esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cross-Layer Approach MAC/NET with Updated-GA (MNUG-CLA)-Based Routing Protocol for VANET Network
## 5 Results and Discussion
### Network Energy Consumption (NEC)  
This represents the energy allocation of all the nodes in the network. In this research, the network energy consumption is defined as the energy being utilized by a node to establish the path (EP). The mathematical expression to calculate the energy consumption is given as follows,  
\[NEC\ =\ Number\ of\ nodes\ *\ Energy\ (EP) \tag{5}\]  
where consumed energy of the network '_NEC_' is calculated based on the number of nodes in the network, as well as the energy taken to build the route path to transmit the data. The unit of energy is represented as Joules. Figure 6 represents the graphical outcome of network energy consumption with respect to the number of vehicles in the network. The performance of the proposed work is compared with earlier work, namely GPSR, MM-GPSR and GEO-LU. By analyzing the table values and graphs, it can be understood that our proposed MNUG-CLA model produced less consumption when compared with other earlier works.  
Figure 5: Network Energy Efficiency Calculation.  
Figure 6: Network Energy Consumption Calculation.
## 4 Wave-Ship Interaction and Green-Water Loads
### Ship Motion Responses and Green-Water Loads
#### 4.4.2 Green-Water Loads Induced by the Freak Wave  
As the amount of water on the deck during the second green-water impact was significantly less than the first impact, the impact pressure generated from the second impact on the deck was also much smaller than that of the first. Therefore, the following sections only study the impact pressure generated by the first green water impact.  
Figures 12 and 13 show the selected time histories of the impact pressure on the deck and breakwater caused by the freak wave. For the deck, the impact pressure starts at 13.8 s and gradually returns to zero after 21.5 s. The maximum impact pressure on the deck is located near the breakwater. The impact pressure at the front of the deck increases slowly, while the area of the deck near the breakwater reaches its peak value at a faster rate. These phenomena may be due to the gradual reduction in water thickness as the water flows over the deck, resulting in a decrease in pressure. However, the velocity of the water flow also increases, and when the water reaches the area of the deck near the breakwater, the flow velocity is the highest. At the same time, a large amount of water accumulates near the breakwater, leading to a large impact pressure in a short period of time. At around 19.6 s, a small impact pressure appears again due to the falling water. Compared with other literature, such as [PERSON] et al. [23], the impact pressure generated from the falling water was relatively smaller here. This is because other models used in previous studies applied higher superstructures rather than the lower breakwater in the present study. Due to the lower height of the breakwater, most water overtops over the breakwater and only a small portion of the water falls and impacts the deck, resulting in a smaller impact pressure from the falling water.  
Figure 11: Three-dimensional snapshots of interaction between freak waves and container ships: (a) t = 13.4 s, (b) t = 15.7 s, (c) t = 16.6 s, (d) t = 27.0 s, (e) t = 29.2 s, (f) t = 34.0 s.  
the water overturning under the influence of gravity, resulting in another impact on the breakwater. It is worth noting that the breakwater was impacted by green-water loads before the deck area near it, as has been elaborated by [PERSON] et al. [51] and [PERSON] et al. [52] in their articles, and will not be reiterated here.
###### Abstract  
Complications in the analysis of volatile sulfur compounds (VSC) in wine using solid-phase microextraction (SPME) arise from sample variability. Constituents of the wine matrix, including ethanol, affect the volatility and adsorption of sulfur volatiles on SPME fiber coatings (Carboxen-polydimethylsiloxane(PDMS); DVB-Carboxen-PDMS and DVB-PDMS), which can impact sensitivity and accuracy. Here, several common wine sulfur volatiles, including hydrogen sulfide (H\({}_{2}\)S), methanethiol (MeSH), dimethyl sulfide (DMS), dimethyl disulfide (DMDS), dimethyl trisulfide (DMTS), diethyl disulfide (DEDS), methyl thioacetate (MeSOAc), and ethyl thioacetate (EtSOAc) are analyzed, using SPME followed by gas chromatography (GC), using a system equipped with a pulsed-flame photometric detection (PFPD) system, at various ethanol concentrations in a synthetic wine matrix. Ethyl methyl sulfide (EMS), diethyl sulfide (DES), methyl isopropyl sulfide (MIS), ethyl isopropyl sulfide (EIS), and diisopropyl disulfide (DIDS) are evaluated as internal standards. The absorption of volatile compounds on the SPME fiber is greatly affected by ethanol. All compounds exhibit a stark decrease in detectability with the addition of ethanol, especially between 0.0 and 0.5% _v/v_. However, the ratio of interested sulfur compounds to the internal standard becomes more stable when the total alcohol concentration exceeds 2%. EMS was found to best resemble DMS. EIS and DES were found to best resemble DMDS, MeSOAc, and EtSOAc. DIDS was found to best resemble DEDS, DMTS, H\({}_{2}\)S, and MeSH.  
v 2019 24 3392 10.3390/m molecules24183392
# Ballast Flow Characteristics of Discharging Pipeline in Shield Slurry System
## 4 Ballast-Carrying Performance and Distribution State in Slurry System
### Influence Law of Ballast Detailed Mass Percentage  
The statistics were calculated on the total mass \(m_{total}\) and the residual mass \(m_{residue}\) of ballast conveying in the pipeline during the 50 s simulation period. We found that the ballast volumetric concentration, slurry flow rate, and ballast size significantly affect the ballast detained mass percentage \(R_{m}\) of the horizontal pipeline, somewhat influence the vertical pipeline, and basically do not influence the inclined pipeline. Therefore, we only studied \(R_{m}\) in the horizontal pipeline. Figure 12 shows that increasing the ballast volumetric concentration and the slurry flow rate can reduce \(R_{m}\), whereas the ballast size has the opposite effect. When the \(Q\) was 636 m\({}^{3}\)/h and the \(C_{v}\) increased from 1.5% to 6.0%, the \(R_{m}\) varying curve of the horizontal pipeline under different ballast sizes was as shown in Figure 12a. When the \(d_{b}\) was 32 mm, \(R_{m}\) decreased from 55.8% to 29.4%, which is the most significant drop. An increase in the \(C_{v}\) increased \(m_{total}\) and \(m_{residue}\), and it also increased the probability of interaction with the ballast to improve the carrying capacity of the pipeline. When the \(d_{b}\) was 32 mm and the \(Q\) increased from 636 to 1400 m\({}^{3}\)/h, the \(R_{m}\) varying curve of horizontal pipeline under different ballast volumetric concentrations was as is shown in Figure 12b. When the \(C_{v}\) was 1.5%, \(R_{m}\) decreased from 55.8% to 7.0%, which was the biggest decrease. Increasing the slurry flow rate can increase \(m_{total}\) and reduce \(m_{residue}\) in each pipeline section, thereby significantly reducing the \(R_{m}\) of each pipeline. Increasing the slurry flow rate can weaken the negative impact of \(C_{v}\) and \(d_{b}\) on the ballast detained mass percentage. When the \(Q\) was 636 m\({}^{3}\)/h and the \(d_{b}\) increased from 17 to 32 mm, the \(R_{m}\) varying curve of the horizontal pipeline under different ballast volumetric concentration conditions was as shown in Figure 12c. When the \(C_{v}\) was 1.5%, the \(R_{m}\) increased from 28.3% to 55.8%, which was the largest increase. The increase in ballast size had little effect on the \(m_{total}\), but it considerably increased the \(m_{residue}\) in the horizontal pipeline, which resulted in a significant increase in \(R_{m}\). Therefore, an increase in ballast size leads to the risk of pipeline stagnation and blockage. Based on the research, the most likely area of blockage is the horizontal pipe section. Increasing the slurry flow rate can reduce the risk of blockage in the pipeline system, and increasing the ballast volumetric concentration can improve the carrying performance of the ballast.  
Figure 12: \(Cont.\)
## 3 Factors Affecting Accuracy and Accuracy-Improvement Methods
### Effect of the Laser Spot Characteristic of the LD
The \(X\)-directional and \(Y\)-directional diameters (\(d_{xf}\), \(d_{yy}\)) of the focused laser spot on the QD, which is placed at the focal plane of the FL, can be expressed by:  
\[d_{xf}=\frac{2.44f\lambda}{2r_{x}}\ =\ \frac{2.44f\lambda}{d_{x}}\ \text{and}\ d_{yf}=\frac{2.44f\lambda}{2r_{y}}\ =\ \frac{2.44f\lambda}{d_{y}}, \tag{5}\]  
where \(\lambda\) is the wavelength of the LD and \(d_{x}\) and \(d_{y}\) represent the \(X\)-directional and \(Y\)-directional diameters of the laser beam before focusing. If the QD is not located at the focal plane of the FL, the \(X\)-directional and \(Y\)-directional diameters (\(d_{x\Delta z}\), \(d_{y\Delta z}\)) of the laser spot on the QD are revised to:  
\[d_{x\Delta z}=d_{xf}[1+(\frac{4\lambda\Delta z}{\pi d_{xf}^{2}})^{2}]^{\frac{ 1}{2}}\ \text{and}\ d_{y\Delta z}=d_{yf}[1+(\frac{4\lambda\Delta z}{\pi d_{yf}^{2}}) ^{2}]^{\frac{1}{2}}, \tag{6}\]  
where \(\Delta z\) is the position offset of the QD from the focal plane of the FL. As can be seen from Equation (6), a suitable \(\Delta z\) can be found so that \(d_{x\Delta z}\) is equal to \(d_{y\Delta z}\). According to Equations (5) and (6), \(\Delta z\) can be expressed by:  
\[\Delta z=\frac{\pi d_{xf}d_{yf}}{4\lambda}=\frac{1.49\pi\lambda f^{2}}{d_{x}d_ {y}}, \tag{7}\]  
A simulation was conducted by setting \(d_{x}\), \(d_{y}\), \(\lambda\) and f to be 5.0 mm, 4.5 mm, 0.635 nm and 20 mm, respectively. It can be seen from the simulation results shown in Figure 4 that when \(\Delta z\) was set to be \(\pm\)52.8 \(\upmu\)m, \(d_{x\Delta z}\) and \(d_{y\Delta z}\) were both equal to 9.5 \(\upmu\)m. It was verified that when the QD is located at a \(\Delta z\) distance from the focal plane of the FL, the elliptical laser spot can be shaped to be a circle laser spot. It should be noted that the LD usually exits at a divergence angle \(\alpha\). The \(X\)-directional and \(Y\)-directional diameters (\(d_{\text{ss}}\), \(d_{\text{ys}}\)) of the laser spot at the random measurement position \(s\) will be varied to be:  
\[d_{\text{ss}}=d_{x}+2s\alpha\ \text{and}\ d_{\text{ys}}=d_{y}+2s\alpha, \tag{8}\]  
When \(\alpha\) is small enough, the influence of \(\alpha\) on \(\Delta z\) and the diameter of the LD along the measurement direction can be ignored.
## 2 Materials and Methods  
In this study, we enrolled patients who had undergone L4/5, L5/S1, or L4/5/S1 posterior lumbar interbody fusion (PLIF) or posterolateral fusion (PLF) for a degenerative disease at the Chonnam National University Hospital (CHUH) from 1992 to 2012. Criteria for inclusion in our study were: age older than 18 years, bilateral pedicle screw fixation for fusion of the thoracolumbar spine, midline surgical approach, and CT scan within 1 year of the surgical procedure. In addition, patients with a history of lumbar surgery or trauma were excluded.  
Adjacent segmental pathology (ASP) was diagnosed when plain radiographs, computed tomography (CT), or magnetic resonance imaging (MRI) demonstrated one or more of the following lesions (absent preoperatively) at the segment adjacent to the fused segment: more than 4 mm of anterolisthesis or retrolisthesis, more than 10\({}^{\circ}\) of angular motion between adjacent vertebral bodies, more than 50% loss of disc height, or more than 1 grade advancement of facet joint degeneration or disc degeneration or spinal canal stenosis [18].  
Asymptomatic patients without disc-space narrowing, segmental instability, or facet arthropathy shown on a follow-up radiograph were defined as non-ASP patients. Finally, 87 patients met all the inclusion criteria of this study. Among the 87 patients, 56 patients were included in the ASP group, and 31 patients were allocated to the non-ASP group.  
Lumbar 3D CT was performed to assess superior facet joint violation caused by transpecicular screws. All the CT results were thoroughly assessed by two experienced spine orthopedists. Two evaluators were trained in advance to gain sufficient expertise in superior facet joint violation. No clinical history or patient information was known by the evaluators during the evaluations. The evaluated outcomes are presented as a score measure, i.e., 0, non-violated (type I); 1, suspected violation (type II); 2, definite violation (type III) (Figure 1). After scoring each of the superior facet joint violation on both sides, the sum of both sides was determined as the final score.
# New Physically-Based Mathematical Model and Experiments for a Recently Invented Solar Pot
## 1 Introduction
Based on the results, the physically-based model not neglecting the heat exchange with the environment can estimate the temperature of the outlet working medium more accurately than the other model version assuming energy balance between the two sides of a heat exchanger. The black-box model proved to be more accurate than both models, in case of considerable environmental heat exchange. In [32] there are physically-based models for tube-in-tube heat exchangers. The first one is a model with distributed parameters, which can be used to calculate the temperature of the working medium on the cold and hot sides of the heat exchanger. The second one is a second-order concentrated parameter model, which can be used to determine the outlet temperature of the cold and hot side working fluids. According to the authors' conclusion, second-order concentrated parameter models based on the logarithmic mean temperature difference approach can reliably describe the dynamics of heat exchangers. The contribution of the present paper is as follows:  
1. A new physically-based mathematical model is proposed to describe the examined, recently invented solar pot. Computer simulations are made with the model, based on which conclusions are drawn regarding the practical applicability of the solar pot. 2. An experimental system of the hydraulically connected solar pot and a solar collector is physically assembled. The different variables of the system (different temperature values) are measured. Based on measured data, conclusions are drawn regarding the practical applicability of the solar pot. The structure of the paper is the following: Section 2 contains the description of the modelled and measured system. Section 3 presents the physically-based model of the solar pot along with the simulation results. In Section 4, the measurements made on the experimental system and the measured data are shown. Section 5 serves with conclusions and future research proposals.
## 2 Materials and Methods
### Employed Sensors  
To establish the data collection system, various specific components have been selected. The DS18B20 temperature sensor (Maxim Integrated, San Jose, CA, USA) has been utilized for temperature measurement, while the Soil Moisture Sensor v1.2 (DFRobot, Shanghai, China) [39] sensor has been chosen for humidity measurement. Additionally, a real-time clock (RTC) module DS3231 (KEYESTUDIO, Shenzhen, China) has been incorporated to record the timestamp of the collected data. For data transfer between the Arduino board and a PC, the HC-05 Bluetooth module (HiLetgo, Shenzhen, China) has been employed [40]. This setup ensures data recording for subsequent analysis.  
Each of the employed sensors underwent capacitive calibration. Furthermore, initial samples were taken to determine their placement on the test specimens. Particularly concerning the humidity sensor, its design exposes its electronic components excessively to various atmospheric agents. To protect these components, three waterproofing tests were conducted: waterproofing with general-purpose silicone adhesive, waterproofing with neutral silicone adhesive, and waterproofing with PVC tubing and neutral silicone adhesive. The effectiveness of the waterproofing tests was assessed by comparing the measurement data obtained from the sensors without waterproofing and with waterproofing.  
In addition to the waterproofing procedures, the reliability of the sensors was verified through a preliminary calibration phase. The DS18B20 temperature sensors were compared against a certified laboratory thermometer under stable environmental conditions to ensure accuracy. For the Soil Moisture v1.2 humidity sensors, controlled saturation and drying cycles were conducted to confirm response stability and detect potential deviations prior to embedding. These checks allowed the exclusion of outlier units and ensured consistent performance during data collection.
# Impact of Cubic Symmetry on Optical Activity of Dielectric 8-srs Networks
## 3 Results and Discussion
### Experimental Characterization  
To characterize optical properties of 8-srs PhCs fabricated with GD-DLW and to quantify the effect of cubic symmetry on OA and transmission, we compared simulated transmission and OA spectra (Figure 5a,b) calculated for structures with a different aspect ratio with experimental spectra of structures fabricated with different R (Figure 5c,d). Both experimental and simulated PhCs have a lattice constant of 3.5 \(\upmu\)m and rod diameter of 550 nm in the xy plane.  
Optical transmissions for different light polarizations were obtained using a Fourier-transformed infrared spectrometer measured with angle resolution along the [001] direction of PhCs, as described in the Materials and Methods section.  
Figure 5c,d shows total experimental transmission (T\({}_{\text{xx}}\) + T\({}_{\text{xy}}\)) and OA respectively, across 8-srs PhCs with a lattice constant of 3.5 \(\upmu\)m, fabricated with GD radius of R = 900 nm (blue) corresponding to an aspect ratio of e \(\approx\) 1 and R = 300 nm (orange) corresponding to an aspect ratio of e \(\approx\) 3. For the complexity of the structure and the chosen period of 3.5 \(\upmu\)m, the transmission spectra of PhCs fabricated with R = 0 nm, corresponding to simple DLW, do not show any PSB (Figure S3), the index of a poor quality structure. Corresponding simulated total transmission and OA for structures of the same geometry can be seen in Figure 5a,b. Whilst the 8\({}^{\circ}\) focused beam, presence of material absorptions, and scattering losses in experiments broaden and weaken spectral features in the experimental results, there is good agreement between experiment and simulated behavior. Specifically, changes in spectral  
Figure 5: Comparison of experimental and theoretical results. (**a**) Theoretical transmission and (**b**) theoretical OA calculated for 8-srs PhCs with lattice constant of 3.5 \(\upmu\)m and rod diameter of 550 nm for aspect ratio e = 1 (blue) and e = 3 (orange). (**c**) Experimental transmission and (**d**) experimental OA for 8-srs PhC fabricated via GD-DLW with different GD radius (R): 900 nm (e\(\sim\)1) (blue) and 300 nm (e\(\sim\)3) (orange).  
position, transmission suppression, and stop-gap bandwidth compare well with changes expected from simulations. In the case of OA, we observe that strength of OA remains constant as expected, whilst bandwidth increases. Quantitatively, we conserve peak OA of 1 Rad at a wavelength of 4.68 \(\upmu\)m and 4.97 \(\upmu\)m through our 8-srs PhCs of height 14 \(\upmu\)m (4-unit cells) and we observe a minimum OA of 0.5 Rad across a bandwidth of 500 nm and 780 nm, centered at a wavelength of 4.50 \(\upmu\)m and 4.20 \(\upmu\)m for \(\text{e}\approx 1\) and \(\text{e}\approx 3\), respectively.
# Evaluation of Rural Visual Landscape Quality Based on Multi-Source Affective Computing
## 2 Materials and Methods
### Data Processing
#### 2.3.2 EEG Data Preprocessing  
The EEG signals were preprocessed using the data analysis software Asalab 4.10.2 (ANT Neuro, Berlin, Germany), which included steps such as downsampling, re-referencing, and artifact removal. Asalab software decodes and converts the received electrical signals into visual representations, while simultaneously storing and analyzing them in the background for detailed monitoring and assessment of EEG signals. In clinical settings, the frequency-band energy ratio (FBER) is commonly utilized as a characteristic parameter to quantitatively assess changes in the basic rhythm of EEG signals [64]. In this study, the FBER value is referred to as the R-value. The R-value reflects the proportion of different waveforms across the EEG electrodes, which include eight points: Fz, Cz, Pz, F3, F4, Fpz, C3, and C4. As a crucial EEG characteristic parameter, the R-value can be used to evaluate the subject's cerebral cortex preference and excitability in response to the rural visual landscape, based on its magnitude and variation. A total of eight EEG characteristics were derived. The frequency-band energy ratio (R-value) is calculated as follows in (2) and (3):  
\[E_{all}(k)=\sum E^{(j)}(k)_{j} \tag{2}\]  
\[R=\frac{E^{(j)}(k)}{E_{all}(k)} \tag{3}\]  
where \(j\) represents any electrode in Fz, Cz, Pz, F3, F4, Fpz, C3, and C4, E\({}^{(j)}(k)\) represents the power value of the electrode point, and \(E_{all}(k)\) represents the total power value of the eight electrode points.
# Characterization of Inter-System Biases in GPS + BDS Precise Point Positioning
## 4 Characterization of ISB
### ISB Characterization Affected by the Ambient Environment  
Four IGS stations (CEDU HOB2 KARR and PNGM) are selected on DOY 002, 2018. Stations CEDU and HOB2 are equipped with SEPT POLARX5 receivers while stations KARR and PNGM are equipped with TRIMBLE NETR9 receivers. For more detailed information, see Table 2.  
Figure 3 shows that the ISBs estimated with the WUM and CODE precise products showed little fluctuation. It is worth noting that even though stations KARR and PNGM were equipped with the same type of receivers and antennas, the ISB estimate were still different when using WUM and CODE precise products. We surmised that the ambient environment of the receiver, probably the temperature, may have affected the characterization of ISBs [10]. However, for the reason that the satellite clock datum of GFZ final products vary over time significantly, the difference between the different stations could not perform notably.