esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Vision through Haze Based on Polarization Imaging
## 3 Real-Time Optical Sensing and Detection System
### Design of Real-Time Optical Sensing and Detection System  
As per the polarization image model, the real-time optical sensing and detection system with four cameras aligned parallel to the optical axis was designed. Analyzers were mounted and integrated inside each lens at angles of \(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), and \(135^{\circ}\) relative to the polarization direction of the incident light. The structure of our optical system is shown in Figure 2.  
A calibration platform, using a luminous theodolite, was adopted for the calibration of the common optical axis of the four-camera array. During calibration, via controlling the linear motion of a two-dimensional rail, the exit pupil of the theodolite was aligned with the entrance pupils of the cameras separately. Next, with the crosshair lit, the orientation of the optical axis of each camera was adjusted individually to center the crosshair image on the target surface of the camera. High-precision calibration of the common optical axis was thereby completed, and the requirements for subsequent image registration were fulfilled. According to the above analysis, we next constructed a four-axis calibration platform, which was composed of the following elements: a luminous theodolite (1), a horizontal rail (2), a vertical rail (3), a load platform (4), a right angle fixed block (5), and a servo drive (6). Each part is marked on Figure 3.  
Figure 2: Real-time optical sensing and detection system.  
In addition, the cameras were equipped with external triggers for synchronized exposure (trigger IN) and image capture (trigger OUT) from the four cameras. Transistor-transistor logic (TTL) levels that had both trigger IN and OUT interfaces were employed such that one signal could be used to trigger multiple cameras. That is, trigger OUT was enabled on the first camera, while the remaining three were enabled with trigger IN, such that synchronized imaging using multiple cameras was enabled. Compared with other polarization imaging systems, such as a focal-plane polarization imaging system [11], micropolarizer arrays polarization imaging system [12], and so on [13; 14; 15; 16], the proposed system has the advantages of low cost and simple imaging system. This system can obtain the polarization images without losing the light energy.
# Powdered Foods: Structure, Processing, and Challenges: A Review
## 6 Improvements and Challenges on Powdered Foods
### Pretreatment  
[PERSON] [51] reported the advantages and importance of pretreatment in the freeze-drying process. Many methodologies can be applied to food, including size reduction, bleaching, osmotic dehydration, ultrasound, and others. For instance, the application of bleaching not only induces enzymatic inactivation in fruits and vegetables but also contributes to a reduction in drying time [51]. Figure 4 illustrates the pretreatment methodologies commonly employed in the drying of foods.  
Ultrasound induces the formation of microchannels, facilitating water removal in the drying process, besides increasing the size of ice crystals in the freezing process. The freeze-drying sublimation process generates high porosity in the powdered food structure, facilitating food rehydration [31; 51].  
Osmotic dehydration is considered a viable alternative as a pretreatment for drying foods since it is low cost and easy to apply industrially [52]. The pretreatment is based on immersion of the food matrix into an osmotic or hypertonic solution, with diffusion of moisture from the food into the osmotic solution and entry of the solute into the product [52; 53]. Water removal occurs as the food interacts directly with the osmotic solution, with the cell wall acting as a semipermeable membrane, allowing for the passage of water while preventing the movement of larger molecules [53].  
To preserve organoleptic properties, sugar solutions (sucrose) are commonly used in fruits and salt solutions (sodium chloride) for vegetables and meat, in which the osmotic solution is characterized by having a high osmotic pressure and low water activity [53]. While osmotic dehydration can remove a significant amount of water (up to 50%), it is considered only a pretreatment since this method does not impart the properties of a dry or powdered food, such as long shelf life and high stability [52; 53].  
Pulse electric field (PEF) and high-pressure processing (HPP) are also two methodologies that are applied as pretreatment for food drying. PEF can create pores via high-voltage electric pulses, while HPP technology modifies the cellular structure via high hydrostatic pressure. Finally, both processes can impart properties to the food that facilitate mass transfer, consequently reducing drying time [54].
## 4 Conclusions  
In this work, we proposed the coupling of simple, planar, chiral nanohole arrays with an ensemble of emitting material, for chiral emission in the near-infrared and visible ranges. Our numerical method treats the chiral properties of a large number of randomly oriented dipoles, which were homogeneously positioned in a refractive index matching matrix above the ENHA. The chiral properties of the emitted far-field strongly depend on the chiral properties of the plasmonic substrate. Moreover, the excitation of the dipoles at shorter wavelengths can tune the electromagnetic field confinement in the volume of the emitting layer. As such, the excitation field can be used to enhance the emission of specific dipoles, and to control it by selecting proper excitation wavelength and polarization. Thus, it provides additional degree of freedom in the tuning of the overall emission in terms of the degrees of intensity and circular polarization. Even though our design involves a simple elliptical nanohole geometry in an Au or Ag layer, it provides rich chiral behavior in both absorption and emission. Therefore, we strongly believe that the ENHA coupled with emitting layers can be optimized towards tunable efficient chiral nanosources.  
Figure 10: (**a**) Typical absorption and emission spectra for Lumogen Red dye [45]. (**b**) CD\({}_{\text{em}}\) for RCP (top) and LCP (bottom) excitations of Ag-ENHA excited at \(\lambda_{exc}=532\) nm, and emitting at \(\lambda_{em}=630\) nm; the inset shows the xy cross-sections of the electric field intensity, as in Figure 7.  
**Author Contributions:** Conceptualization, E.P. and C.S.; methodology, E.P. and C.S.; software, E.P. and R.G.; validation, C.S.; formal analysis, R.G.; investigation, E.P. and R.G.; resources, C.S.; data curation, R.G.; writing--original draft preparation, E.P. and R.G.; writing--review and editing, C.S.; visualization, R.G.; supervision, C.S.; project administration, C.S.; funding acquisition, C.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Acknowledgments:** In this section, you can acknowledge any support given which is not covered by the author contribution or funding sections. This may include administrative and technical support, or donations in kind (e.g., materials used for experiments).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Communication Technologies for WSANs Applied to the SG
### Synthesis  
The study performed in this section reveals that candidate communication technologies for SG management have different characteristics in terms of data rate, range and power consumption, which will impact their employ in WSANs. Therefore, there is a need for interoperability between these technologies in order to fulfill the overall requirements of a SG, from HAN to WAN, such as bi-directional coordination, data gathering and information processing, real-time and online processing, or proactiveness. Figure 7 lists the proposed communication technologies, for each level of the SG.  
Table 2 profiles the most interesting characteristics, from the point of view of their application to grid management using WSANs, of the presented communication protocols.
# Interplanetary Student Nanospacecraft: Development of the LEO Demonstrator ESTCube-2
## 2 ESTCube-2 Mission
### Satellite Design  
The ESTCube-2 was designed taking inspiration from the three-unit (3U) CubeSat standard but adapted to fit in a larger deployer, for example, using the \"Maximum Advised Dimensions Applicable for ISIS satellite dispenser systems\" for ISISpace ISIPOD and QuadPack deployers [43]. The main design driver for the ESTCube-2 satellite was the IPB payload. To satisfy the high spin-rate deployment of the IPB tether, the satellite was developed from scratch, taking into account the lessons learned from ESTCube-1 mission [8]. The satellite design development was split into three major parts: mechanical structure, avionics stack systems, and side panel systems. A detailed description of each structural block, the parameters of each system, and the development challenges were presented by [PERSON] et al. [18].
## 3 Results  
### Effect of HDTMA Strength  
The effectiveness of the different forms of modification are plotted in Figure 1. The plot shows that the 0.2 M modification had the highest fluoride removal of 13.6%. This was so because the 0.2 M modified vermiculite contained the most electropositive surface (lowest solution pH). Thus HDTMA-VMT\({}_{0.2\text{M}}\) was used for the optimisation process after the pH of the adsorbent surface was reduced to 1.5 before drying. This was carried out to further enhance electrostatic attraction between the surface of the organoclay and the fluoride in the solution.  
Hexadecyltrimethylammonium forms a monolayer or bilayer in the interlayer spaces of the vermiculite depending on the concentration used. Below the critical micelle concentration (CMC), HDTMA forms a monolayer (negative charge); however, above the CMC a bilayer is formed which is positively charged and enhances anionic sorption [24]. The CMC of HDTMA is 0.9 mM [25], thus a bilayer is formed when the high concentration of HDTMA was used in this study. This brings about a positive charge on the adsorbent surface which attracts fluoride from the solution. Figure 2 shows a diagram of the monolayer and bilayer arrangement of HDTMA on the interlayer spaces of vermiculite with fluoride attraction.  
Figure 1: Plot of fluoride removal against hexadecyltrimethylammonium bromide (HDTMA) strength.  
Figure 2: Monolayer (**a**) and bilayer (**b**) arrangement of HDTMA. Reproduced with permission from [26], Elsevier, 2012.
## 3 Structure Design for FH Signal Tracking and Sorting
### Sorting of FH Signals
\tag{20}\]  
For the cross-sub-band single hop, the power in each sub-band should not be less than \(\gamma(\gamma<1)\) of the total power. Generally, \(\gamma\) is not less than 5%. The four thresholds in Figure 3 for identification can be expressed as  
\[\left\{ \begin{array}{l}\mu_{L}=\frac{1-\gamma}{\beta}\sigma_{t}\\ \mu_{SL}=(1-\gamma)\sigma_{t}+\frac{\gamma}{\beta}\sigma_{t}\\ \mu_{SH}=(1-\gamma)\sigma_{t}+\gamma\beta\sigma_{t}\\ \mu_{H}=(1-\gamma)\beta\sigma_{t}\end{array} \right. \tag{21}\]  
Suppose the power estimation is \(\sigma_{d}\) after dynamic programming. The identification for the relationships can be expressed as  
\[\left\{ \begin{array}{rl}H0:&\sigma_{d}<\mu_{L}\\ H1:&\mu_{L}\leq\sigma_{d}<\mu_{SL}\\ H2:&\mu_{SL}\leq\sigma_{d}<\mu_{SH}\\ H3:&\mu_{SH}\leq\sigma_{d}<\mu_{H}\\ H4:&\sigma_{d}\geq\mu_{H}\end{array} \right. \tag{22}\]  
If the observed single hop crosses sub-channels, the situations are opposite between two adjacent sub-channels after dynamic programming (as single hop B in Figure 3), which is easy to distinguish. The result of identification is the estimation of support sets. Based on Equation (18), the multiple signal can be separated in the order of sub-bands. The algorithm for the separation is shown in Figure 4.  
Figure 4: Separation strategy of frequency hopping (FH) signals. The detail of the strategy can be summarized as follows:  
**Input:** Received signals. **Step 1:** Detect the sub-channels of FH signals by \(\alpha_{i}[n]\) based on Equation (12). **Step 2:** If a frequency hop occurred, obtain the TOA estimation of the new hop by Equation (13). **Step 3:** Refresh the amplitudes of multi-frequency functions of the sub-bands by dynamic programming in the sub-channels that were detected in Step 1. Extract the sub-bands as the support set by Equation (22). **Step 4:** Recover the signal in the order of sub-bands by Equation (18). **Step 5:** Get the relative power of \(z_{i}[n]\) in Step 4. **Step 6:** Separate the multiple signals by TOA, obtained in Step 2. If the signals cannot be separated by TOA, take relative power into consideration for the separation. **Output:** Separated signals \(z_{r}[n],r=1,\ldots,R\).
# An Expert Artificial Intelligence Model for Discriminating Microseismic Events and Mine Blasts
## 5 Results and Discussions  
Based on the above investigation of optimal model parameters, the optimal models of five intelligent algorithms were established and used to predict the testing set after being trained by the training set. The discrimination results are shown in Table 2 and Figure 11. To better show the discrimination performance of the five models, a ranking method proposed by [PERSON] et al. [63] was adopted in this paper. Firstly, each quality measuring indicator of the five models was sorted from smallest to largest. Then, they were scored, and the model with the smallest indicator value received \(1\) point, and the model with the second smallest indicator value received \(2\) points. In this order, the model with the largest indicator value received \(5\) points. Finally, the scores of the above six indicators of each model are summed, and the higher the total score, the better the discrimination performance. As shown in Table 2, the total score of PSO-ELM is the highest, followed by BPNN, ELM, FDA, and NBC. This shows that the discrimination performance of PSO-ELM is the best, followed by BPNN, ELM, FDA, and NBC. The values of the six indicators of PSO-ELM above are the highest, close to or equal to \(1\), which is almost perfect. This shows that PSO-ELM has an excellent performance in discriminating microseismic events and mine blasts.  
Although some achievements have been obtained in this research, there are still some limitations. First of all, only the PSO-ELM algorithm was discussed and there are other intelligent algorithms that need to be tested for discriminating microseismic events and mine blasts. Secondly, in addition to these \(22\) seismic parameters, the impact of other seismic parameters on the performance of discrimination remains to be investigated. Finally, since the data of only one mine were used to check the performance of PSO-ELM, its universality is not yet clear.
## 3 Results and Discussion
### Changes in Velocity of Mudflow  
To obtain detailed information on the topic of the velocity of mudflow surging from a tailings dam-break during randomly selected experimental runs, velocity trackers were used, and the velocity was recorded. The velocities at four locations \(S_{1}\), \(S_{2}\), \(S_{3}\), and \(S_{4}\) (1.5 m, 3.0 m, 4.5 m, and 6.0 m  
Figure 6: Dynamic pressure curve at locations \(S_{2}\), and \(S_{4}\) (3.0 m and 6.0 m downstream of the gate) for tests with \(H\) = 30 cm as the initial filling height. The dynamic pressure-history comprises of three stages, stage (A): rapid increase stage; Stage (B): rapid reduction stage; Stage (C): slow reduction stage. Note: The time t = 0 in Figure 4a,b are the starting time of mudflow reaching station \(S_{2}\) and \(S_{4}\).  
Figure 7: Peak value of dynamic pressure at sections \(S_{2}\) and \(S_{4}\) (3.0 m and 6.0 m downstream of the gate).  
downstream of the gate) were analyzed, and the velocity of the mudflow is plotted in Figure 8 corresponding to the three different tailing particle sizes. The initial velocity at different monitoring points is shown in Table 2.  
Figure 8 depicts the velocity curve at different monitoring points in the downstream trench. The velocity curve presented two distinct primary stages, namely a rapid reduction stage (A) and a slow reduction stage (B). The velocity is relatively high at the initial stage of the dam-break. At all monitoring points, with an increasing of the particle size of the tailings, the velocity increases simultaneously.  
Table 2 indicates the initial velocity at different monitoring points for the three different tailing particle sizes. As shown in Table 2, along with the increase of the particle size of the tailings, the initial peak velocity gradually increases in value. Analysis of the peak value at the location of \(S_{2}\), the peak values of the initial velocity were 1.9 m\(\cdot\)s\({}^{-1}\), 2.2 m\(\cdot\)s\({}^{-1}\), and 2.6 m\(\cdot\)s\({}^{-1}\) for fine tailings, medium tailings, and coarse tailings, respectively.  
Similarly, it has been pointed out in Section 3.1 that with a decrease in the size of the tailings particles, there is an increase in the viscosity of mudflow. As a result of this, the liquidity of mudflow is worse, leading to an overall decrease in velocity.
## 5 Improvement of ZPC Tracking Performance  
This section validates the interference-free property of ZPC and compares ZPC with other works in tracking performance. The experiment results demonstrate the improvement of ZPC tracking accuracy and its advantages.
## 3 Dynamic Compressive Mechanical Properties of Sandstone after Corrosion by Strong Alkali Solution
### Dynamic Stress-Strain Curves of Sandstone at Different Corrosion Times  
The sandstone specimens were subjected to SHPB impact compression tests after corrosion by strong alkali solutions at different times, and the dynamic stress-strain curves were obtained after treatment, as shown in Figure 5.  
According to the basic principle of the SHPB test [27], data processing by the three-wave method [28] can be used to obtain mechanical parameters, such as dynamic stresses and strains in sandstone specimens.  
The \(\sigma\)-\(\varepsilon\) curve change law is basically the same; the curve length in the elastic deformation stage is larger than that in the plastic deformation stage. When the specimens were non-corroded and corroded at 1 d, the \(\sigma\)-\(\varepsilon\) curve peak front was steeper, and the strain was relatively small from 1 d to 3 d; in the curve of the stage line elastic stage, the slope of the sandstone specimen curve decreased with the increase in corrosion time, that is, the dynamic elastic modulus of the specimen decreased with the increase in corrosion time. In the third stage of the curve, the dynamic peak stress of the sandstone after corrosion at 28 d was much smaller than the other time gradients when loaded, and the corresponding damage strain also increased sharply. Similarly, we can deduce that the dynamic peak stress of the sandstone also had a time effect like the dynamic elastic modulus (with corrosion time extension, the sandstone specimen softening effect will also increase), thus confirming the increased micropore fissures in the sandstone specimen, making the sandstone specimen corrosion softened by corrosion. At the same time, the flexibility became stronger, and the corrosion effect of the alkali solution changed the internal composition and microstructure of the sandstone; with the extension of corrosion time, the length gap between the elastic deformation curve and the plastic deformation decreased. This phenomenon indicates that with the extension of corrosion time, sandstone brittleness gradually weakened, and ductility gradually increased. According to the composition of sandstone in Section 2, we concluded that with the extension of corrosion time, cations such as K\({}^{+}\), Na\({}^{+}\), Ca\({}^{2+}\), Mg\({}^{2+}\) in the sandstone and quartz, the main component of the sandstone, continue to combine with OH\({}^{+}\) ions in the solution and undergo chemical reactions, so that the original mineral composition is destroyed by decomposition, secondary fractures increase, and the sandstone structure becomes loose, which is similar to the conclusion of the study by [PERSON] et al. [29] and [PERSON] et al. [14].  
Figure 5: Dynamic stress–strain curves of sandstone at different corrosion times.
## 6 Testing, Analysis, and Results  
The datasets used in the simulations consist of two distinct sets:  
Figure 6: Simplified overview of four CPUs using the model from Figure 5.  
* **The short job dataset**: this dataset consists of jobs with relatively short execution times. These jobs are designed to simulate processes that are completed quickly.
* **Mixed job dataset**: in contrast, the \"mixed job dataset\" provides a more complex and realistic test environment. It simulates scenarios where the CPU must manage a mix of short- and long-running jobs, closely resembling real-world situations where processes with varying execution times are constantly competing for system resources. This dataset helps us assess how well our scheduling algorithms adapt to the challenges presented by a diverse set of jobs.  
A function generates short jobs using a normal distribution, \(N(\mu,\sigma)\), with \(\mu\) as the mean and \(\sigma\) as the standard deviation. For calculating the total time of a short job, we set \(\mu=25\) and \(\sigma=10\), with values capped at \([1,50]\). Long jobs are generated using different \(\mu\) and \(\sigma\) values, specifically \(\mu=100\), \(\sigma=25\), and capped at \([50,150]\). Each dataset comprises 250 jobs, with the short dataset exclusively containing short jobs. The mixed dataset consists of an equal mix of 50% short and 50% long jobs. In the mixed datasets, short jobs are represented by odd job IDs, while even job IDs correspond to long jobs. Figure 7 provides a visualization of the job execution time distribution for both datasets.  
The niceness parameter for each job is generated using a normal distribution with \(N(0,6)\) and capped at \([-20,19]\). Since most jobs are expected to have a niceness of 0, the normal distribution with \(\mu=0\) is well-suited. A standard deviation of \(\sigma=6\) is used to introduce variability among jobs, assigning higher priority to some jobs and lower priority to others.  
The final step in dataset generation involves setting the arrival times of jobs. To introduce realism, we uniformly assigned arrival times in the range \([1,\left(\sum_{i=1}^{n}T_{(i,total)}\right)/6]\), except for the first 10 jobs, which have an arrival time of 0. These initial jobs serve as a startup phase. Dividing the total running time by 6 accommodates the presence of four CPUs, effectively reducing the overall time by approximately 4. We increased it by 2 to ensure jobs arrive closer to each other. The jobs are sorted based on arrival time, meaning that, for a job with ID \(i\), its arrival time satisfies \(i-1\leq i\leq i+1\).  
We employ two datasets to simulate diverse workloads, comprehensively assessing how scheduling algorithms perform under various conditions. This approach allows us to discern whether a scheduler excels primarily with short jobs or adapts well to a mixed load comprising both interactive and resource-intensive tasks.