esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Cork Closures on the Volatile Profile of Sparkling Wines during Bottle Aging
## Materials and Methods
### Analysis of Volatile Composition by HS-SPME-GC/MS  
The analyses of volatile compounds in sparkling wine samples were performed in 2018 (12 months), 2019 (24 months) and 2020 (42 months) using a HS-SPME-GC/MS method adapted from [PERSON] et al. [12]. Briefly, each sparkling wine sample (250 \(\mu\)L) was placed in a 20 mL glass vial which was incubated for 5 min at 45 \({}^{\circ}\)C, using a Combi-PAL autosampler (Varian Pal Autosampler, Zwingen, Switzerland). The volatile compounds were then extracted by a 50/30 \(\mu\)m divinylbenzene/carboux/polydimethylsiloxane (DVB/CAR/PDMS) fiber (Supelco Inc., Bellefonte, PA, USA) for 30 min at 45 \({}^{\circ}\)C with a stirring speed of 250 rpm. After extraction, the compounds were thermally desorbed into the GC system for 6 min at 250 \({}^{\circ}\)C. All samples were randomly injected.  
A 436-GC model (Bruker Daltonics, Bremen, Germany) coupled to a SCION single quadrupole (SQ) mass spectrometer (Bruker Daltonics, Bremen, Germany) and a Bruker Daltonics MS workstation (version 8.2.1, Bruker Daltonics, Bremen, Germany) were used for volatile analysis and quantification. The GC system was equipped with a fused silica capillary column (Rxi-Ssil MS, 30 m \(\times\) 0.25 mm internal diameter \(\times\) 0.25 \(\mu\)m; Restek Corporation, Bellefonte, PA, USA) and high-purity helium C-60 (Gasin, Leca da Palmeira,Portugal) was used as carrier gas at a constant flow rate of 1.0 mL/min. The oven temperature was programmed at 40 \({}^{\circ}\)C for 1 min, followed by an increase of 5.0 \({}^{\circ}\)C/min to 250 \({}^{\circ}\)C, where it was held for 5 min, and then increased at 5.0 \({}^{\circ}\)C/min to 300 \({}^{\circ}\)C. SQ-MS was conducted in the electron ionization (EI) mode at 70 eV and the transfer line, ion source, and manifold temperatures were maintained at 250, 250, 260, and 41 \({}^{\circ}\)C, respectively. Data acquisition was performed in full scan mode with a mass range of 40 to 400 \(m\)/z and a 500 ms scan time.  
For the quantification of volatile compounds, standard compounds were dissolved in a wine model solution (12% ethanol, 5 g/L of tartaric acid, pH 3.2) and analyzed under the same conditions by HS-SPME/GC-MS. The calibration curves were achieved by injecting a range of known concentrations of each compound and computed by the respective area of the peak versus concentration.
## 3 Results and Discussion
### Active Control Analysis
#### 3.2.3 Control Effect Verification
Changing the velocity feedback gain \(g_{P}\) is equivalent to changing the equivalent damping of the system, and changing the displacement feedback gain \(g_{I}\) is equivalent to changing the equivalent stiffness of the system. The control analysis results are consistent with the expectation. Previous studies only analyzed the beam pointing stability by assuming that the structural vibration is known [29], but did not analyze the structural transmission characteristics of the structure under excitation. The results show that this method can effectively control the peak of the output light angle response of the full frequency domain spatial filter and make it fall within the pointing threshold, and the ability to resist environmental changes is significantly enhanced. The beam stabilization technology is effective for high rate shooting, but it is meaningless for low rate shooting. After the completion of structural design, the space for optimization and improvement is limited. The above two methods may not meet the requirements of a higher beam positioning index. The structure response can be reduced by Optomechanical coupling active control technology to solve the problem of higher beam pointing stability. To achieve better control effect, neural network adaptive online tuning control parameters can be used to intelligently control the structural response in the future, to realize the optimal control under different disturbances. The most relevant work has only considered the influence of vibration factors on beam pointing, and has not studied the control of the Optomechanical system. Other contemporary methods are studied from the beam stabilization technology, this part of the  
Figure 17: Comparison of the peak values of output light angle response of the spatial filter under the control and noncontrol of broadband white noise: (**a**) Displacement excitation signal of broadband white noise; (**b**) velocity feedback gain \(g_{P}\) control; (**c**) displacement feedback gain \(g_{I}\) control; (**d**) velocity feedback gain \(g_{P}\) and displacement feedback gain \(g_{I}\) joint control. influence on the beam pointing is very small, and the main influence factor of the beam misalignment is caused by the vibration of the Optomechanical structure, accounting for more than 60% [23], so controlling the response of the Optomechanical structure is more effective to improve the beam pointing.
## 4 Review: Slant Links Rain Fade Models
### Physical Models
#### Advantages  
It contains in the signal path the non-uniform definition of a heavy and light rain zone. The model also contains the standard statistics necessary for designing the versatile diversity scheme, the interference statistics due to rainfall, and attenuation statistics due to the percentage of the time.
# A Macro-Micro Damage Model for Rock under Compression Loading
## 4 Results
### Verification of the Present Model  
#### 4.2.1 Verification of Current Model by the Sandstone Triaxial Experiment Results  
As shown in Figure 8, the theoretical stress-strain curves are obtained. The changing trend of experimental curves and theoretical curves are consistent. With the increase of axial strain, the axial stress first increases to a peak value, and then decreases gradually. It can be seen that the proposed damage constitutive model can simulate the stress-strain response in the failure process of the sandstone samples well. Especially in the stage before peak stress, the simulation results are very similar to the experimental results, and can objectively characterize the constitutive relationship of sandstone samples before peak stress. For the stress-strain curve at lower pressure, there is a certain deviation between the theoretical curves and the experimental curves in the post-peak stress stage. The reasons for deviation could be as follows. First, the failure from strain localization is approximately studied by global failure of the wing crack model. The global failure is achieved by considering the interaction of the adjacent wing cracks and the additional stress caused by rock bridge damage. Second, the model parameter \(m\) is a constant value, which is used to determine the stress-strain curves under different confining pressures. With the increase of confining pressure, theoretical curves are more consistent with experimental curves of sandstone samples. The peak stress and corresponding peak strain also increase with the increase of confining pressure. It can be observed from Figure 9 that the crack initiation stress and peak stress predicted by the proposed model are in good agreement with the test results. Both of them increase with the increase of confining pressure. Furthermore, the difference between crack initiation stress and peak stress also increased with the increment of confining pressure.
# An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning
## 6 Performance Analysis
### Energy Consumption  
Energy efficiency is a vital metric for IoT systems with constrained devices operating on batteries or energy harvesting. Table 13 provides the energy usage statistics for five representative IoT node types: sensors, smartwatches, smartphones, laptops, and embedded systems. The minimum, maximum, and average power consumption in watts are reported for each node when running the Enhanced SCAFFOLD protocol.  
The sensors unsurprisingly exhibited the lowest power needs with a 1.1 W minimum, 1.4 W maximum, and 1.24 W average consumption. Many sensors run on coin cell batteries and operate intermittently to achieve multi-year lifetimes. Enhanced SCAFFOLD introduces additional cryptography and communication overhead, which impacts the energy budget. Lightweight cryptographic algorithms optimized for sensors could reduce this overhead. Smartwatches saw a 0.8 W minimum, 1.2 W maximum, and 0.92 W average energy utilization. Wearables have tight power budgets, so the 17% increase from average to peak usage highlights the optimization opportunities. Employing idle sleep states with wake-on-demand could conserve smartwatch energy when Enhanced SCAFFOLD is not actively communicating. Offloading non-time-critical tasks could further improve efficiency.  
Smartphones exhibited sizable 2.3 W to 3.1 W energy needs, given their multifunctional capabilities. However, the 2.68 W average is a small proportion of the smartphone battery capacity. There are few constraints to optimizing Enhanced SCAFFOLD for phones, but power-saving WiFi modes could be employed during idle periods to save incremental energy. Laptops unsurprisingly consumed the most power, with a 5.1 W minimum, spiking to a 7.2 W maximum and 6.04 W average. Laptop batteries can readily support these loads. Their higher computing resources could actually be leveraged to offload tasks from more constrained devices to improve the overall ecosystem's efficiency.  
Finally, embedded systems showed an expected 2.2 W to 3.4 W range and 2.78 W average power requirement. Optimization depends on the embedded use case - for battery-powered field systems, duty cycling and wake-on-demand are viable. On continuously powered devices, computation offloading or caching could save energy.
# Collision Study on New Aluminum Alloy W-Beam Guardrail
## 2 Structure Optimization of W-Beam Guardrail
### Theoretical Analysis
Additionally, the cross-sectional shape of the W-beam guardrail exhibits a wavy contour, similar to a shell structure [19; 20; 21]. This unique contour shape ensures both structural mechanical performance and lightweight characteristics. In optimizing the structure of the new guardrail beam, the same wavy shell structure is adopted. The area of the sections further from the centroidal axis is gradually increased, with more area distributed farther from the centroidal axis to improve the moment of inertia of the cross-section. The optimization approach for the W-beam guardrail can be referenced in the schematic shown in Figure 1.  
Based on the above optimization strategy for the guardrail beam cross-section, a total of 36 new guardrail cross-sections were designed. Compared to the traditional cross-sections, these new cross-sections exhibit varying degrees of increase in cross-sectional area. The shape and area of a cross-section both influence its moment of inertia. To facilitate the exploration of the impact of cross-sectional shape on the moment of inertia, the thickness of the 36 new types of guardrails was adjusted to match the cross-sectional area of traditional W-beam guardrails. For example, at a thickness of 3 mm, the cross-sectional area of the 21 st new guardrail increased by 8.1% compared to the traditional guardrail. The thickness of the 21 st new guardrail was adjusted to 2.76 mm to match the cross-sectional area of the 3 mm thick traditional guardrail. The adjusted 21 st new guardrail exhibited an 18.4% increase in moment of inertia compared to the traditional guardrail, as prominently indicated in Figure 2. Detailed results are shown in Figure 2.  
It is important to note that the 2.76 mm thickness of the new corrugated beam presents challenges in practical manufacturing. To facilitate the adoption of the new W-beam guardrail, all subsequent discussions in this paper will pertain to the Type 21 guardrail with a thickness of 3 mm. Figure 1: Optimization strategy for the W-beam guardrail. Figure 2: Curves of rate of increase in moment of inertia of the 36 new types of section.
## 4 Results and Discussion
### Microstructural Properties  
As the physical properties of cementitious composites liaise with the microstructure of the hardened matrix, microstructural analysis can be useful for the prediction of properties of cement-based products [49]. Figure 20 shows the SEM images of broken mortar samples with 0, 5, and 50% waste at 120 days.  
Significant densification can be observed in the SEM images for the mortar mix made with 5% waste (Figure 20b), highlighting the filler effect of waste powders. However, as shown in Figure 20c, several micro-cracks developed in the mortar mix with 50% waste,  
Figure 19: Weight loss percentage of all specimens after 7 days’ immersion in 25% sulphuric acid solution.  
Figure 20: SEM images of Ctrl (**a**), RGE5 (**b**), and RGE50 (**c**) mixes.  
and larger voids are evident, which can be assumed as the main reason of the high water absorption values (Figure 13) [39; 83]. Therefore, whereas application of a limited content of waste powders can lead to pore refinement, increasing the replacement ratio may degrade the microstructural properties. Figure 21 also shows the EDX spectrum of C-S-H paste obtained from the mortar mixes containing 0, 5, and 50% waste, respectively, with the main peaks associated with Ca and Si. As the concentration of calcium in the C-S-H gel indicates density and stability of cement products [84; 85], it can be said that the C-S-H gel formation in the mortars with 0 and 5% waste was highly stable.  
Figure 21: _Cont_.  
Figure 22 shows SEM images of specimens made with 0, 5, and 50% waste powders exposed to sulphuric acid. The images of the specimens that were processed in tap water are also shown in Figure 20. A relatively compact microstructure for specimens in tap water can be seen there, while specimens subjected to sulphuric acid became more porous. In addition, mortars with 0 and 5% replacement, in comparison with the mix with 50% waste powders, had more compact and less porous structures. Therefore, it can be said that microstructure disintegration, along with a noticeable strength loss, are consequences of lower inter-crystalline bond strength [86]. Using 5% waste as cement replacement decreased the porosity and increased the acid resistance in terms of strength loss and disintegration of the microstructure. For 0 and 5% replacement, as shown in Figure 22a,b, needle-like products, possibly ettringite, as described in previous studies [11; 87; 88], were well formed, whereas in the case of 50% replacement, needle-like products cannot be seen (Figure 22c), highlighting the low rate of reaction in these mortars. [PERSON] et al. [11] reported that, by incrementing uncalcined RM in mortar mixes, the needle-like products' content significantly decreased. In their study, specimens containing 30% RM had almost no needle-like products, but specimens with 15% calcined RM had a few ones attributed to the dilution effect of RM's incorporation [89].  
Figure 21: EDX analysis of mortar mixes.  
Figure 22: _Cont._
## 2 Islanding Detection Methods
### Passive Methods  
Passive methods' primary work is dependent on the measurement of system parameters at the point of common coupling (PCC), such as voltage, current, frequency, power, or impedance. When the islanding occurs, significant variations of those parameters which exceed the allowed threshold values are observed. Protective relays monitor the variations and activate the main circuit breaker if necessary. Passive methods include various techniques, such as over/under voltage protection (O/UV) or over/under frequency protection (O/UF), the rate of change of frequency (ROCOF), the rate of change of active and reactive power (ROCOP), voltage unbalance (VU), and phase jump detection (PJD) [3]. The power quality and grid operation are not affected during the implementation of passive methods. NDZ is larger than other systems. However, the detecting speed is slower. Below are thorough explanations of passive techniques.
###### Abstract  
The prediction of bearing remaining useful life (RUL) plays a pivotal role in ensuring the safe operation of machinery and reducing maintenance loss. Traditional prediction methods only consider the features of one domain or integrate the features of multiple domains into a one-dimensional sequence as the model input, which leads to some inaccuracy in prediction. In order to improve the prediction accuracy, a bearing RUL prediction method based on the parallel deep residual convolution neural network (P-ResNet), which is considered both time-domain features and time-frequency features, is proposed in this paper. Synchronous wavelet transform (SWT) is adopted to extract time-frequency features from original vibration signals. Both the time domain features and time-frequency domain features after dimension reduction by PCA are used as input to P-ResNet, which contains two series of parallel convolution operations to learn the time-frequency features and time-domain features, respectively, to ensure the comprehensiveness of information-bearing degradation. The residual layers were added to enhance the learning ability of time-frequency features. Kalman filter algorithm was used to smooth the prediction results. The IEEE PHM 2012 Data Challenge datasets were used as data sources for model training and prediction. Compared with the traditional convolutional neural network (CNN), the P-ResNet model maintains the synchronization of global and local information and has a stronger learning ability. The experiment data validate the effectiveness of the proposed method, and the comparison between the posed methods and the others proves the superiority of the proposed method.  
bearing; remaining useful life prediction; deep learning; convolutional neural network +
Footnote †: journal: Applied sciences
# Revealing Accessibility of Cryptic Protein Binding Sites within the Functional Collagen Fibril
## 4 Computational Studies of the Collagen Fibril  
[PERSON] and [PERSON] first built an all-atom model of the collagen fibril based on the atomic structure and periodic nature of the fibril, as reported by Orgel (Protein Data Bank (PDB) entry: 3 HR2) [53], and performed molecular dynamics (MD) simulations on the collagen model with periodic boundaries to replicate the supermolecular arrangement [99]. The simulation was validated by comparison to experimental observations and theories of the collagen fibril structure. [PERSON] and [PERSON] also studied the inter-protein interactions within a collagen fibril [100]. Simulations of collagen fibrils and monomers were compared to study the atomic level interactions upon fibrillogenesis. They also compared the process of collagen fibrillogenesis with the process of protein folding and concluded that they both act to shield hydrophobic side-chains from solvent, and they differ in the percentages of major intra-protein hydrogen bond types (e.g., backbone-backbone, backbone-side-chain, and side-chain-side-chain) [100].  
[PERSON] and [PERSON] also performed MD simulations on collagen models with periodic boundaries, and evaluated the effect of several factors on D-band shrinkage [101]. They found that the large shrinkage that was observed in earlier simulations was not eliminated by optimizations of temperature/pressure coupling algorithms, salt concentration or hydration level, or the cross-linking of monomers, and concluded that the shrinkage was a force-field artifact. A new force field Amber99 sb-ildn was found to produce a small D-band shrinkage of <3% [101].  
Here, we aimed to create a measure of accessibility for known specific receptor and protein binding sequences within the collagen I fibril. Previously, [PERSON] et al. created a qualitative ranking of accessibility of collagen binding sites based on their distance from and relative occlusion by the fibril surface [54] and calculated SASA within the fibril to evaluate accessibility of the MMP cleavage site [98]. We have now used a modified SASA method to characterize the ability of a binding partner to access its specific binding site within the collagen fibril SRU that is nearest to the fibril surface.
# Load Frequency Optimal Active Disturbance Rejection Control of Hybrid Power System
## 2 Dynamic Model of Load Frequency Control
### Load Frequency Control Model  
The linearized model of a multi-area interconnected power system can be described in Figure 2, in which the \(i\)-th control region of a hybrid power system is described. In this diagram, each region is assumed to have \(n_{i}\) generators. Each area has a participation factor \(\alpha_{ni}\) and satisfies \(0\leq\alpha_{ni}\leq 1\); the sum of all regional participation factors is 1. \(\Delta P_{L,i}\) is the disturbance of power loads, and \(\Delta P_{RES,i}\) is the output power of renewable energy generators. \(R_{i}\) is the \(i\)-th unit droop feature. \(B_{i}\) is the frequency bias factor obtained from the gradient feature \(R_{i,ap}\), via \(B_{i}=D_{i}+1/R_{i,ap}\), where \(D_{i}\) is the load-damping factor [19].  
When quantifying the discrepancy arising from imbalances in power across multiple areas, the disruption encountered by the system is referred to as the Area Control Error (ACE) [27]. The ACE encompasses the deviation in power exchange between different control areas and the deviation in frequency within each area. The calculation formula can be represented as Equation (1).  
\[ACE_{i}=B_{i}\Delta f_{i}+\sum\
olimits_{j\in N_{i}}T_{ij}\frac{1}{s}(\Delta f _{i}-v_{i}) \tag{1}\]  
where \(v_{i}\) and \(T_{ij}\) are area interface signals. \(G_{A,i}\) (\(s\)) is the transfer function from \(\Delta P_{c,i}\) to \(\Delta\omega_{i}\), which can be obtained by Equations (2) and (3) [28].  
\[G_{A,i}(s)=\frac{\Delta f_{i}}{\Delta P_{c,i}}=\frac{G_{rm,i}(s)\Sigma_{j=1}^{ n_{i}}\alpha_{ij}G_{GT,i}(s)}{1+\sum_{j=1}^{n_{i}}\frac{\alpha_{ij}G_{GT,i}(s)}{R _{j}}} \tag{2}\]  
\[G_{rm,i}(s)=\frac{1}{2H_{i}s+D_{i}} \tag{3}\]  
Figure 1: The structure diagram of a three-area interconnected power system.  
Figure 2: Linearization model structure of the system in the i-th control area.  
where \(G_{rm,j}(s)\) is the transfer function of rotating mass inertia of the \(i\)-th region, \(G_{GT,j}\), (\(s\)) is the transfer function governor and turbine, \(\alpha_{ij}\) is the participated factor of the \(j\)-th generation unit, and \(R_{j}\) is the decline rate of the \(j\)-th generator.