esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Prototype Testing
### Manually Supervised M3 TRIOS System Used for Data Comparison  
The M3 TRIOS system deployed during the FRM4 SOC/FICE consists of three TRIOS RAMSES radiometers, one measuring downwelling irradiance, \(E_{d}\), and two measuring radiance with fixed sky- and water-viewing zenith angles of 140\({}^{\circ}\) and 40\({}^{\circ}\) for \(L_{d}\) and \(L_{u}\), respectively. The system was deployed since 2001 at many locations from both large and small ships, and platforms including AAOT [31]. Details of the instrumentation and standard measurement protocol, based on Reference [25], and data processing and quality control can be found in Reference [26]. This is quite similar to the PANTHYR processing described in Section 2.5, except for the following elements:  
* The conversion from DCs to calibrated (ir)radiance is performed by the TriOS MSDA_XE software rather than the equivalent Python routines written for PANTHYR.
* Measurements are made simultaneously for \(E_{d}\), \(L_{d}\), and \(L_{u}\) with a much larger number of replicate scans, at least 30, with a scan every 10 s for 10 min. The first five scans passing the quality control tests described in Web Appendix 1 of Reference [26] are retained for \(E_{d}\), \(L_{d}\), and \(L_{u}\)
* The skyglint correction given as a quadratic function of wind speed by Reference [26] is used as an approximation of the more accurate LUT of Reference [25] described in Section 2.5 for PANTHYR.
* The skyglint correction, Equation (1), and conversion to \(\rho_{ww}\), Equation (2), and subsequent NIR correction, Equations (3) and (4), are applied to each \(E_{d}\), \(L_{d}\), and \(L_{u}\) triplet scan individually to give five scans for \(\rho_{w}\) before mean-averaging to yield \(\overline{\rho_{w}}\).  
Although the instrument has wider spectral range, data are here limited to the range 400-900 nm where quality was checked by previous inter-comparison exercises. For this specific implementation, the downwelling irradiance sensor was mounted on the top deck of AAOT on a telescopic mast at height 5 m above deck. The sky- and water-viewing radiance sensors were mounted at the southwest face of the platform on a frame tailor-made by Tartu Observatory and Plymouth Marine Laboratory to accommodate many radiometers and to ensure 40\({}^{\circ}\) and 140\({}^{\circ}\) zenith-angle viewing. On the other hand, 90\({}^{\circ}\), 135\({}^{\circ}\), or 270\({}^{\circ}\) relative azimuth angle to sun was achieved by manual rotation of the structure before each measurement. Measurements were made typically every 30 min during daytime between 8:00 a.m. and 1:00 p.m. UTC.
# Inkjet-Printed and Paper-Based Electrochemical Sensors
## 3. Ink Materials and Requirements
### Ink Materials  
Although a multitude of materials can be deposited with an inkjet printer, inkjet-printed electrochemical sensors typically require a small class of materials, namely conductors and hydrophobic materials. Obviously, conductors are needed for electrode fabrication whereas hydrophobic materials are useful for defining microfluidic channels. Although not necessarily applicable to the field of electrochemical sensing, inkjet printing can also be utilized with semiconducting and insulating materials for fabrication of other flexible electronics. However, given the application, semiconducting and insulating materials are not necessarily useful in traditional electrochemical sensors.  
Among conductors, the most common materials used are metallic nanoparticles and metallic nanowires as well as carbon-based materials such as carbon nanotubes, carbon powder, and graphene. In addition to using carbon nanotubes purely for their high conductivity, modified carbon nanotubes can also be used to enable detection of different analytes. Due to their nanometer dimensions, these materials can be dispersed in various mediums to develop nanomaterial ink capable of being deposited by an inkjet printer.  
While the conductive materials are obviously required for electrode formation, these materials must be dispersed in a fluid medium to be used in the inkjet printing process. Like many other coatings, ink can either be water-based or solvent-based, each with their own respective advantages. Water-based ink is certainly much easier and safer to handle considering that solvents can be corrosive and potentially harmful if they are inhaled or make contact with the skin. However, water-based ink typically requires the addition of an ionic surfactant to disperse the conductive materials. This not only increases material cost, but it can possibly inhibit contact between the nanomaterials ultimately reducing the electrode conductivity. On the other hand, solvent-based ink is able to naturally disperse small concentrations of conductive materials without the addition of other materials. Additionally, solvent-based ink exhibits much faster dry times when compared to water-based ink. This can enhance the nanomaterial network uniformity ultimately increasing electrode conductivity. Furthermore, with a better network, fewer layers need to be printed to achieve the necessary conductivity. Given these advantages and disadvantages, it is important to consider all of these properties when developing ink and selecting a fluid medium. Fortunately, due to the recent growth in popularity, various types of inkjet-compatible ink are readily available on the commercial market. In fact, many of the publications discussed in this review refrained from developing custom ink and instead purchased ink from a third-party source.
###### Abstract  
(1) To address issues such as a low compression ratio and severe leakage in uniform wall thickness vortex compressors, this paper adopts a new scroll compressor model with a variable diameter double arc combined profile, which has a larger pressure ratio and smaller leakage than the scroll compressor with equal wall thickness that can bring new ideas and methods to the research in the field of scroll compressors. (2) This paper determines the theoretical equation of the variable diameter dual circular arc vortex profile and the combined profile, and infers mathematical models for the leakage line length, working chamber volume, and gas leakage per unit rotation angle of the vortex compressor, followed by simulation. (3) The results show that compared to the uniform wall thickness involute profile, the variable diameter dual circular arc combined profile model reduces the leakage line length by 25%, increases the average total pressure at the working chamber outlet by 2.38%, and decreases the leakage amount by 3.2%, consistent with theoretical analysis. (4) The tangential leakage caused by the radial gap in the variable diameter dual circular arc combined profile model has a significant impact on the uneven distribution of temperature and velocity fields in the working chamber of the vortex compressor, but a smaller impact on the pressure field.  
vortex compressor; combined profile; tangential leakage; numerical simulation +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Average Seasonal Variations of VOCs
Moreover, the sampling period in 3-7 April coincided with the COVID-19 lockdown in Kazakhstan (19 March-13 April) with the absence of traffic activity [35]. [PERSON] et al. [25] assessed the effect of the lockdown restrictions on benzene, toluene, ethylbenzene, and \(o\)-xylene emissions by comparing their concentrations in April 2020 with the same period in the previous years from 2015 to 2019. Authors proposed that an increase of benzene and toluene concentrations by 2-3 times in 2020 indicated the non-traffic sources and can be related to no-precipitation conditions during sampling. In addition, the COVID-19 lockdown period can be associated with higher coal combustion by private houses and bathhouses (saunas) [25]. The significant reduction of \(n\)-heptane, methylene chloride, benzene, \(n\)-decane, toluene, 1,2-dichloroethane, and naphthalene by 27-90% (\(t\)-test, \(p<0.05\)) was observed in the heating period compared to the non-heating period in spring (Figure 3). Temperatures (14.0 vs. 17.6\(\,{}^{\circ}\)C) and pressure (763 vs. 760 mm Hg) were comparable in both spring sampling periods. Therefore, such reduction may indicate that coal combustion during the heating season primarily affected the emissions of the above-mentioned  
Figure 3: Variations of the VOCs concentrations (mean of the three sampling days \(\pm\) SD) in spring sampling periods during heating and non-heating season. VOCs. The obtained results of benzene and toluene emissions are in agreement with [PERSON] et al. [25]. A significant increase by 2-5-fold of ethylbenzene and benzaldehyde concentrations was observed from the heating period to the non-heating period in spring, which can be explained by a substantial reduction of traffic in the heating period due to the lockdown and resuming traffic in the non-heating period [35]. A similar effect of traffic-free conditions was observed by [PERSON] et al. [25], when concentrations of ethylbenzene and \(o\)-xylene decreased by 4 and 2.7 times in April 2020 (lockdown) compared to the same period in 2015-2019. The variations of mean concentrations of the rest of the analytes were insignificant and varied from 0.36 to 3.1 \(\upmu\)g m\({}^{-3}\) (Figure 3).
# Vehicle-Following Control Based on Deep Reinforcement Learning
## 3 Problem Formulation  
Vehicle-following on city roads is different from cruise control since the vehicle will start, stop, and shift frequently. Therefore, it is necessary to adjust the accelerator or brake according to the state of the vehicle in front. The ability to maintain a safe driving distance is the most important indicator of vehicle-following control. Following is a random and interactive process, so vehicle-following control can be modeled as a Markov decision process (MDP). During the MDP process, the following vehicle needs to continuously observe the current state and make decisions. MDP can be represented by a tuple \(\{S,A,P,R\}\), where \(S\) is a set of states. We divide state \(S\) into two states as the input of the policy network, namely the start-stop state \(S_{start-stop}\) and steady state \(S_{\text{steady}}\). \(A\) is the action set and we divide \(A\) into start-stop phase action \(A_{start-stop}\) and stable phase action \(A_{\text{steady}}\). \(P\) is the state transition probability, and \(R\) is the immediate reward obtained after performing action \(A\). Figure 1 shows a schematic diagram of car following control. Below, we will introduce the state space and action space in the process of vehicle-following.
## 4 Discussion
### Tensile Strength
#### 4.2.2 Tensile Strength of BTD Tests  
With the validation of the DIC method, it was confirmed that the crack initiated at the centre of discs in most BTD, so the tensile strength was calculated as Equation (4). However, the contact angle \(\theta\) between the arc jaw and cycle disc was generally uncertain. As the suggestion of the ISRM, the contact angle was set to be approximately \(10^{\circ}\) when the specimen loaded by two steel jaws was 1.5 times larger than the disc in diameter. In this study, the DIC method was applied to acquire the real contact angle \(\theta\) by reading the displacement and strain field at the contacting region in the BTD as shown in Figure 12a. The real final contact angles \(2\theta\) are described in Table 5, which were less than \(10^{\circ}\), as recommended by ISRM. Hence, the real tensile strength was obtained according to Equation (4) using the real contact angle. According to Table 5, the average tensile strength of the BTD was 3.667 MPa, which was quite close to that of 3.405 MPa in the BTC. The average tensile strength of the BTD was only 7.69% larger than that of the BTC as shown in Figure 12b. This suggested that the tensile strength of brittle materials was slightly overestimated by Equation (4) based on the pure elastic theory and hyperbolic distributed loading conditions at the boundaries.  
Figure 11: The strain field of failure initiation obtained by DIC for BTC-01 (**a**) and BTC-03 (**d**), and the sketch of the failure process for BTC-01 (**b–d**) and BTC-03 (**e**,**f**).  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Number**} & **Loading Rate/kN/s** & **Maximum Load/kN** & **Contact Angle 2\(\theta^{\circ}\)** & **Tensile Strength/MPa** \\ \hline
01 & 0.593 & 16.44 & 15.9 & 3.641 \\
02 & 0.605 & 17.15 & 17.8 & 3.777 \\
03 & 0.596 & 16.13 & 18.9 & 3.539 \\
04 & 0.459 & 16.79 & 16.7 & 3.709 \\ Average & 0.563 & 16.63 & 17.3 & 3.667 \\ \hline \end{tabular}
\end{table}
Table 5: Statistics of the maximum of load, loading rate, contact angle, and the tensile strength in the BTD.
## 7 Experimental Results
### Execution and Arrival Time Range  
If the input tasks' load and the period range are kept constant and the execution time range and arrival time range vary, it is foreseen that it will not affect the overhead and hyper-period of the system. The reason for this is that the execution time and arrival time of the tasks have no role in calculating the hyper-period of the tasks but rather on the overall CPU utilization of the input tasks. Table 6 reviews the scenarios used for varying the execution range, and the rest of the parameters are kept fixed. The number of tasks is fixed as 10, the period is selected from 1 to 10, and, for each experiment, the corresponding parameter is changed. For brevity, we represent this in the same table for execution time as well as for arrival time.  
Figure 8(a) reveals the effect of hyper-period, optimal threshold, and overhead on varying range of execution time. Due to adjusting the hyper-period, it always falls below the threshold, but, in contrast, the overhead shows some irregular patterns, and thus no relation of overhead can be drawn with the execution time range.  
Figure 8(b) depicts the behaviour of hyper-period, optimal threshold, and overhead with respect to the change in the range of arrival time. The random plots of overhead and hyper-period confirm that the arrival time of the tasks also has no relationship to the parameters mentioned above.  
Figure 8: Scenario 2: The effect of varying Period Range, given No. of Tasks = 1–10, Execution Time Range = 1–5, Arrival Time Range = 1–5. (a) effect of hyper-period, optimal threshold and overhead on period range; (b) effect of optimal threshold on period range.
## 2 System Model and Methodology  
The primary aim of the paper is to research and analyze the performance of hybrid isolation devices. The focus is on examining the impact of the reactive forces exerted by active control on the lower passive isolation layer when control forces are applied to the upper layer, and how these forces affect the system displacement. GA optimization is used to adjust the gain, integral, and derivative parameters of the PID controller. This section explains the methods used to achieve the research objectives.  
Figure 1 summarizes the research methodology flowchart. The proposed method integrates dynamic iterative analysis with optimization techniques.  
In step 1, the process begins with setting the initial conditions for the entire hybrid isolation system. This includes configuring system structural parameters, PID parameters, and improved genetic algorithm parameters. System structural parameters encompass  
Figure 1: Research methods flowchart.  
mass, stiffness, and damping. PID parameters include proportional gain, integral gain, and derivative gain. Improved genetic algorithm parameters cover fitness function, population size, and number of iterations. Additionally, seismic excitation is inputted and initial responses are set.  
In step 2, the Newmark method is used to calculate displacement, velocity, and acceleration. Based on the current response and the values of Proportional Gain (\(\mathrm{K_{p}}\)), Integral Gain (\(\mathrm{K_{i}}\)), and Derivative Gain (\(\mathrm{K_{d}}\)), the PID control forces are calculated and applied to both the upper and lower layers simultaneously.  
In step 3, the seismic forces and PID control forces are integrated to update the displacement, velocity, and acceleration of the system. The fitness of the current genetic algorithm individuals is calculated based on the displacement results of the upper layer.  
Subsequently, crossover, selection, and mutation operations are performed based on the fitness results to generate a new generation of PID parameters. The optimal PID parameters of the new generation are then applied in the subsequent calculations, continuing until the seismic excitation ends.
## 7 Results and Discussion
### Close-Range Photogrammetry  
Some objects are documented by photogrammetrical technology (IBMR). In this case, there are no suitable objects for typical and traditional photogrammetrical 3D documentation using stereophotogrammetry. IBMR can be a solution, which can replace traditional photogrammetry. As this technology produces a point cloud like laser scanners, it can replace scanners in some cases, or better, complement and merge data with data captured from scanners. By documenting artifacts (construction artifacts, archaeological funds, etc.), the IBMR nowadays is very popular and simple to use. From about 0.5-2 m and with using of a modern SLR camera, the IBMR has significantly better resolution, which reaches sub-millimeter resolution and accuracy and allows for better documenting of hidden areas (Figure 27).  
During our expedition, many artifacts were documented with this method. Important artifacts, such as archaeological finds or non-transportable objects, can then be printed as a facsimile on a 3D printer. It is also perfect that photogrammetric data can already be routinely combined with laser scanning using suitable software (e.g., RealityCapture). Thus, the advantages of both technologies can be used together, and the emerging model is suitably complemented by the second technology.
###### Abstract
The objective of this study was to evaluate the positional accuracy of operating a harvester based on a modern GNSS device logging multiple GNSS constellations under a mature forest canopy, and to develop approaches for forecasting the positional accuracy of these harvesters under such dense canopy cover. The study aimed to fill gaps in our knowledge, such as how accurately GNSS-based harvesters can be positioned and how accurate the latest GNSS technologies are when tested in real-world forest environments, and in challenging conditions, i.e., winter operations, spruce-dominant mature stands, and high canopy cover. This study has the potential to drive advancements in GNSS technology by providing new insights into the impact of canopy cover on GNSS's accuracy, leading to the development of GNSS technologies that are not only better suited for dense forest canopies, but are also more accurate and more efficient.
# Hydraulic Fracture Propagation near the Cavity in a Poroelastic Media
## 3 Numerical Test Results and Discussion  
### Formulation of the Problem  
Let us consider the problem of interaction between a hydraulic fracture and a circular cavity in a plane formulation. Figure 2 shows a 2D area with a circular cavity that has a radius, \(R\). The initial crack is located at a distance, s, from the surface of the cavity and is oriented vertically.  
In numerical experiments, the size of the area is 40 \(\times\) 40 m, and the radius of the cavity is 2.5 m. Figure 2 shows the position of the cavity and the discretization of the area. The minimum element size is 0.1 m. On the outer boundary of the area, we set the conditions providing hydrostatic compression, \(P\), in the vicinity of the cavity (Figure 2a). The boundary of the circular cavity is free. For numerical calculations we define the following parameters: the elasticity modulus of the rock, \(E\) = 3.0 GPa; Poisson's ratio, \(v\) = 0.3; dynamic viscosity of the fluid, \(\eta\) = 0.001 Pa-s; critical energy release rate, \(G_{\text{IC}}\) = 280 N/m; critical stress, \(\sigma_{c}\) = 0.75 MPa; fluid injection rate into the crack, \(q\) = 0.001 m\({}^{2}\)/s, and porosity, \(\gamma\) = 0.1. The medium permeability, \(k\), Biot coefficient, \(\alpha\) and Biot modulus, \(M\), are changed during the calculations.
## 3 Results and Discussion
### Interfacial Parameters and Gibbs Adsorption Isotherm
#### 3.5.1 Surface Excess Concentration (\(\Gamma\))
[45], which provides a good explanation of the surface tension behavior of amino acids with water and surfactant. For Glycine and L-Alanine, probably at higher temperatures, weaker ion-dipole interactions get disrupted and give way for the formation of new hydrogen bond between the water molecules. Consequently, this will lead to an increase in \(\Gamma\) values. Due to the C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)- aromatic group, L-Phenylalanine acts as a surfactant molecule with a steady decrease in surface tension with an increased concentration with water. It is interesting to note that decreased \(\gamma\) values upon the increase in L-Phenylalanine concentration produce a hydrophobically rich environment at the air-liquid interface, which dominates over the effect of \(\pi\)-conjugation. Thereby, it contributes to the fact that on hydrogen bonding, the surfactants act as structure-makers, but this effect is stronger with \(\pi\)-conjugated L-Phenylalanine due to the shift in charge on sp\({}^{2}\) hybridization. Hence, it is very obvious that taurine resembles the Gly/L-Ala behavior and  
Figure 6: Surface excess concentration (G) versus temperature (T) plot of aqueous tyramine with D-glucose at different concentrations. tyramine, like phenylalanine, which supports the accuracy of the work presented in this paper. Therefore, it is concluded that at lower concentrations, with the C\({}_{6}\)H\({}_{5}\)CH\({}_{2}\)-group, tyramine acts as a surfactant, but on a further increase in concentration, its hydrophobic part engages the solute molecules. Thus, it results in releasing water molecules from amino acids, leading to the formation of hydrogen bonds, with a steady increase in surface tension. Hence, the side chain of amino acids contributes to the hydrogen bonding capacity, hydrophobicity, and related interactions. These considerations led us to undertake the study of \(\beta\)-amino acids with glucose.