esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Combination of GC-IMS and HS-SPME-GC-Ms  
The Venn diagram (Figure 5) showed that 10-11 common compounds were detected in each sample through GC-IMS and GC-MS. And much more compounds were detected only in GC-IMS or GC-MS. And the flavor compounds detected by GC-IMS had 65 varieties in FTL, 64 varieties in BT, and 65 varieties in GT. The flavor compounds detected by GC-MS had 49 varieties in FTL, 48 varieties in BT, and 47 varieties in GT. Specifically, heptanal, hexanal, (E,E)-2,4-heptadienal, nonanal, decanal, linalool, and 2-pentyl-furan were all the flavor compounds detected in the three samples by both GC-IMS and GC-MS, which were speculated to be the key flavor compounds of teas due to the high ROAV value. Other key compounds in tea, including linalool oxide I, sulactone and methyl salicate, were detected in BT and GT, but not in FTL. This result indicated that the compounds accumulated during processing. The two methods have different detection principles. IMS technology separates ions according to the different migration rates when they pass through the gas in the electric field at ambient pressure [11], and MS technology separates ions according to the mass-to-charge ratio of the ions in the samples [13]. Most volatile compounds detected by GC-IMS are small molecules with low content, and most volatile compounds detected by GC-MS are large molecules with high content; therefore, GC-MS has low sensitivity to low concentrations. Consequently, the content value of compounds affects the results of the two detection methods, especially compounds with low content. Some key compounds with high content had the same change trends in tea aroma in the GC-MS and GC-IMS results. However, the results of the two methods are inconsistent on some compounds with low content, such as hexanal, nonanal, and decanal. Sampled and enriched by HS-SPME, volatile compounds detected by GC-MS present the same odors we can smell in practice. Therefore, we used GC-MS coupled with ROAV to analyze key aroma compounds in tea. The varieties of flavor compounds detected in tea by only one method alone ranged from 47 to 65 kinds, which were far lower than the 122-124 kinds detected by the combination of the two methods. Therefore, the combination of the two methods can detect more material changes. The main flavor compounds detected only in GC-IMS were benzaldehyde, furfural, (E)-2-hexenol, 1-octen-3-one, ethyl acetate, and 2-ethyl pyrazine. The main flavor compounds detected only in GC-MS were safranal, \(\beta\)-homocycloctitral, benzylacohol, phenylethyl alcohol,  
Figure 5: Venn diagram of the flavor of GC-MS and GC-IMS in different teas.
## 4 Results and Discussion
### The Effect of CO2 Solubility on Steam-CO2 Mixture Condensation
The contrast of \(\Delta HTC\) and \(S\) shows an increase in the total pressure and CO\({}_{2}\) concentration, and the low solubility of CO\({}_{2}\) is not negligible. Figure 7a,b show the average temperature (\(T_{i}\)) and average mass percentage (\(\alpha_{i}\)) curves at the gas-liquid interface under total pressure conditions of 1 atm and 10 MPa. Figure 7c shows a variation in the condensate film thickness along the horizontal plate at a subcooling rate of 7 K. The solubility of CO\({}_{2}\) is larger under a total pressure condition of 10 MPa, and its influence on \(T_{i}\) and \(\alpha_{i}\) is more obvious. Similarly, CO\({}_{2}\) solubility is larger for a 30% CO\({}_{2}\) inlet condition than a 2% inlet CO\({}_{2}\) at the inlet because of the larger partial pressure, so it also has an influence on \(T_{i}\) and \(\alpha_{i}\). Through Figure 7b, the \(\alpha_{i}\) calculated by the CG model is smaller than that calculated by the NG model, which indicates that the mechanism of CO\({}_{2}\) can promote condensation in order to reduce the accumulation of CO\({}_{2}\) at the gas-liquid interface. In Figure 7c, the effect of CO\({}_{2}\) solubility on the accumulation of condensate film on the plate can also be seen, but it is not very remarkable. The thickness of the liquid film in the CG model is greater than that in the NG model. Therefore, when the total pressure is higher than 1 MPa, it is wrong to regard CO\({}_{2}\) as an NCG. It is necessary to consider the solubility of CO\({}_{2}\) in order to estimate the heat transfer of steam-CO\({}_{2}\) mixture condensation under high-pressure conditions. CO\({}_{2}\) solubility has an enhanced effect on condensation.
# Methane Production Potential from Apple Pomace, Cabbage Leaves, Pumpkin Residue and Walnut Husks
###### Abstract  
Circular economy aims to eliminate organic waste through its transformation, composting and processing into other products or energy. The main aim of the study was to determine the specific methane yield (SMY) of anaerobic digestion (AD) of four different fruit and vegetable residues (FVR). In addition, the reduction in greenhouse gas (GHG) emissions was calculated based on the assumption that maize will be replaced by the FVR as a feedstock for biogas production. The SMY of four residues (apple pomace, cabbage leaves, pumpkin peels and fibrous strands and walnut husks) was measured in the biomethane potential test (BMP) in wet anaerobic digestion technology. The highest SMY (297.81 \(\pm\) 0.65 NL kgys\({}^{-1}\)) was observed for cabbage leaves while the lowest SMY (131.07 \(\pm\) 1.30 kgys\({}^{-1}\)) was found for walnut husks. The concentrations of two inhibitory gasses (NH\({}_{3}\) and H\({}_{2}\)S) in biogas were low and did not affect the AD process. Only biogas produced from cabbage leaves was characterised by higher NH\({}_{3}\) and H\({}_{2}\)S concentrations resulting from the highest protein concentration in this waste. FVR used as feedstock in biogas production may decrease the area of maize cultivation. Therefore, the GHG emissions from maize cultivation will be reduced. In Poland only, the use of four studied FVR as feedstock for biogas production would contribute to the reduction of GHG emissions by 43,682 t CO\({}_{2}\) eq.  
renewable energy; biogas; apple pomace; cabbage leaves; pumpkin residue; walnut husks
# The Thermal Analysis and Heat Dissipation Structure Optimization of a Propeller Driver System
## 3 Thermal Analysis and Optimization of Heat Sink Based on Sealed Cabin
### The Influences of Fin Numbers on Thermal Performance  
The influences of fin numbers on thermal performance are studied from cases with a base thickness of 40 mm, fin thickness of 2 mm, and fin length of 32 mm. The temperatures of devices U1, U2, U3, and U4 are also used to monitor how the temperature changes with the fin numbers, as illustrated in Figure 17. It is shown that the driver temperature decreases as the fin number increases. That is also on account of the increase in the heat transfer surface area. Compared to the wing-free sections, the temperatures are reduced by about 1.96%, 5.97%, 12.9%, and 19.3% for U1, U2, U3, and U4, respectively, as the fin numbers increase from 4 to 24. When the number of fins is 24, the temperature of U1, U2, U3, and U4 is the lowest, and the temperature is 39.14 \({}^{\circ}\)C, 41.92 \({}^{\circ}\)C, 45.29 \({}^{\circ}\)C, and 45.33 \({}^{\circ}\)C.  
Figure 16: Influences of fin length on thermal performance.  
Figure 15: Influences of base thickness on driver thermal performance.  
According to the analysis results above, when the base thickness is 40 mm, the fin length is 32 mm, and the fin number is 24, the heat sink has the best presentation on heat dissipation.
# From Lean Production to Lean 4.0: A Systematic Literature Review with a Historical Perspective
## 3 Results
### Answering RQ3. Historical Evolution of the Term \"Lean\"
#### 3.2.15. Lean 4.0 (2017)  
_Lean 4.0_ (L4.0) is the most recent specifier. It was introduced in 2017 by [PERSON] et al. [123] in the German language paper _Lean 4.0--Between Contradiction and Vision_, which combined lean with Industry 4.0 (I4.0). The authors reflect on the compatibility between lean philosophy and technologies under the umbrella of I4.0, concluding that lean appears to be a prerequisite for digitization.  
In 2018, [PERSON] et al. [124] agreed that lean enabled the successful introduction of I4.0 and concluded that both views complement each other. They present a detailed overview on how the most relevant lean tools can be complemented with I4.0 technologies.  
In 2020, [PERSON] et al. [125] went further by taking a holistic view to identify 25 synergy points between lean tools and 4.0 technologies. [PERSON] et al. [126] proposed new perspectives on how to incorporate artificial intelligence to support human decisions in key lean 4.0 topics (production control, maintaining continuous pull flow, and early prediction of machine failure). Under the denomination \"lean Industry 4.0\", [PERSON] et al. [127] identified the research trends combining \"lean management\" and I4.0. to go further in reducing waste to achieve a new level of operational excellence.  
At the present moment, only four papers have been found with \"lean 4.0\" in the title, although increasing interest (see Figure 19) is being generated in the relationships between Industry 4.0 and different lean aspects: lean production [49; 128], lean manufacturing [54; 129], lean and green [85; 130], lean construction [74], lean enterprise [70], lean healthcare [131], lean management [132], lean six sigma [133], lean supply chain [64], and lean thinking [86].  
Figure 18. Lean startup evolution.  
In total, 83 papers have been identified linking \"lean\" and I4.0. An analysis based on the address of the corresponding author shows countries leading the research in this topic: Germany is in the first position as the term \"Industry 4.0\" was coined in Germany. Nevertheless, an arising interest is shown in different nations, particularly in Brazil, Portugal and Italy (see Figure 20).  
As a conclusion, lean 4.0 is a combination of lean manufacturing (or lean production) principles and tools with Industry 4.0 technologies. It deals with the synergies and complementarity of I4.0 with lean with the intention of reducing waste and complexity. It appears to be a promising field of research in the coming years.
# Head-Related Transfer Functions for Dynamic Listeners in Virtual Reality
## 4 Experiment I: Static Localization  
### Stimuli  
The experiment was constructed using an audio-visual virtual environment created in Max 8. Head-tracking, visual display, and user interaction system were provided by the HTC Vive Pro head-mounted display (HMD), using the SteamVR Version 2.0 tracking system. A visual interface in VR showed an aiming cross-hair and three concentric guide circles around the head, denoting the median, frontal, and horizontal planes. The cross-hair was coupled to the head direction. A starting location marker for each trial was drawn in VR as a circle with \(2^{\circ}\) radius as seen from the central viewpoint at (0, 0) degrees. The stimulus sound was pulsed pink noise with a total duration of 3 s, a pulse length of 400 ms with pauses of 100 ms. The pulsed noise was used to provide onset and offset cues for auditory localization [36]. For each trial, the pulse train was convolved with HRTFs from either the measured or modeled HRTF database, matching the participant's own HRTFs, or using the generic HRTFs. Room acoustic cues were not modeled in this localization experiment, to focus only on the localization cues provided by the HRTFs. Target locations were selected as follows for the azimuth angles: \(\phi=\{0,15,75,90,105,165,180\}^{\circ}\); and elevation angles: \(\theta=\{-40,-20,0,20,40\}^{\circ}\). All combinations of the target azimuths and elevations were presented to the participants once in random order with random assignment to either the left or right hemisphere, assuming a left-right symmetry in auditory localization. The HRTFs were randomly interleaved from the three databases, i.e., the participant was unaware of the HRTF condition in each trial. The audio was played back at 68 dB\({}_{A}\) sound pressure level through an RME Fireface UCX audio interface and Beyerdynamic DT770 Pro headphones, diffuse-field equalized by the manufacturer.
# Key Issues on Integrating 5G into Industrial Systems
## 2 Composition and Communication Networks of Industrial Systems  
### System Structure and Main Equipment  
There are two categories of industrial system architectures: process-oriented and discrete-oriented. Process-oriented industrial systems mainly rely on the DCS (Distributed Control System) to achieve precise control, while discrete-oriented industrial systems mainly rely on a PLC (Programmable Logic Controller) to complete control tasks. DCS is typically employed in large-scale continuous process control, offering greater flexibility and programmability to support complex control strategies and algorithms, as well as handling intricate control logic and vast amounts of real-time data. Conversely, PLC is geared towards discrete event and sequence control, featuring lower costs and better suitability for medium- to small-scale control tasks. There is not much difference in automation control between the discrete industry and the process industry. But the production processes are more dispersed and the production equipment is more diverse in the latter than in the former. Here, we choose the DCS system as a representative for in-depth analysis and its structure is shown in Figure 1[24]. The DCS system is based on computer control, display, and communication technology to achieve decentralized control of on-site processes. Itadopts a multi-layer hierarchical and cooperative autonomous structural form, thereby achieving centralized management, control, and operation of the production process.
## 3 Results and Discussion
#### 3.2.2 Signal Count  
For the purpose of analyzing the influences of the F-T cycles on the splitting fracture damage, the variation curves between signal count and load level versus time during splitting were obtained for the SBS-modified asphalt mixtures with basalt fiber under various F-T cycles. As shown in Figure 9a, the entire loading process and AE signal count can be divided into three stages by combining the intensity of the AE signal count and the load level. During the first stage, when the splitting loading load level was below 0.2, there were almost no AE signal counts generated except several AE counts due to the mechanical noise while loading. After entering the second stage, the AE signal started to come alive. A significant increase in the AE signal count began to appear when the load level was  
Figure 8: The AE amplitude and load level versus time during splitting at \(-10\)\({}^{\circ}\)C for SBS-modified asphalt mixture with basalt fiber under various F-T cycles. (**a**) 0 F-T cycle; (**b**) 3 F-T cycles; (**c**) 6 F-T cycles; (**d**) 9 F-T cycles; (**e**) 12 F-T cycles; (**f**) 15 F-T cycles; (**g**) 18 F-T cycles; and (**h**) 21 F-T cycles.  
between 0.2 and 0.8. The AE signal count values increased with increasing load level. At the same time, the AE signal count values at the second stage increased overall and the signal was dense as compared with the first stage. The variation of the AE signal count implies that cracks occurred gradually and preliminarily formed at this stage, and then the cracks also developed into stable crack growth under continuous splitting loading. As the splitting loading continued, the AE signal reached the third stage. During this stage, the highest value of the AE signal count occurred, which means that the cracks in the asphalt mixture specimen developed rapidly. The AE signal count increased significantly, which means obvious cracks appeared inside the specimen, eventually losing resistance and causing damage.  
Figure 9: Cont.  
With respect to the influences of the F-T cycles on the AE signal count of the asphalt mixtures under splitting loading, it is obvious that the variations of AE signal count and load level have similar trends under various repeated F-T cycles. Compared with the AE signal counts of various F-T cycles, it is clear that the density of the AE signal count gradually became slightly smaller, at the same time, the loading time was shortened to a certain degree with increasing F-T cycles. The first stage was gradually shortened, and the third stage was gradually increased. The occurrence of the second stage gradually moved forward. The F-T cycle processing accelerated the damage of asphalt mixture specimen, i.e., the F-T cycle had a visible adverse influence on the splitting strength of SBS-modified asphalt mixture with basalt fiber.
## 3 Monitoring System Architecture and Software Architecture
### Software Architecture of the Cloud Server
#### 3.2.1 Software Architecture of the Front-End Application Layer  
A data module is designed to display the running state parameters and historical data. The web page information browsing service is used to display the running state parameter page and historical data monitoring page. The running state parameter page shows the  
Figure 4: Hardware used in the data acquisition module.  
Figure 5: Workflow of the cloud server.  
running state parameters of the monitoring terminals (see Table 3). The historical data monitoring page displays the historical data of the user's monitoring terminals in charts.  
The functional modules of the front-end application layer are presented in Figure 6.  
To ensure loose coupling, flexibility, and reusability of the frontend application layer, the MVVM design pattern is adopted to separate the view layer and the business logic layer. The software architecture of the front-end application layer is constructed as shown in Figure 7.  
Figure 6: Functional modules of the front-end application layer.  
Figure 7: Software architecture of front-end application layer.  
The view layer (View) serves as the entrance for users to interact with the cloud server, including a registration and login view, data module view, user management module view, fault alarm view, and log recording view. Among these, the user management module view is only accessible to administrators.  
The business logic layer (ViewModel) is an intermediate layer and a key component of the front-end application layer. It orchestrates the interactions between the view layer and the model layer. It is responsible for logic control for users and permission control and logic control for administrators.  
The model layer (Model) stores temporary data of front-end pages and interfaces with the ViewModel. It stores data from registration and login, data module, permission management module, fault alarm and log recording, etc., and processes the data of the ViewModel. When the ViewModel requests data from the back-end service layer, the model layer caches these data.
###### Abstract  
Conducting computational stress-strain analysis using finite element methods (FEM) is a common approach when dealing with the complex geometries of atherosclerosis, which is a leading cause of global mortality and complex cardiovascular disease. The considerable expense linked to FEM analysis encourages the substitution of FEM with a considerably faster data-driven machine learning (ML) approach. This study investigated the potential of end-to-end deep learning tools as a more effective substitute for FEM in predicting stress-strain fields within 2D cross sections of arterial walls. We first proposed a U-Net-based fully convolutional neural network (CNN) to predict the von Mises stress and strain distribution based on the spatial arrangement of calcification within arterial wall cross-sections. Further, we developed a conditional generative adversarial network (cGAN) to enhance, particularly from the perceptual perspective, the prediction accuracy of stress and strain field maps for arterial walls with various calcification quantities and spatial configurations. On top of U-Net and cGAN, we also proposed their ensemble approaches to improve the prediction accuracy of field maps further. Our dataset, consisting of input and output images, was generated by implementing boundary conditions and extracting stress-strain field maps. The trained U-Net models can accurately predict von Mises stress and strain fields, with structural similarity index scores (SSIM) of 0.854 and 0.830 and mean squared errors of 0.017 and 0.018 for stress and strain, respectively, on a reserved test set. Meanwhile, the cGAN models in a combination of ensemble and transfer learning techniques demonstrate high accuracy in predicting von Mises stress and strain fields, as evidenced by SSIM scores of 0.890 for stress and 0.803 for strain. Additionally, mean squared errors of 0.008 for stress and 0.017 for strain further support the model's performance on a designated test set. Overall, this study developed a surrogate model for finite element analysis, which can accurately and efficiently predict stress-strain fields of arterial walls regardless of complex geometries and boundary conditions.  
finite element methods (FEM); cardiovascular disease; convolutional neural network (CNN); U-Net; conditional generative adversarial neural network (cGAN); stress-strain field maps +
Footnote †: journal: Applied Sciences
## 2 Design of Path Tracking Controller with Constraints on Tire Slip Angles
### Design of LQR  
When designing LQR, a linear quadratic cost function (LQCF) is to be defined. LQCFs, \(J_{1},J_{2}\) and \(J_{3}\), for PTC with three input configurations, _IC#1_, _IC#2_, and _IC#3_, are defined as Equation (11), respectively. These LQCFs are transformed into the vector-matrix form as Equation (12). The weighting matrices \(\mathbf{Q}\) and \(\mathbf{R}_{i}\) in Equation (12) are defined in Equation (13). The weight \(\rho_{i}\) in LQCFs is determined with Bryson's rule, as given in Equation (14), where \(\zeta_{i}\) represents the maximum allowable value for the associated terms [31; 32; 46]. For better path tracking performance, \(\zeta_{i}\) and \(k_{v}\) are tuned. The control inputs \(\mathbf{u}_{1}\), \(\mathbf{u}_{2}\) and \(\mathbf{u}_{3}\) for _IC#1_, _IC#2_ and _IC#3_ are obtained as Equation (15). In Equation (15), \(\mathbf{P}_{i}\) stands for the solution to the Riccati equation for _IC#i_.  
\[\begin{array}{ll}J_{0}=\int_{0}^{\infty}\left\{\rho_{1}\epsilon_{y}^{2}(t)+ \rho_{2}\epsilon_{y}^{2}(t)+\rho_{3}\beta^{2}(t)+\rho_{4}\gamma^{2}(t)\right\} dt&\\ \begin{cases}J_{1}=J_{0}+\int_{0}^{\infty}\left\{\rho_{5}\delta_{f}^{2}(t) \right\}dt&\text{for }IC\#1\\ J_{2}=J_{0}+\int_{0}^{\infty}\left\{\rho_{5}\delta_{f}^{2}(t)+\rho_{6}\delta_{r }^{2}(t)\right\}dt&\text{for }IC\#2\\ J_{3}=J_{0}+\int_{0}^{\infty}\left\{\rho_{5}\delta_{f}^{2}(t)+\rho_{7}\Delta M_ {z}^{2}(t)\right\}dt&\text{for }IC\#3\end{cases}\\ \end{array} \tag{11}\]  
\[J_{i}=\int_{0}^{\infty}\left[\begin{matrix}\mathbf{x}(t)\\ \mathbf{u}_{i}(t)\end{matrix}\right]^{T}\left[\begin{matrix}\mathbf{Q}& \mathbf{0}\\ \mathbf{0}&\mathbf{R}_{i}\end{matrix}\right]\left[\begin{matrix}\mathbf{x}(t) \\ \mathbf{u}_{i}(t)\end{matrix}\right]dt,\ i=1,2,3 \tag{12}\]  
\[\mathbf{Q}=diag(\rho_{1},\rho_{2},\rho_{3},\rho_{4}),\ \begin{cases}\mathbf{R}_{1}= \rho_{5}&\text{for }IC\#1\\ \mathbf{R}_{2}=diag(\rho_{5},\rho_{6})&\text{for }IC\#2\\ \mathbf{R}_{3}=diag(\rho_{5},\rho_{7})&\text{for }IC\#3\end{cases} \tag{13}\]  
\[\rho_{i}=\frac{1}{\zeta_{i}^{2}} \tag{14}\]  
\[\mathbf{u}_{i}(t)=-\mathbf{K}_{i}\mathbf{x}(t)=-\mathbf{R}_{i}^{-1}\mathbf{B} _{2i}^{T}\mathbf{P}_{i}\mathbf{x}(t),\ i=1,2,3 \tag{15}\]