esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of a Microbial Cocktail Used as a Starter Culture on Cocoa Fermentation and Chocolate Flavor
## 3 Discussion
In addition, to produce primary metabolites, such as ethanol, and lactic and acetic acids during cocoa fermentation, starter cultures also produce a vast array of volatile secondary metabolites, such as higher alcohols, acids, esters, aldehydes, ketones [24; 27], and others that could influence cocoa flavor [25]. The most important volatile compound groups detected were esters and alcohols, in both fermentations (Table 2 and Figure 4). These compounds are already described as important in cocoa products [24]. The esters are correlated to fruity notes [28] and the alcohols with flowery and candy notes [29; 30], e.g., 2,3-butanediol, 2-heptanol, guaiaicol, benzyl alcohol, and phenylethyl alcohol found in this study, being that the latter two compounds were found in all chocolate process stages of both fermentations (PH15 I and PH15 NI) (Table 2) [4]. Acids are generally related to unpleasant odors present in cocoa products [24; 30; 31]. A total of five compounds were identified, some being related to rancid, sour, or fatty odors. However, some acids detected here may present pleasant odors, e.g., 4-hydroxybutanoic acid and hexanoic acid, with sweetish odors, as shown in Table 2[4]. In order to investigate the influence of a starter culture on the final product, two chocolates were produced and their sensory analyses were evaluated. The judges noted differences between the two chocolate samples (PH15 I Ch and PH15 NI Ch) during the tasting time. Significant differences were observed as described in Figure 5. Bitter was the dominant taste in PH15 NI Ch (Figure 5A) and, in PH15 I Ch, bitter, sweet, and cocoa tastes were dominant (Figure 5B). These results can be corroborated by the analysis of the volatile compounds in the chocolate samples. The 2,3-butanediol, which gives flavor to cocoa butter (sweet chocolate), and 2,3-dimethylpyrazine, which gives caramel and cocoa flavors, were detected only in PH15 I Ch (Table 2). Therefore, that dominant flavor detected in PH15 I Ch could be related to these compounds, indicating the inoculation influence in the final product. The cocoa pods were manually opened with a machete, and the beans were immediately
# An Automated Framework for Streamlined CFD-Based Design and Optimization of Fixed-Wing UAV Wings
## Appendix A Blending Between Sections
### Meshing Module-Geometry Pre-Processing Features
#### Control Volume Boundaries/Far-Field  
Wing aerodynamic simulations focus on analyzing the aerodynamic performance of a wing under free atmospheric conditions. To accurately represent these conditions, the control volume (far-field) should be large enough so that the flow field at its boundaries remain unaffected by disturbances caused by the wing \"wall\". However, the size of the control volume should also be carefully managed to avoid exceeding computational capacity.  
Control volumes can take on various shapes, such as rectangular parallelepipeds, bullet-shaped geometries, or semi-spheres. To the best of the authors' knowledge, there is no universally established standard regarding the optimal shape of the control volume, as the choice often depends on the specific simulation requirements. Based on previous, in-house experience, and in order to facilitate automation, the rectangular parallelepiped shape was selected to represent the far-field bounds.  
The parametrized dimensions that define the artificial far-field boundaries adhere to common practices [41; 43; 48] and are illustrated in Figure 10. The origin of reference is located at the leading edge of the root airfoil. According to best practices, the distance between the upstream and downstream boundaries is set to 25 chord lengths, with the root chord leading edge is positioned at 25% of this distance. Vertically, the far-field boundaries extend 10 chord lengths above and below the origin. For the lateral extent, the boundary is defined by the semispan of the wing (\(b_{2}\)), which has been specified as five semispan lengths.  
Figure 10: Control volume dimensions and layout. The yellow outline denotes the outer boundary of the computational domain, while the blue lines represent internal sizing boxes used to define mesh refinement regions around the geometry of interest.
## 3 Review of WBASN MAC Protocols for IEEE 802.15.4 and IEEE 802.15.6
### Priority-Based Approaches  
These approaches improve IEEE 802.15.4/ IEEE 802.15.6 MAC by considering the priorities of the channel access mechanism. These approaches introduce mechanisms to recognise the priority of the nodes. The weighted-fair-queue (FQ-CSMA/CA) [90] algorithm differentiates between the alarm signals in an emergency event and normal signals. The preference for the signals is given based on urgency. The algorithm aims to reduce the latency for alarm signals without disturbing the traffic of normal signals by introducing weighted queues. Overall signals are divided into five categories according to their urgency, including sensor data traffic, ACK traffic, command control traffic, system settings and alarm signal traffic.  
A Markov-based analytical model that deals with nodes having a different set of access parameters, with two priority classes, including high and low priorities, has been designed [91] for CAP. These priorities consider contention widow values to adjust the prioritised traffic, which means a prioritised node will get more chance to access the channel.  
Differentiated services are delivered to prioritised traffic classes, which are made for critical data traffic. The work in [92] tunes the MAC layer transmission parameters, including macMinBE, macMaxBE and the initial size of CW. This tuning is applied based on priority; mostly, data traffic is assigned the lowest priority, whereas alarm reports, command frames and GTS data are considered high priority. Lower back-offtime-periodvalues are given to high-priority traffic and vice versa; moreover, for high-priority traffic, a priority queue is introduced.  
An explicit priority scheme has been designed [93] for IEEE 802.15.4 by categorising the transmitting nodes into critical and non-critical nodes. Critical nodes have urgent data to send, whereas the non-critical nodes have normal traffic data and can tolerate the delay. A secondary beacon is used by the nodes to inform the coordinator about the urgency, and the coordinator only allows those nodes in CAP that have informed and requested it to send urgent data. Moreover, after receiving the secondary beacon, the coordinator generates the primary beacon and informs the other nodes about activities in the upcoming CAP.  
Two mechanisms have been proposed to provide differentiation services to IEEE 802.15.4-based nodes in saturation conditions, including contention window differentiation (CWD) and backoff exponent differentiation (BED). Nodes are categorised into various priority classes, i.e., emergency and high bandwidths, etc. These priorities are assigned based on data traffic. Overall, CW and binary exponent/backoff values are used to provide these prioritised services. Further, CWD and BED are used to make another scheme known as the backoff counter selection (BCS). BCS is used to select the next backoff period when it is medium-busy. A Markov-based model is developed to validate the performance of CWD and BED for IEEE 802.15.4.
## 4 Analysis and Discussions
### Limitations and Development Trends
In terms of model optimization, researchers will work on developing adaptive parameter optimization algorithms, using genetic algorithms, particle swarm optimization algorithms, and other intelligent algorithms to automatically search for the optimal parameters of the model and improve the accuracy and efficiency of the model. With the development of computer hardware technology, especially the popularization of high-performance computing and cloud computing, the computational efficiency of the model will be significantly improved, and it will be able to process a large amount of settlement data during the construction period more quickly and realize real-time or near-real-time prediction. In addition, to improve the generalization ability of the model, big data and data mining technology play important roles in constructing a more generalized prediction model by analyzing and learning from a large amount of data from different metro tunnel construction cases and extracting common features and laws. At the same time, multimodal fusion is another future development direction, combining the SSA-VMD neural network model with physical mechanism-based models, other machine learning models, etc., to complement each other's strengths and weaknesses to further improve the reliability and accuracy of the prediction of settlement during the construction period of subway tunnels, as well as to provide a strong guarantee for the safe construction of subway tunnels.
# TinyML Algorithms for Big Data Management in Large-Scale IoT Systems
## 4 Experimental Results  
### Overview  
In this section, we assess the performance of our five proposed algorithms--TinyCleanEDF, EdgeClusterML, CompressEdgeML, CacheEdgeML, and TinyHybridSenseQ--across multiple key performance metrics. The evaluation is conducted by varying the number of Raspberry Pi devices used in the deployment, ranging from one to ten. Each algorithm's performance is measured across elements such as accuracy, compression efficiency, data processing time (ms), training time (ms), overall efficiency, and scalability. The metrics utilized in this work are provided in detail in Section 4.2 below.
###### Abstract  
The aim of the study is to evaluate the anti-tyrosinase activity of different aqueous extracts obtained from pomegranate juice processing by-products. External pomegranate peels of two certified cultivars (Akko and Wonderful), were extracted using only water as the extraction solvent. A traditional decoction and a pulsed ultrasound-assisted extraction (PUAE), both 10 min long, were performed and compared. All the aqueous extracts proved to be rich in bioactive compounds. In particular, the total phenolic content (TPC) ranged from 148 to 237 mg gallic acid equivalent (GAE)/g of dried peels (DW), the radical-scavenging ability (RSA) ranged from 307 to 472 mg ascorbic acid equivalent (AAE)/g DW, the free ellagic acid content (EA) ranged from 49 to 94 mg/mL, and the ellagitannins (ETs) ranged from 242 to 340 mg/mL. For both cultivars, PUAE extracts had higher ET content and a lower EC50, while the decoctions had slightly higher TPC, RSA, and free EA amounts. Principal component analysis (PCA) highlighted the direct correlation between the ET content and the tyrosinase enzyme inhibition (lower values of EC50). These findings suggest the potential use of both these natural extracts as low-cost lightening and/or anti-browning ingredients exploitable in several formulations (e.g., cosmetics) or extemporarily usable.  
pomegranate peels; anti-tyrosinase activity; waste recovery; green extraction 2020 10 2795 10.3390/app10082795
# Image-Caption Model Based on Fusion Feature
## 2 Related Work
### Region-Level Feature  
Since the proposed encoder-decoder model, the research on image high-level semantics extraction has been slow due to technical limitations. Before [PERSON] et al. [12] proposed GCN, GCN [13] showed a high performance in extracting non-Euclidean data signatures (as shown in the structure) and remained high when untrained. Graph structure has the advantage that other data structures cannot compare to in representing semantic information. It can express the high-level semantics of images, such as objects in an image, their properties, and the interrelationship between objects.  
[PERSON] et al. [14] proposed a GCN-LSTM architecture for an image-captioning model, the first GCN application to the image-caption-generation field. The model relies on target detection techniques such as Faster-RCNN. It first detects the relationship between object, object attribute, and object in the image by target-detection techniques; then it constructs a graph structure. GCN was used to extract the graph structure's features to guide the LSTM to generate the caption. Graph convolutional neural networks are widely used in image captioning. It is worth mentioning that, with the advent of graph convolutional neural networks, the features of the image have been changed from the direct extraction of grid-level features to the extraction of region-level features of the image by the target-detection technique first.  
[PERSON] et al. [13] proposed the Hierarchy Parsing (HIP) architecture. It was developed by combining Faster-RCNN and Mask-RCNN techniques for region-level and instance-level segmentation of images, constructing the image into a tree structure, where \(I\) represents the image, and \(R\) represents the region-level object. \(M\) represents the instance-level object, representing the relationship in the tree structure, and then using GCN to extract the features of the tree structure, passing the features into the up-to-down attention mechanism to compute the most relevant few objects. The model adopts the Analytic Hierarchy Process (AHP) to extract the semantics of images at three levels. The acquired semantics are richer, and the model's generalization is more robust than the regular model. However, the treestructure has some limitations in expressing the complex relationship of objects in the image. Therefore, [PERSON] et al. [15] proposed a framework for Caption-Guided Visual Relationship Graph (CGVRG). The framework first takes objects from the image by Faster-RCNN and then by text scene graphical parsers to extract relational triads from the caption. Then CGVRG is constructed by using weak-supervised learning to correspond to objects and predicates. The CGVRG is input to GCN, and GCN extracts the features and context vectors of CGVRG. The model uses the features and context vectors of the graph structure to guide the decoder to generate captions so that the model has better semantic information than the regular model.
# Optimization Design of Lattice Structures in Internal Cooling Channel of Turbine Blade
## 3 The Mathematical Optimization Model and Two Selected Optimization Problems
### The Mathematical Optimization Model  
An LS channel may improve both heat transfer and mechanical performances of turbine blades. Relevance between heat transfer and mechanical performances and geometric parameters of a LS channel was established, and their functions were obtained. Based on these functions, functional integration design and the multidisciplinary performance requirements were taken into account, and a mathematical optimization design model was established at the start of structural design.  
Diameter (\(D\)) and inclination angle (\(\omega\)) of the LS channel were taken as geometric variables; \(Nu\), \(freq1\), \(E\), and \(\overline{\rho}\) were selected as objectives to characterize the performance of  
Figure 5: Force analysis of the single member of the PLS core: (**a**) PLS core; and (**b**) force analysis.  
heat transfer, vibration, load bearing, and light weight, respectively, and the corresponding mathematical optimization model was developed as follows.  
Variables of the model are two geometric variables of the LS channel, which are:  
\[x=(x_{1},x_{2})^{\text{T}}=(D,\omega)^{\text{T}} \tag{27}\]  
Three functions of heat transfer and mechanical performance of mathematical optimization model are given as:  
\[\left\{\begin{array}{ll}f_{1}(x)&=Nu=\dfrac{1}{N}\sum_{n=1}^{N}Nu(n)\\ f_{2}(x)&=freq1=f(D,\omega)\\ f_{4}(x)&=\overline{\rho}=\dfrac{V_{members}}{V_{core}}\end{array}\right. \tag{28}\]  
Constraints of the variables of the model are:  
\[2\text{ mm}\leq x_{1}\leq 5\text{ mm; }40^{\circ}\leq x_{2}\leq 70^{\circ} \tag{29}\]  
Additionally, its equivalent elastic modulus represented bearing load capacity of LS channel, and the value of equivalent elastic modulus was taken greater than the value of its crushing strength, which was also a constraint for LS. The function is expressed as:  
\[f_{3}(x)\ >\sigma \tag{30}\]
## 5 Flying Base Station Capacity Gain Benefit
### Defining Beneficial and Capacity Gain  
We analyze when the flying base station can be beneficial and quantify the benefit of using a flying base station in capacity gain. The flying base station is _beneficial_ if its channel capacity to the user is greater than the stationary base station's channel capacity to the user, i.e., \(C_{FU}>C_{SU}\). We quantify the benefit of using a flying base station in _capacity gain_\(\mathcal{G}\), which is the difference between the channel capacities, i.e., \(\mathcal{G}=C_{FU}-C_{SU}\). The flying base station is beneficial and adds benefits over the existing stationary base station if \(\mathcal{G}>0\); otherwise, the user would opt to use the stationary base station.
## 1 Introduction
### Related Work
#### 1.1.2 Privacy-Preserving Outsourcing Convolutional Neural Network to Cloud  
Outsourcing technology hands over the complex computation to the cloud so as to realize the rational allocation of resources and reduce the computing and storage burden on the local side. However, data and model information outsourced to the cloud may contain sensitive information. How to achieve privacy protection of sensitive information is a problem that is worth studying.  
[PERSON] et al. in [22] proposed a CNN prediction scheme, which protects privacy in the outsourcing environment; that is, the model hosting server cannot learn results and models. By using two noncommunication servers with secret sharing and triplet generation and minimizing the use of heavyweight passwords, overall latency and accuracy are optimized. [PERSON] et al. in [23] proposed a secure cloud-based image service framework that allows privacy-preserving and effective image denoising in the cloud to generate high-quality images. It also shows how to make use of encryption technologies to support DNN image-de noising services. The security design can achieve the same denoising quality as plaintext with high efficiency locally and affordable cost in the cloud. [PERSON] et al. in [24] present and evaluate a lightweight secure outsourcing neural network inference computation scheme, Sonic. It fully outsources the secure inference service to the cloud, freeing end devices and model owners from real-time online. A series of secure and effective function protocols using lightweight cryptography primitives are designed in Sonic to protect user input and model privacy in the whole inference service. A large number of evaluations show that sonic is more efficient than the existing technology in online phrases.  
The above scheme uses garbled circuits (GC) and homomorphic encryption technologies [25] to realize outsourcing deep neural network models. However, they may bring a heavy computation burden. Therefore, we propose a lightweight cryptography privacy-preserving scheme with secret sharing to send the convolutional neural network model to the third-party cloud platform, which can save the computing resource [26] of users and servers. The comparison of different privacy-preserving outsourcing convolutional neural network schemes is shown in Table 2.
# Software-Defined NB-IoT Uplink Framework--The Design, Implementation and Use Cases
## 5 Uplink Implementation
### Channel Coding
#### 5.1.2 Turbo Encoder  
Thereafter, the output of the CRC module is transferred to the turboencoder with the 1/3 coding rate that is presented in Figure 3:  
It is a combination of the two parallel shift registers (with \(D\) indicating consecutive delay blocks) that may be derived from the following transfer function:  
\[G(D)=\left[1,\frac{g_{1}(D)}{g_{0}(D)}\right], \tag{2}\]  
where \(g_{0}(D)=1+D^{2}+D^{3}\) and \(g_{1}(D)=1+D+D^{3}\).  
The original input data stream is shifted through the first register, whilst interleaved input bits are passed along the second one. The interleaving procedure is specified in [21]. The given encoder produces three bits for each bit of the transmitted codeword through \(x_{k}\), \(z_{k}\), \(z^{\prime}_{k}\) streams and an additional 12 bits due to register reset, including also \(x^{\prime}_{k}\) stream.
# Towards Understanding Clustering Problems and Algorithms: An Instance Space Analysis
## 4 Building an Instance Space from Artificial Datasets
### Algorithm Selection in the Instance Space--Performance Prediction  
MATILDA uses SVM-based meta-models to predict in which regions of the instance space a particular algorithm would be the best choice for a given instance. Herewith, a specific module trains one SVM classification model per algorithm that predicts whether the algorithm will perform well or not for a given instance (see liveDemoIS.mlx file available at [[https://github.com/andremun/InstanceSpace](https://github.com/andremun/InstanceSpace)]([https://github.com/andremun/InstanceSpace](https://github.com/andremun/InstanceSpace)), last accessed on 18 March 2021). It does this using the MATLAB fitcsvm package. In this process, the following operations are performed: (i) it produces resubstitution results using a stratified K-fold cross-validation (CV); (ii) it gives a probability for each instance of belonging to a given class, by fitting a posterior probability model; (iii) it uses Bayesian Optimization to fine tune the SVM hyperparameters \(\{C,\gamma\}\) that are the cost or regularization term and the radial-basis function kernel (RBF) hyper-parameter, respectively.  
Table 4 shows the results obtained by the SVM predictors for each clustering algorithm (10 in total), as well as the results of an idealized oracle that always picks the right algorithm, and a selector that combines the decisions made by all the SVMs models to predict the likely best algorithm for each instance using the highest precision SVM to break ties. This assumes that the most precise SVM (CV-Precision) is the most reliable. The table includes the average and standard deviation of the performance measure across all instances (the average ranking obtained for all datasets), probability of good performance and 10-fold CV performance of the SVM models (accuracy, precision and recall). The SVM parameter values employed are also shown. The average ranking and its standard deviation are the expected performance if we were to use each algorithm across all instances. As already mentioned in the paper, the threshold for good performance defined in MATLAB was set as an avg_rank performance metric of at least 0.50. The probability of good performance is based on this parameter and represents the fraction of instances for which the performance measure is greater than or equal to 0.50. The clustering algorithm that presented the best performance on average was CC (probability of good 0.85), while the worst was KMO (probability of good 0.35). The results also indicate that the class of hierarchical algorithms in general presented better performance. The Selector is successful since it has a probability of 0.91 of selecting an algorithm with good performance and a CV-Precision of 91.30--better than always selecting the best on-average algorithm, which is CC (0.85). Nonetheless, the relatively low CV Recall of the Selector indicates that it could be further improved.