esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantum Dot-Based White Organic Light-Emitting Diodes Excited by a Blue OLED
## 3 Results and Discussion  
### The Comparison of Electron Transport Layer Materials  
As part of the fabrication of the blue OLED, an experiment was conducted to determine the material that was most suitable for the electron transport layer (ETL). Several ETL materials were considered, such as X = ET036, LG201, TmPyPB, and TPBi, and the current-voltage and luminance characteristics of OLEDs with these materials were compared. The device structure was ITO/PEDOT: PSS/Poly-TPD/mCP (15 nm)/MADN: v-DABNA (50 nm)/X/LiF (0.8 nm)/Al (150 nm). Here, Poly-TPD was dissolved in chlorobenzene into a solution with a concentration of 5 mg/mL. After thorough stirring, it was spin coated on the top of PEDOT: PSS was used for the hole transport layer. The energy band diagram of the device is shown in Figure 7. Table 1 shows the structural parameters of the devices for modulating the ETL materials. Figure 8 shows the (a) current density-voltage and (b) the luminance-voltage characteristic of the blue OLEDs that modulated various ETL materials. As shown in Figure 8, compared with other ETL materials, TPBi had a better effect on electron injection, thereby increasing the probability of hole-electron recombination, and improving the luminance of the OLEDs. It could be seen that the current density and luminance were higher with TPBi as the ETL material. At a voltage of 8 V, the current density was 20.39 mA/cm\({}^{2}\) and the luminance was 89.55 cd/m\({}^{2}\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Device**} & **HIL** & **HTL-1** & **HTL-2** & **EML** & **ETL** & **EIL** & **Cathode** \\ \cline{2-7}  & **PEDOT: PSS** & **Poly-TPD(Spin)** & **mCP** & **MADN(90\%):** & **X** & **LiF** & **Al** \\ \hline ET036 & & & & & & & \\ \hline LG201 & & & & & & & \\ \hline TmPyPB & & & & & & & \\ \hline TPBi & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Device parameters with modulated ETL materials (unit: nm).  
Figure 7: Energy band diagram of the blue OLED for the modulating ETL material.
## 5 Experiments
### Collaborative Framework Performance Tests
#### 5.1.3 Interference Influence  
Although the average latency barely varies for regular packet transmissions, as the number of concurrent users increases, more and more packets are dropped, the lag quickly becoming noticeable. This effect can be clearly observed when the nodes receive movement updates (i.e., the affected users see virtual objects \"teleport\" from one place to another). Such a behavior is justified in the view of Figure 10: the larger the number of concurrent devices, the larger the number of lost packets.  
Figure 10: Linear representation of the number of packets lost over the number of users.  
Despite the relevant number of packets lost in some cases, it is relevant to point out that, since UDP packets are sent every 200 ms, losing one is not a big deal. However, the percentage of lost packets can harm the user experience significantly in certain scenarios. For instance, for more than 30 users (an actually large number of users in applications that share the same AR experience in real time), in the case of the tested 2.4 GHz network, almost 25% of the packets are dropped, noticeably harming the user experience. However, it can be seen that network performance can be significantly improved by using the less crowded 5 GHz band and a slightly faster router: the number of lost packets drops to about 1%. With this type of network, the number of lost packets stays below 10% until there are more than 80 concurrent users.
## 4 Discussion of Results  
### Reduction of Feature Space  
One observation from the results in Table 2 is that the highest score is constantly achieved without resorting to reducing the dimensionality of the HEVC feature set. Dimensionality reduction methods aim to retain the most representative features and discard the features that are deemed unnecessary, redundant, or non-representative of the original image information. The fact that retaining all and not some of the 64 HEVC features yields higher scores means that all 64 HEVC features are excellent representatives, and none of them can be discarded.  
This is also true for the second-highest scores across all learning architectures in the SumMe dataset but with the PCA-based temporal subsampling of frames per training set. This means that for the SumMe dataset, the quality of the features used is more important or influential than the method used for the temporal subsampling of frames due to the difficult nature of the videos it contains, which were intended to be used with importance- and interestingness-based applications of video summarization [52]. According to the presented results, HEVC features successfully capture importance and interestingness information of video frames.  
For TVSum, OVP, and VSUMM, the second-highest score across all three learning architectures is achieved when HEVC features are reduced with stepwise regression regardless of the temporal subsampling method used. The interesting finding with the TVSum and VSUMM datasets is that the F-score of the video summarization is negatively affected by less than 2% when the HEVC-based temporal subsampling of frames is used compared to the best scores. Even in the case of the second-highest scores in the OVP dataset, in which dimensionality reduction is applied and cosine similarity is used for temporal subsampling, the decrease in the F-score is less than 2% as well. This indicates that even when some of the HEVC features are removed, regardless of the method being used for temporal subsampling, the retained features are still highly representative of the frame content and contain close and comparable information compared to the full set of HEVC features.  
In general, the use of stepwise regression did not generate the best results in any of our experiments. This can be justified by the fact that stepwise regression uses linear multivariate regression for variable selection. However, the problem at hand, which is mapping feature variables to key frames, is clearly non-linear, leading the stepwise regression approach to fail.  
The following are examples of using stepwise regression with the TVSum and SumMe datasets. For the TVsum dataset, we found that the most significant features pertain to the following IDs from Table 1: 4, 5, 8, and 9 and standard deviations of (2-4, 6-8), 10-13, 23-25, and 30, and 10 bins of MVx histogram and 12 bins of the MVy histogram whereas for the SumMe dataset, we found that the most significant features pertain to the following IDs from Table 1: 3 and 5-7, standard deviations of 1 and 4, 23, standard deviations of 23 and 30, and 7 bins of MVx histogram and 5 bins of the MVy histogram.
# Using a Pair of Different-Sized Spheres to Calibrate Radar Data in the Microwave Anechoic Chamber
## 3 Results
As we stated above, only in the Ku-band, can the radar data of the small sphere be used for calibrating that of the larger sphere. Since the sizes of the seagull and drone are also bigger than the large sphere, the small sphere can be used for calibrating that of the seagull and drone within the Ku-band. Radar calibration in the optic region requires no restrictions on the material composition of the calibration object, but the selected size of the calibration object must ensure that the calibration object's data remain in the optic region. The projected area can be used to calculate the theoretical RCS value of the calibration object. If the calibration object is a sphere, Equation (2) can be used for calculating the RCS value of the sphere. Calibration in the Mie region is more complex than calibration in the optic region. Different resonance effects occur in the data in the resonance region of targets composed of different materials, and it is hard to quantify the contribution of the resonance effect in the Mie region to the RCS values of objects. As shown in Table 3, the relative numbers of the drones in the Ku-band and S-band were 7.933 dB and 10.987 dB, respectively, but the values of these numbers of the seagull are \(-8.155\) dB and \(-6.624\) dB, respectively. This means that the resonance effect within the S-band contributes more to the bird than the drone. Moreover, since the RCS values of the calibration object (e.g., spheres) were not equal to their physical cross-section in the Mie region, it is not advisable to use the small sphere to calibrate the radar data of the drone and the bird in the Mie region. In conclusion, it is not proper to use the small sphere to calibrate any one object without considering the scattering region, and more importantly, it is not reasonable to choose any sphere (small sphere or large sphere) to calibrate one specific object. The scattering region must be the first rule for radar calibration. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Objects** & **S-Band** & **X-Band** & **Ku-Band** \\ \hline Small sphere (0.1 m) & 0 dB & 0 dB & 0 dB \\ Big sphere (0.3 m) & 13.162 dB & 13.000 dB & 8.723 dB \\ Seagull (0.4 m) & \(-6.624\) dB & \(-2.496\) dB & \(-8.155\) dB \\ Drone (0.4 m) & 10.987 dB & 7.831 dB & 7.933 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Relative values of radar scattering power of different objects. Figure 4: _Cont._
# Personalized Chinese Tourism Recommendation Algorithm Based on Knowledge Graph
## 3 Methodology
### Personalized Travel Recommendation Algorithm Based on Knowledge Graph
#### 3.3.2 Weight Calculation of User- Attraction Scoring Matrix  
The user- attraction scoring matrix is constructed according to the scores of all users on the attractions, and the users' scoring can reflect the degree of their preference for attractions. The degree of the user's preference for each attraction is expressed as the weight of the attraction in all the rated attractions of the user through the scoring matrix. Assuming that in the recommendation algorithm, the user set is represented as \(U=U_{1},U_{2},...,U_{m}\), the set of the tourist attractions is represented as \(I=I_{1},I_{2},...,I_{n}\), and the constructed user-attractions scoring matrix is represented as the matrix \(R_{m\times n}\),\(wp_{uj}\) represents the weight of the rated attraction \(j\) among all the rated attractions of user \(u\), \(R_{uj}\) represents the weight of the rated attractions \(j\) by user \(u\), \(N_{u}\) represents the set of attractions that user has rated, and the calculation method is the rating of the attraction divided by the sum of the ratings of all the rated attractions of the user. The calculation formula is as follows:  
\[wp_{uj}=\frac{R_{uj}}{\sum_{j\in N_{(u)}}R_{uj}} \tag{9}\]
# NDN Content Store and Caching Policies: Performance Evaluation
## 5 Simulation and Discussion
### Simulations Results
#### 5.2.5 Interest Retransmissions  
The consumer request rate and the consequent network traffic considered in this work is relatively low when compared to real networks. However, given the reduced size of the transmission buffer in each router considered for this work, some scenarios (i.e., NDN Testbed, and Abilene, with \(\alpha=1.1\)) observed congestion and a consequent packet drop, triggering the NACK response and the interest retransmission from the consumers (see Figure 9a), which shows the number of retransmissions slightly increasing with the increased number of CS. However, scenarios with \(\alpha=0.75\) did not present retransmissions (see Figure 9b).
# Research on Backstepping Linear Active Disturbance Rejection Control of Hypersonic Vehicle
## 1 Citation
Considering the two situations of standard parameters and parameter perturbation in a large range, the control system can demonstrate good control performance. However, there are too many controller parameters in ADRC. Many intelligent optimization algorithms such as the foraging algorithm [25] and particle swarm algorithm [26; 27] are used to optimize controller parameters. In the literature [28], a mass adaptive control method combining with robust sliding mode control (SMC) and linear active disturbance rejection control (LADRC) is designed for the quadrotor load unmanned aerial vehicle (UAV) with mass variation, but they are still difficult to adjust and have large parameter errors. An improved active disturbance rejection controller (ADRC) based on particle swarm optimization (PSO) is proposed to solve the problems of control accuracy, response speed and parameter tuning in the current dynamic communication antenna servo system [29]. A non-singular finite-time terminal sliding mode control is proposed in the literature [30]. The particle swarm optimization algorithm is used to obtain the design parameters of the proposed disturbance observer and controller. The literature [31] proposes an integrated filtered extended state filter into an active disturbance rejection control (ADRC) system and derives an improved ADRC. A general nonlinear ADRC algorithm is introduced in the literature [32]. The controller of a hypersonic vehicle needs a strong ability to restrain all kinds of disturbances in order to achieve higher control accuracy. Therefore, this paper uses a backstepping controller to make a more accurate nonlinear modeling of the system and uses an LADRC controller to observe and compensate for all kinds of disturbances of hypersonic vehicles. The main innovations of this paper are as follows:  
1. Aiming at the strong coupling and nonlinearity of hypersonic vehicles, backstepping control is adopted to model the system close to the actual system model. 2. In view of the influence of unmodeled dynamic parameter disturbance and external disturbance on hypersonic vehicles in flight, the LESO is used to observe the disturbance. 3. In order to simulate the real flight of hypersonic vehicles, we consider the disturbance of the internal parameters of hypersonic vehicles and give the parameter fault tolerance range within 6%. We use Gaussian white noise to simulate the external disturbance in flight to reflect the randomness and uncertainty of the disturbance.
## 2 Methodology
### Initial LST Estimated by High-Resolution Multispectral Data  
The land surface temperature (LST) of subpixels in finer resolutions represents the ultimate outcome of our downscaling efforts. Consequently, it is necessary to employ alternative methods to estimate the LST for the computation of weights for sub-objects or subpixels; the estimated LST at this stage, as the initial downscaled LST for the subpixels, is well established by surface structures, which shape the thermal performance of the ground surface. Remote sensing indices (RSIs) derived from high-spatial-resolution visible and near-infrared (VNIR) data can effectively reflect the spatial distribution of the LST. Utilizing VNIR data from ASTER and ETM+, some typical RSIs, such as the normalized difference vegetation index (NDVI), vegetation fraction (\(f_{v}\)), normalized difference built-up index (NDBI), impervious surface area (ISA), and fractions of endmembers (\(F_{i}\)), were used to estimate the initial temperature for the subpixels. The primary regression relationships between LST and RSIs are presented in Table 1. While individual RSIs can perform effectively within specific landcover types, it is essential to combine their indices across different landcover scenarios.  
\(\rho_{\text{RED}}\), \(\rho_{\text{NIR}}\) and \(\rho_{\text{SWIR}}\) are, respectively, the reflectance of the red band, near-infrared band, and the short wave infrared band; \(F_{\text{high\_albedo}}\) and \(F_{\text{low\_albedo}}\) are the fraction of the high albedo and the low albedo endmember in a pixel. \(T_{i}\) is the LST of endmember \(i\). \(\rho_{i,b}\) is the reflectance of endmember \(i\) in band \(b\), and \(e_{b}\) is the model residual.  
For the natural surface covered by some vegetation, we identified that the NDVI, \(f_{v}\), and \(F_{i}\) had a good relationship with the LST. So, an average of the LST, as estimated by the three indices, could obtain the initial decomposed LST.  
\[LST_{\text{ini}}=\text{Average}(f(NDVI),f(f_{v}),f(F_{i})) \tag{12}\]  
For the urban areas and surfaces with less vegetation, we identified that the NDBI, ISA, and \(F_{i}\) had a good relationship with the LST. So, the average of the LST, as estimated by the three indices, can generate the initial decomposed LST.  
\[LST_{\text{ini}}=\text{Average}(f(NDBI),f(ISA),f(F_{i})) \tag{13}\]  
Since the LST of a water body in an ETM+ or ASTER image may not change more than 1 K, a constant value obtained from the MODIS water body's LST for the water body was chosen for the initial decomposed LST in the finer scales.  
The relationship between the MODIS LST and the RSI could be calculated by MODIS VNIR/SWIR data, or the temperature of different endmembers could be estimated through the LST and RSI feature space. Then, the LST-RSI relationship was used alongside the high-spatial-resolution ETM+ or ASTER assistant data to obtain the initial temperature for the subpixels.
## 4 Results
However, the _Next button_ was needed, for example, to more accurately determine the _Out of screen_ values. Using the above data, we determined whether there is a relationship between concentration (based on the above interpretation of eye tracking data) and _Start HR_. Specifically, we determined whether the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**User ID** & **Question** & **Next Button** & **Other** & **Out of Screen** \\ \hline
4 & 85 & 8 & 86 & 91 \\
5 & 122 & 9 & 123 & 16 \\
6 & 122 & 17 & 103 & 28 \\
7 & 51 & 0 & 187 & 32 \\
8 & 87 & 9 & 70 & 104 \\
9 & 79 & 47 & 91 & 53 \\
10 & 79 & 25 & 141 & 25 \\
13 & 109 & 8 & 132 & 21 \\
14 & 71 & 26 & 126 & 47 \\
15 & 75 & 6 & 34 & 155 \\
17 & 78 & 8 & 123 & 61 \\
18 & 104 & 4 & 113 & 49 \\
19 & 55 & 25 & 172 & 18 \\
20 & 88 & 5 & 131 & 46 \\
21 & 136 & 12 & 75 & 47 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Eye tracking records. Figure 2: Areas of research (interest). degree of correlation between the variables _Out of screen_ and _Start HR_ is statistically significant. Since the variable _Out of screen_ does not have a normal distribution and we work with a relatively small sample, it was necessary to use a nonparametric statistical method to evaluate the correlation. The Kendall coefficient tau expresses the difference between the probability that the values of two variables are in the same order compared to the probability that the values are not in the same order [42]:  
\[\tau=\frac{n_{c}-n_{d}}{n(n-1)/2} \tag{4}\]  
where \(n_{c}\) is the number of matching pairs and \(n_{d}\) is the number of mismatched pairs. Pair (\(x_{i}\), \(y_{i}\)) and (\(x_{j}\), \(y_{j}\)), where \(i\), \(j=1\),..., \(n\) and \(i\
eq j\) is concordant if \(x_{i}>x_{j}\) and \(y_{i}>y_{j}\) or \(x_{i}<x_{j}\) and \(y_{i}<y_{j}\). A pair is discordant if \(x_{i}>x_{j}\) and \(y_{i}<y_{j}\) or \(x_{i}<x_{j}\) and \(y_{i}>y_{j}\). A pair is indecisive if \(x_{i}=x_{j}\) or \(y_{i}=y_{j}\). The variables _Out of screen_ and _Start HR_ have a statistically significant correlation, as Kendall tau came out to 0.39, which in this case is a statistically significant dependence on the level of statistical significance of 0.05. Based on the above, we can say that the heart rate that the student had at the beginning of the quiz had a statistically significant effect on its concentration during the quiz. The correlation turned out to be positive, which means that if the student had a higher HR, he often looked around and was not focused on solving the assigned tasks.
## 2 Materials and Methods
### Field Test  
Field tests were performed near the mine field in the Karaganda region (Kazakhstan). The soils on the site included a topsoil layer, brown carbonate sandy loams with fine and medium sands (layer thickness 0.2-1 m) and dark gray, hard loams (layer thickness 2-6 m), described in detail in Table 1.  
To monitor ground surface deformations caused by underground mineral extraction, a 3 \(\times\) 3 m grid was established on the ground surface (Figure 7). Measurement benchmarks,  
\begin{table}
\begin{tabular}{c c c c} \hline
**Soil Characteristics** & **Unit** & **Natural Soils** & **Equivalent Material** \\ \hline bulk unit weight, \(\gamma\) & N/cm\({}^{3}\) & 21.2 & 17.5 \\ moisture, W & \% & 11.8 & 8.1 \\ cohesion, c & kPa & 39 & 0.9 \\ angle of internal friction, \(\varphi\) & degree & 25 & 37 \\ modulus of deformation, E & MPa & 21.8 & 0.24 \\ Poisson ratio, \(\
u\) & - & 0.35 & 0.25 \\ \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical properties of soils at the experimental site and of the equivalent material used in laboratory modeling.  
Figure 6: Laboratory testing in the expandable box: (**a**) static loading; (**b**) testing models.  
embedded to a depth of 1.5 m, were installed at the intersections of the grid lines. In the centers of the cells of this grid, models of foundations with a conical top and a standing pile were placed. The load on the test foundations was applied incrementally, using metal plates, each weighing 12.5 \(\times\) 10\({}^{3}\) N. Settlements of the foundations were measured using vertical displacement sensors. Displacement readings were taken at each load increment and monitored until stabilization, at each stage. After reaching the maximum test load (100 kN), unloading was also performed in stages, to observe rebound behavior. The settlement monitoring during the static load test was carried out in the absence of horizontal soil displacement, as well as with a horizontal displacement, measured at 2.7 mm per 1 m.  
For a comparative analysis of the bearing capacity of cone-top pile foundations and isolated shallow foundations, identical static loading of isolated shallow (columnar) foundations was carried out. As a model of an isolated shallow foundation, round plate indenters with an area of A = 2462 cm\({}^{2}\) were used, corresponding to the diameter of cone-top pile foundations at the intersection with the ground surface level. The use of circular plates ensured more uniform load distribution across the foundation base, whereas square plates tended to produce cracks at their corners, indicating stress concentration and non-uniform pressure distribution in those zones.  
As a movable compensator, 50 mm diameter steel balls were used, secured to a \(500\times 500\times 20\) mm steel plate with the aid of viscous oils. The number of balls was  
Figure 7: Field testing: (**a**) design of a cone-top pile model for field testing; (**b**) testing models from steel; (**c**) field testing process.  
determined based on the final load, ensuring that the load on each individual ball did not exceed the contact strength of the material.