esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Extraction Methods
### Extraction in Supercritical Fluids
#### 4.1.2 Influence of Temperature  
Polyphenolic compounds are sensitive to high temperatures and can undergo degradation. Therefore, extraction under supercritical conditions may lead to the breakdown of compounds into their components [93]. However, the key difference lies in the duration of the process and, consequently, the exposure time of the compounds to high temperatures. In conventional extraction methods, the process typically lasts around 3 to 10 h [23, 56, 71]. In the case of supercritical extraction, higher density and reduced viscosity resulting from increased pressure and temperature lead to increased diffusion coefficients, which can shorten the exposure time of compounds to high temperatures to a maximum of 3 h [71]. Therefore, limiting the process time has not only economic significance due to reduced processing duration but also reduces the exposure time of tannins to high temperatures. Studies have shown that longer processing times decrease both the extraction yield and the antioxidant activity of the products, likely due to secondary reactions such as degradation and hydrolysis [94]. Additionally, by using CO\({}_{2}\) as a co-solvent, the critical conditions of the entire mixture are lowered (40-70 \({}^{\circ}\)C) [47, 75, 95], usually not exceeding the temperature of conventional solvent extraction 40-100 \({}^{\circ}\)C [5, 71]. Therefore, supercritical fluid extraction can be viewed as a method that preserves the chemical composition of polyphenols. Simultaneously, the ability to control process parameters can be a promising advantage in increasing the selectivity of tannin extraction using supercritical fluids.
## 5 Experiments and Results
### Evaluation Metrics
#### 5.2.2 SSIM (Structural Similarity Index Measure)  
SSIM is a metric that evaluates the visual impact of three characteristics of an image: luminance, contrast, and structure, comparing changes between a reference and a processed image. Values range from \(-1\) to \(1\), where \(1\) indicates perfect similarity.  
\[\text{SSIM}(x,y)=\frac{(2\mu_{x}\mu_{y}+c_{1})(2\sigma_{xy}+c_{2})}{(\mu_{x}^{2 }+\mu_{y}^{2}+c_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+c_{2})} \tag{4}\]  
where  
* \(\mu_{x}\) and \(\mu_{y}\) are the average pixel values of images \(x\) and \(y\), respectively;
* \(\sigma_{x}^{2}\) and \(\sigma_{y}^{2}\) are the variances of images \(x\) and \(y\), respectively;
* \(\sigma_{xy}\) is the covariance between images \(x\) and \(y\);
* \(c_{1}=(k_{1}L)^{2}\) and \(c_{2}=(k_{2}L)^{2}\) are constants used to stabilize the division with a weak denominator. Here, \(L\) is the dynamic range of the pixel values (255 for 8-bit greyscale images), and \(k_{1}=0.01\) and \(k_{2}=0.03\) are default values.
# Water-Soluble Sugars of Pedigreed Sorghum Mutant Stalks and Their Recovery after Pretreatment
## 3 Results and Discussion
### Chemical Composition of Pretreated Solid Biomass  
For raw biomass, the maximum cellulose content of the selected 16 sorghum mutants was 31.3%, whereas the minimum was only 18.1%. The average cellulose content was 24.6% with a standard deviation of 4.8% (Table 3). Large variations of hemicellulose content were also observed from 14.7% to 26.6%, with an average of 20.4% and standard deviation of 4.20%. Relatively low amounts of lignin content were observed in this study, ranging from 6.6% to 13.9% with a mean value of 10.4% and standard deviation of 2.3%, as compared to corn stover and herbaceous grass [3; 12]. Significant amounts of hemicellulose were hydrolyzed into xylose and arabinose after the dilute acid pretreatment. Hemicellulose content after the dilute acid pretreatment was reduced to approximately 6%, which resulted in significantly higher cellulose and lignin content. The majority of hemicellulose (>85%) was hydrolyzed during the pretreatment, which could increase the pore size and provide greater access for enzymes to digest cellulose [7; 23]. Lignin relocation and removal were reported to enhance the enzymatic hydrolysis of treated sweet sorghum bagasse [30; 31]. [PERSON] and [PERSON] reported that sweet sorghum bagasse contained 45% cellulose, 27% hemicellulose, and 21% lignin [32]. The maximum cellulose content after dilute acid pretreatment was 55.8%, whereas the minimum was still as high as 47.0% with an average value of 50.7% and standard deviation of 2.2% (Table 3), which were higher than the cellulose content of acid-treated sweet sorghum bagasse reported by [PERSON] et al. [33]. The increased cellulose content contributed to higher fermentable sugar concentration after enzymatic hydrolysis as compared to the untreated biomass [34; 35]. A relatively high proportion of solid biomass was lost after dilute acid pretreatment, especially for those samples with high water-soluble sugar percentages. For pretreated pedigreed sorghum mutant stalks, the maximum solid recovery was 53.7%, and the minimum solid recovery was only 29.3% (E52A) (Table 3). However, it was more important to preserve as much of the cellulose present in the pretreated biomass. Cellulose recovery was defined as the cellulose ratio before and after pretreatment multiplied by the mass recovery. All the sorghum samples obtained greater than 82.3% cellulose recovery with a maximum recovery of 92.5%.
## 2 OpenFOAM Tritium Transport Model Upgrades
### Two Species BC Correlation  
For two hydrogen species--\(A\), \(B\)--the rate equations are  
\[J_{A}=\Delta_{sA}(C_{s,0\_A}-C_{s\_A}) = \left(C_{g\_A}kT\right)K_{d\_A}-C_{s,0\_A}^{2}K_{r\_A}+\ \frac{1}{2}\left(C_{g\_AB}kTK_{d\_AB}-C_{s\_0\_A}C_{s\_0\_B}K_{r\_AB}\right) \tag{1}\] \[J_{B}=\Delta_{sB}(C_{s\_0\_B}-C_{s\_B}) = \left(C_{g\_B}kT\right)K_{d\_B}-C_{s\_0\_B}^{2}K_{r\_B}+\ \frac{1}{2}\left(C_{g\_AB}kTK_{d\_AB}-C_{s\_0\_A}C_{s\_0\_B}K_{r\_AB}\right)\] (2) \[C_{s\_A(B)}\ \text{solid species $A$ ($B$) interface cell center concentration (atoms/m${}^{3}$)}\] \[C_{s\_0\_A(B)}\ \text{solid species $A$ ($B$) fluid to solid interface concentration (atoms/m${}^{3}$)}\] \[C_{g\_A(B)}\ \text{fluid species $A$ ($B$) interface cell center concentration (molecules/m${}^{3}$)}\] \[C_{g\_AB}\ \text{fluid species $AB$ interface cell center concentration (molecules/m${}^{3}$)}\] \[K_{d\_A(B)}\ \text{dissociation rate constant of $A$ ($B$) ($molecules/m${}^{2}/Pa/s$) = (molecules $\times$ s/kg/m$)}\] \[K_{r\_A(B)}\ \text{recombination rate constant for $A$ ($B$) ($m${}^{4}/s/molecules)}\] \[K_{d\_AB}\ \text{dissociation rate constant of $AB$ (molecules/m${}^{2}/Pa/s$)}\] \[K_{r\_AB}\ \text{recombination rate constant for $AB$ ($m${}^{4}/s/molecules)}\] \[\Delta_{sA(B)}\ =\ \frac{D_{A(B)}}{2L}\ =\ \frac{\text{diffusion constant of species $A$(B) in solid $\left(m${}^{2}/s$) }\right)}\]  
into account molecules vs. atoms).  
In the first version of the model, this system of two equations was transformed to a fourth order polynomial and solved for \(C_{s\_0\_A(B)}\) with a quartic solver [10]. However, the stability and accuracy of the results turned out to be unsatisfactory in practice. Therefore, Equations (1) and (2) are now iterated alternately by a Newton-Raphson formalism. In practice, the very first problem's time-step will often require numerous iterations, especially for rate constants of large numeric values, which typically are used to simulate diffusion-limited cases. Nevertheless, this method turned out quite practicable in transients when the results for the species interface concentrations of the previous time-step served as initial values for the next step. The key advantage is that an external numeric solver for a system of equations is not required.  
The updated two-species model had been verified with the TMAP [11] code in \"rat-edep\" mode (similarly as in Section 5.2 of [10]). The results of OpenFOAM and TMAP were basically identical. It turned out that OpenFOAM can handle higher absolute numeric values of the rate constants in the first time-step than TMAP, which is a hint of the robustness of the OpenFOAM numerical model.
## 3 Numerical Results and Experimental Validation  
To validate the analytical framework and compare the performance of the two correlation-based estimators, this section presents numerical results alongside field experiments. The outcomes provide practical insight into how real-world uncertainties affect time period estimation for speed measurement in PIR-based systems.
# Bell-Clauser-Horne-Shimony-Holt Behavior Under Quantum Loss and Decoherence
## 2 Background, Notation
### Discrete Memoryless Channels  
A discrete memoryless channel without feedback, which will be referred to as a \"channel\" in what follows, is specified by the triple \((X,A,p(a|x))\), where \(X\) is a finite set of input symbols, \(x\in X\), \(A\) is a finite set of output symbols, \(a\in A\), and \(P(a|x)\) is the conditional probability that the channel outputs, \(a\), when the input is \(x\). The channel is often considered to connect two parties; somewhat unusually, we shall call the sender [PERSON], and the receiver [PERSON], in the present case.  
Having an input probability distribution, \(p_{x}^{\text{in}}\), on [PERSON]'s side, the output probability distribution \(p_{a}^{\text{out}}\) at [PERSON]'s side is calculated as follows:  
\[p_{a}^{\text{out}}=\sum_{x}p_{x}^{\text{in}}P(a|x). \tag{1}\]  
In information theory, it is common to define the channel transfer matrix  
\[T_{x,a}=P(a|x). \tag{2}\]  
Considering the input and output probability distributions as row vectors, \(\mathbf{p}^{\text{in}}\) and \(\mathbf{p}^{\text{out}}\), respectively, we have  
\[\mathbf{p}^{\text{out}}=\mathbf{p}^{\text{in}}T. \tag{3}\]  
For a matrix \(T\) to represent a channel, it should be a _stochastic_ matrix: its elements have to be between 0 and 1, and the sum of each row should be 1.  
Assume now that [PERSON] sends her received symbols through another channel, \(T^{\prime}\), to [PERSON]. (Thus, \(T^{\prime}\) represents a channel \((A,R,P(r|a))\) in the sense of (2).) It is straightforward to see that the matrix of the so-arising channel between [PERSON] and [PERSON] will be \(T\,T^{\prime}\). When composing channels, the subsequent channel's matrix multiplies the preceding one from the right.
# Development of Active Microvibration Isolation System for Precision Space Payload
## 3 Hybrid Control Design
### Simulation Experiment  
Simulation experiments of a single vibration isolation module were conducted to compare the dynamic characteristics of the feedback and hybrid control. The dynamic model of the system refers to Equation (4) and the simulated physical parameters are close to the real system parameters. The vibration signal in the time domain and the power spectral density obtained from the experimental results are shown in Figure 7.  
Figure 7a,b show the acceleration signal in the time domain and their power spectral density (PSD). A random vibration with a value of 0.2 mg is added to the base platform as an input disturbance. It can be seen that the feedback control has a considerable suppression effect in the entire frequency band, and the acceleration amplitude of the load platform is suppressed to 2 \(\upmu\)g. With hybrid control, the acceleration amplitude is further suppressed to 0.6 \(\upmu\)g. Below the frequency of 10 Hz, the residual disturbance of the load platform also obtains a more anticipated suppression performance. Therefore, the feedback control method can remove the residual acceleration, and the hybrid control method can further improve the active vibration isolation performance.
## 3 System Description  
### Basic Assumptions  
It is assumed that \(N\) numbers of identical underwater acoustic sensor nodes are submerged in a given oceanic region to monitor environmental events and record meaningful ecological changes for applications described in [6; 7; 8]. These sensor nodes are scarce in resources such as power, data storage, data processing and sensing range capabilities. These sensor nodes are also equipped with communication modules that have limited range. Sensor nodes can adjust their transmission power to maintain one hop communication link. Each sensor node is aware of its geographical location and its unique identification number which is called an address.  
It is also considered that an AUV is available for exploring the designated area. The AUV uses onboard guidance and navigational tools to traverse a specific path called a tour path. It is assumed that the AUV achieves error free localization using techniques based on inertial navigation system (INS) and acoustic navigation as described in [19; 20]. The deployment information of the sensor nodes is known to the AUV. Therefore, the AUV can navigate using this information to stop near a sensor node for data gathering. It is also assumed that the AUV has mechanism to adjust its speed and achieve a certain depth in a designated area. The AUV is also capable of establishing a communication link with a range much longer than normal nodes. Similarly, the AUV can also process and store a large amount of data for later usage.
## 3 Methodology  
A novel optimization algorithm, SCEHO-IPSO, is introduced for task scheduling. There are several indicators utilized for evaluating the efficacy of the task-scheduling algorithm. The following goals are needed to be achieved for an efficient task-scheduling algorithm:  
* Minimization of total cost: According to the user's QoS parameters, the limited total monetary cost states that the SCEHO-IPSO algorithm is efficient.
* Maximization of the QoS parameters: The QoS parameters play a crucial role in cloud computing environments and are utilized to analyze the effectiveness of the SCEHO-IPSO algorithm. The higher QoS is superior, while other parameters remain unchanged.  
Workload balancing: Workload balancing is closely related to the resource utilization rate. If it is an excellent task-scheduling algorithm, the majority of resources should be fully used in cloud environments.
* Minimization of makespan: It represents that the proposed optimization algorithm completes the scheduling of tasks with limited execution time.
* Minimization of latency: Latency is an important measure for evaluating the proposed task-scheduling algorithm. The latency and response time should be limited if it is an excellent task-scheduling algorithm. The flow diagram of the proposed work is mentioned in Figure 1.
# Multiuser Equalizer for Hybrid Massive MIMO mmWave CE-OFDM Systems
## 3 Precoder and Equalizer Design
### Hybrid Equalizer  
In this section, we design the proposed analog-digital multi-user equalizer to efficiently remove both the multi-user and residual inter carrier interferences. In the following derivation, the closed-form iterative digital equalizer is firstly obtained as a function of the analog part of the equalizer, then, the analog part of the equalizer, which cannot be obtained iteratively, is derived assuming that the digital part will fully remove the interference. The analog part is composed by a set of vectors selected from a dictionary based on the receive array response matrix. Finally, the digital equalizer is iteratively computed using the analog fixed coefficients.
# Prediction of Air Quality Combining Wavelet Transform, DCCA Correlation Analysis and LSTM Model
## 3 Results
### DCCA-LSTM Predictive Analysis at Short-Medium and Long Scales
#### 3.3.2 Medium-Scale and Long-Scale Application of the DCCA-LSTM Model  
When the length of the window fell within the medium-long scale, all climatic characteristics were significantly connected with the AQI. Here, the DCCA coefficient was chosen as the weighting factor to quantify the relevance of the feature when the window length was 5000, and the prediction results are displayed in Figure 7 and Table 4.  
Since both the AQI and variable characteristics exhibited correlation features at the medium and long scales, variable screening was not practicable; thus, DCCA coefficients were added into the LSTM model as weighting factors, which improved the accuracy by 5% and the running time by 22 s.
# Adaptive Feedback Control for Four-Phase Interleaved Boost Converter Used with PEM Fuel Cell +
## 3 Controller Design  
To regulate the converter's output voltage, it is effective to transition from direct current control to indirect control by using an inner loop to adjust line currents around a reference current generated by the external voltage loop controller. The effectiveness of this method has been demonstrated in several studies for controlling interleaved boost converters in both single-phase and four-phase operations; see [11; 18; 19]. In other words, a two-loop control system must be adopted to prevent output voltage instability. A two-loop controller based on the system's feedback state is designed. Additionally, three controllers, namely, the proportional-integral (PI) controller, the double-loop super-twisting sliding mode (DL-STSM) algorithm, and the backstepping with STSM controller, are selected for comparison purposes.
## 3 Results
Actually, a simple approximate model where one is substituting uranium with niobium atoms (without allowing for any disorder or structural relaxations) explains this stabilization, see Figure 9. In this model, it is clear that 25 at.% Nb favors \(\gamma\) over \(\alpha\) uranium. Intuitively, one associates the \"stabilization\" of the metastable \(\gamma\) phase as being due to alloying with a metal of the same structure (Nb is also BCC). However, this is not the driving force of the stabilization, but rather more generally the presence of 4d electrons that belongs to the transition metal. The \(\alpha\) phase originates from the bonding 5f states of the uranium atom [37; 38], while the BCC phase is a result of a balance between 5f and 4d states, where the latter provides the preference for the BCC phase [39]. To show this, we illustrate in Figure 9 that replacing the Nb atom with an atom from a metal that is face-centered cubic (FCC), namely Rh, actually has a greater effect on stabilizing the \(\gamma\) phase than Nb itself (dashed lines in the middle panel). This might seem counterintuitive, but the reason is that Rh provides more 4d electrons than Nb and these electrons help in stabilizing the \(\gamma\) phase. However, for practical purposes, Rh is far too rare and expensive to be considered a viable stabilizer. Figure 9: DFT-FPLMTO energies for \(\alpha\) and \(\gamma\) (BCC) U-Nb alloys as functions of the atomic volume. At 25 at.% Nb the \(\gamma\) phase is stable over the \(\alpha\) phase. The dashed lines in the middle panel refers to calculations where Nb is replaced by the FCC metal Rh. Lastly, we show the energetics of the metastable \(\gamma^{\rm s}\) phase of the U\({}_{3}\)Nb compound in Figure 10. In this figure, the parameter \(x\) distorts the BCC \(\gamma\) phase (\(x=0.25\)) and defines the \(\gamma^{\rm s}\) phase. As opposed to the calculation in Figure 9, the structures are all fully relaxed and for the U\({}_{3}\)Nb compound, the \(\alpha\) phase remains slightly below the BCC (\(\gamma\)) phase. The metastable \(\gamma^{\rm s}\) phase is observed for the specific U\({}_{77.5}\)Nb\({}_{22.2}\) alloy with the specific value of the parameter, \(x=0.241\pm 0.001\), according to [PERSON] and [PERSON] [34]. In Figure 10, we notice that energetics of the metastable \(\gamma^{\rm s}\) phase of the U\({}_{3}\)Nb compound, within the interval \(0.242\leq x\leq 0.248\), is only slightly higher either then that of the \(\gamma\) or \(\alpha\) forms of this compound.
## 2 State of the Art
Other authors do not analyze the probability of impact occurrence, but the intentionality of the pedestrian when they see an approaching vehicle from the road curbside, considering different approaches to predict the pedestrian path [19]. Machine learning classification techniques are especially worthwhile for estimating pedestrian intention, allowing the identification of the main factors explaining pedestrian movement and reactions, using bagging techniques such as Random Forest [20] and Support Vector Machine (SVM) [21]. Some other studies revolve around the generation of algorithms that are also focused on the development and optimization of emergency braking systems and autonomous vehicles through the generation of computationally scalable decision-making models that provide robustness to uncertainty in the pedestrian state, such as Markov decision processes [22; 23; 24]. The most common technology integrated into vehicles for the detection of pedestrians and other vehicles on the road is the fusion sensor, which combines the performance of a camera housed in the windshield and a LIDAR (light detection and ranging) or radar sensor. Regarding pedestrian identification, some recent approaches are based on the application of a unified deep learning model, where different convolutional layers (CNN- Convolutional Neural Network) are combined for feature extraction using the HOG (Histogram of Oriented gradients) technique, part deformation and occlusion handling, and a final SVM classifier [25]. The use of the CNN for image processing continues to be extended in recent works, as in [26], where the pedestrian detection task is performed using a 3D CNN that allows rejecting true false pedestrians through the vehicle fusion sensor; or in [27], with a multitask R-CNN for complex nighttime scenarios, including distance estimation. LIDAR, on the other hand, is more accurate but less economical than radar, and shows certain advantages over the camera, since it is not affected by light conditions and is computationally faster in detecting the distance and direction of the object [28]. Hence, with the development of these safety-oriented technologies, the general study of the movement of VRU (Vulnerable Road Users, including pedestrians, cyclists, motorcycle riders, users of personal mobility devices, children seven years and under, and elder people) is being considered to enhance the effectiveness and reliability of these systems. Thus, intending to reduce potential crashes, some surveys focus on understanding the limb motions [29], and the corresponding comparison between crossing and non-crossing cases. For the case of fully autonomous vehicles, some models based on perception-decision-action models [30] have demonstrated a certain level of natural harm-avoidance result, by calculating the possibility of occurrence of a vehicle-to-pedestrian collision as a function of human avoidance capability.