esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Plant protein concentrates are used to enhance the nutritional quality of bread and to respond to the demand of consumers with respect to increased protein intake. In the present study, bread samples were produced using pea protein concentrate (PP) and soy protein concentrate (SP) substituting wheat flour by 5%, 10%, and 15%. The protein levels were between 1.2- and 1.7-fold (PP) and 1.1- and 1.3-fold (SP) higher than the control bread. The incorporation of 10% and 15% PP allowed for the achievement of a \"high protein\" claim. Water absorption was correlated with the protein contents of the breads (\(r=0.9441\)). The decrease in bread volume was higher for the PP than SP incorporations, and it was highly negatively correlated with the protein content (\(r=-0.9356\)). Soy breads had a softer crumb than pea breads. The total change in crumb colour was higher in the PP than SP breads. The soy breads had an overall acceptability between 6.3 and 6.8, which did not differ (\(p>0.05\)) from the control. PP breads were statistically less liked (\(p<0.05\)). The results underlined that the choice of the type and amount of protein concentrates influenced the bread properties differently.  
s 2021 11 436 1102 11 436 1102  
inhibitors [5]. The health benefits of peas are represented by the level of protein, starch, fibre, vitamins, minerals, and phytochemical components. It has been demonstrated that pea ingredients can influence the glycaemic response, insulin resistance, and cardiovascular and gastrointestinal health [6]. Research has been done for producing bread with a 10% protein content through the replacement of wheat flour with 30% raw, germinated, and toasted pea flour [7]. In addition, in bread formulations, pea protein isolate has been used to substitute 15% of the wheat flour, and the effect on the dough properties and bread quality have been analysed [8].  
[PERSON] et al. [9] showed that different types of soy products used in bread manufacturing prevented the formation of the gluten, and reduced dough strength and gas retention ability of the dough. The addition of soy protein to 30% impacted the dough rheology and the quality of the end products--the specific bread volume decreased and the hardness increased [10]. On the other hand, [PERSON] et al. [11] proved that soy flour at a 20% substitution level to wheat flour used as a protein supplement in bread manufacturing did not negatively affect the flavour and texture properties.  
Studies on the supplementation of bread with 5% pea protein isolate showed little adverse effects on the bread volume and physical attributes of the crumb, and had a good overall sensory acceptance. Higher amounts of soy protein concentrate and pea isolate in spelt flour (20% and 10%, respectively) determined the decrease in the loaf volume [12].  
The aim of this study was to obtain and analyse the quality of high-protein breads with the addition of plant-based proteins, which can be a \"source\" of protein or can be claimed to be \"rich\" in proteins, based on EFSA (European Food Safety Authority) recommendations.
## 3 Methodology
### Machine Learning Model Development
#### 3.4.3 Training Process  
The model was trained using the Adam optimizer (initial learning rate of 0.001), with mean squared error (MSE) chosen as the loss function:  
\[MSE=\frac{1}{n}{\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2}} \tag{1}\]  
Figure 6: Detailed schematic of neural network layer connectivity.  
where \(y_{i}\) denotes the true value and \(\hat{y}_{i}\) denotes the predicted value. The core steps of the training procedure are outlined below:  
1. Data Partitioning - Seventy percent of the data were assigned for training, 15% for validation, and the final 15% for testing.
2. Batch Training - Mini-batches of size 64 were employed for 100 epochs, facilitating steady gradient updates and helping to deter overfitting.
3. Early Stopping - Training was halted once validation loss failed to improve for 10 consecutive epochs, a safeguard against excessive overfitting.
4. Adaptive Learning Rate - The learning rate was halved whenever the validation loss showed no progress for 5 straight epochs, helping stabilize training.  
Figure 7 plots both training and validation loss over 100 epochs. Initially, both losses decrease rapidly, reflecting significant learning in the early stages. The training loss stabilizes at 0.031, while the validation loss ends at 0.160, showing some fluctuation indicative of partial overfitting.  
The convergence point around epoch 50 (marked by the green dashed line) indicates diminishing returns from further training, as validation loss becomes more volatile while training loss continues to decline. This suggests overfitting, highlighting the need for techniques such as increased regularization or additional hyperparameter tuning to enhance generalization.  
Figure 8 shows a parallel coordinates plot where each blue line represents a hyperparameter trial--darker regions appear where multiple trials share similar parameter values. The red line beneath tracks the best mean-squared error (MSE) found so far, illustrating how performance steadily improves until converging near trial 40.  
Figure 7: Training and validation loss over epochs.
# MIMO Channel Estimation in an SDR Platform for Evaluation of D&F Relay Nodes
## 1 Introduction
Nevertheless, the associated implementation and development costs hinder their usage by research communities. In [14], a Wireless Open Access Research Platform (WARP) that uses the A&F relaying scheme has been presented, based on the OFDM technique and implementing the transmit diversity scheme. Nonetheless, the drawback of this approach is that, due to the absence of an external clock interface in the hardware platform, multi-relay synchronization is difficult. Moreover, it is very expensive. The best solution, considering these problems, is using Software Defined Radio (SDR) that is performed using a general purpose processor. This is more suitable for the implementation of signal processing, through PHY and MAC layer functions. USRP (Universal Software Radio Peripheral) and GNU Radio are the most widely used SDR platforms at present [15,16]. On the other hand, this is only the first step to the implementation of channel estimation and equalization techniques in the SDR platform developed. We are working to upgrade it to be compatible with 5G technology, which will allow us to implement the NOMA technique [17] and new algorithms for the channel estimation of the pilot symbols and interpolation of data symbols [18]. Furthermore, we will consider a turbo operation between the equalization and channel estimation steps [19]. In this paper, we propose the use of USRP and MatlabTM to implement a co-operative testbed. In [20], the feasibility of the implementation of D&F RNs through a platform of these characteristics was shown. Nevertheless, it did not allow the implementation of determined functionalities. Several key issues and significant improvements have been introduced in [21]. Non-LOS scenarios and downlink performance evaluation, with several modulation schemes between eNB and in-band D&F RN, have been analyzed and implemented. Furthermore, two pieces of hardware equipment (NI-USRP-2944R) with better performance are incorporated into the core network. The main contributions of this paper are related to the core network of the platform. We focus on MIMO wireless channel estimation, in which the LS technique is employed to estimate the Channel Frequency Response with respect to pilot symbols. Furthermore, BCI, BLI, and BNNI interpolation methods are developed for channel estimation of data symbols. MMSE and ZF equalizers are also considered. The rest of the paper is organized as follows: The equipment and functionalities that comprise the SDR Platform developed in [21] are presented in Section 2. The MIMO system model in the core network is explained in Section 3. In Section 4, the mathematical models and algorithms for the channel estimation, interpolation, and equalization processes are formulated. Implementation of Decode-and-Forward Relay Nodes using SDR is described in Section 6. Measurement scenarios, results, and discussions regarding the usefulness of the Platform to test the implemented techniques are given in Section 6. Finally, our conclusions are presented in Section 7.
# Practical Work of Three-Phased Induction Motor Modeling in a Classical Spreadsheet Software
## 5 Practice
### Exercise 6  
We consider that the mechanical characteristic \(T_{u}=f(\Omega_{r})\) of an induction motor, when powered by a 400 V/50 Hz three-phase network, is rectilinear in its useful part and that we have \(T_{u}=0\) for \(\Omega_{r}=\Omega_{s}\).  
1. Determine the expression of the useful torque \(T_{u}\) as a function of the rotational speed \(\Omega_{r}\), with \(T_{u}\) in N\(\cdot\)m and \(\Omega_{r}\) in rpm, since for \(\Omega_{r}=1450\) rpm and \(T_{u}=50\) N\(\cdot\)m. The load driven by the motor has a resistive torque \(T_{load}\) proportional to the rotational speed of the axis \(\Omega_{r}\) for \(\Omega_{r}=1000\) rpm and \(T_{load}=35\) N\(\cdot\)m.
2. Give the expression of \(T_{load}\) as a function of \(\Omega_{r}\).
3. Determine the operation point of the motor-load set. Specify both torque and rotational speed.
4. At this operation point, the following measures have been taken: the absorbed power \(P_{abs}=8.6\) kW and the RMS value of the intensity in the line \(I_{line}=14.5\) A. Calculate the power factor of the motor \(\cos\varphi\) and its efficiency \(\eta\).  
_Solutions:_  
1. Since we know two points of the line expressing \(T_{u}\) in terms of \(\Omega_{r}\) (1500 rpm, 0 N\(\cdot\)m) and (1450 rpm, 50 N\(\cdot\)m), we can calculate both the slope \(m\) and the ordinate at the origin \(p\) of the line \(T_{u}=m\cdot\Omega_{r}+p\). \(m=\frac{0-50}{1500-1450}=-1\) N\(\cdot\)m/rpm since \(0=-1.1500+p\), \(p=1500\). Thus, \(T_{u}=1500-\Omega_{r}\).
2. Similarly, we know two points of the desired line, (0 rpm, 0 N\(\cdot\)m) and (1000 rpm, 35 N\(\cdot\)m). Then, \(T_{load}=m\cdot\Omega_{r}\), with \(m=\frac{35}{1000}=0.035\).
3. The operating point of the motor-load set is then given by the intersection of the two lines found in Questions 1 and 2. \(T_{u}=T_{load}\) when \(1500-\Omega_{r}=0.035\cdot\Omega_{r}\). Then, \(\Omega_{r}=\frac{1500}{1+0.035}=1449.28\) rpm and \(T_{u}=T_{load}=0.035\cdot 1449.28=50.7\) N\(\cdot\)m. The visualization of this solution is visible in Figure 9.  
4. The power factor \(\cos\varphi\) of the motor at this operating point is given by \(\cos\varphi=\frac{P}{\sqrt{3}\cdot\text{{\it{U}}}\cdot\text{{\it{I}}}\cdot\text {{\it{1}}}}=\frac{8600}{\sqrt{3}\cdot 400\cdot 14.5}=0.856\). Similarly, the efficiency of the motor is the ratio of the useful power \(P_{u}\) divided by the absorbed power \(P_{abs}\). With the characteristics of the operating point, we have \(P_{u}=T_{u}\cdot\Omega_{r}=50.7\cdot(1449,28\cdot\frac{2.77}{60})=7694.65\) W, and \(\eta=\frac{7694.65}{8600}=89.5\%\).
## 4 Experiments and Results
### Energy Consumption and Supplementary of Sink Nodes with Different Sink Rates  
In this experiment, we discuss how to deploy enough chargers with different sink rates. In the experiment, an average of four sink nodes (SN) were deployed in a 100 \(\times\) 100 unit experimental environment, with coordinates SN_1 (25, 25), SN_2 (25, 75), SN_3 (75, 25), SN_4 (75, 75). As all sensors need to send data to the sink node, it is assumed that the radius of the received data of the sink is 36 units in the experiment to cover the entire experimental environment. Every time a sensor transmits data to the sink node, it increases the sink energy consumption by 0.6 units. Each charger can charge 1 unit of energy to the sink. The total number of sensors deployed in the experimental environment was 20. This experiment is based on being able to replenish all the energy needed by the sink nodes, and deploying enough chargers around the sink node to replenish the power. Figure 13 shows the energy consumption and supplementary of sink nodes with different sink rates.
## 2 Materials and Methods
### Frequency-Guided Sequential Demodulation
The optimizer finds the best values of the intermediate frequencies \(\hat{\omega}_{xa}(u,v)\) and \(\hat{\omega}_{ya}(u,v)\), that are fed to the sign-determining block of the FSD. At the algorithm's end, the wrapped phase \(\hat{\varphi}_{w}(x,y)\) is obtained, which depends on the method used: FSD, FSD-HSO or FSD-PSO. To obtain the unwrapped phase \(\hat{\varphi}_{u}(x,y)\), standard unwrapping algorithms can be used. The unwrapping algorithm used in this work was least squares [30]. The operation of the PSO optimizer within the optimization block of Figure 1 is detailed below. The position of a set of particles is randomly initialized. Then, it is necessary to  
Figure 1: Flowchart of the FSD algorithm optimized with HSO and PSO algorithms. iteratively update the \((x,y)\) positions of the particles in the swarm to find the best positions through a cost function; that is:  
\[cost(u,v)=\big{[}I_{n}(u,v)-\cos\big{(}w_{x}(u-x)+w_{y}(v-y)+\hat{\varphi}_{wa}( x,y)\big{)}\big{]}^{2}, \tag{8}\]  
where \(w_{x}\) and \(w_{y}\) are the current positions in \((x,y)\), respectively. In addition, a random update of the particles is carried out, iteratively comparing the current position versus the best position within the swarm. At the end of the interactions, the best \((x,y)\) positions found will correspond to the intermediate frequencies \(\hat{\omega}_{xa}\) and \(\hat{\omega}_{ya}\), respectively. The PSO algorithm applied to FSD is as follows [18; 19]:  
1. Inputs: normalized interferogram \(I_{n}\) and local enveloped phase \(\hat{\varphi}_{wa}\). 2. The PSO parameters are established. 3. The position of the particles is initialized randomly. 1. The best-known position of the particle swarm is updated by means of the cost function. 4. The speed of the particles is initialized, randomly. 1. The speed of the particles is updated based on their best position with respect to the current position; 2. The best position within the swarm is updated. 5. The intermediate frequencies \(\hat{\omega}_{xa}\) and \(\hat{\omega}_{ya}\) are found based on the best positions found within the swarm, during the total number of valid iterations. The implementation details of the algorithm PSO were particle number 80, inertia weight 0.5, cognitive coefficient 0.3, social coefficient 0.2 and iterations 20. Similarly, for the HSO algorithm were harmony search solutions 100, harmony memory considering rate 0.9, pitch adjusting rate 0.2 and iterations 100. In both algorithms, the following parameters will be the same: dimensions of problem 2, lower and upper limits [\(-\)1, 1], neighborhood region for energy function 11 and optimal cost value 0.25, as well as the iterations mentioned before were applied, as long as the cost function was less than 2.5. The operation of the FSD-HSO algorithm can be consulted in reference [1].
# Pressure-Temperature Phase Diagram of Ta-H System up to 9 GPa and 600 \({}^{\circ}\)C
## 1 Introduction  
Hydrides have a variety of functionalities, including high-temperature superconductivity [1, 2, 3], fast ionic conductivity [4, 5], and H\({}^{-}\) conduction [6, 7]. Recent progress in synthesis techniques, theoretical calculations, and fundamental studies of hydrides have enabled these functionalities to be realized [8]. Applying high pressure is an essential technique for investigating functional hydrides, because novel hydrides can be synthesized under high pressure due to the extremely high chemical potential of hydrogen. On the other hand, hydrogenation reactions of pure metals have received little attention. However, such studies provide us with useful information for the high-pressure synthesis of novel functional hydrides.  
Recently, the hydrogenation reaction of tantalum (Ta) monohydride (TaH) [9] has been reported to form Ta dihydride (TaH-2) [10]. The formation of TaH-2, in which Ta atoms occupy the hcp lattice's nodes (hereafter, referred to as hcp TaH-2), has been predicted theoretically [11]. [PERSON] et al. confirmed that hydrogenation Ta above 5 GPa at room temperature could synthesize theoretically predicted hcp TaH-2. During depressurization at room temperature, the formed dihydride decomposed into monohydride at 2.2 GPa. The crystal structure of the monohydride has been reported to be a face-centered orthorhombic one in which lattice constant \(a\) is comparable to \(b\) and \(c\) is nearly equal to \(a/\sqrt{2}\). The face-centered orthorhombic phase cannot be distinguished from the bcc structure by the measurement system used in the present study, which we referred to as distorted bcc TaH. They also reported that hcp TaH-2 could be recovered at ambient pressure by quenching at liquid-N2 temperature and depressurization. Based on the weight change during hydrogen desorption at ambient pressure, the hydrogen concentration was estimated to beH/M \(\sim\) 2.2. Hydrogen-induced volume expansion, d\(V\)/d\(x\), was reported to be 2.4 A\({}^{3}\)/H atom, where \(V\) is the unit cell volume divided by the number of atoms in the unit cell (unit cell volume/formula unit (f. u.) and \(x\) is the hydrogen concentration for TaH\({}_{x}\). Although the hydrogenation behavior of Ta has been carefully investigated under high pressure at room temperature in ref [10], no results on the high-pressure and high-temperature hydrogenation of Ta have been reported to date.  
In the present study, we determined the pressure-temperature (\(p\)-\(T\)) phase diagram of the Ta-H system using in situ synchrotron radiation X-ray diffraction measurement. At room temperature, the obtained phase boundary between distorted bcc TaH and hcp TaH\({}_{-2}\) was consistent with the previously reported transition pressure [10]. However, no novel hydride phase was observed below 9 GPa and 900 \({}^{\circ}\)C.
# A Novel Base-Station Selection Strategy for Cellular Vehicle-to-Everything (C-V2X) Communications
## 1 Introduction
In Section 6, the Markov decision policy topology is presented. The simulation results are shown and discussed in Section 7. Finally, we present our conclusions and outline future work.
###### Abstract  
To mitigate the challenges posed by transient oscillations and steady-state deviations in the traditional virtual synchronous generator (TVSG) that is subjected to active power and grid frequency disturbances, a VSG control strategy based on Transient Damping Compensation and Virtual Inertia Adaptation is presented. Initially, a closed-loop small-signal model for the grid-connected active power loop (APL) of the TVSG is constructed, which highlights the contradiction between the dynamic and static characteristics of TVSG output power through the analysis of root locus distribution trends. Secondly, a VSG control strategy based on Transient Damping Compensation (TDC) is proposed. The influence of APL system parameters introduced by TDC on system stability is qualitatively analyzed based on pole distribution trends and frequency response, and a comprehensive parameter design scheme is presented. In addition, based on the TDC algorithm, an improved virtual inertia adaptive strategy utilizing the Inverse Square Root Unit (ISRU) approach is designed, and the tuning range of parameters is provided. Finally, simulations and experiments verify that the proposed strategy exhibits superior active response performance and transient oscillation suppression capabilities, effectively eliminating active steady-state deviations caused by frequency disturbances in the power grid.  
virtual synchronous generator; transient damping compensation; steady-state deviation; virtual inertia; adaptive control; root locus analysis Article
## 3 Simulation Analysis of FSS
### Effect of Ambient Temperature Change on Support Pressure under Different Air Contents  
The support pressures of the upper and lower chambers of the support cylinder under different working pressures are affected by changes in the ambient temperature and air content, as shown in Figure 6. From the simulation results, when the working pressure of the support cylinder is higher, the variation in ambient temperature and air content has a greater influence on the working pressure error of the upper and lower chambers. When the absolute working pressure of the support cylinder is 1.0 MPa, the maximum change in the upper chamber is 0.0075 MPa, and the maximum change in the lower chamber is 0.0074 MPa. When the absolute working pressure of the support cylinder is 0 MPa, the change in the upper chamber is 0.00125 MPa, and the affected change in the lower chamber is 0.00112 MPa. Therefore, from the standpoint of the influence of the working pressure of the support cylinder alone, the degree of influence on the support rigidity is low when the working pressure of the support cylinder is low.  
Figure 5: Effect of ambient temperature change on support height at different air contents.
## 4 Efficient Band Ordering
### Bhattacharyya Distance-Based Band Ordering  
We have also considered the Bhattacharyya distance [23]. For two random distributions, it is defined by probability functions \(p_{1}(x)\), \(p_{2}(x)\) as:  
\[B\!\!=-\ln(\int\sqrt{p_{1}(x)\,p_{2}(x)}dx)\]  
When both the distributions are univariate and are assumed to follow normal distribution, the Bhattacharyya distance is defined as:\[B =\frac{1}{8}\big{(}\mu_{1}-\mu_{2}\big{)}^{2}\frac{2}{\sigma_{1}^{2}+ \sigma_{2}^{2}}+\frac{1}{2}\ln\!\left(\frac{\sigma_{1}^{2}+\sigma_{2}^{2}}{2 \sigma_{1}\sigma_{2}}\right)\]  
where \(\mu_{1}\), \(\mu_{2}\) are the means and \(\sigma_{1}\), \(\sigma_{2}\) the standard deviations of the two distributions respectively. The result can take values from zero (inclusive) to infinity.
# Towards Sulphite-Free Winemaking: A New Horizon of Vinification and Maturation
## 3 Results and Discussion
### Alcoholic and Malolactic Fermentation Trends  
During winemaking, the yeast strains present in the must, both those autochthonous naturally occurring on the grapevines and those selected, are responsible for alcoholic fermentation, during which sugars are converted into ethanol and carbon dioxide [45]. The alcoholic is a step of pivotal importance, and the monitoring of glucose and ethanol during the fermentation process is important to control the quality of the wine. Usually, after alcoholic fermentation, red wines undergo malolactic fermentation, which consists in the transformation of malic acid into lactic acid, as it is carried out by lactic bacteria of the _Lactobacillus_, _Leuconostoc_, and _Pedicoccus_ genera. This fermentation determines the elimination of the sour taste caused by malic acid and the increase in lactic acid, which are responsible for a smooth and round taste, in turn due to the reduced fixed acidity [47].  
In the present study, the trends of sugars and alcohols during alcoholic fermentation (Figure 3), as well as of malic and lactic acids during the malolactic fermentation (Figure 4), were assessed and were found to be similar for the wines produced with traditional and alternative winemaking processes, evidencing no significant alterations in the fermentative processes. In detail, at the end of the fermentation, sugars were detected at a concentration of almost 3 g/L and alcohol at 15% \(v/v\) in both the traditional and Bioma wines, while malic acid and acetic acid accounted for 0.12 and 0.15 g/L, and 1.34 and 1.19 in Bioma and traditional wines, respectively.  
Figure 2: Total sulphur dioxide trend during winemaking (24 September 2022–4 December 2022) and ageing (4 December 2022–4 October 2023) of traditional and Bioma wines. Error bars represent standard deviation values (n = 3). Letters indicate statistically significant differences between Bioma and traditional samples at the same time.