Connect is shown in Figure 1, with the specific steps as follows:  
1. Initialize the starting point \(q_{start}\), the endpoint \(q_{goal}\), and obstacles, and initialize the starting search tree \(T_{start}\) and the ending search tree \(T_{goal}\), as well as the step length step.
2. A randomly selected sampling point \(q_{rand}\) in space is used to guide the expansion of the random tree. This point needs to satisfy motion constraints and capture collisions with objects in the environment. Then, iterate over all the nodes on the random tree \(T_{start}\), compute the distance between them and the sampling point \(q_{rand}\), and filter the node \(q_{nearest1}\) that is closest to that point.
3. The node \(q_{nearest1}\) on the random tree \(T_{start}\) grows a fixed number of steps in the direction of the sampling point \(q_{rand}\) to obtain a new leaf node \(q_{new1}\). If there is no collision with an obstacle during the movement in the direction from the node \(q_{nearest1}\) to the new node \(q_{new1}\), the node \(q_{new1}\) will be added to the random tree \(T_{start}\). Otherwise, the point will be discarded, and the process will revert to step 2.
4. Then, iterate over all the nodes on the random tree \(T_{goal}\), and calculate their distances to the new node \(q_{new2}\). Perform the same process as in step 3 on the random tree \(T_{goal}\) to obtain the new node \(q_{new2}\) on it.
5. Using the greedy search algorithm, step 4 is repeated on the random tree \(T_{goal}\). When an obstacle is encountered during the growth of the random tree, the growth is stopped.
6. Then, exchange the two random trees and repeat step 2-step 6. Within the specified number of searches, when \(q_{new1}\) and \(q_{new2}\) are the same, a path connecting the initial point and the goal point can be obtained, indicating successful path planning.
###### Abstract  
Carbon-based nanoparticles (CNPs) have demonstrated great potential in biomedical applications because of their unique physical and chemical properties, and excellent biocompatibility. Herein, we have studied two types of CNPs with gadolinium (Gd) impurities (Gd-CNPs), which were prepared by microwave synthesis (MWS) and hydrothermal synthesis (HTS), for potential applications as photoluminescent (PL) labels and contrast agents in magnetic resonance imaging (MRI). The prepared Gd-CNPs were investigated by means of transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy, UV-visible absorption spectroscopy, and magnetic-resonance relaxometry, which allowed us to reveal specific features and functional properties of the prepared samples. While the TEM data showed similar size distributions of both types of Gd-CNPs with mean sizes of 4-5 nm, the optical absorption spectroscopy showed higher absorption in the visible spectral region and stronger PL in the red and near-infrared (NIR) spectral regions for the MWS samples in comparison with those prepared by HTS. Under green light excitation the former samples exhibited the bright red-NIR PL with quantum efficiency of the order of 10%. The proton relaxometry measurements demonstrated that the HTS samples possessed longitudinal and transverse relaxivities of about 42 and 70 mM\({}^{-1}\)s\({}^{-1}\), whereas the corresponding values for the MWS samples were about 8 and 13 mM\({}^{-1}\)s\({}^{-1}\), respectively. The obtained results can be useful for the selection of appropriate synthesis conditions for carbon-based nanoparticles for bimodal bioimaging applications.  
carbon; nanoparticles; dots; gadolinium; photoluminescence; contrast agent; MRI; relaxivity; bioimaging +
Footnote †: journal: Applied Sciences
## 4 Discussion
The static headspace chromatogram gives the best representation of monoterpenes and monoterpenoids but a weaker signal for sesquiterpenes and sesquiterpenoids. Solid-phase microextraction gives a significant spectrum of monoterpenes and monoterpenoids with shorter retention times and weaker signal for monoterpenes and monoterpenoids. The determination of substances in the sample depends on the sensitivity of the method used. The volatility of the given substance and the amount of sample used are also important. Therefore, some substances were not identified by all three methods. It seems that the results of Liq and SPME are the most similar (but not in all cases), so the analysis of the extract prepared with an organic solvent (hexane) will be the most suitable for the quantitative determination of these substances. Altogether, 26 terpenes/terpenoids were among the ten main present in four different chemotypes. They were divided with Liq containing 16 terpenes/terpenoids (2 terpenes, 1 terpenoid, 9 sesquiterpenes, and 4 sesquiterpenoids), 15 in HS (5 terpenes, 2 terpenoids, 8 sesquiterpenes), and 17 in SPME (4 terpenes, 3 terpenoids, 10 sesquiterpenoids). The main terpene in chemotype LOH LL3 from the HS and SPME methods was limonene, but as we can see from Table 3, the other terpenes/terpenoids do not follow the same relative ratio. Chemotype LOH LL4 has a similar situation. The main terpene in Liq and SPME is \(\beta\)-caryophyllene, but as we can see from Table 4, the other terpenes/terpenoids do not follow the same relative ratio. As we can see from Tables 1-4, the terpenes found most often from amongst the ten mains were \(\beta\)-myrcene, limonene, \(\beta\)-caryophyllene, \(\alpha\)-humulene, \(\gamma\)-selinene, and selina-3,7(11)-diene. The most reproducible analysis for quantitative analysis (three repetitions were performed for each analysis) appears to be from Liq samples. It is now well known that different cannabis chemotypes (also mentioned as variety, strain, chemovar, cultivar, phenotype, or genotype) with the same content of major cannabinoids act differently in the treatment of the same patient. Not every chemotype is suitable for a given patient. _Cannabis_ constituents (whether cannabinoids, terpenes/terpenoids, or the other bioactive substances) interact with each other and can thus increase (or decrease) the effectiveness of a given chemotype [17; 61]. If these effects are independent, synergistic, or entourage, they still need to be thoroughly studied. In addition, each of us is genetically different, and therefore, one chemotype that is suitable for one patient might not be suitable for another one. It should also be emphasized that a given chemotype of cannabis which is suitable for a specific patient witha given disease may not (but sometimes may) cure a different disease.
## 3 Distance-Based Opportunistic Push/Subscribe Content Dissemination Model
### Application Layer
#### 3.3.2 Subscription Management  
When a user subscribes to some new content, the user sets how many days it can wait for the content. If a user has received the requested content before the deadline, it responds to the CSS and removes its subscription for this content. If a user fails to receive the requested content, the user can extend the deadline. However, if the user does not extend the deadline, CSS deletes the user's subscription after a certain time. When a user subscribes to the content, the user just sets how many days or hours it is willing to wait from the current time rather than an exact time point. CSS calculates the exact time point of the deadline of this subscription, so it is not necessary that all users' smartphones clocks be synchronous. All subscriptions are listed according to the sequence of their deadlines. If the deadline of a subscription is very near the current time, the subscription is at the top of the subscription table. If the deadline of a subscription is still many days from now, the subscription is at the bottom of the subscription table. When a provider accesses CSS and check the subscription list, it will check the subscription list from top to bottom.
# Analyzing the Enablers of Customer Engagement in Healthcare Using TISM and Fuzzy MICMAC
## 6 Conclusions  
Healthcare and medical facilities are significantly shifting online, giving rise to e-Health. Traditional ways of maintaining and updating medical information have shifted to online mode [62] by increasingly using information systems [63]. This prompts healthcare service providers to create a positive reputation and increase the customers' trust by stressing the significance of content. Thus, the current study aims to identify and model the enablers of customer engagement in healthcare. The enabling factors were identified using extant literature and validated with expert opinions. Initially, 13 enablers were identified, subsequently reduced to 8 with expert's opinions. Then, the enablers were modelled using the TISM approach and classified into clusters using Fuzzy MICMAC analysis.  
It emerged that informative content is the most significant enabler of customer engagement in healthcare. The first thing healthcare customers look for in the content is a complete set of information that solves their purpose. Apart from informativeness, the other crucial enablers are authenticity and quality of content. Healthcare services being most crucial, customers seek more authentic information by verifying the content provider. A trustworthy provider provides accurate and reliable information. Quality of information cannot be ignored as customers do not want to risk their lives or health by accessing incorrect or vague information. The enablers discussed above increase the usefulness of the information. The customers can use the information embedded with all the above criteria to help them to get their problems solved or satisfy their curiosity for knowledge and information.  
Further, the visually attractive design of content and the content embedded with jokes and humorous stories about health are very influential in seeking customers' attention. However, it was found that design and entertainment value have less impact on other enablers in driving engagement. Together with other enablers, they play a prominent role in increasing engagement but have no relationship with other enablers. Healthcarecustomers prefer to have entertaining content and content in video form rather than text or audio format. However, it would be useless if the provider did not provide them with the crucial information they were looking for. Henceforth, the customers will be more prompted to share useful information in their network as they are more concerned about the well-being of their close ones. When the content is shared with customers, they get a platform to share their concerns and opinions with others by co-creating the content.
## 2 Materials and Methods
### ML Methodologies Used for LA Prediction Modeling
#### 2.4.4 Artificial Neural Networks (ANNs) Models  
ANNs are data-driven, non-linear, and self-adaptive techniques capable of establishing a relationship between the input data and target output data even when they have an unknown relationship [82]. The mathematical structures of ANN models have a resemblance with the neural system of the human brain [78]. They process data collectively through interconnected neurons depending on factors including thresholds, adjustable weights, and mathematical transformation functions of the images [83]. The ANN architecture used in our study comprised of three layers, where the input and output layers depict the independent and dependent variables, and the hidden layer (inter layer) deals with the computation of data and establishes interconnection between the input and output layers through the hidden neurons. The optimum neurons in the hidden layer were determined through the trial and error approach [84]. In our study, a single hidden layer was found  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**GBR**} & \multicolumn{3}{c}{**ANN**} \\ \hline
**Hyper Parameters** & **Values** & **Descriptions** & **Hyper Parameters** & **Values** & **Descriptions** \\ \hline n\_estimators & 500 & No of decision tree in the ensemble & alpha & 0.001 & Regularization parameter \\ \hline max\_depth & 4 & Maximum depth of the decision tree & hidden\_layer\_sizes & (3,); (5); (10); (3,3) & Four architecture 2-3-1, 2-5-1, 2-10-1, 2-3-3-1 \\ \hline min\_samples\_split & 5 & Minimum number if sample required to split an internal node & max\_iter & 1000 & No. of iteration \\ \hline learning\_rate & 0.01 & Determine the impact of each tree on final outcome. & activation & ’logistic’ & Logistic sigmoid function \(\frac{1}{(1+exp^{-x})}\) \\ \hline loss & \(\textasci{1  
to provide better accuracy than the double hidden layers which is in accordance with the literature [61]. Figure 3 and Table 2 depict the different ANN models where nine types of inputs (as described in Table 1) were combined with four types of ANN architectures, i.e., input layer-hidden layer-output layer (2-3-1, 2-5-1, 2-10-1, 2-3-3-1) in total calculating to 4 \(\times\) 9 = 36 models of ANN. This was performed to observe the effects of different hidden layers on ANN models leading to the selection of the nine best models based on the coefficient of determination (R\({}^{2}\)), mean absolute errors (MAEs), and root mean square errors (RMSEs) values of the testing phase.  
The learning rate was adaptive in nature, i.e., constant as the initial learning rate (0.001) as long as the training loss decreased and number of iterations were determined as 1000 epochs for convergence. To capture the non-linearity of the data an activation function, sigmoid, was used. The hyperparameters, their values and descriptions used for ANN model building are described in Table 3.