esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Ultrasonic Treatment on Taste and Flavor Quality of Japonic Rice
## 3 Results
### GC-MS Data Analysis  
#### 3.5.1 Composition of Aroma Compounds  
A total of 65 volatile compounds in cooked rice were identified in this study. The relative contents of each compound were quantitatively calculated by the peak area normalization method. The quantities and relative contents of various volatile compounds in cooked rice are shown in Figure 5. Out of the 65 volatile compounds detected, 23 (40.1%) were aldehydes, 11 (2.94%) were alcohols, 5 (13.38%) were ketones, 3 (0.6%) were esters, 2 (4.88%) were phenols, 13 (28.08%) were hydrocarbons, 7 (9.72%) were heterocyclic compounds, and 1 (0.3%) was other compounds. The main volatile compounds in cooked rice were aldehydes, heterocyclic compounds, and hydrocarbons, which aligns with the findings of previous studies [21].  
Figure 4: Electronic nose response radar images of five types of cooked Japonica rice under different ultrasound treatments. Radar response map of the electronic nose of LJ3013 under different ultrasound treatments (**a**); Radar response map of the electronic nose of LJ66 under different ultrasound treatments (**b**); Radar response map of the electronic nose of JJ816 under different ultrasound treatments (**c**); Radar response map of the electronic nose of JJ830 under different ultrasound treatments (**d**); Radar response map of the electronic nose of JJ305 under different ultrasound treatments (**e**).  
The changes in the number and content of various types of volatile compounds in the five types of cooked Japonica rice after ultrasound treatment are shown in Figure 6. The formation of cracks on the surface of cooked rice grains caused by ultrasound treatment facilitated the penetration of water into the grains during cooking, thereby enhancing the release of volatile compounds [22]. This phenomenon might be attributed to the progressive increase in the types of volatile compounds with increasing ultrasound treatment time. Meanwhile, ultrasound treatment increased lipid dispersion in the sample [10], facilitating lipid oxidation, thereby increasing lipid oxidation products, such as aldehydes and alcohols. The total concentration of volatile compounds gradually increased, mainly due to the increase in the concentration of hydrocarbons and heterocyclic compounds.  
Figure 5: The relative content and quantity of different volatile chemicals in cooked Japonica rice following ultrasound treatment.  
Figure 6: Quantity (**a**) and content (**b**) of various volatile substances in cooked Japonica rice after different ultrasound treatments.
## 3 Working Mechanism  
### Characteristic Mode (CM) Theory  
To ease the illustration of the working mechanism of the CP bandwidth enhancement, here we review the main theoretical elements of CMA first.  
According to the CM theory [34], an impedance matrix \(\mathbf{Z}\) can be written as  
\[\mathbf{Z}=\mathbf{R}+j\mathbf{X},\]  
where \(\mathbf{R}\) and \(\mathbf{X}\) are the real and imaginary parts of the impedance matrix, respectively. Accordingly, a generalized eigenvalue problem is defined as:  
\[\mathbf{X}\mathbf{J}_{\mathbf{n}}=\lambda_{n}\mathbf{R}\mathbf{J}_{\mathbf{n}},\]  
where \(\mathbf{J}_{\mathbf{n}}\) is the \(n\)th characteristic current mode of a conductive structure and \(\lambda_{n}\) is the corresponding eigenvalue of the \(n\)th mode. For each mode, modal significance MS is defined as [35]:  
\[\text{MS}=|1/(1+j\lambda_{n})|.\]  
It is an intrinsic property of the structure and is independent of external excitations. Thus, by investigating the MSs of a certain structure, the properties of the structure can be envisioned and modified. Especially for \(\lambda_{n}=0\), i.e., \(\text{MS}=1\), the \(n\)th mode is in resonance. If we take an antenna as an example, with proper feeding excitation, the antenna may effectively radiate.  
Another important factor for antennas design in CM theory is the characteristic angle (CA), which is defined as [35]:  
\[\alpha_{n}=180^{\text{o}}-\tan^{-1}\lambda_{n}.\]  
It presents the constant phase lag between the current \(\mathbf{J}_{\mathbf{n}}\) and the tangential component of characteristic fields \(\mathbf{E}_{\mathbf{n}}\), where \(\mathbf{E}_{\mathbf{n}}\) is the field produced by current \(\mathbf{J}_{\mathbf{n}}\).  
To design a CP antenna, in practice, it is required that two orthogonal modes be effectively excited, and their MSs be close. Meanwhile, the characteristic angles should have a phase difference of nearly 90\({}^{\circ}\) (70\({}^{\circ}\)-110\({}^{\circ}\)).
## 4 Proposed Framework
### Encoder Decoder with Temporal Attention  
The encoder decoder model is a variant of Recurrent Neural Network (RNN) that has well suited modeling temporal sequences. These networks process input sequences within the context of their internal hidden state (\"memory\") in order to arrive at the output, the internal hidden state is an abstract representation of previously seen inputs. Thus, they are capable of modeling dynamic contextual behavior. We use Gated Recurrent Units (GRU) [26] as our choice of RNN in our implementation. The proposed EDTAM model consists of three building blocks--encoder, decoder, and temporal attention module. These are described below.  
The encoder is an RNN that reads each timestep of the detector arrival waveform sequentially and updates its hidden state conditioned on the current input and its previous hidden state, Equation (3).  
\[\mathbf{h_{e(t)}}=f\big{(}\mathbf{h_{e(t-1)}},\mathbf{y_{t}}\big{)} \tag{3}\]  
where \(\mathbf{h_{e(t)}}\), \(\mathbf{y_{t}}\) are hidden state, input to the encoder at time step \(t\) respectively. The hidden state of the encoder is stored after each time step, and the final output of the encoder is \(\mathbf{H_{e(T)}}=\Big{(}\mathbf{h_{e(1)}},\mathbf{h_{e(2)}}\dots,\mathbf{h_ {e(t)}}\Big{)}\).  
The decoder is also an RNN that is trained to generate the output sequentially based on its input and hidden state. At each time step, the input to the decoder is conditioned on the decoder's output at the previous timestep, encoder's output, and current hidden state of the decoder. The Temporal Attention Module (TAM) acts as an interface between the encoder's outputs and the input of the decoder.  
The Temporal Attention Module (TAM) is a fully-connected network with the inputs being the decoder's output at a previous time step and its hidden state. The output of TAM is a vector, attention scores, which is used to compute the weighted sum of the encoder's hidden states. This weighted sum is fed as input to the decoder, the attention helps the model to focus on specific parts of the encoder's outputs for prediction at each step.  
The final hidden state of the encoder is used to initialize the hidden state of the decoder. The attention scores for predicting at timestep, \(t\) are calculated as follows. Let \(h_{d}\langle t\rangle\), \(z_{t}\) represent the decoder's hidden state, output respectively for time step \(t\)  
\[A_{T}=\frac{\exp\Big{(}\mathbf{w}_{j}[h_{d\langle t\rangle}\
eq z_{t-1}]\Big{)} }{\sum_{j=1}^{K}\exp\Big{(}\mathbf{w}_{f}[h_{d\langle t\rangle}\
eq z_{t-1}] \Big{)}} \tag{4}\]  
where \(w_{j}\) are the rows of learnable weight matrix \(\mathbf{W}\), and \(A_{T}\) represents the computed attention scores. The input to the decoder is the dot product of these attention scores with the hidden states of the encoder, \(A_{T}.H_{e(T)}\).
## 5 AEDG: The Proposed Scheme
### Initialization Phase
#### 5.1.1 GN Selection Criterion  
In the AEDG, an AUV moves along a fixed elliptical trajectory, as shown in Figure 9. The selection of the GN is based on the RSSI value and the residual energy of the individual node. The GN election is carried out multiple times in our proposed scheme, as shown in Figure 8. The criterion for the selection of the GN and the association of the member nodes is given below in detail.  
The AUV regularly broadcasts hello packets after a specific interval of time. Nodes within direct communication range of the AUV listen for the hello packet and broadcast it to their neighbours. Each node calculates the distance between itself and the AUV. The node whose RSSI value is greater is selected as the GN. As the network evolves, the energy of the GN is consumed with time, so in order to avoid the \"hot spot problem\", the GN is rotated when its energy drops below a pre-defined threshold.  
Moreover, the GNs are selected from the region in direct communication range of the AUV. In our protocol, the communication range of the AUV is greater than that of the AEER  
Figure 9: Association of member nodes with the GN through the SPT and communication between the GN and the AUV.  
the AEDG is greater than the AEERP, because the area for the selection of the GNs increases due to the long communication range of the AUV as compared to the AEERP, and hence, more nodes are eligible for GN selection. Moreover, the restriction on the number of member nodes with the GN enhances the stability period and reduces the burden on the GNs.
## 5 Simulation Results and Analysis
### Coverage Probability with Obstacles  
In Figure 15, we also plot the statistics for coverage probability in an environment with obstacles. A similar no. of UE was taken in an obstacle environment to evaluate the probability of covering all UE in an area with different schemes. In Figure 15, the ICBM-UAV scheme works slightly more efficiently than the distributed and centralized algorithms. Our main focus is to cover all UE in the area, either with obstacles or without.
## 2 Materials and Methods
#### 2.1.4 MAIRCA (Multi Attributive Ideal Real Comparative Analysis) Technique  
MAIRCA, introduced to the MCDM literature by [PERSON] and his colleagues, is a technique that identifies the gaps between ideal and empirical ratings [65]. By summing the gaps for each criterion, the total gap value for decision alternatives is calculated. At the end of the application, the alternative with the values closest to the ideal ratings, i.e., the one with the least total gap value, is determined as the best option. The MAIRCA technique is implemented with seven steps by using Equations (17)-(26) [66; 67].  
**Step 1. Creating the decision matrix.** The matrix X in Equation (1), which consists of m rows and n columns, is created.  
**Step 2. Normalizing the decision matrix.** The normalized matrix is derived from decision matrix (1) using Equation (18) for maximization-oriented criteria and Equation (19)for minimization-oriented criteria. The normalized matrix shown in Equation (20) is obtained.  
\[n_{ij}=\frac{x_{ij}-x_{i}^{-}}{x_{i}^{+}-x_{i}^{-}} \tag{17}\]  
\[n_{ij}=\frac{x_{ij}-x_{i}^{+}}{x_{i}^{-}-x_{i}^{+}} \tag{18}\]  
\[N=\begin{bmatrix}n_{11}&\dots&n_{1n}\\ \vdots&\ddots&\vdots\\ n_{m1}&\dots&n_{mm}\end{bmatrix} \tag{19}\]  
**Step 3. Determination of selection probabilities (\(P_{i}\)).** It is calculated using Equation (20).  
\[P_{i}=P_{i+1}=\dots=P_{m}=1/m \tag{20}\]  
**Step 4. Creation of the theoretical evaluation matrix.** The theoretical evaluation matrix is calculated using Equation (21).  
\[t_{ij}=P_{i}*w_{ij} \tag{21}\]  
**Step 5. Creation of the real evaluation matrix.** The R real evaluation matrix shown in Equation (24) is created with Equation (22).  
\[r_{ij}=t_{ij}*n_{ij} \tag{22}\]  
\[R=\begin{bmatrix}t_{11}n_{11}&\dots&t_{1n}n_{1n}\\ \vdots&\ddots&\vdots\\ t_{11}n_{m1}&\dots&t_{mn}n_{mm}\end{bmatrix} \tag{23}\]  
**Step 6. Formation of the Total Difference Matrix.** The difference matrix shown in Equation (25) is created using Equation (24).  
\[G=T-R \tag{24}\]  
\[G=\begin{bmatrix}t_{11}-r_{11}&\dots&t_{1n}-r_{1n}\\ \vdots&\ddots&\vdots\\ t_{m1}-r_{m1}&\dots&t_{mn}-r_{mm}\end{bmatrix} \tag{25}\]  
**Step 7. Calculation of criterion function values of alternatives.** The criterion function is calculated with Equation (26)  
\[Q_{i}=\sum_{j}^{n}g_{ij} \tag{26}\]  
The process is completed by sorting the \(Q_{i}\) values of the options from smallest to biggest.
## 3 Test Results and Analyses  
### Initial Strain Induced by the Static Stress  
In this study, the distribution characteristics of the static strain field on the free surface of the specimen under different static loading conditions were analyzed.  
For the first group of tests, initial static load was not applied. Hence, the initial static strain was 0 in this case.  
For the second group of tests, uniaxial static compressive stresses of \(\sigma_{y0}=\sigma_{0}=-30\) MPa were applied on both ends of the specimen along the \(y\) direction. The strain components and principal strain near the free surface induced by the static stresses were  
\[\begin{split}\varepsilon_{10}=\varepsilon_{x0}=-\frac{\mu \omega_{0}}{E}=605\times 10^{-6}\\ \varepsilon_{20}=\varepsilon_{y0}=\frac{\sigma_{0}}{E}=-2240 \times 10^{-6}\\ \varepsilon_{z0}=-\frac{\mu\omega_{0}}{E}=605\times 10^{-6}\end{split} \tag{1}\]  
where \(\varepsilon_{x0}\), \(\varepsilon_{y0}\), and \(\varepsilon_{z0}\) are the linear strains in the \(x\), \(y\), and \(z\) directions, respectively, \(\varepsilon_{10}\) and \(\varepsilon_{20}\) are the maximum and minimum principal strains on the free surface, respectively, and \(\mu\) is the Poisson's ratio. The tensile strain was positive.  
Tensile strains were observed in both \(x\) and \(z\) directions owing to the uniaxial static compressive stresses. The maximum principal strain on the free surface was tensile. Its magnitude and direction were the same as those of the linear strain in the \(x\) direction. The minimum principal strain was compressive. Its magnitude and direction were the same as those of the linear strain in the \(y\) direction.  
For the third group of tests, biaxial static compressive stresses of \(\sigma_{x0}=\sigma_{y0}=\sigma_{0}=-30\) MPa were applied on the top, bottom, left, and right sides of the specimen. The strain components and principal strain near the free surface induced by the static stresses were  
\[\begin{split}\varepsilon_{20}=\varepsilon_{x0}=\varepsilon_{y0}= \frac{(1-\mu)\sigma_{0}}{E}=-1636\times 10^{-6}\\ \varepsilon_{10}=\varepsilon_{z0}=-\frac{2\mu\omega_{0}}{E}=1210 \times 10^{-6}\end{split} \tag{2}\]  
Compressive strains were observed in both \(x\) and \(y\) directions. The maximum principal strain changed from tensile to compressive, compared to the case with the uniaxial compression. Its magnitude and direction were the same as those of the linear strain. It exhibited anisotropy characteristics. A tensile strain was observed only along the \(z\) direction, perpendicular to the free surface. Its magnitude was twice that under the uniaxial compression.
## 1 Introduction  
### Research Background  
As carbon dioxide emission control has strengthened, owing to the concerns over limited resources and environmental pollution, an interest in eco-friendly energy production technologies, such as solar photovoltaic power generation and wind turbines, has been growing [1]. Among them, the wind turbine generators that use aerodynamics have been extensively researched and developed, because they can be easily installed even in a rough terrain or the ocean.  
The main components of the existing wind turbine generators are gearboxes, generators, and frequency converters. However, it has been reported that the major failures of wind turbine generators occur in the main components, and an interest in maintenance has been increasing since one to three failures occur in each wind turbine generator every year [2; 3; 4; 5]. In recent years, the development of permanent magnet generators, using rare earth, has allowed for the development of small-sizedgenerators with a high torque density. However, the requirement for 600-800 kg rare earth per MW may significantly affect development costs. Furthermore, generators with high torque density may contribute to light weight, but the fundamental problems with maintenance cannot be addressed [6]. The maintenance costs of wind turbine generators are significantly affected by the structure complexity and marine conditions. Wind turbine generators with hydraulic transmission systems have been proposed to overcome such problems [7].  
The stability of the internal components of wind turbine generators significantly affects the power quality, and the generating voltage and frequency must be as stable as possible in order to produce high-quality power [8]. Hydraulic transmission can maintain the output at an almost constant voltage and frequency by continuously controlling the capacity of the hydraulic motor according to the variable wind speed conditions. Therefore, it is possible to produce high-quality power, even if the gearbox and frequency converter, which experience classic problems, are removed, in which case the safety circuit becomes relatively simpler [9]. In addition, as the generator can be moved to the ground, only the hydraulic pump is installed in the nacelle, making it possible to reduce the head mass by 20-40% and tower mass by up to 50%, as well as to significantly save on maintenance costs [10]. However, as hydraulic transmission has a lower overall system efficiency compared to mechanical transmission, a detailed study on power generation efficiency is required for system development [11; 12].
# Targeted Substituted-Phenol Production by Strategic Hydrogenolysis of Sugar-Cane Lignin
## 3 Results and Discussion
### Sugar-Cane Lignin (SCL) Characterization
#### 3.2.7 Catalysts Screening  
Pt/Al\({}_{2}\)O\({}_{3}\), Rh/Al\({}_{2}\)O\({}_{3}\), Ni/Al\({}_{2}\)O\({}_{3}\) and Fe/Al\({}_{2}\)O\({}_{3}\) were compared under the same reaction conditions. Overall monophonic yield for Pt-, Rh-, Ni- and Fe/alumina was 11.4%, 12.9%, 8.3% and 6.6%, respectively, without ring-hydrogenic activities. These values, up to three times higher compared to thermolysis and Al\({}_{2}\)O\({}_{3}\) tests, revealed substantial catalyst chemoselectivity. The negligible contribution from thermolysis or alumina allows  
Figure 2: Estimated yield in g/100g of phenolic compounds in solvolysis (reference) and reactions in the presence of Al\({}_{2}\)O\({}_{3}\) support. Standard deviation value: 0.1. Products identified by GC-MS: (1) 2-methoxyphenol, (2) 4-methyl-2-methoxyphenol, (2A) 4-ethylphenol, (3) 4-ethyl-2-methoxyphenol, (4) 4-propyl-2-methoxyphenol, (5) 1,2-dihydroxybenzene, (6A) 2,6-dimethoxyphenol, (7) 4- (3-hydroxypropyl)-2-methoxyphenol, (8) 4-(3-methoxypropyl)-2-methoxyphenol, (9) 4-methyl-2,6-dimethoxyphenol, (10) 4-(2-hydroxyethyl)-2,6-dimethoxyphenol, (11) 4-ethyl-2,6-dimethoxyphenol, (12) 4-propyl-2,6-dimethoxyphenol, (13) 4-(2-hydroxyethyl)-2-methoxyphenol, (14) 4-propyl-2,6-dimethoxyphenol, (15) 4-(1-hydroxy-2-methyl-pent-3-enyl)-2,6-dimethoxyphenol. Conditions: 573 K, 20 barg H\({}_{2}\), solvent, acetone/water 50:50 \(v/v\).  
the assignment of the reaction specificity to the metal. At the studied conditions, acetone was at a high enough temperature and pressure to reach the supercritical state (SC) [39], though this is not the case for \(\mathrm{{}_{sc}H_{2}O}\)[40]. Conditions to reach supercritical fluid in the reaction could be affected due to intermolecular interactions between the solvents as they were mixed (acetone/water 50:50 \(\mathrm{\SIUnitSymbolMicro v}\)/\(\mathrm{\SIUnitSymbolMicro}\)). Still, variations in dielectric constants giving higher lignin fragment solubility and stabilization were expected [40]. These aforementioned characteristics, enhancement of hydrogen diffusion, and the presence of heterogenous catalysts could be among the combined factors associated with improvement in product formation. Detailed results of each catalyst's monophenolics yield and distribution is discussed in the following sections.
## 3 Data Collected to Verify the Model
### Residential and Commercial Tenement House in Katowice, Poland  
A corner residential and commercial tenement house located in the northern frontage of the market square in Katowice was built in 1869 by the builder [PERSON]. The building has three floors aboveground and one underground, and it is covered with a flat roof. The tenement house was partly rebuilt several times, e.g., in 1900, 1911 and 1933. The last renovation, which included works conducted on the facades, took place in 2017. As a  
Figure 4: The Golden Gate in Gdansk: (**a**) eastern facade; (**b,c**) exemplary views of the basements; (**d**) a fragment of the brick wall of the basement wall.  
result of these works, the building lost its original appearance, especially when it comes to the facade decoration, and is now referred to as a styleless building (it has no decoration that would allow the building to be placed in a specific historical context). The tenement house is listed in the municipal register of monuments of the city of Katowice.  
The external and internal walls of the basements, which are made of ceramic bricks and lime mortar, were subjected to the tests (samples were taken from bricks). Same as in the Golden Gate building, the measurement points were placed at different heights above the floor level and the location of the measuring points was agreed upon with the conservation services. The view of the tenement house, an example view of the basements, and a view of a fragment of the tested wall are shown in Figure 5. Table 3 shows 17 sets of data collected during the tests for the purpose of experimental verification of the RF model.  
Table 4 summarizes the selected descriptive statistics that characterize the data collected during the experimental research that was carried out in the tenement house. The arithmetic average \(\bar{x_{i}}\) and standard deviation \(S_{x}\) were determined, and the minimum \(x_{min}\) and maximum \(x_{max}\) values are also provided.
# Robust Algorithm for Calculating the Alignment of Guide Rolls in Slab Continuous Casting Machines
## 3 Angle Conversion Algorithms
### Optimization-Based Approach
#### 3.3.2 Loss Function  
The next step in the loop is the calculation of the loss function. The loss function's main task is to return a value that is representative of the difference between the measured angles and the result of the forward function. The sum of squared errors  
\[loss_{\text{angles}}=\sum_{k=1}^{n-1}(\rho_{\text{calc},k}-\rho_{\text{meas}, k})^{2} \tag{25}\]  
fulfills this task. Here, \(\rho_{\text{calc},k}\) and \(\rho_{\text{meas},k}\) are the calculated and measured tangential angles between rolls \(k\) and \(k+1\).  
Additionally, to ensure that the assumption of an ideally positioned footroll (roll 1) is met, the term  
\[loss_{\text{footroll}}=e_{1}^{2} \tag{26}\]  
is included in the loss function. \(e_{k}\) represents the radial component of the position error of roll \(k\). Since the total value of the loss function gets minimized, \(e_{1}\) will be close to zero after the optimization.  
Lastly, a third term  
\[loss_{\text{errors}}=w_{\text{errors}}\cdot\sum_{k=1}^{n}e_{k}^{2}. \tag{27}\]  
is added to the loss function. Here, all positional roll errors are directly contributing to the loss function, which actually represents a conflict with the first term. However, this term is helpful because it prevents the algorithm from overfitting to the measured angles--which the geometric algorithms suffer from--and keeps the computed position errors within a reasonable range. This term must be carefully weighed using the factor \(w_{errors}\) such that the first and third term both contribute sensible amounts to the loss. By carefully choosing this factor, the optimization algorithm can be tuned to produce a good fit to the measured angles while simultaneously rejecting excessive measurement errors.  
The complete loss function  
\[loss=loss_{\text{angles}}+loss_{\text{footroll}}+loss_{\text{errors}} \tag{28}\]  
is the sum of the three terms described above.