esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Selection of Suitable Radio Frequency Protocol
### Choice of the Wireless Radio Protocol Solution
The results of the simulations on the Radio Mobile software (one of those simulations, Figure 8) show that the choice of the carrier frequency at 915 MHz is judicious because the area covered is large enough, and without any surprise, it is larger than with a carrier frequency of 2.4 GHz. The technological choice for radio frequency modules at the transmitter and receiver is based on XTends [8] modules using the Zigbee IEEE.802.15.4 [9] protocol with a carrier frequency of 915 MHz. The modulation used with XTend boards is of the non-coherent Frequency Shift Keying (FSK) type. However, before continuing with the solution development, we need to qualify the system performances more accurately with the XTends radio protocol parameters. Therefore, detailed telecommunication system modeling is necessary to simulate the effects of the environment where this solution will be implemented.
# Influence of Terroir on Microbial Diversity and Wine [PERSON]
## 1 Introduction  
Alcoholic fermentation, the process of transforming grape must into wine, has been extensively studied for many years. This process is carried out by yeasts, which convert the sugars present in the must into alcohol and carbon dioxide [1, 2]. The microbiota of the must is sometimes unknown, which makes spontaneous fermentation a risky and unpredictable practice. In addition to yeast, filamentous fungi and bacteria are also found in the microbiota of a must [3, 4].  
Non-_Saccharomyces_, once considered undesirable during wine fermentation, have recently gained recognition for their positive contributions to wine quality [5]. These yeasts are found in vineyards and on the surface of the grapes, and they are the first to appear during spontaneous fermentation [6]. This type of fermentation is the most traditional method for carrying out alcoholic fermentation in wineries, producing a more complex organoleptic profile. Their concentration is influenced by grape variety, geographical-climatic conditions, and viticultural practices [7]. They produce a high concentration of glycerol, extracellular enzymes involved in the decrease in ethanol content and the organoleptic profile [8, 9, 10, 11]. [PERSON] et al. [12] used Non-_Saccharomyces_ yeasts as an alternative to reduce the alcohol content in beverage fermentation. This measure could be used to mitigate the effect that global warming has had on over-ripening, producing wines with a high alcohol content.  
The aroma of fruit wine is influenced by several factors, one of the main factors being the alcoholic fermentation carried out by microorganisms [10]. [PERSON] et al. [13] studied the enzymatic activity of Non-_Saccharomyces_ for the production of kiwi fruit wine, where \(\beta\)-glucosidase activity improved the aromatic complexity by releasing volatile compounds such as higher alcohols, ethyl esters, acetates, and other volatile aromatics, thus improving the aromatic quality of the fruit wine.  
In the present study, some genera of Non-_Saccharomyces_ were isolated: _Hanseniispora_ spp., which is the main genus present in ripe grapes, produces enzymes and aromatic compounds that increase the diversity of flavors; _Lachancea_ spp. is also present on the surface of the grape and produces lactic acid, increasing acidification; _Pichia_ spp. is known for its ability to increase the composition of aromatic compounds, providing thiols, terpenes, and fruity esters; and _Torulaspora_ spp. is a genus commonly used in oenology and is recognized for its ability to improve wine quality. The objective of this study was to distinguish the production and quality areas of the Protected Designation of Origin (F.D.O.) Montilla-Moriles (Cordoba, Spain) and assess their oenological potential through analysis of microbiota and their volatilome.
# New Four Points Initialization for Digital Image Correlation in Metal-Sheet Strain Measurements
###### Abstract  
Nowadays, the deformation measurement in metal sheets is important for industries such as the automotive and aerospace industries during its mechanical stamping processes. In this sense, Digital Image Correlation (DIC) has become the most relevant measurement technique in the field of experimental mechanics. This is mainly due to its versatility and low-cost compared with other techniques. However, traditionally, DIC global image registration implemented in software, such as MATLAB 2018, did not find the complete perspective transformation needed successfully and with high precision, because those algorithms use an image registration of the type \"afine\" or \"similarity\", based on a 2D information. Therefore, in this paper, a DIC initialization method is presented to estimate the surface deformation of metal sheets used in the bodywork automotive industry. The method starts with the 3D points reconstruction from a stereoscopic digital camera system. Due to the problem complexity, it is first proposed that the user indicates four points, belonging to reference marks of a \"Circle grid\". Following this, an automatic search is performed among the nearby marks, as far as one desires to reconstruct it. After this, the local DIC is used to verify that those are the correct marks. The results show reliability by reason of the high coincidence of marks in experimental cases. We also consider that the quality of mark stamping, lighting, and the initial conditions also contribute to trustworthy effects.  
DIC; initial condition; image registration; strain measurement +
Footnote †: journal: Applied Mechanics and Physics Letters
## 2 Introduction to the Support Vector Machine  
The SVM enables linear separation by transforming an input space with nonlinear characteristics into a feature space of more dimensions by using a kernel function that can provide an optimal solution. It has attracted a great deal of attention due to the novelty of the concepts that it brings to pattern recognition, its strong mathematical foundation, and its excellent results in practical problems [20; 21].  
We assume that data samples are as follows:  
\[S=(\mathbf{X}_{i},d_{i})_{i=1}^{N},\;d_{i}=-1,\;1., \tag{1}\]  
where \(\mathbf{X}\) and \(N\) are input samples and the number of samples, respectively.  
We find a hyper-plane \(y=\mathbf{W}\cdot\mathbf{X}+b\) with the smallest norm of the coefficient \(\|\mathbf{W}\|^{2}\) having the largest margin (\(2/\|\mathbf{W}\|\)), where \(\mathbf{W}\) and \(b\) are weight vector and bias, respectively. To find an optimal hyper-plane, we use the minimized function \(\Phi(\mathbf{W})=\frac{1}{2}\|\mathbf{W}\|^{2}\) under the constraint \(d_{i}[(\mathbf{X}_{i}\cdot\mathbf{W})+b]\geq 1,\ i=1,2,\dots,\ N\), where the operation is an inner product. The solution to this optimization is given by the saddle point of the Lagrangian as:  
\[L(\mathbf{W},b,\alpha)=\frac{1}{2}\|\mathbf{W}\|^{2}-\sum_{i=1}^{N}\alpha_{i} \Big{\{}y_{i}[\mathbf{W}^{T}\cdot\mathbf{X}_{i}+b]-1\Big{\}}, \tag{2}\]  
where \(\alpha_{i}\) is the Lagrange multiplier.  
A multi-layer perceptron neural network (MLP NN) and support vector machine (SVM) were used to analyze the classification performance and SVM showed better results for sonar signals [20]. In [20], MLP NN had been shown to be easily affected by learning algorithms, input samples, and initial variables. In contrast, it showed that SVM has a relatively robust classification capability. The performance analysis of four classifier algorithms, multivariate Gaussian, evidential K-nearest neighbor (K-NN), probabilistic neural network (PNN), and SVM, were conducted from data for near-field detection [22]. The result showed that the SVM had better performance for high dimensional features and the kernel function was an important factor. In [22], the results showed that SVM was more robust for an unbalanced number of training samples, that is, the number of the target sample is small and the clutter is huge. In addition, SVM was less affected by the scattered and mixed characteristics of the underwater environment. A study on sonar data classification according to the kernel function of SVM [23] was performed. In [23], the results showed that the radial basis function (RBF) kernel had better performance than polynomial kernel. For these reasons, we use a SVM classifier and a Gaussian function type RBF kernel, which has already been widely used for sonar classification.
## 2 Related Work  
### Consumer Satisfaction and Product Reputation  
Consumer satisfaction is an indicator of how pleased consumers are with a product or service, typically measured by comparing their expectations with their actual experience [16]. Consumers often express their satisfaction by providing direct feedback, completing surveys, participating in interviews, writing online reviews, sharing experiences on social media, and joining focus group discussions [17; 18; 19]. The expression of consumer satisfaction shapes a product's reputation [20; 21], which in turn influences product sales [22; 23]. Therefore, businesses should pay close attention to how consumers articulate their satisfaction action and to the state of product reputation, using these as important clues for product improvement [24; 25].  
Consumer satisfaction refers to the overall evaluation of a product throughout the entire purchase cycle, considering aspects such as quality, price, performance, service, and more. Many factors influence consumer satisfaction, including price [10; 11], packaging [12; 13], logistics services [14; 15], product services [26; 27], and product quality [28; 29; 30]. Pricing and packaging design are often addressed from a marketing perspective, while product and logistics services are refined to enhance consumer experience. Product quality, however, is closely tied to the product's inherent design. For food products, taste is a crucial aspect of product quality and a key factor influencing consumer satisfaction [31; 32].  
Traditional methods of obtaining consumer satisfaction data include surveys [10; 11; 33] and interviews [13; 27; 34], which can provide highly targeted, in-depth insights. However, due to time and cost constraints, the scale of data collected through surveys and interviews is often limited to a few hundred consumers. With the continuous development of e-commerce technology, numerous e-commerce platforms now offer rich information resources. Researchers have therefore begun using product ratings and reviews on these platforms [28; 30] to gather consumer satisfaction data on a larger scale. Studies measuring satisfaction based on consumer sentiment in product review texts have been conducted on major domestic and international e-commerce platforms, including JD.com [14; 35], Taobao [14; 36], and Amazon [37; 38].  
Food products represent a significant category on e-commerce platforms. Understanding consumer reputation through a large volume of user ratings and reviews can support product improvement and further development. Currently, research on consumer satisfaction with food products based on e-commerce reviews remains relatively limited, warranting deeper investigation in this field.
## 2 Related Works
### In Situ Data Storage Management  
The storage method of in situ data is closely related to the functionality of data-generating devices, data volume, processing requirements, and storage durability demands [25]. In edge computing architectures, the servers used for storage are particularly critical, as they directly affect storage efficiency, access speed, and data persistence. Selecting appropriate servers based on different application requirements and device capabilities ensures that data can be effectively stored, accessed rapidly, and retained over long periods.  
Traditional in situ data storage typically relies on a single server without detailed differentiation based on specific storage requirements of in situ data. The advantage of this approach lies in its relatively simple system architecture and fixed cost, making it suitable for scenarios with stable data volumes and low processing demands. However, this method also exhibits several limitations, such as low storage space utilization, slower response speeds, and difficulties in handling diverse data types and scales [26].  
In summary, existing in situ data storage methods can effectively improve storage efficiency and access speed in certain scenarios, but still face numerous challenges when dealing with complex data of various types and scales. Further research is needed to achieve more flexible and efficient in situ data storage in complex environments such as smart grids.
## 4 Recurrent Neural Networks (RNN)-Based Model
### Embeddings  
(Word) embeddings are representations obtained for words used for text analysis, typically in the form of a real valued vector that encodes the meaning of the word, such that the words that are closer in the vector space are expected to be similar in meaning. In this paper, our RNN model makes use of fastText word embeddings [32]. The network receives a sequence of vectors (i.e., word embeddings) as input, which was obtained for each input sentence using the fastText tool. The fastText tool was recently open-sourced by Facebook Research, and it enables a fast and effective method to learn word embeddings. This tool proved to be effective and useful in different applications such as text classification, clustering, and information retrieval.  
FastText is similar to the Word2 Vec approach [33] although it builds on Word2 Vec's specific limitation. Specifically, FastText can handle new, OOV terms by extending the Word2 vec skip-gram (SG) model with internal subword information in the form of character n-grams (i.e., sequences of adjacent characters). The method can build a vector representation for a word based on its constituent subparts (or subword components), which allows the model to represent the morphology and lexical similarity of words, in addition to being able to construct vectors for unseen words. This feature is particularly helpful to enhance learning on heavily inflected languages [34]. This would trigger, for example, the words  
Figure 1: Histogram of amount of samples per class and average sample length per class.  
close, closer, and closed to all have similar embeddings/vectors, even if they tend to appear in different contexts.
# The Free-Energy Landscape of a Mechanically Bistable DNA Origami
## 3 Results  
### Origami Design and Structure  
The bistable jointed origami consists of four rod-like blocks of parallel double helices based on a hexagonal lattice that are linked through scaffold connections forming a roughly planar arrangement (Figure 1). In Ref. [24] these blocks have been termed the frame (dark blue), compliant (red), crank (green) and coupler (cyan) blocks. The central section of the compliant block is a 6-helix bundle and is designed to bend more easily than the other blocks which are all based on 10-helix bundles. For example, using the oxDNA model such a 10-helix bundle has been found to have a bending persistence length that is about three times larger than a 6-helix bundle [29].  
Joint 1 between the frame and compliant block is designed to be rigid, whereas joints 2-4 are designed to behave like hinges allowing free rotational motion of the blocks in the plane of the structure. Joints 2 and 3 are connected by short single-stranded linkers on one side of the block, so that the block can rotate about that edge. The linkers for joint 4 are somewhat longer to provide additional flexibility so that the joint can bend both inwards and outwards.  
The origami has been designed to have a free-energy landscape with two minima (termed the \"closed\" state S1 and the \"open\" state S2) as a function of \(\theta\) the angle between the crank and the frame. Since joint 1 is rigid, in order for the origami to transition between the two stable states, the compliant block needs to bend outwards to accommodate the extra length between the ends of the crank and the coupler blocks. When the crank and the coupler blocks are collinear, the bending in the compliant block is at its maximum, and so this intermediate state U is expected to be unstable, i.e., a local maximum on the free-energy landscape. A simple mechanical model that accounts for the cost of bending the compliant block, while assuming free rotation at joints 2-4 predicts a free-energy barrier of 3-4 \(k_{B}T\)[24]. Representative oxDNA configurations at the \(\theta\) values corresponding to S1, U and S2 are depicted in Figure 1; the substantial bending of the compliant block in the intermediate state U is very apparent, but it is interesting to note that in the open state some bending of this block still remains.  
Experimentally, the free-energy landscape for this origami has been inferred from ensembles of structures visualized using electron microscopy [24]. In particular, the probability distribution for \(\theta\) was measured (the free-energy landscape is proportional to the log of this distribution). The main peaks at 28\({}^{\circ}\) and 80\({}^{\circ}\) correspond to states S1 and S2, but surprisingly there was a small sub-peak in the probability distribution at intermediate values of \(\theta\) rather than a minimum. It was suggested that this sub-population in the U state might be a result of coaxial stacking between the ends of the crank and coupler blocks when they are collinear.
# Phishing Detection in Blockchain Transaction Networks Using Ensemble Learning
## 3 Methods
### Deep Convolutional Neural Networks
#### 3.4.1 Long Short-Term Memory (LSTM)  
Long short-term memory (LSTM) is a type of architecture for recurrent neural networks (RNNs) that, unlike ordinary RNNs, can learn long-term dependencies. Unlike traditional RNNs, each neuron in them is a memory cell whose contents may be changed or reset. Figure 2 depicts a schematic representation of the LSTM memory cells. Like feedforward networks, recurrent networks have input, hidden, and output layers. LSTM employs a more complicated calculation approach that uses three filters: input filter \(i\), forget filter \(f\), and output filter \(o\). Let us imagine there are \(K\), \(M\), and \(L\) neurons in the input, hidden, and output layers, respectively.  
The activation vectors of these layers are given in Equations (1)-(3), respectively.  
\[x[n]=\{x_{1}[n],\ x_{2}[n],\dots,x_{K}[n]\}^{t} \tag{1}\]  
\[h[n]=\{h_{1}[n],\ h_{2}[n],\dots,h_{M}[n]\}^{t} \tag{2}\]  
\[y[n]=\{y_{1}[n],\ y_{2}[n],\dots,y_{L}[n]\}^{t} \tag{3}\]  
Figure 2: Schematic representation of LSTM architecture: \(IN\) are inputs, \(OUT\) are outputs, \(i\) is input filter, \(f\) is forget filter, \(o\) is output filter, \(c\) and \(c^{\prime}\) are memory cells.  
where \(t\) is the transposition operation, the weights of the neuron layers are:  
\[w^{in}=\left(w^{in}_{ij}\right)_{M\times K},\;w^{h}=\left(w^{h}_{ij}\right)_{M \times M},\;w^{out}=\left(w^{out}_{ij}\right)_{L\times M} \tag{4}\]  
In the direct transfer mode, using Equations (5) and (6), the value of activation of the neuron \(j\) of the hidden level is obtained.  
\[h^{\prime}_{j}[n]=\sum_{k=1}^{K}\left(w^{in}_{jk}\times x_{k}[n]\right)+\sum_{ m=1}^{M}\left(w^{h}_{jn}\times h^{\prime}_{m}[n-1]\right), \tag{5}\]  
\[h_{j}[n]=\theta_{j}\left(h^{\prime}_{j}[n]\right) \tag{6}\]  
where \(\theta_{j}\) is the nonlinear activation function of neuron \(j\) in the hidden layer. The value of the output neuron \(y_{l}\) is calculated using Equation (7).  
\[y_{l}[n]=\sum_{m=1}^{M}\left(w^{out}_{lm}\times h_{m}[n]\right) \tag{7}\]
## 5 Simulation and Analysis
### Case Study 3  
To verify the effectiveness of the proposed AGFTSMC method in a more realistic power system model, IEEE 39-bus system model is established. This model consists of 10 generators,  
At \(t=5\) s, a 0.032 p.u, disturbance is added to bus 8 in area 1; at \(t=35\) s, a 0.072 p.u, disturbance is added to bus 16 in area 2; and at \(t=70\) s, a 0.054 p.u disturbance is added to bus 3 in area 3. The simulation runs for 100 s. The frequency deviations and tie-line power deviations of the system in the three areas are shown in Figure 6.  
From Figure 6a,b, it can be observed that when a 0.032 p.u disturbance is added to bus 8 in area 1, the maximum frequency deviation of the system in area 1 is approximately \(2.527\times 10^{-5}\) Hz, making the response time about 10.48 s. Additionally, the maximum tie-line power deviation of the system between area 1 and area 2 is about 58.16 W. At \(t=35\) s, when a 0.072 p.u perturbation is added to bus 16 in area 2, the maximum frequency deviation of the system in area 2 is roughly \(6.606\times 10^{-5}\) Hz, making the response time about 12.82 s. Additionally, the maximum tie-line power deviation of the system between area 1 and area 3 is roughly \(62.18\) W. When a 0.054 p.u disturbance is added to bus 3 in area 3, the maximum frequency deviation of the system in area 3 is about \(3.914\times 10^{-5}\) Hz, making the response time about 11.32 s. In addition, the maximum tie-line power deviation of the system between area 1 and area 3 is approximately \(40.18\) W. It can be seen from Figure 6a,b that adopting the proposed AGFTSMC approach can cope with load disturbances effectively, which makes frequency deviation and tie-line power deviation of the system converge to zero in a short time. The proposed control method has the characteristics of fast response and low overshoot, demonstrating superior control performance.
## 3 Results and Discussion
### The Antioxidant Activity of Soy Sauces
Dark soy sauce (Singapore brands) values ranged from 47.1 to 147.33 TEAC mM (ABTS) [59], and our results are more similar to Oyster or plum sauces from the same country. According to [48], the soy sauce showed strong DPPH radical-scavenging activity (87.7% scavenging activity). According to TEAC of commercial seasonings, it was indicated that common additives used in commercial soy sauces, such as fructose syrup, caramel, malt syrup, and monosodium glutamate, show no antioxidant effect or even act as a pro-oxidant. However, the preservative sodium benzoate did not react significantly with ABTS*+ and did not lead to an overestimation of the sauces' TEAC [59]. The observation of decreasing antioxidant activity after acidic hydrolysis of dark soy sauces suggests that melanoidins as colored components are formed by Maillard reactions between reducing sugars and free amino \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Total Polyphenol** & **Total Flavonoid** & \multicolumn{2}{c}{**The Antioxidant Capacities**} & \multicolumn{2}{c}{**The Antioxidant Capacities**} \\  & **Content** & **Content** & **Measured by DPPH** & \multicolumn{2}{c}{**Measured by ABTS**} \\ \cline{2-7}
**Soy Sauce** & \(\upmu\)g **Gallic** & \(\upmu\)g & **mg TE/mL** & \(\upmu\)mol TE/mL** & **mg TE/mL** & \(\upmu\)mol TE/mL** \\  & **Acid/mL** & **Quercetin/mL** & & & & \\ \hline KS\_C & 226.26 \(\pm\) 7.81 b  & 41.55 \(\pm\) 2.95 b  & 0.316 \(\pm\) 0.008 b  & 1.258 \(\pm\) 0.030 b  & 3.689 \(\pm\) 0.124 c  & 14.900 \(\pm\) 0.500 c  \\ KS\_O & 202.43 \(\pm\) 10.10 a  & 34.02 \(\pm\) 3.33 a  & 0.284 \(\pm\) 0.022 b  & 1.129 \(\pm\) 0.087 b  & 3.162 \(\pm\) 0.119 b  & 12.771 \(\pm\) 0.481 b  \\ TS\_C & 225.87 \(\pm\) 7.65 b  & 32.39 \(\pm\) 1.81 a  & 0.201 \(\pm\) 0.033 a  & 0.799 \(\pm\) 0.131 a  & 2.848 \(\pm\) 0.119 a  & 11.501 \(\pm\) 0.481 a  \\ TS\_O & 206.43 \(\pm\) 2.56 a  & 46.31 \(\pm\) 3.37 b  & 0.399 \(\pm\) 0.018 c  & 1.590 \(\pm\) 0.071 c  & 3.470 \(\pm\) 0.082 c  & 14.015 \(\pm\) 0.332 c  \\ \hline \hline \end{tabular}  
* Each value is the average of three analyses’ standard deviation. Values with different uppercase letters in columns are significantly different using [PERSON]’s test (\(p<0.05\)).  
\end{table} 
Table 2: The total polyphenol and flavonoid content and the antioxidant capacities of soy sauces. acids and the amino groups of peptides can influence that parameter [57]. However, the color of soy sauce was not proportional to its antioxidant capacity in different studies [60]. The current results do not indicate that shoyu sauces commercially available in Poland, both standard and organic, show excellent in vitro antioxidant capacity.