esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Study on the Production of Beer from Buckwheat and Buckwheat Malt under Laboratory Conditions
Beer color is a consequence, among others, of products resulting from the Maillard reaction between FAN compounds and reducing carbohydrates [60]. In the study conducted by [PERSON] et al. (2012), a higher FAN content in 100% barley malt caused an increase in beer color due to the Maillard reaction [69]. The results obtained are in accordance with those obtained by [PERSON] et al. (2012) and [PERSON] et al. (2019) in their works [66; 70]. For example, a study realized by [PERSON] et al. (2019) concluded that doubling the amount of buckwheat malt as a brewing adjunct (from 20% to 40%) led to a further increase in beer color to approximately 5.1 EBC [66]. The bitter value of buckwheat beer decreased slightly compared to that of 100% barley malt beer. These differences may be due to evaporation losses when boiling the word with hops. Because the same hopping regime was used, the isohumulone content was similar among the four varieties. The estimated global overall influence of unmallted buckwheat and buckwheat malt on the original extract, apparent extract, alcohol content, and density of the beer is shown in Figure 3. \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Characteristic**} & \multicolumn{5}{c}{**Brewing Recipe Variant**} \\ \cline{2-6}  & **CS** & **B1** & **B2** & **B3** & **B4** \\ \hline Wort extract, \({}^{\circ}\)P & 11.00 \(\pm\) 0.20 \({}^{\text{a}}\) & 9.10 \(\pm\) 0.10 \({}^{\text{b,A}}\) & 9.20 \(\pm\) 0.10 \({}^{\text{c,A}}\) & 9.00 \(\pm\) 0.20 \({}^{\text{d,A}}\) & 8.90 \(\pm\) 0.16 \({}^{\text{e,B}}\) \\ Colour, EBC & 4.40 \(\pm\) 0.18 \({}^{\text{a}}\) & 11.80 \(\pm\) 0.14 \({}^{\text{b,A}}\) & 11.00 \(\pm\) 0.12 \({}^{\text{c,B}}\) & 11.40 \(\pm\) 0.10 \({}^{\text{d,C}}\) & 12.00 \(\pm\) 0.12 \({}^{\text{e,D}}\) \\ pH & 5.98 \(\pm\) 0.02 \({}^{\text{a}}\) & 6.04 \(\pm\) 0.04 \({}^{\text{a,A}}\) & 6.05 \(\pm\) 0.01 \({}^{\text{a,A}}\) & 6.04 \(\pm\) 0.02 \({}^{\text{a,A}}\) & 6.10 \(\pm\) 0.04 \({}^{\text{a,A}}\) \\ Bitterness value, IBU & 38.90 \(\pm\) 0.42 \({}^{\text{a}}\) & 41.90 \(\pm\) 0.38 \({}^{\text{a,A}}\) & 38.60 \(\pm\)0.54 \({}^{\text{b,B}}\) & 39.60 \(\pm\) 0.26 \({}^{\text{c,C}}\) & 41.80 \(\pm\) 0.32 \({}^{\text{d,A}}\) \\ \hline \end{tabular} Results represent mean values \(\pm\) standard deviation (SD), \(n=3\); Different letters indicate (a, b, c, d) that the result presents significant differences from the control (CS) at \(p<0.01\) level; Different letters indicate (A, B, C, D) that the result presents significant differences between the variants (B1, B2, B3, B4) at \(p<0.01\) level.  
\end{table} 
Table 7: Physico-chemical properties of wort after boiling.
## 3 Application Results
### The Results of the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF)  
The correlograms of monthly streamflow, as an input variable determined by autocorrelation, and partial autocorrelation functions for both stations of Dizaj and Tapik were illustrated for 20 months. These figures demonstrate the true value of inputs for each station. With 95% confidence, the limits of ACF and PACF are given by \(\pm\) 1.96/\(\surd\)\(n\), and were demonstrated by red color dash lines (Figure 4). The lag number could be defined when the lags were considered between these two lines [64]. As shown in Figure 4, six months of lag were considered for Dizaj station as input variables; however, for Tapik station, lag number 4 was considered as the optimum input variable.
# An Efficient Automatic Midsagittal Plane Extraction in Brain MRI
###### Abstract  
In this paper, a fully automatic and computationally efficient midsagittal plane (MSP) extraction technique in brain magnetic resonance images (MRIs) has been proposed. Automatic detection of MSP in neuroimages can significantly aid in registration of medical images, asymmetric analysis, and alignment or tilt correction (recenter and reorientation) in brain MRIs. The parameters of MSP are estimated in two steps. In the first step, symmetric features and principal component analysis (PCA)-based technique is used to vertically align the bilateral symmetric axis of the brain. In the second step, PCA is used to achieve a set of parallel lines (principal axes) from the selected two-dimensional (2-D) elliptical slices of brain MRIs, followed by a plane fitting using orthogonal regression. The developed algorithm has been tested on 157 real T\({}_{1}\)-weighted brain MRI datasets including 14 cases from the patients with brain tumors. The presented algorithm is compared with a state-of-the-art approach based on bilateral symmetry maximization. Experimental results revealed that the proposed algorithm is fast (<1.04 s per MRI volume) and exhibits superior performance in terms of accuracy and precision (a mean \(z\)-distance of 0.336 voxels and a mean angle difference of 0.06).  
medical image registration; image alignment in medical images; misalignment correction in MRI; midsagittal plane extraction; symmetry detection; PCA +
Footnote †: journal: Medical Image Analysis
## 4 Experimental Setup
### Performance Metrics  
Multi-objective optimization (MOO) algorithms are designed to solve problems with multiple conflicting objectives, where the goal is to find a set of solutions that optimally balance these objectives. Evaluating the performance of MOO algorithms is crucial for assessing their effectiveness in finding high-quality solutions across the entire Pareto front, the set of non-dominated solutions.  
In this paper, we take the Hypervolume (HV), Spacing (SP), and Pure Diversity (PD) metrics together as evaluation criteria to evaluate the performance of algorithms. Choosing the HV, SP, and PD metrics together provides a comprehensive assessment of the algorithm's performance across different aspects:  
HV assesses both convergence and overall spread, SP ensures even distribution of solutions, and PD guarantees exploration of the entire search space. These metrics collectively ensure that the algorithm is not only converging to the optimal solutions but also maintaining a diverse set of solutions. This balance is critical in multi-objective optimization as it provides a set of well-distributed, high-quality solutions for decision-makers.  
By considering all three metrics, we can gain a complete understanding of the algorithm's strengths and weaknesses in solving multi-objective optimization problems, leading to more informed decisions in algorithm selection and development. Here, is a detailed description of them:
## 2 Materials and Methods  
### Powder and Substrates  
For the experiments, AP&C-GE Additive (Saint Eustache, QC, Canada) plasma atomized IN718 powder was used. The chemical composition of the powder conforms to the ASTM B637 (grade UNS N07718) standard [28]. Figure 1 is an SEM image of the powder. Because of the plasma atomization process, the particles are extremely round and smooth. The nominal powder granulometry ranges from 25 to 45 \(\upmu\)m. The particle size distribution of the utilized powder was measured using a Morphologi 4 optical particle size analyzer from Malvern Panalytical Ltd. (Malvern, UK). Figure 2 depicts the outcome. Stainless steel AISI 304 substrates with dimensions of 400 \(\times\) 400 \(\times\) 30 mm were used for the experiments.  
Figure 1: SEM image of the used IN718 powder.
## 4 Result  
### Accuracy Evaluation of TVMPDI  
The Pearson correlation coefficient and scatter plot of the TVMPDI and SPEI in the growing season are shown in Figure 2. In general, the correlation coefficient between the TVMPDI and SPEI was above 0.5 from April to October (p < 0.01). Among them, the correlation coefficients of April, May, September and October were all between 0.5 and 0.6, which indicates a moderate correlation. The correlation coefficients were between 0.6 and 0.7 in June, July and August, reaching a high correlation (p < 0.005).  
\begin{table}
\begin{tabular}{c c c} \hline
**Stvmpdi** & **Z** & **Trend of TVMPDI** \\ \hline \(\geq\)0.005 & \(\geq\)1.96 & Dry \\ \(\geq\)0.005 & \(-\)1.96–1.96 & Slight dry \\ \(-\)0.005–0.005 & \(-\)1.96–1.96 & Stable invariance \\ \(\leq\)\(-\)0.005 & \(-\)1.96–1.96 & Slight wet \\ \(\leq\)\(-\)0.005 & \(\leq\)\(-\)1.96 & Wet \\ \hline \end{tabular}
\end{table}
Table 4: Statistics of the TVMPDI trend.  
According to the theory behind each dryness indicator, the lower the VHI, SDCI, SM, TVPDI, and TVMPDI values are, the drier the area is. Therefore, the monitoring findings of the TVMPDI are consistent with these indices when VHI, SDCI, SMCI, and TVPDI are positively correlated with the TVMPDI; otherwise, the monitoring results are inconsistent.  
The spatial distribution of the time-series correlation between the TVMPDI and remote sensing drought indices (VHI, SDCI, SMCI and TVPDI) are shown in Figure 3. The correlations of the TVMPDI with VHI and SDCI were above 0.8 (Figure 3a,b, respectively), which reached the level of extreme correlation, while the correlations of the TVMPDI with SMCI and TVPDI are different from those above. The correlation was slightly lower between the TVMPDI and TVPDI, especially in the western and central regions of the study area. In these areas the correlation remains around 0.6 (Figure 3d). Additionally, the correlation between the TVMPDI and SMCI in this region was only 0.3-0.6 (Figure 3c). These weakly correlated areas accounted for about 28%.
# A Novel FFT_YOLOX Model for Underwater Precious Marine Product Detection
## 1 Introduction
To address this issue, a unique backbone is proposed by replacing the 3 \(\times\) 3 kernel in the original backbone of YOLOX with a conceptually simple, and computationally efficient, block, named an FFT_filter. A 2D discrete fast Fourier transform (FFT) [33] has been determined to be equivalent to a circular convolution, of which its size is the same as the feature map. Therefore, it can capture the global feature within the input feature map. The basic idea behind this architecture is to increase the receptive field of the FFT by learning the global interactions of spatial location. In this study, three species of precious marine products, sea cucumber, sea urchin and scallop, were obtained as the research objects. A novel detection model based on a modified YOLOX is proposed. The contributions of this work are: (1) a unique FFT_filter is presented to replace the standard 3 \(\times\) 3 kernel in the original backbone of YOLOX to capture global features and enlarge the receptive field; (2) a unified and modified YOLOX model, named FFT_YOLOX, is proposed for precious marine products detection in real underwater situation. Extensive experimental results demonstrate that the proposed detection method can outperform the original YOLOX model with fewer parameters. The remainder of this paper is organized as follows: Section 2 presents an overview of YOLOX architecture and the 2D FFT method which are the basis of the proposed algorithm. Then, the FFT_filter is introduced and the main framework proposed. Section 3 presents the experiment results and discussion. Finally, the concluding remarks are illustrated in Section 4.
## 1 Introduction
[13] presented a theoretical model for the shear strain transfer of the embedded FBG sensors, based on the assumption that the strain of the host material was the same as the strain of the center of the FBG, but they ignored the stress coupling effect of the adhesive layer. [PERSON] et al. [14] considered the viscoelasticity of the adhesive layer and proposed a time-dependent equation of strain transfer. Their research results also had an important impact on this paper. In this paper, a new type of FBG dynamometry bolt is designed. The difference with the FBG sensor designed by predecessors is that the FBG sensor studied in this paper adds a desensitization device. Taking the ground pressure monitoring of return airway 81303 in the Yangquan No. 1 coal mine as the engineering background, we establish the FBG online monitoring system for the ground pressure, and focus on the transmission mechanism and sensing characteristics of FBG dynamometry bolt. Furthermore, the theoretical analysis previously mentioned is validated by a uniaxial tensile test and a long-term constant load tensile test. Finally, it is confirmed that the FBG dynamometry bolt can replace the traditional resistance dynamometry bolt to monitor the stress of the bolt body by four characteristic indexes: the correlation coefficient, measuring range, linearity, and sensitivity.
## 5 Settlement Prediction
### Probabilistic Analysis  
The numerical model presented above can reasonably reproduce the observed settlement in the large-scale load tests. However, this section also shows the model's capacity to provide a settlement as a statistic variable, so that the confidence interval can be obtained.  
The following procedure is proposed:  
Figure 14: Model analytical outputs pile and soil settlements.  
Figure 13: Model analytical outputs for the SPT average compared to a large-scale load test.  
1. SPT strength profiles are generated which meet the expected average and standard deviation as it has been discussed in previous sections. In this way, statistical uncertainty due to the randomness of the soil profile is introduced.
2. For each of these profiles, it is calculated the settlement for a given load. As a result, a settlement distribution function is obtained.
3. Finally, the uncertainty of the model prediction is assessed on this distribution. If large-scale load tests are available, as in this case, comparison allows for the verification of the procedure.  
The density distribution function is calculated by entering in the model the synthetic SPT profiles generated according to its distribution function.  
The rest of the parameters remain constant to the values entered for the adjustment of the settlement made in the previous section. It is therefore a deterministic model that is fed by a statistic variable. The result is a statistic variable, which in this case is the settlement density function.  
Figure 15 shows this settlement function, which is compared to the large-scale test available in this particular case.
# Unraveling the Impact of Class Imbalance on Deep-Learning Models for Medical Image Classification
## 2 Materials and Methods
### Generation of Classification Models  
After preprocessing the image dataset, as shown in Figure 8, experiments were conducted using different packages to primarily obtain two types of models: classification models with artificial neural networks and classification models with deep neural networks. These models were generated for both balanced and imbalanced datasets, dividing the data in a 70-30 ratio for training and testing, respectively. Among the selected packages to build the models were Neuralnet, RSNNS, and Keras for artificial neural networks and Deepnet and Keras for deep neural networks. Although preliminary tests were conducted with the nnet package, these proved unsuccessful as the package could not handle the dataset used. Regarding the topology of the models (see Section 2.2), they were developed to have 1, 2, 3, and 4 layers.  
The model generation involves an exploration routine to determine the number of neurons to be set in each layer, consisting of two parts:  
* Coarse Exploration: The exploration spans from 10 to 500 neurons, increasing by increments of 10 for the first 100, then by 50. Its aim is to identify the neuron count yielding the highest accuracy in both training and testing. Each increment in neuron count is trained for 100 epochs. Data are collected epoch by epoch for single-layer models, and for models with 2, 3, and 4 layers, they are collected at the end of the 100 epochs. At the end of training, the best model per epoch is saved along with the collected data.
* Fine Exploration: The data collected during the initial exploration are reviewed to select the best-performing model characterized by high accuracy and a specific number of neurons. This model undergoes a training and testing process, consisting of 1000 and 300 epochs, respectively, aimed at further improving its final accuracy.  
Figure 9 displays the flowchart guiding the execution of this experiment.  
Figure 8: Image preprocessing.
# Intelligent Total Transportation Management System for Future Smart Cities
## 4 Discussion on the Possible Use and Preliminary Evaluation of the Concept
### Action Made in Concept Implementation
A smart, intelligent total transportation management system coupled with drones was introduced and investigated [72, 73]. * Urban transportation was classified depending on the transportation means and management techniques being applied. Such a system includes users, operators, service providers, transportation management, infrastructure, nature/builtenvironment, regulations, competence and knowledge centers, supporting sub-systems, and passive and active interactions with other essential systems. * Obstacles were modeled as cylinders used in the collision avoidance process in the path planning of drone formation operating in smart cities. * A methodology to determine and calculate UAVs' (unmanned aerial vehicles) landing stages was developed and investigated. * A cube of a transportation system was introduced and used to identify the urban total transportation system's classification. * A method to optimize the total transportation impact was developed, which can be applied to optimize the energy used by the vehicles over their routes. With the above comprehensive discussion and experimental results, we could conclude that the proposed system introduced in this paper has numerous advantages compared to the present concepts:  
* it deals with transportation as a single and total system (including the legacy control, vehicles, infrastructure, connecting supply chains),
* it is multi-layered (dealing separately with the cooperative, prioritized vehicles), and
* it introduces new solutions (on the system level as active/proactive control and the sub-system levels like smart parking, harmonization of the transportation means on the use of short-term prediction based on transportation monitoring).
# Superimposed Perfect Binary Array-Aided Channel Estimation for OTFS Systems
## 2 System Model
For the fractional Doppler scenario, the proposed method is still valid if the correlation interval on the Doppler axis is increased. Considering the effects of the multipath time-varying channel conditions, the received signal can be expressed as  
\[r(t)=\iint h(\tau,v)e^{j2\pi v(t-\tau)}s(t-\tau)d\tau dv+n(t), \tag{6}\]  
where \(n(t)\) is additive Gaussian white noise distributed as \(\mathcal{CN}\big{(}0,\sigma_{n}^{2}\big{)}\). At the receiver side, the received symbols \(Y[n,m]\) in the TF domain are obtained by applying the Wigner transform to \(r(t)\) with the received pulse \(g_{\mathrm{rx}}(t)\). Finally, by performing the symplectic finite Fourier transform (SFFT) on \(Y[n,m]\), the received symbols are converted back to the DD domain, where they are expressed as \(y[k,l]\). According to [11], the relationship between the transmitted and received symbols in the DD domain is  
\[y[k,l]=\sum_{i=1}^{P}h_{i}e^{-j2\pi\frac{lk_{i}}{MN}}\alpha_{i}[k,l]x[(k-k_{i} )_{N^{\prime}},(l-l_{i})_{M}]+\omega[k,l], \tag{7}\]  
where \(\omega[k,l]\) denotes the noise in the DD domain, distributed as \(\mathcal{CN}\big{(}0,\sigma_{\omega}^{2}\big{)}\), and the subscript \((\cdot)_{N}\) denotes the modulo operation with divisor \(N\). \(\alpha_{i}[k,l]=1\) when the transmitted and received pulses are ideal, meaning that they satisfy the biorthogonality conditions [11]. Figure 2: The transmitted data frames of (**a**) SP-OTFS systems and (**b**) SPBA-OTFS systems. Otherwise, affected by the imperfect biorthogonality of the rectangular pulses, \(\alpha_{i}[k,l]\) is expressed as  
\[\alpha_{i}[k,l]=\left\{ \begin{array}{l}e^{j2\pi\frac{l_{i}}{MN}},\ l_{i}\leq l<M,\\ \frac{N-1}{N}e^{j2\pi\left(\frac{l_{i}}{MN}-\frac{(l-k_{i})}{N}\right)},\ 0<l<l_{i}. \end{array} \right. \tag{8}\]  
By substituting (1) into (7) and re-expressing the equation in matrix form, the received symbol matrix of an SPBA-OTFS system is given by  
\[\mathbf{Y}=\sum_{i=1}^{P}h_{i}e^{-j2\pi\frac{l_{i}}{MN}}\mathbf{A}_{i}\circ \left(\mathbf{D}_{k_{i},l_{i}}+\mathbf{P}_{k_{i},l_{i}}\right)+\mathbf{W}, \tag{9}\]  
where \(\mathbf{Y}\in\mathbb{C}^{N\times M}\), \(\mathbf{W}\in\mathbb{C}^{N\times M}\), \(\mathbf{A}_{i}\in\mathbb{C}^{N\times M}\), \(\mathbf{D}_{k_{i},l_{i}}\in\mathbb{C}^{N\times M}\) and \(\mathbf{P}_{k_{i},l_{i}}\in\mathbb{C}^{N\times M}\) are the matrix forms of \(y[k,l]\), \(\omega[k,l]\), \(a_{i}[k,l]\), \(x_{d}[(k-k_{i})_{N^{\prime}},(l-l_{i})_{M}]\) and \(x_{p}[(k-k_{i})_{N^{\prime}},(l-l_{i})_{M}]\), respectively. For two matrices \(\mathbf{A}\) and \(\mathbf{B}\), the operation \(\mathbf{A}\circ\mathbf{B}\) denotes their Hadamard product.
# Development of a Novel Variable-Diameter Wheel
## 2 Wheel Configuration and Transmission System
### Transmission System  
The planetary gear train of a variable-diameter wheel can transmit two power sources that are used to enable the wheel's rolling and changes in diameter. The three ring gears in the transmission system and the three wheel hubs are assembled under the corresponding interference fits. The three wheel hubs (left, middle, and right) transfer the rotations from the transmission system to the spring spokes. The electric motor \(M_{1}\) adjusts the wheel diameter, whereas the electric motor \(M_{2}\) provides the energy for the wheel rotation. Figure 3 shows that \(M_{2}\) drives the rotation of the three ring gears with the same speed when \(M_{1}\) is locked (\(M_{1}\)'s worm reducer has a self-locking ability). Thus, the wheel with an invariant diameter rotates on the terrain. If \(M_{2}\) rotates, then the wheel diameter adjusts.  
Therefore, to match the two power sources, the transmission system has two routes.  
First, if \(M_{1}\) does not rotate (i.e., the left sun gear \(a_{1}\) is locked), then the three ring gears have the same rotating speed. The route can be elaborated as follows.  
Electric motor \((M_{2})\)\(\rightarrow\) right (\(H_{2}\)) and left planet carriers \((H_{1})\)\(\rightarrow\) left (\(c_{1}\)), middle (\(c_{2}\)), and right planet gears \((c_{3})\)\(\rightarrow\) left (\(b_{1}\)), middle (\(b_{2}\)), and right ring gears \((b_{3})\).  
Second, if \(M_{1}\) rotates, the three ring gears rotate at different speeds. Note that the two ring gears on both sides have the same speed.  
(1) \(M_{1}\)\(\rightarrow\) left sun gear \((a_{1})\)\(\rightarrow\) middle planet gear \((c_{2})\)\(\rightarrow\) middle ring gear \((b_{2})\).  
(2) Electric motor \((M_{2})\)\(\rightarrow\) right planet carrier \((H_{2})\) and left planet carrier \((H_{1})\)\(\rightarrow\) left (\(c_{1}\)), middle (\(c_{2}\)), and right planet gears \((c_{3})\)\(\rightarrow\) left (\(b_{1}\)), middle (\(b_{2}\)), and right ring gears \((b_{3})\).  
In summary, on the basis of the power-transmitting routes of the transmission system, the three hubs rotate at different speeds. Consequently, the relative rotations between the hubs cause deflections of the helical torsion springs, from where the wheel feet gain radial mobility.