esa-sceva/satcom-chunk-collection huggingface dataset:
# Microencapsulation of Polyphenols and Their Application in Food Technology
## 4 Microencapsulation of Polyphenols
### Encapsulation Carriers  
Microencapsulation is a technology that enables the protection and controlled release of bioactive compounds, such as polyphenols, by enclosing them within a carrier material. In particular, the selection of an appropriate encapsulation carrier is essential for determining the stability, bioavailability, and release profile of polyphenols. Moreover, the carriers used for microencapsulation can vary widely, including carbohydrates, proteins, lipids,and biopolymers, each offering unique benefits and limitations based on their structural properties and interactions with polyphenols [154; 155].
## 4 Multi-Objective Parameter Tuning
### M-Race
#### 4.3.2 Determining Non-Dominated Configurations  
Non-dominated sorting was introduced by [PERSON] et al. [24] for use in a multi-objective evolutionary algorithm. It is a technique for ranking points based on the 'level' of dominance. This ranking is used to eliminate underperforming candidates that consistently have a low rank.  
Figure 1: Example of a Pareto front.  
```
Sample \(N\) configurations uniformly with \(d\) control parameters each using a Sobol sequence. Let \(\alpha\) be the chosen significance level. \(k\gets 0\) Let \(\Theta_{k}\) be the surviving configs at iteration \(k\) Let \(P\) be the history of \(p\)-values while\(|\Theta_{k}|>n_{min}\)do  Evaluate the performance of each \(\theta\in\Theta_{k}\)  Rank \(\Theta_{k}\) using NDS  Perform Friedman test on \(\Theta_{k}\)  Let \(p\) be the \(p\)-value of the test \(P\gets P\cup p\) if\(p<\alpha\)then  Remove worst configs from \(\Theta_{k}\) \(P\leftarrow\emptyset\) else  Perform least-squares regression on P  Let \(m\) be the gradient of regression line if\(m\geq 0\)then return\(\Theta_{k}\) endif  endif \(k\gets k+1\) endwhile return\(\Theta_{0}\)
```  
**Algorithm 2** M-race.  
Suppose a set of parameter configurations \(\Theta_{1}\), with each configuration \(\theta_{i}\in\Theta_{1}\) having a corresponding performance vector \(\mathbf{C}_{i}\). Ranking of the configurations proceed by determining the Pareto-optimal front of the performance vectors. All the parameter configurations that have corresponding performance vectors in this front are assigned the best rank of 1. Let this set of Pareto-optimal configurations be \(\tilde{\Theta}_{1}\subset\Theta_{1}\). Then, all configurations in \(\tilde{\Theta}_{1}\) are removed from \(\Theta_{1}\) to form \(\Theta_{2}=\Theta_{1}\setminus\tilde{\Theta}_{1}\). This new set contains all the configurations that were dominated by those in the Pareto-optimal front. Now, the Pareto-optimal front of \(\Theta_{2}\) is calculated. Configurations in this front form another Pareto-optimal set \(\tilde{\Theta}_{2}\) and are assigned a rank of 2. Again these configurations are removed to form \(\Theta_{3}=\Theta_{2}\setminus\tilde{\Theta}_{2}\). This process is repeated until all configurations have been assigned a rank. Figure 2 shows an example of non-dominated sorting. The red dots indicate the non-dominated solutions that form the Pareto front, while the blue dots represent dominated solutions that are assigned higher ranks in subsequent iterations of the sorting process.  
Figure 2: _Cont._
## 2 Numerical Modeling and Validation
### Numerical Models
#### 2.2.2 Material Models  
The material model for the infilled concrete in GSPCW walls was selected as the total strain crack model in DIANA. The input parameters for this concrete model include the elastic concrete properties, cracking direction, and uniaxial behavior of concrete under compression and tension. The secant unloading method was used for total strain crack model and the internal damage variables \(\alpha_{k}\) were used to monitor the strain of the concrete due to cracking and crushing, which is related to the constitutive model of concrete, see Figure 4. The cracking direction was selected as the rotating direction, and the shear stiffness remained constant. Figure 4 also shows the stress-strain constitutive relationship for the uniaxial behavior of concrete. In this study, the model proposed by [PERSON] et al. [12] was adopted as the constitutive model of concrete under compression, and is applicable to concrete constrained by both circular- and rectangular-shaped reinforcement. The constraint effects of the steel plates on concrete can be regarded as a constraint effect with many transverse reinforcements on concrete, as in the following formula:  
\[f_{c}=\frac{f^{\prime}_{cc}xr}{r-1+x^{\prime}} \tag{1}\]  
\[x=\frac{\varepsilon_{c}}{\varepsilon_{cc}} \tag{2}\]  
\[\varepsilon_{cc}=\varepsilon_{co}\bigg{[}1+5\bigg{(}\frac{f^{\prime}_{cc}}{f^{ \prime}_{co}}-1\bigg{)}\bigg{]} \tag{3}\]  
\[r=\frac{E_{C}}{E_{C}-E_{\text{sec}}} \tag{4}\]  
\[E_{\text{sec}}=\frac{f^{\prime}_{cc}}{\varepsilon_{cc}} \tag{5}\]  
where \(f^{\prime}_{cc}\) is the compressive strength of confined concrete, \(\varepsilon_{c}\) is the longitudinal compressive concrete strain, \(f^{\prime}_{co}\) is the unconfined concrete strength and \(\varepsilon^{\prime}_{co}\) is the corresponding strain, which can be assumed to be 0.002. \(E_{c}\) is the elastic modulus of concrete, which can be calculated using \(E_{c}=5000\sqrt{f^{\prime}_{co}}\). The compressive strength of confined concrete \(f^{\prime}_{cc}\) can be determined by the confinement effectiveness of steel plates and calculated as described by [PERSON] et al. [13]. The constitutive model of concrete under tension was calculated according to CEB-FIP Model 1990 [14], and the formula is given by:  
\[f_{t}=\begin{cases}0.26f^{2/3}_{cu}&(f_{cu}\leq 50\text{ MPa})\\ 0.21f^{2/3}_{cu}&(f_{cu}>50\text{ MPa})\end{cases} \tag{6}\]  
Figure 3: GSPCW wall models in DIANA: (**a**) model schematic; (**b**) meshed model with loading conditions and constraints.  
The constitutive model for steel in the study adopts a simple bilinear elastic hardening material model. The elastic modulus, yielding strength, and ultimate strength were all based on material properties determined by tests in Ref. [9]. The Von Mises yielding criterion was selected to describe the yielding status of steel.
# A Low-Power WLAN Communication Scheme for IoT WLAN Devices Using Wake-Up Receivers
## 1 Introduction
Because the wake-up radio defined in IEEE 802.11 ba can be added to any IEEE 802.11 standard, the MU operation in IEEE 802.11 ax is adopted in order to significantly reduce the transmission delay. In addition, the scheduling method may utilize the wake-up delay to transmit to the other group of STAs, which reduces the entire transmission delay in the massive uplink MU scenario. In this case, MU transmission in wake-up radio may cause collisions, additional delay and power consumption because of simultaneous wake up and transmission. To resolve this inefficiency, a simple and efficient method to avoid collisions is added to the proposed method. The proposed scheme in this paper is able to ameliorate the inefficiencies of the previous works. The proposed scheme provides an efficient communication scheme to control trade-offs between latency and power consumption for multiple IoT devices with mobility without requiring proceeding transmission tree construction. The proposed scheme is based on realistic standards, IEEE 802.11 ba and IEEE 802.11 ax. The proposed scheme can be implemented without any standard change because IEEE 802.11 ba can be used with any IEEE 802.11 standard, and in the proposed scheme, existing parameters are reused and make use of certain proposed values. This paper is an extension of our previous work [17], which was a preliminary study on the wake-up receiver-based WLAN MU communication scheme. Because the main focus of [17] was waking up multiple STAs for MU communication, only power and delay comparisons between the conventional scheme and the proposed basic multiuser scheme are provided. In this paper, we propose a full extension of the MU transmission scheme along with a detailed description of the wake-up packet structure as well as the scheduling scheme and performance analysis. To do this, we performed extensive simulations and we present here our analysis. In the proposed scheme, we exploit salient features of IEEE 802.11 ax to provide a low-delay communication scheme, while maintaining low transmission power. The rest of this paper is organized as follows: In Section 2, background on power saving algorithms and low-power wake-up receivers in accordance with IEEE 802.11 ba are presented. In Section 3, the target scenario and the conventional transmission scheme are explained. In Section 4, the proposed structure and transmission schemes are presented. In Section 5, the analysis of each method is given. In Section 6, the simulation results are shown. Finally, Section 7 concludes the paper.
## 6 Detailed Review of CS Based ECG IoT Framework and Analysis
### Learning Algorithms on ECG
#### 6.2.2 Learning on Direct CS Measurements  
At the decision-making block, the reconstruction of ECG signal is time consuming and in order to reduce the time and power usage various researches have carried out to analyze the signal directly on CS measurements without reconstructing the signal. Fundamentally CS reconstruction is all about learning the signal using iterative algorithms from the linear CS measurements [24; 25] and it would be a smart way to integrate the learning along with ML. Anyway, a systematic approach of compressed learning of ECG signal had not been done, but few have implemented compressed learning on ECG. [PERSON] et al. [230] proposed a biometric user identification using ECG known as Compressed alignment-aided compressive analysis (CA-CA) algorithm, this CA-CA algorithm uses PCA based dictionary in compressed domain, where the reconstruction of ECG signal is avoided and the information is directly recovered from compressed domain resulted in a reduction of computation time by 81.08% and classified the compressed ECG signal with high accuracy. Compared to compressed learning (CL), the accuracy of the proposed algorithm was improved by 7.11%, the accuracy dropped with respect to Reconstruction learning with alignment (RL-A) and this algorithm was tested only on a small ECG database.  
Another article related to compressed domain by [PERSON] et al. [231] investigates the problem of heart rate estimation from ECG recordings, on compressed signal. QRS complex locations are detected directly in the compressed sensed domain; a template matched filtering based QRS detection approach is considered, by calculating the correlation of the known QRS template and a compressed ECG. Orthogonal Daubechies 4 wavelet was considered as a sparsifying transform. Using the cross correlation and matched-filtering detection method we compared the results with the Pan-Tompkins (PT) detection algorithm for CR = 75% average Sensitivity and the Positive Predictive value was about 95%.  
[PERSON] et al. [232] discussed the heartbeat classification on wearable devices in CS domain. The original ECG signal is observed using a sparse binary random matrix, a template based QRS detection algorithm was developed to locate the QRS complexes directly by calculating the correlation of the compressive ECG measurements. Here signal reconstruction is avoided. Based on the detected QRS complexes from the compressed  
\begin{table}
\begin{tabular}{c c c c c c} \hline
\begin{tabular}{c} Classification \\ accuracy for KNN \\ and MLP = 92.5\% \\ and 93.1\% \\ respectively \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._ECG signals, the deep Boltzmann machine is used for heartbeat classification algorithms under varying CRs, compared to the benchmarking method the performance was slightly reduced. However, it achieved lower energy consumption and is thus suitable for wearable devices.  
Table 4 lists down the summary of these publications and their performance in compressed domain.
## 3 Results and Discussion
### Synveresis and Whey Composition  
Synereesis in FMs is the separation of the liquid phase from the gel. According to [PERSON] (2009) [47] it can occur spontaneously or when the gel is mechanically disrupted during cutting, shaking or freezing, and is undesirable in firm and stirred fermented milk because it can negatively influence consumer acceptance of the food product. In previous studies, it has been observed that when the protein content and total solids are increased, an increase of whey retention capacity is observed, and then spontaneous syneresis process disappear, obtaining products with greater apparent viscosity and firmness [47; 48]. Tables 6 and 7 show the percentage of syneresis and composition of the expelled whey. It is observed that the syneresis values are slightly higher in FMs with YO-MIXTM 300 compared to those obtained with MA400. Similar values (average 64%) were found in previous studies [46]. Other authors [48] compared the percentage of syneresis 14 days after fermentation in yogurts based on different types of milk, obtaining considerably higher values in goat milk (39%) compared to cow milk (25%) or sheep (17%). [PERSON] et al. (2003) [41] explained that when goat milk is used for FMs, it is required the inclusion of a fortification to improve the coagulum characteristics because goat milk presents slightly lower casein content (\(\alpha\)-s1-casein) than cow's milk. The content of \(\alpha\)s1-casein in goat milk depends on the genetic polymorphisms, whereas goats with alleles A, B or C have contents of \(\alpha\)s1-casein up to 25% of the total milk protein, goats with O or N alleles have no \(\alpha\)s1-casein. The lower the content of \(\alpha\)s1-casein the larger the casein micelles and the number of hydrated pores yielding a less dense gel structure than cow milk [49]. It is important to mention that no significant differences on syneresis were observed between storage times, as opposed to observations by [PERSON], (2009) [47] who did appreciate a decrease due to storage. Other authors also reported increased percentage of syneresis with storage time in cow's milk yogurt [22]. In a previous study, whey composition was as follows: 0.32% protein, 0.24% fat, 4.4% lactose, and 3.5% of total solids [46] In the present study, lactose was much lower, presenting values <2.5%. It should be noted that the protein values were around 0.30% and decreased considerably in all fermented milks regardless of the feeding, at late lactation. It can be observed that FMs cultured with MA400 lost more fat with the whey than those cultured with YO-MIXTM 300.
# Pocs-Augmented CycleGAN for MR Image Reconstruction
## 2 Materials and Methods
### Loss Function
#### 2.4.4 Cycle Loss  
While the above loss function pushes the generator to transform an image with aliasing artifacts \(y_{u}[\text{m}]\) into an aliasing-free output \(y[\text{-}n]\), the output of the generator may not carry the same content as the input. This is because the discriminator was not trained with paired data (aliased image \(y_{u}[\text{m}]\) as the input and the corresponding aliasing-free image \(y[\text{m}]\) as the reference). To make sure the input and the output of the generator were images with the same contents, a cycle loss \(\mathcal{L}_{cyc}\) was used. \(\mathcal{L}_{cyc}\) calculated the difference between the output of \(G_{u2f}\Big{(}G_{f2u}(y)\Big{)}\) and \(y\) as well as \(G_{f2u}\Big{(}G_{u2f}(y_{u})\Big{)}\) and \(y_{u}\). \(\mathcal{L}_{cyc}\) was defined by the following equation:  
\[\mathcal{L}_{cyc}\Big{(}G_{u2f},G_{f2u}\Big{)}=E_{y\sim p_{data(y)}}\Big{|}G_ {u2f}\Big{(}G_{f2u}(y)\Big{)},y|_{l_{1}}^{1}+E_{x\sim p_{data(x)}}\Big{|}G_{ f2u}\Big{(}G_{u2f}(y_{u})\Big{)},y_{u}|_{l_{1}}^{1} \tag{8}\]  
The overall loss function was given by a weighted sum of the above sub-loss functions:  
\[\mathcal{L}\Big{(}G_{u2f},G_{f2u},D_{y_{u}},D_{y}\Big{)}=\mathcal{L}_{\mathcal{ GAN}}\Big{(}G_{u2f},D_{y},x,y\Big{)}+\mathcal{L}_{\mathcal{GAN}}\Big{(}G_{f2u},D_{y _{u}},y,x\Big{)}+\beta\mathcal{L}_{\left\lvert+\right\rvert}\Big{(}G_{u2f},G_{ f2u}\Big{)} \tag{9}\]  
where \(\beta\) is a regularization parameter to balance the weights of generative loss and cycle loss.
# Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles
## 2 Requirement Analysis
### Target Resolution and Identification  
The resolution of targets is the ability to resolve different objects. This is important for driver assistance systems to quantify the number of targets and to track them over time. Nearby located targets can be erroneously merged by on-board ranging sensors into one single object. The processing capabilities inside ranging sensors and on-board the vehicle are constrained, and consequently, the maximum number of detected and tracked targets is limited. Depending on the road environment, it will be required to track up to 100 targets in order to obtain the required awareness for safety-relevant applications. Additionally, the unambiguous identification of targets over time is a further requirement for relative positioning solutions. This involves giving the same target the same ID when its detection and tracking were interrupted.
# Estimation of Inter-System Biases between BDS-3/GPS/Galileo and Its Application in RTK Positioning
## 3 Datasets and Experiments
### Datasets
2[FOOTNOTE:2]Footnote 2: The duration of the station GODN and GODS is from day-of-year (DOY) 29, 2020 to DOY 150, 2021. 2 The duration of the station GODE and USN7 is from DOY 36, 2021 to DOY 150, 2021. 2[FOOTNOTE:2]Footnote 2: The duration of the station GODE and USN7 is from DOY 36, 2021 to DOY 150, 2021. 2[FOOTNOTE:2]Footnote 2: The duration of the station GODN and GODS is from day-of-year (DOY) 29, 2020 to DOY 150, 2021. 2 The duration of the station GODE and USN7 is from DOY 36, 2021 to DOY 150, 2021. 2[FOOTNOTE:2]Footnote 2: The duration of the station GODN and GODS is from day-of-year (DOY) 29, 2020 to DOY 150, 2021. 2 The duration of the station GODE and USN7 is from DOY 36, 2021 to DOY 150, 2021. 2[FOOTNOTE:2]Footnote 2: The duration of the station GODN and GODS is from day-of-year (DOY) 29, 2020 to DOY 150, 2021. 2 The duration of the station GODE and USN7 is from DOY 36, 2021 to DOY 150, 2021. 2[FOOTNOTE:2]Footnote 2: The duration of the station GODN and GODS is from day-of-year (DOY) 29, 2020 to DOY 150, 2021. 2[FOOTNOTE:2]Footnote 2: The duration of the station GODN and GODS is from day-of-year (DOY) 29, 2020 to DOY 150, 2021. 2 The duration of the station GODE and USN7 is from DOY 36, 2021 to DOY 150, 2021. 2[FOOTNOTE:2]Footnote 2: The duration of the station GODE and USN7 is from DOY 36, 2021 to DOY 150, 2021. 2[FOOTNOTE:2]Footnote 2: The duration of the station GODN and GODS is from day-of-year (DOY) 29, 2020 to DOY 150, 2021. 2 The duration of the station GODE and USN7 is from DOY 36, 2021 to DOY 150, 2021. 2[FOOTNOTE:2]Footnote 2: The duration of the station GODN and GODS is from day-of-year (DOY) 29, 2020 to DOY 150, 2021. 2 The duration of the station GODE and USN7 is from DOY 36, 2021 to DOY 150, 2021. 2[FOOTNOTE:2]Footnote 2: The duration of the station GODE and USN7 is from DOY 36, 2021 to DOY 150, 2021.  
constellation with a PDOP lower than 3.0 is regarded as having a good distribution [6; 32]. The time series of PDOP of BDS-3, GPS, Galileo, and triple-system combined constellation are shown in the bottom of Figure 1. With the largest number of visible satellites, the BDS-3/GPS/Galileo combined constellation corresponds has the lowest PDOP of 1.07. As for the single systems, the average PDOP of BDS-3, GPS, and Galileo is 2.18, 1.94, and 2.58, respectively. Figure 1: **(a)** Sky plots of BDS-3 (in blue), GPS (in green), and Galileo (in magenta) on day of year (DOY) 120, 2021 at GODN. **(b)** The number of visible satellites of the different global navigation satellite systems (GNSSs) and all the GNSSs (in red). **(c)** The position dilution of precision (PDOP) time-series of different GNSSs and all the GNSSs. In addition, the average number of visible satellites within the day is shown in the legend and all the statistics were computed with a cutoff elevation angle of 10\({}^{\circ}\).
# State-of-the-Art Review of Structural Vibration Control: Overview and Research Gaps
## 2 Overview of Structural Control Systems  
It is crucial to recognize that the control system and the structure do not operate as isolated dynamic entities; instead, they significantly interact and mutually influence each other. The control system, integrated with the structure, manages seismic forces by providing counteracting forces or dissipating seismic energy, thereby preventing a catastrophic failure of the structural system. Structural control class selection depends on various factors, including budget constraints, desired performance, specific structural characteristics, and project requirements. Each class has its advantages and limitations, and informed choices are to be made based on the type of structure, control goals, and constraints. This section presents the classification of the control systems and their critical features.  
Structural control can be broadly classified based on the amount of external energy required; the different control systems can be categorized as passive, active, semi-active, hybrid, and passive control systems (Figure 1) [13]. Based on the mathematical modeling of the control systems, active and semi-active systems are further classified according to their mathematical modeling [11], as shown in Figure 2.
## 3 The mmWave Band  
This section presents an overview of the potential bands for 6G networks and assesses the migration to mmWave frequencies. Within the sub-6 GHz bands, approximately 2.5 GHz of licensed bandwidth is designated, with a significant portion allocated at 3.5 GHz. However, when considering the mmWave band, the responsible regulatory bodies for spectrum allocation, such as the FCC in the United States [35] and the International Telecommunication Union-Radio (ITU-R) [36], have released numerous bands.  
The mmWave bands have been designated at frequencies ranging from 28 to 30 GHz, 38 to 40 GHz, and a free-licensed band extending from 57 to 71 GHz, providing a valuable 14 GHz of contiguous band. Notably, this allocation represents 15 times the unlicensed spectrum of Wi-Fi [37]. Additionally, a 12.9 GHz band is located at 71-76 GHz, 81-86 GHz, and 92-95 GHz [38; 39]. Furthermore, in the UK, Ofcom has released the 26 GHz frequency band, spanning from 24.25 up to 27.5 GHz, for potential 6G utilization [40]. In China, considerations are being made for 45 GHz bands for both licensed and unlicensed wireless networks. For bands above 100 GHz, ITU has released a wide bandwidth of more than 20 GHz of unlicensed spectrum for wireless communication and applications [41; 42]; this spectrum will shape the future of wireless communications [43]. For more details about the potential bandwidth allocation for 6G, please refer to Table 2.  
Integrated satellite-terrestrial networks can also share the mmWave bandwidth with mobile networks; these satellites adopt digital beamforming to form multibeam transmission [44]. To provide a visual representation, Figure 4 illustrates the candidate bandwidths for 6G, encompassing the various mmWave bands and their allocated frequencies [10].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Sub-6 GHz Band**} & \multicolumn{4}{c}{**Centimeter- and Millimeter-Wave Band**} & \multicolumn{4}{c}{**Complementary Sub THz Band**} \\  & & **[4,5]**  
The following path loss and foliage loss models are both adopted in the simulation as part of the parameters of the simulation setting and the calculation of the performance metrics to evaluate the DBS LPN and HPN network models.