esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Precise Orbit Determination for the HY2D Satellite Using Onboard GPS/BDS Observations
## 2 Materials
### HY2D Spacecraft
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & & **X+** & **X\(-\)** & **Y+** & **Y\(-\)** & **Z+** & **Z\(-\)** & **SA+** & **SA\(-\)** \\ \hline \multirow{8}{*}{Spacecraft surfaces} & \multicolumn{2}{c}{Projected area (m\({}^{2}\))} & 3.62 & 3.92 & 5.17 & 5.46 & 3.06 & 6.22 & - & - \\  & \multicolumn{2}{c}{Specular} & 0.65 & 0.65 & 0.65 & 0.65 & 0.65 & 0.65 & - & - \\  & \multicolumn{2}{c}{Diffuse} & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & & \\  & \multicolumn{2}{c}{Absorbed} & 0.35 & 0.35 & 0.35 & 0.35 & 0.35 & - & - \\  & \multicolumn{2}{c}{Specular} & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & - & - \\  & \multicolumn{2}{c}{Infra-red} & 0.31 & 0.31 & 0.31 & 0.31 & 0.31 & 0.31 & & \\  & \multicolumn{2}{c}{Absorbed} & 0.69 & 0.69 & 0.69 & 0.69 & 0.69 & 0.69 & - & - \\ \hline \multirow{8}{*}{Radiators and solar arrays} & \multicolumn{2}{c}{Projected area (m\({}^{2}\))} & 0.33 & 0.37 & 2.61 & 2.33 & 4.88 & 1.72 & 18.12 & 18.12 \\  & \multicolumn{2}{c}{Specular} & 0.87 & 0.87 & 0.87 & 0.87 & 0.00 & 0.87 & 0.10 & 0.00 \\ \cline{1-1}  & \multicolumn{2}{c}{Diffuse} & 0.00 & 0.00 & 0.00 & 0.00 & 0.15 & 0.00 & 0.00 & 0.10 \\ \cline{1-1}  & \multicolumn{2}{c}{Absorbed} & 0.13 & 0.13 & 0.13 & 0.85 & 0.13 & 0.90 & 0.90 \\ \cline{1-1}  & \multicolumn{2}{c}{Specular} & 0.22 & 0.22 & 0.22 & 0.22 & 0.00 & 0.22 & 0.08 & 0.00 \\ \cline{1-1}  & \multicolumn{2}{c}{Infra-red} & 0.00 & 0.00 & 0.00 & 0.00 & 0.15 & 0.00 & 0.00 & 0.10 \\ \cline{1-1}  & \multicolumn{2}{c}{Absorbed} & 0.78 & 0.78 & 0.78 & 0.78 & 0.85 & 0.78 & 0.92 & 0.90 \\ \hline \end{tabular}
\end{table} 
Table 2: Projected areas and optical characteristics of HY2D.
# Experimental Study on a Prediction Model of the Shrinkage and Creep of Recycled Aggregate Concrete
## 4 Creep and Shrinkage Model of Recycled Aggregate Concrete
### Shrinkage Increasing Coefficient of Attached Mortar  
Concrete is constrained by aggregates, so its dry shrinkage is lower than that of pure grout [33]. The shrinkage of concrete is mainly caused by the hydration of cement mortar. The size of a few coarse aggregates is unstable. However, most of the coarse aggregates have the same size, a large elastic modulus, high crush index, and good restraint on concrete shrinkage. Therefore, the shrinkage of concrete depends on the amount and rigidity of the coarse aggregate in concrete. Considering the combined effect of aggregate content and rigidity, the prediction equation of the shrinkage strain of ordinary concrete is as follows:  
\[S_{\rm NAC}=S_{\rm TP}^{\rm NAC}(V_{\rm TP}^{\rm NAC})^{\alpha}. \tag{15}\]  
In the equation, \(S_{\rm NAC}\)--the shrinkage strain of ordinary concrete; \(S_{\rm TP}\)--the shrinkage strain of cement slurry in common concrete under the same conditions; \(V_{\rm TP}^{NAC}\)--the volume of the total cement slurry in the ordinary concrete; \(\alpha\)--empirical coefficient. The change range is 1.2-1.7 and the average is 1.45.  
Because of the two-phase nature of RCA, the total mortar content in RAC is composed of attached mortar and fresh mortar. The shrinkage strain of RAC can be expressed as the following equation:  
\[S_{\text{RAC}}=S_{\text{TP}}^{\text{RAC}}(V_{\text{TP}}^{\text{RAC}})^{\alpha}. \tag{16}\]  
It is assumed that the shrinkage characteristics of cement mortar in RAC are the same as those of ordinary concrete, which simultaneously gives the following equation:  
\[\frac{S_{\text{RAC}}}{S_{\text{NAC}}}=\left(\frac{V_{\text{TP}}^{\text{RAC}}} {V_{\text{TP}}^{\text{NAC}}}\right)^{\alpha}. \tag{17}\]  
The definition of SAM is the shrinkage increase coefficient of adhesive mortar, \(S_{\text{AM}}=\left(V_{\text{TP}}^{\text{RAC}}/V_{\text{TP}}^{\text{NAC}} \right)^{\alpha}\). Since the shrinkage of RAC is related to the volume of the total mortar, the volume \(V_{\text{TP}}^{\text{RAC}}\) of the total mortar in RAC can be determined according to Equation (9). Then there is the following equation:  
\[S_{\text{AM}}=\left(\frac{1-V_{\text{RCA}}^{\text{RAC}}\times[\frac{1-r}{r} \times\frac{\rho_{\text{RCA}}}{\rho_{\text{NCA}}}+(1-M_{\text{AM}})]}{1-V_{ \text{RCA}}^{\text{RAC}}\times(1+\frac{1-r}{r}\times\frac{\rho_{\text{RCA}}} {\rho_{\text{NCA}}})}\right)^{1.45}. \tag{18}\]  
When \(r=100\%\), it is all RAC:  
\[S_{\text{AM}}=\left(\frac{1-V_{\text{RCA}}^{\text{RAC}}(1-M_{\text{AM}})}{1-V _{\text{RCA}}^{\text{RAC}}}\right)^{1.45}. \tag{19}\]
## 5 Results and Discussion  
### Combustion Characteristics  
#### 5.1.1 Cylinder Pressure  
Cylinder pressure is an indication of fuel efficiency and the ability to mix air with fuel. Figure 5 shows the change in pressure in the cylinder for diesel and biodiesel blends. It was observed that the maximum pressure reached was 56.5, 63.28, 67.8, and 72.32 bar for operating modes A, B, C, and D. In all operating modes, diesel generated the maximum pressures. This behavior is consistent with similar studies [39].  
Figure 4: Engine operating conditions.  
Figure 5: Influence of fuel on cylinder pressure for (**a**) mode A, (**b**) mode B, (**c**) mode C, and (**d**) mode D.  
The blends of biodiesel PB5 and PB10 showed a decrease in chamber pressure of 5.1% and 9.8%, compared to diesel, respectively. Similarly, blends of biodiesel PB5 SB5 and PB10 SB5 showed a decrease in in-cylinder pressure of 12% and 15.6%, compared to diesel, respectively. The greater pressure reduction in biodiesel with sunflower blends is attributed to the lower calorific value of this biodiesel, as shown in Table 4. [PERSON] et al. [40] study present a similar behavior with other types of biodiesel.
# Planetary Gearbox Fault Diagnosis Based on ICEEMD-Time-Frequency Information Entropy and VPMCD
## 2 Feature Extraction Method Based on ICEEMD-Time-Frequency Information Entropy
### A Description of Improved Complementary Ensemble Empirical Mode Decomposition (ICEEMD)
In order to test the advantages of the method proposed in this paper, the ICEEMD method and CEEMD method were used to decompose the simulation signal, where \(Y\) is the simulation signal, \(x_{n}\) is the noise signal, and S is the simulation signal with noise. \[Y=2.5 sin(2\pi 8t+\pi/6)+3 tsin(2\pi 4t+\pi/2) \tag{6}\]  
The simulation signal, noise signal, and simulation signal with noise are shown in Figure 2.  
The seven sub-signals decomposed by ICEEMD are shown in Figure 3 and the seven sub-signals decomposed by CEEMD are shown in Figure 1:0  
Figure 1:1 Seven sub-signals decomposed by ICEEMD. Figure 1:2 Simulation signal, noise signal, and simulation signal with noise. As shown in Figures 3 and 4, seven IMFs are sequentially decomposed in order of their frequency using the two methods. We can see that the decomposed components of CEEMD are more severely affected by noise than those of ICEEMD. In order to further test the performance of the two methods in eliminating noise, the simulation signal was compared with the reconstructed signals of ICEEMD and CEEMD, respectively, and the results were shown in Figure 1:3  
By comparison, the correlation between the simulation signal and the reconstructed signal of ICEEMD is higher and the mean squared error is smaller than that of CEEMD. In addition, compared with CEEMD, the components of the ICEEMD method have better orthogonality. Therefore, the method proposed in this paper is better at removing noise and restoring the signal characteristics.
## 4 Experimental Results and Discussion
### Experimental Setup  
In experiments, each HSI dataset was randomly divided into training and test sets. The training samples were utilized to learn a feature extracted model to obtain their low-dimensional embedding  
Figure 4: Indian Pines hyperspectral image: (**a**) HSI in false color; (**b**) ground truth; and (**c**) spectral curve. (Note that the number of samples for each class is shown in brackets.)  
Figure 5: PaviaU hyperspectral image: (**a**) HSI in false color; (**b**) ground truth; and (**c**) spectral curve. (Note that the number of samples for each class is shown in brackets.)  
features. After that, the classifier was applied to obtain the class labels of test samples. The overall classification accuracy (OA), average classification accuracy of each class (AA), and kappa coefficient (KC) were employed to evaluate classification results.  
To demonstrate the effectiveness of the proposed method, we compared SSRHE with four spectral-based approaches, LPP, LDA, MFA, and RLDE; two spatial-spectral combined approaches, LPNPE and SSCE; and a hypergraph method, DHLP. For all approaches, we tuned the parameters by cross-validation to achieve good results. In LPNPE, the size of spatial-spectral window was set to 15 for the PaviaU data and 11 for the Indian Pines data. In SSCE, the window scale and the local neighbor size were both set to 5 on both datasets. For RLDE and MFA, the intraclass neighbor size was set to 3 and 8, while the interclass neighbor size was set to 5 and 60, respectively. The number of nearest neighbor in DHLP was selected as 9.  
The nearest neighbor (1-NN) classifier was applied for classification. Each experiment was randomly repeated ten times. All experiments were performed on a personal computer with i5-4590 central processing unit, 8-G memory, and 64-bit Windows 7 using MATLAB 2014a.
# Reversible Image Processing for Color Images with Flexible Control
## 3 Proposed Method
### Image Processing  
Here, we explain the outline of image processing in Figure 2a. We can select one or two types of functions from among five brightness controls: sharpening and smoothing, brightness increase and decrease, and CE functions. Here, our method can carry out two types of brightness controls simultaneously.  
The largest, middle, and smallest values in the \(R\), \(G\), and \(B\) components of each pixel are assigned to _Max_, _Median_, and _Min_, respectively. We regulate the _Max_ and _Min_ values so as to use the selected brightness controls (see Figure 2a(I)). Depending on the user's intention, the saturation is subsequently improved (see Figure 2a(II)). The _Median_ value is then adjusted to avoid hue distortion (see Figure 2a(III)). The _Max\({}^{\prime}\)_, _Median\({}^{\prime}\)_, and _Min\({}^{\prime}\)_ values are further calibrated to maintain the magnitude relation among RGB components and turned back into \(R\), \(G\), and \(B\) values, respectively (see Figure 2a(IV)). Recovery information is finally embedded into each color component by using the PEE-HS method [19] (see Figure 2a(V)). Accordingly, our new method can reversibly control the brightness and improve the saturation.  
As described above, our proposed method can conduct one or two functions for brightness from five control types: sharpening and smoothing, brightness increase and decrease, and CE functions. It also performs brightness control and saturation improvement  
Figure 1: Block diagram of our previous method [13].  
independently. The recovery information is required for each function. In the case where the number of functions is increased, the amount of recovery information is accumulated and might exceed the hiding capacity. In such a case, reversibility cannot be fully ensured. Through our experiments, reversibility was constantly ensured under the combination of up to two types of brightness controls and saturation improvement. We separately explain the sharpening and smoothing function, the brightness increase and decrease function, and how to guarantee reversibility.
# Launch Dynamic Simulation of a Compressed-Air Launcher for Fire Suppression
###### Abstract  
This paper focuses on improving fire suppression performance through the use of compressed-air launching technology. A launch dynamics calculation model of a compressed-air launcher is presented, developed using VC++ programming, to simulate the acceleration process of a fire-extinguishing bomb in a barrel. By analyzing the influences of various structural and initial parameters on interior ballistics variations, the effectiveness of the calculation model and program in accurately simulating the launching process is demonstrated. The calculation results indicate that the bore pressure follows a similar trend to that of traditional gumpowder launching. Additionally, it is found that specific structural parameters, such as nozzle diameter and gas cylinder volume, have a direct impact on interior ballistics variations. Notably, the nozzle diameter positively affects the peak pressure, muzzle velocity, gas transfer efficiency, and launch efficiency. To ensure an optimal launch effect and efficiency, the nozzle diameter should be selected to be more than half of the launcher caliber. Similarly, the gas cylinder volume positively influences the peak pressure and muzzle velocity while negatively affecting the gas transfer efficiency and launch efficiency. Furthermore, the initial pressure in the gas cylinder exhibits a positive linear relationship with both the peak pressure and muzzle velocity but a negative linear relationship with the gas transfer efficiency and launch efficiency. The loading position minimally impacts the peak pressure and muzzle velocity but slightly enhances the gas transfer efficiency and launch efficiency. Finally, it is observed that launch angles do not affect the interior ballistic process. The research findings provide valuable theoretical guidance for determining the working parameters of compressed-air accelerated fire-extinguishing bombs.  
compressed air; launch dynamic; fire suppression; interior ballistics +
Footnote †: journal: Applied Sciences
## 3 Analytical Non-Stationary S2A Channel Model
### Time-Frequency ACFs
\[R_{\text{LOS}}\left(\Delta t,\Delta f\right)= \frac{\lambda^{2}G_{\text{T,LOS}}G_{\text{R,LOS}}}{16\pi^{2}d_{ \text{LOS}}^{2}}\cdot\exp\left(\mathrm{i}2\pi\Delta f\tau_{\text{LOS}} \right)\cdot\exp\left(\mathrm{i}2\pi\frac{\mathbf{n}_{\text{RT}}^{\text{T}} \cdot\mathbf{v}_{\text{RT}}}{\lambda}\cdot\Delta t\right) \tag{33}\] \[R_{\text{SPE}}\left(\Delta t,\Delta f\right)=\frac{|\boldsymbol{ \Gamma}|^{2}\lambda^{2}G_{\text{T,SPE}}G_{\text{R,SPE}}}{16\pi^{2}d_{\text{ SPE}}^{2}}\cdot\exp\left(\mathrm{i}2\pi\Delta f\tau_{\text{SPE}}\right)\] (34) \[R_{\text{LOS}}\left(t,\Delta t;f,\Delta f\right) =E\left\{H_{\text{LOS}}^{\ast}\left(t,f\right)\cdot H_{\text{ LOS}}\left(t+\Delta t,f+\Delta f\right)\right\}\]0 \[R_{\text{LOS}}\left(t,\Delta t;f,\Delta f\right) =E\left\{H_{\text{LOS}}^{\ast}\left(t,f\right)\cdot H_{\text{ LOS}}\left(t+\Delta t,f+\Delta f\right)\right\}\]1  
With time variant ACFs, different coherence time can be obtained, which is essential parameter for system design. Through Fourier transform [31] as in Equation (36) with Equations (33)-(35), for the WSSUS channel, the Doppler delay spectrum of each path can be obtained, where \(R\left(u,\Delta f\right)\) is \(R_{\text{LOS}}\left(\Delta t,\Delta f\right)\), \(R_{\text{SPE}}\left(\Delta t,\Delta f\right)\), or \(R_{\text{DBF}}\left(\Delta t,\Delta f\right)\), \(u\) denotes \(\Delta t\). If more precise information of the channel is desired, the time variant features should not be ignored. Thus other method towards the frequency spectrum features such as Wigner Ville spectrum should be adopted, for example, as discussed in [13]. \[R_{\text{LOS}}\left(t,\Delta t;f,\Delta f\right) =E\left\{H_{\text{LOS}}^{\ast}\left(t,f\right)\cdot H_{\text{ LOS}}\left(t+\Delta t,f+\Delta f\right)\right\}\]2
## 3 Results and Discussion
### Non-Saccharomyces as Inhibiting Species
pulcherrima_ strains exert their antagonistic influence only toward _P. fermentans_ M105A30, in agreement with the evidence observed by [PERSON] et al. [27]. Surprisingly, we found no antimicrobial action on spoilage yeasts _Brettanomyces_/_Dekera_[62]. Taken together, these results proved the strain-dependent character of _M. pulcherrima_ antimicrobial activity. This is probably connected to the intraspecific diversity in terms of the molecular basis responsible for the antagonistic phenotype [64]. The inhibitory activity of _M. pulcherrima_ is addressable to the production of low molecular weight and heat-sensitive metabolites, principally pulcherriminic acid [63]. The acid forms an insoluble red pigment pulcherrimin in the presence of iron (III) ions, with subsequent precipitation. This iron sequestration which depletes the medium of iron, making it unavailable to the other microorganisms' mechanism appeared to be strain-dependent [64]. Additionally, the inhibition of _S. cerevisiae_ by _M. pulcherrima_ could also come from the competition for nutrients: in the sequential inoculation, the consumption of the nutrients by the non-_Saccharomyces_ yeast at the beginning of AF could prevent the following growth of _S. cerevisiae_[62]. In this study, _T. delbrueckii_ 291, a commercial strain, showed inhibitory activity toward some bacterial strains on culture medium agar. This might contribute to explaining a negative influence found after inoculation of commercial _T. delbrueckii_ on spontaneous malotactic consortium [30] strains, which is perfectly in accordance with the evidence reported by [PERSON] et al. [65] that successfully tested the combination of _T. delbrueckii_ and _O. oeni_ strains in red Barbera wine. Together with results reported for _M. pulcherrima_ 346, these findings added a piece to the intricate puzzle of the possible interaction among non-_Saccharomyces_ and malolactic bacteria in oenology [65, 66, 67, 68, 69, 13]. Concerning the other non-_Saccharomyces_ yeasts, some inhibitions on culture medium and grape juice testify to the occurrence of other phenomena of interest. In particular, _P. fermentans_ M105A30 was found largely inhibited. _P. fermentans_ B05A29 displayed a certain negative activity against _T. delbrueckii_ and _B. bruxellensis_ (some _Pichia_ strains have been found to produce a killer toxin called zymocins [70, 71]). Finally, all the strains of _B. bruxellensis_ tested in this study inhibited _Pediococcus_ spp. and _O. oeni_ 6 on grape juice agar.
# Evaluation of Different Chemical Mechanisms on O3 and PM2.5 Predictions in Alberta, Canada
## 3 Results and Discussion
### Fine Particulate (PM\({}_{2.5}\))
#### 3.3.1 Spatial Variation  
The spatial distribution of primary PM\({}_{2.5}\) emission by season is presented in Figure 2. Non-point sources are the largest source of primary PM\({}_{2.5}\) (Table 2), so there is no surprise that there is a strong spatial correlation of this component of PM\({}_{2.5}\) with roadways as well as with urban centres--Grande Prairie, Beaverlodge, and Edson. The spatial variation of average PM\({}_{2.5}\) predicted by CB6R3 in summer and winter, as well as the respective differences with SAPRC07 and RACM2, are shown in Figure 7. During the summer, low concentrations are observed in the south and southwest of the domain, with slightly higher concentrations to the north and east. There is a strong spatial correlation during the summer months between these slightly higher concentrations and roadways. During the winter, the primary PM\({}_{2.5}\) is elevated throughout the modelling domain (Figure 2), with noticeable enhancements around the urban centres. The spatial pattern for winter now runs from northwest to southeast, aligning more with the NOx and VOC emissions. Generally, the winter concentrations were higher than the summer concentrations. In summer, SAPRC07 and RACM2 produced up to 11% and 14% more PM\({}_{2.5}\) than CB6R3, respectively. In winter, CB6R3 and SAPRC07 were similar, but RACM2 produced up to 7% more than CB6R3.  
The change in spatial correlation between winter and summer may suggest a change in the source of the PM\({}_{2.5}\). Figure 8 shows mean concentrations in the study area for various species of PM\({}_{2.5}\) such as nitrate (ANO3), sulfate (ASO4), ammonium (ANH4), and Secondary Organic Aerosols (SOA) for anthropogenic and biogenic sources produced by CB6R3, SAPRC07, and RACM2 mechanisms. Clearly there is a difference in PM\({}_{2.5}\) composition between summer and winter.  
Figure 9 depicts the relative percentage composition of nitrate, sulfate, ammonium, SOA-Anthropogenic (SOA-Anth), and SOA Biogenic (SOA-Bio) predicted by three mechanisms for both summer and winter. Sulfate and SOA-Bio dominates PM\({}_{2.5}\) in summer and were approximately 50% and 35%, respectively, of the total. Nitrate production during the summer was very low for all mechanisms. Other species combined were about 15%, but SAPRC07 produced about 10% more SOA-Bio and 7% more SOA-Anth. There is a noticeable difference in the production of nitrate compounds during winter. CB6R3 produced the most sulfate, followed by SAPRC07 and RACM2. In the winter, the most dominant PM\({}_{2.5}\) species were nitrate (64%), sulfate (21%), and ammonium (10%). Other species were at less than 10%. The three mechanisms showed no major difference in terms of relative compositions in the winter.
# Assessing Ambisonics Sound Source Localization by Means of Virtual Reality and Gamification Tools
## 3 Materials and Methods
### Auditory Stimuli  
The auditory stimuli used in this study were the call of the Southern Lapwing (Vanellus chilensis), commonly referred to as the 'Queltehue'. This choice was influenced by its relevance to the _Soundlapse_ project, which explores the natural soundscape of Southern Chile. The Queltehue's call was selected for its broad spectral characteristics, especially its content around 4 kHz and higher, which are known to significantly enhance vertical and front/back localization [28]. The sound file, extracted from a soundscape recording, was also used in previous research by [PERSON] and [PERSON] [12; 16] (Figure 4). It was in mono format and spatialized as a virtual sound source using a 2 nd-order Ambisonics encoder in an original patch created in Max with the Spat libraries (Figure 5).  
Figure 4: Spectrogram of the auditory stimulus used in this study: call of the bird Vanellus chilensis, showing frequency content of the call, emphasizing the spectral range with significant components relevant for vertical and front/back localization.  
Figure 3: User interface for stimulus control within the VR environment.  
In the first part of the experiment, stimuli were randomly presented from 16 virtual sound sources at specific positions on the horizontal plane (Figure 2a). In the second part of the test, stimuli were presented from 12 positions, including some with an elevation angle of 35\({}^{\circ}\) to introduce vertical localization tasks (Figure 2b).  
The reproduced sound pressure level of the stimuli was calibrated to ensure consistent loudness across all trials. The randomization of stimulus presentation positions in both parts of the experiment helped prevent learning effects and ensured that participants had to rely on their auditory localization abilities.