esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Optical System of Dim Target Simulator Based on Polarization Stray Light Suppression
## 4 Experimental Results Verification  
### Verification Experiment of Polarization Stray Light Suppression Effect  
In order to verify the suppression effect of the depolarization optical engine on polarized stray light, a dim target simulator based on polarized stray light suppression is constructed. A space detection camera was used to test the grayscale contrast of the simulated image of the simulator, as shown in Figure 15.  
Under the condition that the relative position between the camera and the target simulator and the exposure time of the camera remain unchanged, the black and white grid images of the target simulator without depolarization stray light and the target simulator with depolarization optical engine are observed, respectively, and the grid data of the 2 \(\times\) 2 black and white grid region are obtained and analyzed. The overall grayscale distribution and horizontal, vertical, and sloping grayscale distribution curves of bright areas before and after stray light elimination are obtained, respectively, as shown in Figures 16 and 17.  
Figure 14: High-image quality collimating optical system. (**a**) Two-dimensional structure diagram of optical system; (**b**) spot diagrams; (**c**) field curvature and distortion curve; (**d**) transfer function curve.  
The average grayscale value \(g_{light}\) in the bright area and the average grayscale value \(g_{dark}\) in the dark area, as well as the maximum grayscale value \(g_{MAX}\) and the minimum grayscale value \(g_{MIN}\) in the bright area, can be obtained from the grid data. The image contrast \(C\) and grayscale non-uniformity \(\eta\) in the bright area before and after the stray light elimination can be calculated by using Equations (20) and (21):  
\[C=\frac{g_{light}}{g_{dark}} \tag{20}\]  
Figure 16: Black and white grid observation image and grayscale distribution without depolarizing stray light.  
Figure 17: Black and white grid observation image and grayscale distribution of target plane after adding depolarization optical engine.  
Figure 15: Stray light test site of dim target simulator based on polarization stray light suppression.  
\[\eta=\frac{g_{MAX}-g_{MIN}}{g_{MAX}+g_{MIN}} \tag{21}\]  
The results show that without depolarizing stray light, the grayscale contrast of the image is only 2.51, and there is an obvious transition between the bright and dark areas; there is a serious grayscale non-uniformity in the bright area; the non-uniformity is as high as 33.99%, which cannot meet the needs of dim target simulation. After the polarization stray light is eliminated, the image contrast is 6.96, which is increased by nearly 2.8 times, and the space occupied by the transition area between the bright area and the dark area is greatly reduced; the grayscale non-uniformity of the bright area is 5.88%, which is only 17.3% before the polarization stray light, meeting the simulation requirements of the dim target.
## 3 Results and Discussion
### Heat Content  
Figure 4 shows the heat content of the sample at different mass losses. It demonstrates that the LC and HC samples had similar behavior with the extent of torrefraction. Although there existed some scatters in the results (due to the heterogeneous nature of the sample), it indicates a trend that heat  
Figure 3: Organic chlorine removal efficiency vs. mass loss.  
content of both LC and HC samples increased due to the release of volatiles as the mass loss increased, reaching a maximum of \(\sim\)34 MJ/kg at \(\sim\)45% mass loss. After 45% mass loss, the heat content started decreasing as the mass loss increased, due to the formation of fixed carbon, and it reached \(\sim\)32 MJ/kg at \(\sim\)58% mass loss. As compared to PRB coal with a heat content of \(\sim\)17 to 19 MJ/kg [36], these numbers were rather encouraging. In order to help further study the behavior of the effect on the heat content of the material, the experimental results were fitted mathematically as shown in Figure 4. It is essential to note that Figure 4 is only applicable to this specific blend of material since different compositions will have different heat content. If we were to produce chlorine-free solid fuels, predicting the heat content of such a fuel is essential. After all the organic chlorine was removed, the remaining inorganic chlorine (salts) could potentially be removed through a mechanical process (pulverizing and sifting). Since the calorific value of inorganic chlorine is negligible and the mechanical processes did not affect the calorific value of other materials in the waste, we could assume that the heat content of fully dechlorinated waste was comparable to the one after all the organic chlorine was removed.  
Since the torrefaction process releases volatiles, which also contain some energy, it is essential to understand the energy retained at the different extents of torrefaction compared to the initial amount. Figure 5 shows the normalized retained energy vs. mass loss, which indicated that the energy retained for both LC and HC samples had similar behavior with the extent of torrefaction. The retained energy decreased as the mass loss increased to a final value of \(\sim\)50% of retained energy at \(\sim\)58% mass loss.
## 3 Results and Discussion
### Analyzing Submitted Jobs after Integration
#### 3.2.2 Characteristics of Preempted Jobs  
When a slot was preempted, the job assigned to the slot had to be restarted when an idle slot was available. Therefore, the job with a NumJobStarts value of 2 or more was judged as preempted, and the characteristics of the job were examined. The distribution of NumJobStarts used in the analysis is shown in Figure 8. Percentages of 17.10% in Group A and 0.76% in Group B were preempted job ratios. It was noted that the ratio difference between the two groups resulted from a fourfold difference in size.  
Figure 8: Job-start distribution.  
Figure 7: Comparison of queued jobs for two groups in 2019.  
We also looked at the situation of preempted jobs and preempted possibility along their committed time. The result is shown in Figure 9. In both groups, the longer the execution time was, the higher the probability of being preempted was. However, Group A maintained a high probability, while Group B tended to have nothing preempted after a certain period.  
In the probability graph at Figure 10, redrawing only up to the percentage of committed time 99 would look like Figure 11. Both groups tend to grow linearly, but Group A grows faster than Group B.  
Figure 10: _Cont._  
Figure 9: Distribution of preempted jobs by committed time.
## 3 Results
### The Coupling Effect of Optical Characteristic Parameters on Radiative Characteristics
As evidenced by the data in Figure 4, when \(\omega_{0}\) is initially small, it drives a rapid exponential enhancement of electron radiation power. However, once a critical threshold of \(\omega_{0}\) is reached, the scaling transitions to a steady linear regime, reflecting a saturation of geometrically focused benefits. Building on the prior findings that laser intensity (\(a_{0}\)) dominates radiation power scaling, we analyze the case of \(\omega_{0}=7\) to amplify the effects of \(a_{0}\). As shown in Figure 5, the radiation power exhibits exponential growth for \(a_{0}=1\)-\(6\), transitioning to a quasi-linear regime at \(a_{0}=6\)-\(7\). This clearly demonstrates that the exponential phase governed by \(a_{0}\) spans a broader parameter range, offering greater potential for optimizing radiation power compared to the linear saturation stage. Figure 2: A three-dimensional diagram of electronic radiation power. Longitudinally, parts (**A(1)–A(4)**) correspond to the case where \(a_{0}\) is 1; parts (**B(1)–B(4)**) correspond to the case where \(a_{0}\) is 3; parts (**C(1)–C(4)**) correspond to the case where \(a_{0}\) is 5; and parts (**D(1)–D(4)**) correspond to the case where \(a_{0}\) is 7. Horizontally, they represent the ranges of w0 as 1, 3, 5, and 7, respectively. Figure 4: Radiation power variation diagram with \(\omega_{\theta}\) ranging from 1 to 7 under the condition that \(a_{0}\) = 7. Among them, \(\omega_{\theta}\) was normalized, so there is no unit. Figure 3: Fitting surface diagram of radiation power under the coupling of \(a_{0}\) and \(\omega_{0}\). Take the logarithm of the radiation power with base 10.
## 5 System Implementation and Field Testing
### Implementation Details
#### 5.1.2 Ticket-Scanning Subsystem  
The ticket-scanning subsystem is implemented using Visual Studio C#, hosted on a notebook with an embedded webcam. The booking tickets are generated in the form of QR codes. Because the digital booking tickets are in the form of QR codes, the implemented program running on the laptop includes a QR code reader to scan and decode the tourist's QR code booking ticket. In the subsystem, the reservation entrance gate is emulated by a program which exhibits a virtual gate on the notebook screen. The program also includes a virtual gate to show on the laptop screen. When scanning in a QR code booking ticket, the program decodes the ticket information and sends the information to the central subsystem for ticket verification. Upon receiving the verification result from the central subsystem, the virtual gate would be shown as opening up if the result is valid, or shown as keeping closed if the result is invalid.
## 4 EPMRS Mathematical Model
### Weighted Feature Extraction
\]  
Based on Equation (6), we can easily determine the dominant feature of \(u_{i}\) by selecting the maximal value of \(w_{i,k}\) where \(k=1,2,\ldots,v\). For instance, the value \(w_{2,3}=0.238\) represents that the feature \(f_{3}\) is the dominant feature of the user \(u_{2}\). Let matrix \(\overline{M}=(\overline{m}_{j,k})\in\mathbf{Z}^{+m\times v}\) be a new song-to-feature matrix containing the songs that are not listened by \(u_{i}\in U\), where \(\overline{M}\subset M\). Following the example of matrices \(N\) and \(M\), let us consider that user \(u_{1}\) has not listened to the songs presented in the matrix \(\overline{M}=(\overline{m}_{j,k})\in\mathbf{Z}^{+2\times 4}\) with two songs and four features as  
\[ \begin{array}{cccc}f_{1}&f_{2}&f_{3}&f_{4}\\ \overline{M}=\begin{array}{c}s_{5}\\ s_{8}\end{array}\left(\begin{array}{cccc}1&0&1&1\\ 1&1&0&1\end{array}\right).\end{array} \]  
Now, we calculate the \(o_{t}(u,s)\) values for \(u_{i}\in U\) with respect to the songs presented in matrix \(\overline{M}\) and generate a weighted user-to-song matrix \(\overline{N}=(\overline{n}_{i,j})\in\mathbf{Z}^{+n\times m}\) as  
\[\overline{N}=W\overline{M}^{T}. \tag{6}\]  
Based on \(\overline{N}\), the EPMRS can easily recommend the songs to \(u_{i}\) by selecting the maximum value of \(\overline{n}_{i,j}\) where \(j=1,2,\ldots,m\). Following the above-mentioned example matrix \(W\) with respect to the user \(u_{1}\), we apply \(\overline{M}\) on Equation (6) to obtain \(\overline{N}=(\overline{n}_{i,j})\in\mathbf{Z}^{+1\times 2}\) as  
\[ \begin{array}{cc}s_{7}&s_{8}\\ \overline{N}=u_{1}\ \left(\begin{array}{cc}0.238&0.079\end{array}\right). \end{array}  \tag{7}\]  
Finally, the EPMRS will recommend the song \(s_{5}\) to \(u_{1}\) as the value \(\overline{n}_{1,1}=0.238\) is the maximum value in the record of \(\overline{N}\) of Equation (7). Let \(l_{u}=\{l_{u_{1}},l_{u_{2}},\ldots,l_{u_{n}}\}\) denote the set of the latent features of \(u_{i}\in U\) and \(l_{s}=\{l_{s_{1}},l_{s_{2}},\ldots,l_{s_{m}}\}\) represent the set of the latent features of \(s_{j}\in S\). The optimized WFE function is  
\[\min_{u_{i}\sim j}\sum_{u_{i}\sim j}o_{t}(u_{i},s_{j})\big{(}\overline{N}-l_{ u_{i}}^{T}l_{s_{j}}\big{)}^{2}+\lambda\left(\sum_{u_{i}}||l_{u_{i}}||^{2}+\sum_{s_ {j}}||l_{s_{j}}||^{2}\right), \tag{8}\]  
where \(\lambda\) is the regularization parameter, \(i=1,2,\ldots,n\), and \(j=1,2,\ldots,m\). The optimized WFE function as shown in the Equation (8) consists of two terms: the weighted mean square error term and the \(L2\) normalization term. The first term of the Equation (4) calculates the weighted mean square error of all the users and all songs in the database. The overall interest variable \(o_{u,S}\) gives weights to all possible combinations that may also contain songs that are not rated by the user \(u\). The second term of the Equation (8) normalizes the latent features extracted from the user \(u\) and the song \(s\).
## 4 Results
For the case with the exposed electrode at 0 w (Figure 10a,b), the effects of the plasma on film cooling effectiveness were higher for the lower blowing ratio case (Figure 10a) because of the lower coolant momentum. For the higher blowing ratio case (Figure 10b), the plasma-induced flow might not have been strong enough to drag down more coolant toward the endwall. The film cooling effectiveness increased slightly even for the 7 kV input voltage case. For the cases with the exposed electrode at 10 w, the higher blowing ratio case (Figure 10d) presented slightly better improvement in averaged film cooling effectiveness than the case with the exposed electrode at 0 w. Since the momentum of the coolant downstream from \(x/w\) = 10 was lower than that near the slot, more coolant could be dragged down by the plasma-induced flow, and this resulted in improved film cooling. In order to investigate the effect of the ionized gas caused by the DBD plasma on the PSP emitting intensity, a separate test was conducted. Figure 11 shows the setup for the test. The widths of the exposed and the insulated electrodes were 5 and 10 mm, respectively. Both electrodes were made of 0.07-mm thick copper tape and attached on the 3-mm thick acrylic plate. The mainstream velocity was 10 m/s, and the input voltage was set as 6 kV. Figure 12 presents the spanwise averaged intensity difference for the cases with and without plasma. The results showed that the averaged intensity difference reached its maximum point near the exposed electrode, and it continuously decreased downstream. The maximum difference was about 0.5%. For the film cooling effectiveness of 0.8, a 0.5% increase in \(I_{\mathrm{air}}\) and \(I_{\mathrm{fg}}\) resulted in about a 0.03% increase in film cooling effectiveness. Therefore, overestimation in the film cooling effectiveness due to the plasma and ionized gas had very little effect on the measured film cooling effectiveness. Figure 9: Laterally-averaged film cooling effectiveness (\(L_{\mathrm{e}}\) = 10 w). Figure 11: Test setup to investigate the effect of plasma on the PSP emission. Figure 3:0 Overall area averaged film cooling effectiveness.
## 5 Application Scenario: State and Disturbance Estimation for an Underactuated Hovercraft
### Modeling
For the use of the proposed ellipsoidal state estimation scheme, that will be applied to the closed-loop system, the state equations are discretized in time with the step size \(T\) according to  
\[ \begin{bmatrix}x_{1,k+1}\\ x_{2,k+1}\\ x_{3,k+1}\\ d_{1,k+1}\\ d_{2,k+1}\\ d_{3,k+1}\end{bmatrix} =\left( \begin{array}{cccccc}-\frac{c+k_{11}}{m}&x_{3, k}-\frac{k_{12}}{m}&-\frac{k_{13}}{m}&-\frac{1}{m}&0&0\\ -x_{3,k}&-\frac{c}{m}&0&0&-\frac{1}{m}&0\\ -\frac{k_{21}}{7}&-\frac{k_{22}}{7}&-\frac{c+k_{23}}{7}&0&0&\frac{1}{7}\\ 0&0&0&0&0&0\\ 0&0&0&0&0&0\end{array} \right)\cdot \begin{bmatrix}x_{1,k}\\ x_{2,k}\\ d_{1,k}\\ d_{2,k}\\ d_{3,k}\end{bmatrix} \enspace. \tag{89}\]  
The (normalized) parameters used for the following simulations are: \(c=0.01\), \(m=5\), \(J=0.8\), \(c_{\rm r}=0.01\), \(k_{11}=0.5\), \(k_{12}=1.5\), \(k_{13}=0\), \(k_{21}=0\), \(k_{22}=0\), \(k_{23}=0.1\), and \(T=0.1\). As measured system outputs, all three velocities (surge, sway, and yaw) are assumed to be available. For the simulation, they are described by the three independent sensor models  
\[\frac{1}{\delta_{i}^{2}}\cdot\left(x_{i,k}-y_{{\rm m}i,k}\right)^{2}\leq 1\enspace, \tag{90}\]  
where \(y_{{\rm m}i,k}\), \(i\in\{1,2,3\}\), are the data provided by the velocity sensors and \(\delta_{i}>0\) their corresponding maximum uncertainty. Besides filtering the velocity signals, the proposed observer is used to reconstruct the ranges of the disturbance inputs \(d_{i}\) that are compatible with the system model and the sensor data. Due to the assumption of constant but uncertain disturbances, see Equations (87) and (89), it is permitted to intersect disturbance estimates at a specific time step \(k\) with those of previous steps and to employ those intersected results as _virtual_ measurements in analogy to the sensor models (90). In the system model (89), the influence of the yaw rate \(x_{3}\) on the system matrix during the state prediction is accounted for by an interval parameter that corresponds to its estimated range from the preceding correction step. Without any modification, the proposed method is capable of handling imprecisely known damping coefficients \(c\in[\underline{c}\ ;\ \overline{c}]\) and \(c_{\mathrm{r}}\in[\underline{c}_{\mathrm{r}}\ ;\ \overline{c}_{\mathrm{r}}]\) or uncertain masses and mass moments of inertia.
# Detection of Smoking in Indoor Environment Using Machine Learning
###### Abstract  
Revealed by the effect of indoor pollutants on the human body, indoor air quality management is increasing. In particular, indoor smoking is one of the common sources of indoor air pollution, and its harmfulness has been well studied. Accordingly, the regulation of indoor smoking is emerging all over the world. Technical approaches are also being carried out to regulate indoor smoking, but research is focused on detection hardware. This study includes analytical and machine learning approach of cigarette detection by detecting typical gases (total volatile organic compounds, CO\({}_{2}\) etc.) being collected from IoT sensors. In detail, data set for machine learning was built using IoT sensors, including training data set securely collected from the rotary smoking machine and test data set gained from actual indoor environment with spontaneous smokers. The prediction accuracy was evaluated with accuracy, precision, and recall. As a result, the non-linear support vector machine (SVM) model showed the best performance with 93% in accuracy and 88% in the F1 score. The supervised learning k-nearest neighbors (KNN) and multilayer perceptron (MLP) models also showed relatively fine results, but shows effectivity simplifying prediction with binary classification to improve accuracy and speed.  
smoking detection; sensor fusion; indoor air quality sensing; cooking & heating detection; machine learning; F1 score +
Footnote †: journal: Applied Sciences
# Self-Optimizing Traffic Steering for 5G mmWave Heterogeneous Networks
## 2 Overview of mmWave HetNets
### Triple-Band Network Structure  
Within the proposed mmWave HetNet, we further introduce a novel triple-band network structure which utilizes the licensed bands, sub-6 GHz unlicensed bands, and mmWave bands together to support various user types. The proposed network structure works in the following way. The superior controller first evaluates the data rate requirements of users according to the information provided by subordinate controllers. Thereafter, each user is associated with a proper network. Specifically, users with gigabit-level data rate requirements prefer the mmWave network; high-QoS-demanding users with megabit-level data rate requirements prefer the LTE-LAA network; low-QoS-demanding users with megabit-level or even lower data rate requirements prefer the WiFi network; and high-QoS-requirement users with lower data rate requirements prefer the LTE network. With the information of all networks, each user can select a suitable access point (mmWave AP, LTE SBS, or WiFi AP) according to the real-time system environment.  
Thus far, we have introduced a novel hierarchical and triple-band network structure for the mmWave HetNet. The main benefits of our proposals are summarized as follows.  
* The proposed mmWave HetNet is very compelling, as the large available mmWave bandwidth enables extremely high data rate transmission and new service requirements.
* Centralized approaches generally have high computational complexity and large signalling overhead, whereas distributed approaches cannot effectively handle the fluctuation in system environments. Combining both centralized and distributed approaches, we can not only decrease the computational complexity but also guarantee the user performance.
* Compared with the widely used dual-band network structure, our proposal mainly has the following merits: (1) It can release the traffic burden in both licensed and mmWave networks. (2) It can improve the QoS of users with megabit-level data rate requirements by associating them with the more stable LTE-LAA networks. (3) It can ensure the performance of users with gigabit-level data rate requirements by associating them with the bandwidth-richer mmWave network.  
To further improve the system performance, we should seek potential solutions to user association in the triple-band network structure. Traffic steering, which can be flexibly and easily utilized by steering different types of users to different networks, is one of the best approaches. In the next section, we propose a self-optimizing traffic steering strategy for the mmWave HetNet.
# Flexural Performance of Reinforced Concrete Members with Steel Bars
## 4 Conclusions  
To evaluate the applicability of the regulations presented in the current standards for the design of flexural members using the SD600 steel bar, the flexural performance of six beam members was measured using the yield strength (f\({}_{\text{y}}\) = 400 MPa, f\({}_{\text{y}}\) = 600 MPa) and tensile steel ratio (1.0\(\rho_{\text{max}}\), 0.75\(\rho_{\text{max}}\), 0.5\(\rho_{\text{max}}\)) of tensile steel bars as variables.  
The conclusions from the experiment are as follows:  
1. When the maximum-steel-ratio equation was tested, every specimen failed according to the typical flexural failure mode of beams preceded by the yield of tensile steel bars. As the load increased, the number and width of cracks at the centre increased. Diagonal cracks were generated at both ends after the yield load was reached. Thereafter, the displacement increased sharply with gradual increases of the load, and the specimen failed. When the SD600 steel bar was used up to the maximum steel ratio, the specimen showed a typical ductile flexural failure mode regardless of the concrete strength. When the maximum strength was reached, the steel bar had a strain of \(\epsilon_{\text{t}}\) = 0.006, which is the minimum allowable strain for steel bars presented in the  
Figure 9: Comparison of yield load according to the tensile steel ratio.  
standard. This result suggests that the maximum-steel-ratio regulation in the current standard is applicable even when high-tension bars are used in flexural members.
2. When the nominal-flexural-strength equation was tested, the results for flexural members showed that on average the yield load and maximum load of every specimen were higher by 7.3% and 21.8% than the nominal load, respectively. Comparing steel bars of different strengths, it was found that when the SD400 bar was used, the yield load and maximum load were higher by approximately 10% and 24% than the nominal load, respectively. When the SD600 bar was used, the yield load and maximum load were higher by approximately 4% and 20% than the nominal load, respectively. The specimens showed the ductile fracture mode even with the SD600 steel bar, and the yield load of every specimen was higher than the nominal strength given by the equation presented in the standard [53]. In view of this, it is believed that the nominal flexural strength of members using the SD600 can be determined by applying the nominal-strength equation presented in the current standard [53].  
Conceptualisation, M.L.; methodology, M.L.; investigation, M.L.; data curation, M.L.; writing--original draft preparation, C.L.; writing--review and editing, C.L. Both authors have read and agreed to the published version of the manuscript.  
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (NRF-2018R1D1A1B07049390).  
Not applicable.  
The authors declare no conflict of interest.