esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effects of STL Addition on BT VMs
#### 3.2.5 OAV Analysis  
OAV is widely used in the contribution evaluation of VMs to tea aroma; specifically, an OAV > 10 indicates a key contribution to odor composition, whereas an OAV > 1 indicates an important contribution to tea aroma [27; 28; 29]. We applied the 33 differential VMs (Table S3), screened using PLS-DA/MEV/S-plot analysis, to the OAV analysis to identify the key VMs that determine the aroma quality in the BT, BoS, and STL samples. Combined with absolute sensory thresholds, the aroma characteristics and OAVs of 17 key VMs are listed in Table 3. There were 16 key differential volatiles with OAVs > 1, and the OAVs of benzaldehyde, 1-octen-3-ol, benzeneacetaldehyde, isophorone, 2-methoxy-3-(2-methylpropyl)-pyrazine,  
Figure 4: Component analysis showing the thermography of differential volatile metabolites in BT, STL, and BoS samples. STL, _Strobilanthes tonkinensis_ Lindau; BT, black tea; BoS, black tea with added STL. Red represents high metabolite content, and green represents low metabolite content. 1-dodecanol, \(\alpha\)-ionone, and _trans_-\(\beta\)-ionone were above 500, indicating key contributions to the odor composition of the tea samples [28]. Among them, benzaldehyde and trans-\(\beta\)-ionone were confirmed as the major aroma contributors to sweet and floral odor in black tea [2; 25; 26], and their OAVs were 390/515 and 59942/177451 in BT/STL samples. The addition of STL significantly changed the volatile composition of black tea and directly affected the aroma quality. The OAVs of (E)-2-hexenal, hexanal, (E)-3-hexen-1-ol, linalool, and linalool oxide (pyranoid) were above 10, which also indicated strong contributions to tea odor composition (Table 3). (E)-3-hexen-1-ol, imparting a floral odor [23; 28], with OAVs of 11/13/0.8, was identified in BT/BoS/STL samples. This indicated that the combination of BT and STL promoted the formation and enrichment of (E)-3-hexen-1-ol, which enhanced the aroma and flavor of BoS. Subsequently, 1-hexanol, phenylethyl alcohol, and nerol (OAV \(>\) 1) were identified as important contributors to sample aroma. When combined with the sensory evaluation results (Table 2), these results indicated that the OAVs of benzaldehyde, 1-octen-3-ol, isophorone, 2-methoxy-3-(2-methylpropyl)-pyrazine, linalool oxide (pyranoid), 1-dodecanol, \(\alpha\)-ionone, and _trans_-\(\beta\)-ionone were the highest in the STL samples (\(p<0.05\)), which were the key VMs of a glutinous rice aroma, and the OAVs of hexanal, (E)-2-hexenal, benzeneacetaldehyde, linalool, phenylethyl alcohol, and nerol were the highest in the BT samples, representing a sweet aroma. The combination of the BT and STL samples improved the contents and OAVs of hexanal, (E)-2-hexenal, (E)-3-hexen-1-ol, 1-hexanol, 1-octen-3-ol, benzeneacetaldehyde, linalool, phenylethyl alcohol, 2-methoxy-3-(2-methylpropyl)-pyrazine, 1-dodecanol, and nerol in the BoS samples, as they contributed distinctly to a sweet and glutinous rice aroma.
## 3 Results and Discussion
### Distribution of Residual Oil in Heterogeneous Porous Media
#### 3.1.4 Distribution of Residual Oil at Different Flow Rates  
The groundwater flow rate at different locations is different. In order to combine with the actual experiment, in this simulation, taking into account the typical groundwater flow rate, taking chip 1 as an example, the flow rates of 1.13, 2.26, 4.52 and 6.78 m/d are applied to analyze the change of residual oil in the chip under different flow rates. Figure 8 is the oil volume fraction diagram of the model varying with time at different flow rates.  
It can be seen from the Figure 9 that at different inlet flow rates, the water phases all break through from the high permeable zone and form a dominant channel, and the dominant channel formation time in the high permeable zone will increase with the decrease of the inlet flow rate, and the residual oil volume in the high permeable zone does not differ much when reaching the proposed steady state; for the low permeable zone, although it is difficult to displace the oil phase as a whole, the residual oil volume will decrease with the decrease of the inlet velocity. It can be seen that in the non-homogeneous zone, the low flow velocity will increase the ripple area in the low permeable zone, which helps to reduce the residual oil volume.  
Figure 7: Curves of residual oil in the high and low permeable areas with time for different wall wetting degrees of chip 1. LP represents low permeable zone, HP represents high permeable zone.
# Experimental Methods to Evaluate the Carbonation Degree in Concrete--State of the Art Review
## 2 Qualitative Methods
### Phase Analysis  
Phase analysis is also a common method used to evaluate the carbonation degree of composite. The principle of these methods is based on the change in phase of concrete. Another logical way to determine the carbonation degree is to study the carbonation depth based on the changes in microstructure on the pore solution of the powder sample. Carbonation could result in the modification of the microstructure owing to the variation of the molar volume of the solid compound in the hydrates and carbonated phases [13]. In detail, calcium hydroxide, calcium silicates and calcium silicate hydrate were carbonated in turn, respectively, to generate calcium carbonates. The formation of this greater proportion of solids is strongly evidenced through the observation of the quantity of carbon uptake, the strength improvement, and calcite and endothermal peaks in TGA, XRD and DTG graphs, respectively [3; 7; 60; 61; 62].
# A New Soil Conditioner for Highly Permeable Sandy Gravel Stratum in EPBs
## 4 Conclusions  
When EPB is used in the excavation of full-face water-rich sandy pebble stratum with high permeability, applying traditional conditioner may be insufficient, which will lead to accidents such as the spewing of the screw machine and ground collapse. In view of it, a new conditioner, modified slurry, was proposed in this paper. Modified slurry was prepared by using ammonium chloride as a viscosity modifier and sodium silicate solution and polymer as water shutoff agents. Two main aspects were studied: (1) viscosity modifier effect of modified slurry conditioner's performance on the plasticity of sandy gravel soils with different particle sizes; (2) the effect of concentration and type of water shutoff agent on the improvement of conditioned soil impermeability.  
Ammonium chloride can act as the viscosity modifier of traditional slurry and instantaneously increased the apparent viscosity of traditional slurry. The developed new modified slurry has an effect on improving the plasticity of sandy gravel soils. Sodium silicate and high-molecular polymer as the main water shutoff agents for modified slurry greatly enhanced the impermeability of conditioned samples. The optimum slurry is composed of B:A:S:H = 10:1:2:2 and its suggested injection ratio is 30%.  
In addition, the suggested field application method is, separately, the injection of B, A, S and H (A and H should better be premixed) at the ratio of 10:1:2:2 into the excavation chamber through the slurry pipelines. After stirring through the cutterhead, these components react rapidly to form a modified slurry, which will consequently increase the plasticity and impermeability of the conditioned soils.  
Figure 9: (**a**) Effect of two polymers mixing on seepage flow. (**b**) Effect of B/A/S/H at the optimal ratio on gravel impermeability.  
**Author Contributions:** Conceptualization, X.L., X.Z. and Y.Y.; methodology, X.L. and X.Z.; formal analysis, X.Z. and Y.Y.; investigation, X.Z.; writing--original draft preparation, X.L. and X.Z.; writing--review and editing, X.L, X.Z. and Y.Y.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Program of China, grant number 2017 YC0805008.  
**Institutional Review Board Statement:** \"Not applicable\" for studies not involving humans or animals.  
**Informed Consent Statement:** \"Not applicable\" for studies not involving humans.  
**Data Availability Statement:** Data available in a publicly accessible repository.  
**Acknowledgments:** Special thanks are due to [PERSON] for her suggestion and assistance in the experimental work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Digitalization and Dynamic Criticality Analysis for Railway Asset Management
## 3 Methodology
### Generalized Mathematical Model for Criticality Assessment
#### 3.3.3 Frequency and Consequence Models  
* Failure Frequency (FF): The frequency of failures (FF\({}_{i}\)) can be modeled using historical data or predictions based on asset attributes, such as operating time, number of runs, or track type. A standard model for failure frequency function based on asset characteristics is as follows: \[\text{FF}_{i}=\alpha+\beta_{1}\alpha_{1}+\beta_{2}\alpha_{2}+\cdots+\beta_{n} \alpha_{n}\] where (\(\alpha\)) is a base constant, and (\(\beta\)) are coefficients that adjust the impact of each characteristic on the failure frequency (\(a_{n}\)). (The choice of a linear regression model for predicting failure frequency (FF) was made due to its simplicity and interpretability, especially when the relationship between variables is approximately linear and the data are sufficient. We also acknowledged that alternative models, including machine learning approaches such as decision trees and non-linear regression, are being explored by our research team for future studies. However, these models typically require larger datasets and greater computational resources, which are beyond this study's scope)
* Consequences (C): Each consequence can be modeled (C\({}_{i,j}\)) using historical failure impact data or by simulations of potential failures. A standardized scale of consequences (e.g., from 1 to 10) can be used for each dimension. The weighted sum of the consequences in the different dimensions gives the total impact of an asset failure; these consequences can also be simplified into qualitative equivalents such as high, medium, low, and unacceptable.
# Structure-Function Coupling Reveals Seizure Onset Connectivity Patterns
## 4 Discussion
The placement of the electrodes in the electrode warp and automated mapping cannot be deemed identical to the original, physical placement of the electrodes during the EEG recording. The electrode warp placements represented the expected actual electrode placement during the recording. This work highlights the possible variation inherent in demystifying the electrical signals captured on scalp EEG. There is an intrinsic between-patient variance in cortex and scalp morphology and thickness. The nature of the clinical procedure introduces further variance through the measurement estimation of different technicians during the application and re-application of electrodes. The Curry sensor coherence algorithm is confined to broadband frequencies and does not compute coherence from narrow-band frequencies. Further, the Curry algorithm does not consider the spatial, topographical, morphological or biophysical implications of the scalp signal; it is calculated purely on the raw EEG wave. Therefore, our inverse square mapping method was constrained in accounting for the spatial and biophysical properties of the scalp signal measurements. It is possible that the single neurologist's assessment of the congruency results may introduce bias. Future work will include evaluation of the EEG data by several neurologists blinded to the methodology, and an inter-rater reliability assessment (such as the Kappa statistic). Lastly, lack of control data restricts the distinction between the structure-function coupling in seizures and normal coupling in resting state or non-ictal periods. Indeed, linking brain structure and function remains an imperfect science, confounded by individual differences in structure-function coupling [47]. Therefore extending this study to a larger cohort, with the addition of control data, is the subject of ongoing work in our lab. The inclusion of pre-ictal and cross-hemisphere connectivity data will enable further comparison and quantification of the active electrodes across varying brain states. Additionally, using coherence matrices derived from open source software could provide a point of comparison to [PERSON]'s sensor coherence maps. Despite these restrictions, our work provides evidence that dMRI is a promising additional tool to classify patients for further investigation or surgical candidature. Our model may be practical in identifying the most active locations for sub-scalp electrodes and the patients who could benefit from ultra long-term monitoring. We show that all regions of high connectivity are not necessarily the best place for sub-scalp electrodes. We present a feasible method to distinguish patients, and patient-specific brain regions, that may be candidates for sub-scalp electrodes. With further refinement, our method could be utilised in identifying the optimal position for sub-scalp electrode placement, removing the need for more invasive EEG methods.
## 3 Approaches to Building a Parallel Algorithm and Its Software Implementation
### Parallelizable Operations  
In the considered implementation of the conjugate gradient method for solving Problem (1), all calculations fall on the following four operations.  
1. The scalar product of two vectors of dimension \(N\). This operation requires \(N\) multiplications and \(N-1\) additions--for a total of \(2N-1\) arithmetic operations. It is customary to denote \(2N-1=O(N^{1})\), which indicates the linear computational complexity of the scalar product operation.
2. Multiplication of a matrix of dimension \(M\times N\) by a vector of dimension \(N\). To obtain each element of the final vector, it is necessary to scalarly multiply the corresponding row of the \(A\) matrix by the multiplied vector. Such calculations must be carried out for each element of the vector, which is the result of multiplication. Thus, \(M\cdot(2N-1)\) arithmetic operations must be performed in total. In the case of a square matrix (\(M=N\)), there will be \(O(N^{2})\) of such operations, which indicates the quadratic computational complexity of the operation of multiplying a matrix by a vector.
3. Multiplication of a transposed matrix of dimension \(N\times M\) by a vector of dimension \(M\). The computational complexity of this operation is equivalent to the operation of multiplying a matrix by a vector.
4. Addition of two vectors of dimension \(N\).  
Figure 1: Graph of dependence of \(x_{n}^{\text{rs}}\) component values on component number \(n\).  
This operation requires \(N\) additions. That is, the computational complexity of this operation is linear.  
Next, the algorithms used to parallelize these operations are considered, which will be used to build a parallel version of Algorithm 2 and its software implementation.
## 3 The Production of a Noise Study for the Evaluation of Construction Site Noise
### A Case Study--Assessment of Noise from the Construction of an Office Building
#### 3.2.4 Sources of Noise during Construction Activities  
According to the data presented in the summarising technical report, the execution of building SO01 is planned in four stages.  
Earthworks: excavation of a construction pit, excavation of trenches, technical infrastructure.  
The foundation of the building, rough substructure: foundation strips, foundation slab, waterproofing of the substructure.  
Rough superstructure: lining of load-bearing walls and partitions, execution of the ceiling structure, roofing of the building.  
Interior and finishing work: execution of plastering, painting, floors and all interiors.  
Final landscing and transport infrastructure on the plot.  
Details, including the construction schedule, are planned in the next phase of the project documentation.  
The calculation of noise from construction activities will be performed for the technical stages listed in the project documentation (except for interior and finishing works). With regard to the information from the accompanying and technical report on the demolition of the existing foundation strips, a demolition works stage will also be added.  
For these stages, the parallel deployment of heavy construction plant at the site is expected due to the requirements of the given technological stages as well as the volume of work. As the construction work progresses, depending on the complexity of the given stage and the simultaneity of the involvement of acoustically dominant machines, the emitted noise will also change. The sound power level of a given machine is stated by the  
Figure 3: The area of the construction site and the area for the trajectories of construction machines during the construction of the investigated building project.  
supplier in the technical sheet for the machine. The expected construction machines and their sound power levels are shown in Table 2.  
The most acoustically significant activities include demolition work, excavation and pile-driving work.  
With regard to the phase of construction, the abovementioned plant will be deployed according to the necessary parallel processes and calculations will be entered as follows:  
1. Demolition work: Removal of demolished material, machine 5, 3, 1
2. Earthworks for utility networks, machine 10, 3, 1
3. Earthworks: Excavations + excavated material removal, machine 2, 3, 1
4. Foundation of the building, deep foundations: pile-driving, machine 4, 3, 1
5. Foundation of the building, shallow foundations: concreting, machine 9, 11
6. Rough superstructure + material transfer, machine 6, 7
7. Final landscing, machine 10, 8
## 3 Proposal for Smart Energy Management
### Firmware  
The WSAN performs the real-time energy consumption monitoring and control by using the required sensors and actuators at the nodes. Therefore, it acts as physical interface between the software part of the proposed system and the machinery whose performance is being optimized. Figure 5 shows a flowchart of the logic implemented at the WSAN nodes.  
Figure 4: Laboratory tests of the final system before been released.  
Figure 5: Firmware flowchart.  
For energy efficiency monitoring, these devices take samples from their sensors periodically and these readings are duly processed before being transmitted. The required signal-conditioning is performed to optimize the measurement range, ensuring the coherence and reliability of the collected data. Furthermore, the hardware is always on the alert for specific requests, which may come from the software part of the system, for an appropriate control of the power supply to the attached machinery. When all of the above-mentioned tasks have been duly executed, these subsystems become idle and nodes enter in stand-by mode to reduce their energy consumption. This is particularly appropriate for the WSAN, where there may be nodes supported by batteries while performing energy harvesting.  
The WSAN has been implemented using a ZigBee PRO Stack, supplied by the manufacturer, which simplifies the firmware design and the interoperation with ZigBee devices of other manufacturers, if necessary. The information has been organized and packaged in data frames (Figure 6), which are the communication units used through the WSN. Nodes transmit and receive their corresponding data, but can also act as routers for frames related to other nodes.
## 4 Performance Analysis for Variable-Gain Relaying
### Average BER  
Based on various binary modulations, the average BER can be derived by using CDF as:  
\[\bar{P}_{e}=\frac{q^{p}}{2\Gamma(p)}\int_{0}^{\infty}\text{exp}(-q\gamma) \gamma^{p-1}F_{\gamma}(\gamma)d\gamma \tag{35}\]  
where \(p\) and \(q\) are determined by particular types of modulation schemes [35]. The coherent binary frequency shift keying (CBFSK), coherent binary phase shift keying (CBPSK), non-coherent binary frequency shift keying (NBFSK), and differential binary phase shift keying (DBPSK) are given by (\(p\), \(q\)) = (0.5, 0.5), (0.5, 1), (1, 0.5), (1, 1), respectively.  
Inserting (23) into (35), the average BER of variable-gain relay can be expressed as:  
\[\begin{split}\bar{P}_{e}^{V}&=\frac{q^{p}}{2\Gamma(p )}\int_{0}^{\infty}\text{exp}(-q\gamma)\gamma^{p-1}d\gamma\\ &-\frac{q^{p}}{2\Gamma(p)}\int_{0}^{\infty}\text{exp}\left[- \left(q+\frac{1}{\gamma_{RF}}\right)\gamma\right]\gamma^{p-1}d\gamma\\ &+\frac{m^{2}q^{p}}{2\Gamma(p)\Gamma(a)\Gamma(b)}\int_{0}^{\infty }\text{exp}\left[-\left(q+\frac{1}{\gamma_{RF}}\right)\gamma\right]\gamma^{ p-1}\\ &\times G_{3,3}^{22}\left[\frac{\varphi_{m}\gamma^{1/t}}{\zeta_{ FSO}^{1/t}}\begin{matrix} 1-b,1,1+m^{2}\\ a,m^{2},0\end{matrix}\right]d\gamma\end{split} \tag{36}\]  
Utilizing [33] (Equation (11)) and [34] (Equation (07.34.21.0013.01)), and the definition of \(\Gamma(x)=\int_{0}^{\infty}t^{x-1}e^{-t}dt\), the average BER expression for variable-gain relaying is obtained as:\[\bar{P}_{e}^{V} =\frac{1}{2}-\frac{q^{p}}{2(q+1/\hat{\gamma}_{RF})^{p}}+A_{1}\frac{q^ {p}}{2\Gamma(p)(q+1/\hat{\gamma}_{RF})^{p}} \tag{37}\] \[\times G_{3t+1,3t}^{2t,2t+1}\bigg{[}\frac{\phi_{m}^{t}}{\hat{\gamma }_{FSO}(q+1/\hat{\gamma}_{RF})}\big{|}Q_{1},\Delta(1,1-p)\big{]}\]
###### Abstract  
In the current era, which is characterized by increasing demand for high-precision location and navigation capabilities, various industries, including those involved in intelligent vehicle systems, logistics, augmented reality, and more, heavily rely on accurate location information to optimize processes and deliver personalized experiences. In this context, the integration of Global Navigation Satellite System (GNSS) and inertial sensor technologies in smartphones has emerged as a critical solution to meet these demands. This research paper presents an algorithm that combines a GNSS with a modified downdate algorithm (MDDA) for satellite selection and integrates inertial navigation systems (INS) in both loosely and tightly coupled configurations. The primary objective was to harness the inherent strengths of these onboard sensors for navigation in challenging environments. These algorithms were meticulously designed to enhance performance and address the limitations encountered in harsh terrain. To evaluate the effectiveness of these proposed systems, vehicular experiments were conducted under diverse GNSS observation conditions. The experimental results clearly illustrate the considerable improvements achieved by the recommended tightly coupled (TC) algorithm when integrated with MDDA, in contrast to the loosely coupled (LC) algorithm. Specifically, the TC algorithm demonstrated a remarkable reduction of over 90% in 2D position root mean square error (RMSE) and a 75% reduction in 3D position RMSE when compared to solutions utilizing the weighting matrix provided by Google with all visible satellites. These findings underscore the substantial advancements in precision resulting from the integration of GNSS and INS technologies, thereby unlocking the full potential of transformative applications in the realm of intelligent vehicle navigation.  
Keywords:global navigation satellite system (GNSS); smartphone positioning; GNSS raw measurements; inertial navigation system (INS); GNSS/INS integration; tightly coupled integration (TC) +
Footnote †: journal: Sensors
# Design of a Tri-Band Wearable Antenna for Millimeter-Wave 5G Applications
## 2 Antenna Design Methodology
### Bending Analysis of the Proposed Antenna
#### 2.5.2 Bending along \(Y\)-Axis  
While it comes to researching the behavior of the \(S_{II}\) parameter when the antenna is in a bent state, the bending radius along the \(y\)-axis of the antenna is selected to be between 30 mm and 90 mm. When the antenna is bent along the \(y\)-axis, the results of the simulated \(S_{II}\) are compared across various radii, as illustrated in Figure 6. It was determined that the antenna maintains a steady \(S_{II}\) even when bent at radii of 30 mm, 60 mm, and 90 mm, respectively.  
Figure 5: Antenna bending analysis along \(x\)-axis.  
Figure 6: Antenna bending analysis along \(y\)-axis.