esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Parametric Modeling of GSPCW Walls  
To extend our understanding of GSPCW wall systems beyond the limited test data, parametric modeling of GSPCW walls based on test walls was performed. In this study, a total of 18 wall models with varying design parameters were simulated. Table 3 lists all of the variations of the parametric wall models. These parameters include: (1) Steel ratio, defined as the ratio of the total steel area to the gross wall section area. Note that the variations in steel ratio are achieved by varying the thickness of the steel faceplates and the steel tie plates; (2) Axial load ratio, defined as the ratio of total vertical load to the product of concrete compressive strength and the wall gross section area; (3) Height-to-width ratio, defined as the ratio of the wall height to the wall section width; (4) Aspect ratio of the grille-type steel, defined as the ratio of the spacing of the steel tie plates to the thickness of the wall section; and (5) Concrete compressive strength. In the process of parametric analysis, the values for material strength were taken from the Chinese Code for Design of Concrete Structures (GB50010-2010) [15] and Code for Design of Steel Structures (GB50017-2017) [16].  
Figure 5: Comparison of experimental and simulation results: load–displacement hysteresis curves of specimen (**a**) GSPCW-\(1\) and (**b**) GSPCW-2; secant stiffness degradation of specimen (**c**) GSPCW-\(1\) and (**d**) GSPCW-2.
## 3 Results  
In Table 3, the results of the repeatability and the accuracy studies are shown.  
Parameters obtained for the real signal are shown in Table 4. Search ranges and values used in original work were also included.  
The convergence curve of the genetic algorithm is presented in Figure 6.  
It can be seen that the initial random population had the best individual about 2.4 worse than the final population (2.02 vs. 0.85).  
Figure 5: Plots of the measured signal: (**a**) full signal, (**b**) spectrogram of the full signal, (**c**) analyzed fragment with the most apparent alpha-waves, (**d**) the discrete Fourier transform of the (**c**) plot.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Accuracy (Mean)** & **Accuracy (Std)** & **ICC** \\ \hline \(A\) (mV) & 0.794 & 0.272 & 0.897 \\ \(B\) (mV) & 0.724 & 0.343 & 0.910 \\ C & 0.853 & 0.189 & 0.978 \\ \(v0\) (mV) & 0.756 & 0.306 & 0.865 \\ e0 (s\({}^{-1}\)) & 0.662 & 0.403 & 0.676 \\ r & 0.700 & 0.384 & 0.733 \\ Lower noise limit (pps) & 0.761 & 0.320 & 0.877 \\ Noise range (pps) & 0.863 & 0.191 & 0.966 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy and repeatability results.  
Figure 7 shows 5-s fragments of the original signal (7a) and the modeled signal (7c) with corresponding spectra (plots 7b and 7d, respectively). Even though the signals are slightly different in the time plane, their frequency planes are almost identical. It is to be expected, as the function minimized by the algorithm was defined in the spectral plane.  
After evaluating defined function for 100,000 times, the mean time needed for one evaluation was determined to be about 0.013 s. Multiplying this value by the number of individuals in each population (256) and the number of populations (150), the maximal time for the whole genetic algorithm was calculated to be about 9 min (Intel Core i7-3740 QM  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Lower Range** & **Upper Range** & **Default Value** & **Obtained Value** \\ \hline \(A\) (mV) & 2.25 & 4.25 & 3.25 & 4.21 \\ \(B\) (mV) & 12 & 32 & 22 & 12.06 \\ C & 70 & 675 & 135 & 406.50 \\ \(\upsilon\) (mV) & 5 & 7 & 6 & 6.60 \\ \(\epsilon\)0 (s\({}^{-1}\)) & 2 & 3 & 2.5 & 2.92 \\ r & 0.5 & 0.6 & 0.56 & 0.6 \\ Lower noise limit (pps) & 50 & 300 & 120 & 278.77 \\ Noise range (pps) & 200 & 1000 & 200 & 897.34 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Values of parameters obtained for the measured signal. Values in column “default” were used in original works.  
Figure 6: The convergence curve of the genetic algorithm. Minimized function, so the score, is dimensionless.  
Figure 7: Visual illustration of optimal parameters—comparison of the modeled and real signals.  
CPU with four 2.7 GHz cores, 16 GB RAM). Time is the main disadvantage of the proposed method. It was not focus of this study, however, to obtain results in an online manner.
## 3 Experimental Results and Evaluations
Furthermore, CIEL*a*b*-based Hue [30] is evaluated and compared in Figure 13. In the Hue comparative evaluation, the proposed method records lower scores than its conventional counterparts in several images like A and CWF, and in the other images [PERSON]'s method alone records lower scores. The result also shows that images like A and CWF tone-mapped by conventional methods, especially iCAM06 and [PERSON]'s methods, introduce a larger hue shift relative to the proposed method. Figure 5:0 CIEL*a*b*-based color difference (\(\Delta\)E_ab) comparison between the conventional and proposed tone mapping methods with the captured images under five different standard illuminations. Figure 5:1 CIEL*a*b*-based colorfulness comparison between the conventional and proposed tone mapping methods with the captured images under five standard illuminations. Figure 5:2 CIEL*a*b*-based hue comparison between the conventional and proposed tone mapping methods with the captured images under five different standard illuminations. As a subjective evaluation, a psychophysical experiment was conducted. The color vision test was joined by a total of 15 male participants and used 30 different images captured under 5 standard illuminations and then processed by various tone mapping methods. In the test, the 30 images went through color rendering process and the outcomes were assessed and compared in the categories of the color reproduction, brightness, and colorfulness. These test images were displayed on 32\" LG 32 QK500C which has been calibrated. The resolution of the monitor is 2560 \(\times\) 1440 (QHD) at 30-83 kHz. Its brightness is max = 300 cd/m\({}^{2}\) and min = 250 cd/m\({}^{2}\). Therefore, the contrast ratio is approximately 1000:1, DFC: Mega. The viewing distance for the test is 80 cm. The screen is behind a dark mask. The maximum time allowed to choose between two tone-mapped images is 13 s which was decided based on a pilot study. All 15 observers are aged between 30 and 34. They are working for M.D. and Ph.D. in the color image rendering field in a university and therefore have a deep understanding of their task. For the psychophysical experiment, a pair of images were compared. The 30 images were processed by the iCAM06, [PERSON]'s method, [PERSON]'s method, and the proposed method and the resulting images were compared. The experiment was conducted in a dark environment to overcome a veiling glare limit in a white or bright background. The parameters used in each algorithm are fixed at the values suggested in the paper of each method mentioned above. The observers judged a pair of images at a time and gave \"1\" to the image they judged accepted and \"0\" to the image they judged rejected. When they judged the image a draw, they gave \"0.5\" to each image. The scores were all added and converted into the preference scores [31]. Figure 14 shows the product of the psychophysical experiment. The higher preference scores were given to the proposed method relative to its conventional counterparts.
## 4 Results and Discussion
### Results of the Simplified Fire Model
#### 4.2.1 Oscillatory Flow through Ceiling Vents
Figure 5a also shows the results for the evolution of the CO\({}_{2}\) mass flow rate through the west vent divided by the constant \(C=7\times 10^{-3}\) (triangle symbols). The approximate coincidence between these results and the outward mass flow rate through the west vent means that the concentration of CO\({}_{2}\) in the hot gases leaving the compartment is fairly constant, and varies from approximately \(7\times 10^{-3}\) during the first two minutes after the instant at which the gases begin to flow (around \(t=10\) s) to \(8.5\times 10^{-3}\) once the simulation reaches a steady state. Figure 5b compares the results of Figure 5a, for \(T_{\rm in}=1500\) K, with those obtained with \(T_{\rm in}=2436\) K. Note that the frequency of the mass flow rate oscillations is only slightly smaller and their amplitude remains more constant with time for the higher \(T_{\rm in}\) value. The absolute values of the net mass flow rates across the west and east vents obtained with both \(T_{\rm in}\) values are in phase to each other and oscillate with very similar amplitudes. Also note that the mentioned delay of about 10 s between the outward and inward mass flow rate oscillation waves also exists in the results obtained with \(T_{\rm in}=2436\) K. Therefore, the influence of \(T_{\rm in}\) on the overall oscillatory behavior of the flow at the open vents is apparently small.
## 4 Related Research on Service Discovery Protocols  
Traditionally, service discovery architectures are classified into two types: directory-based architectures and directory-less architectures. Surveys that focus on service discovery in both fixed and _ad hoc_ networks can be found in [24, 25].
# Multi-Descriptor Random Sampling for Patch-Based Face Recognition
## 3 Experiments and Analysis
### Extended Yale B Dataset  
The Extended Yale B database [34] consists of 2414 frontal face images generated from 38 persons using 64 different illumination conditions. In addition, an image with ambient illumination was also captured for every subject in all poses. Then, the images are grouped into four different subsets depending on the lighting angle with respect to the axis of the camera. Typically, subset1 and subset2 cover the range of 0\({}^{\circ}\) to 25\({}^{\circ}\) while subset3 covers the angular range of 25\({}^{\circ}\) to 50\({}^{\circ}\). subset4 covers 50\({}^{\circ}\) to 77\({}^{\circ}\) and subset5 covers angles which are larger than 78\({}^{\circ}\). To allow a simulation of different levels of contiguous occlusions, the most widely used technique described in [35] is used to replace a randomly located square patch from each test image with a baboon image, this is because it has a texture similar to that of the human face. Moreover, the location of the occlusion is randomly selected. The sizes of the synthetic occlusions vary in the range of 10% to 80% of the original image size. Figure 14 shows some samples of randomly occluded faces generated from the Extended Yale B database.  
For this set of experiments, subset1 was used for training while the remaining 4 subsets were used for testing. For the other parameters, the best performing ones from the previous experiments were used. First, the original image size of 192 \(\times\) 168 was kept, and 50% overlapping patches were sized equally at 32 \(\times\) 28 each. The classifier size was set to \(p\) = 3, the HOG cell size to 6 \(\times\) 6 combined with multi-LBP.  
The average recognition accuracy for each subset for an occlusion level ranging between 10% and 80% is depicted in Figure 15.  
The obtained results have been evaluated and compared against some state-of-the-art algorithms and Table 2 depicts the accuracy percentages. From the table, it can be observed that our proposed method achieves consistent results throughout the experiments. Despite  
Figure 14: Sample images from the Extended Yale B dataset with randomly located occlusions.  
not reaching a higher accuracy at small occlusions, its increase does not affect it as does the SSR-P/W method proposed in [36]. Finally, it eventually outperforms it when occlusion is at 50%, reaching 90.58% as compared to 88.6% for SSR-W, in subset5.  
Further results can be seen in Table 3, which are consistent even when occlusion increases. The accuracy remains above 85% for any occlusion level and under different lighting conditions. This could also be seen in Figure 15, where the average accuracy for each of the four testing sets has been illustrated.
# A Review of Deep Learning Based Methods for Acoustic Scene Classification
## 2 Signal Representations
### Monaural vs. Multi-Channel Signals  
ASC algorithms commonly process monaural audio signals. Sound sources in acoustic scenes are spatially distributed by nature. If multi-channel audio recordings are available, the inherent spatial information can be exploited to better localize sound sources. The joint localization and detection of sound events was first addressed in Task 3 of the DCASE 2019 challenge ([[http://dcase.community/challenge2019/task-sound-event-localization-and-detection](http://dcase.community/challenge2019/task-sound-event-localization-and-detection)]([http://dcase.community/challenge2019/task-sound-event-localization-and-detection](http://dcase.community/challenge2019/task-sound-event-localization-and-detection))).  
In addition to the left/right channels, a mid/side channel representation can be used as additional signal representation [7; 8]. As an example for using a larger number of audio channels, [PERSON] and [PERSON] [9] classified acoustic scene recordings of fourth-order Ambisonics by combining spatial features describing the direction of sound arrival with band-wise spectral diffuseness measures. Similarly, [PERSON] and [PERSON] combined spatial features from binaural recordings with spectro-temporal features to characterize the foreground/background sound distribution in acoustic scenes [10]. Current state-of-the-art ASC algorithms process either mono or stereo signals without a clear trend.  
Figure 1: The flowchart summarizes the article’s structure and lists the typical processing flow of an acoustic scene classification (ASC) algorithm.
## 3 The Framework of CPSS-UPTDN
### Basic Modules in CPSS-UPTDN
The elements and rules in a real system are applied to the artificial system, including various learning strategies, optimization algorithms, and transportation scenes. **Small Loop for real-time status feedback**: The small loop handles the local information concerned by managers. When real urban public transportation feeds back traffic status, the cyber system outputs the predicted traffic status and dispatching scheme according to relevant computational experiments. Then the effective scheme will be transmitted to the real system through parallel execution in real-time. **Big Loop for periodical data update**: The big loop deals with the global information in urban public transportation, and updates all the models in the artificial system using multi-source data periodically. The real system generates new data about traffic status constantly. It is unrealistic that feedback these data to distributed storage module real-timely. So a big loop is designed to periodically update these massive data and then used to optimize the models in the artificial system. Through real-time feedback and periodical update, this kind of interaction between the real and artificial systems can effectively manage urban public transportation.
## 4 Results and Discussion
### Trend Estimates of GPP
These results could help us understand the latitude or elevation range in which the change of GPP mainly occurred, providing a basis for understanding the changes of the Arctic GPP. Figure 16c displays the boxplots of the interannual trends by combining the pixels for each land cover type. It can be seen that almost each land cover type presents positive trends. However, their magnitude, as well as their spatial variation, depend on the land cover types. Forests have the largest interannual trend, followed by savannas and shrublands. The interannual trend of grassland is not as large as we expected and is even slightly smaller than the trend of permanent wetlands. Savannas and forests show the largest spatial variations in interannual trends, as indicated by the most widespread distribution of boxplots. Permanent wetlands and grasslands present the smallest spatial variations when excluding permanent snow and ice as well as barren land. These results demonstrate that the interannual trends of savannas and forests are more influenced by other factors, while those of permanent wetlands and grasslands are less sensitive to other factors.
## 2 Materials and Methods
### Exergo-Economic Analysis  
Economic issues as an inevitable part of engineering were considered in a thermodynamic so-called exergo-economic analysis. Price estimates, assumptions and calculated values for the equipment cost rate are shown in Appendix A, which shows the cost functions for all components.  
* See Table 2 for the equations needed for the exergo-economic analysis.  
Equation (6) determines the cost rate of equipment that \(PEC_{k}\) is the amortization cost for a particular component and \(\Phi_{k}\) is a maintenance coefficient and determines the impact of maintenance costs on total cost of equipment that is assumed (1.06). The number of system operating hours in a year for power plant (N) is considered (8100) for the ORC cycle and (4380) for the topping cycle. The (Capital Recovery Factor) CRF estimated (0.0825) by Equation (11) where (i) is the interest rate and (n) is the number of lifetime years considered as 30 years.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Definition** & **Equation** \\ \hline The equipment cost rate, & \\ [PERSON] et al. [44]. & \(\dot{Z}_{k}=\frac{\Phi_{k}PEC_{k}CRF}{3600\text{-}N}\) & (10) \\ The Capital Recovery Factor, & \(CRF=\frac{(1+i)^{i}\dot{x}}{((1+i)^{i})-1}\) & (11) \\ [PERSON] et al. [44]. & \(\dot{C}_{l}=c_{l}\dot{E}_{l}\) & (12) \\ The exergo-economic balance for each component, & \(\dot{C}_{P,k}=\dot{C}_{F,k}-\dot{C}_{L,k}+\dot{Z}_{k}\) & (13) \\ [PERSON] et al. [20]. & \(\dot{C}_{D,k}=c_{F,k}\dot{E}_{D,k}\) & (14) \\ The exergo-economic factor, [PERSON] et al. [20]. & \(f_{k}=\frac{\dot{Z}_{k}}{\dot{Z}_{4}+c_{F,k}\dot{E}_{D,k}}\) & (15) \\ The relative cost difference of the equipment indicates, & \(r_{k}=\frac{c_{D,k}-c_{D,k}}{c_{F,k}}=\frac{1-\psi_{k}}{\psi_{k}}+\frac{\dot{Z} _{k}}{c_{F,k}\dot{E}_{p,k}}\) & (16) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Equations related to the exergo-economic analysis.  
For calculating the cost per exergy unit, the exergy-economic balance of each component should be obtained according to Equations (12) and (13) where \(c_{i}\) is the cost of the streams, \(\hat{C}_{P}\) is the cost rate of the equipment, \(\hat{C}_{F}\) is the cost rate of fuel flow and \(\hat{Z}_{k}\) is the cost rate dependent on the maintenance cost and capital cost rate of each component.
# Stability Control of Multilayer Roof Strata in the Large Mining Height Gob-Side Entry: A Case Study
## 2 Engineering Background
### Mining Conditions and Entry Support Scheme  
The head entry of the 22301 working face has a rectangular cross-section, with a net width of 4.5 m and a net height of 3.5 m. The entry is driven along the coal seam roof, with a retained strike length of 1600 m. The steel ladder beams are combined with rock bolts, anchor cables and mesh in the integrated support systems to enhance the structural stability of the roof and side wall structures. Furthermore, they provide anchoring points for these additional support structures, which help to distribute the load from overlying rock to the surrounding rock mass, as well as to stabilize loose or fractured rock masses [28]. The specific support parameters of the roof include: \(\phi\)20 mm \(\times\) 2400 mm threaded steel anchor bolts, \(\phi\)16 mm round steel ladder beams, \(\phi\)21.6 mm \(\times\) 6300 mm anchor cables, and No. 10 diamond-shaped metal mesh. The spacing of the roof anchor bolts is 1000 mm \(\times\) 900 mm, and the anchor cables are spaced at 2000 mm \(\times\) 1800 mm, arranged in a pattern of alternating rows with two and three anchors, all installed into the ladder beam holes. The two sides of entry are supported with a combination of \(\phi\)20 mm \(\times\) 1800 mm threaded steel anchor bolts, \(\phi\)10 mm round steel ladder beams, and No. 10 diamond-shaped metal mesh. The spacing between the side anchor bolts is 800 mm \(\times\) 900 mm. A detailed support diagram is shown in Figure 2.  
Figure 2: Support diagram of 22301 head entry.
# A Hybrid Key Generator Model Based on Multiscale Prime Sieve and Quantum-Inspired Approaches
## 10 Fusion Effects: An Example, Results, Discussion, and Perspectives
This creates a diffusion effect similar to quantum decoherence: Small differences in the initial values lead to significantly divergent results, a phenomenon often associated with chaotic systems. Each of these transformations increases the entropy and unpredictability of the final result of the key. Since permutations are influenced by a private key and depend dynamically on time, even slight variations in input or execution times produce completely different keys. This crypto-agility contributes to post-quantum resilience and greatly increases the complexity of brute force or structure-based attacks. The cumulative effect of these steps (padding, reshaping, private permutation, XOR, and quantum-inspired cross-fragmentation) constitutes a non-reversible fusion path. The non-linearity, increased entropy, and lack of temporal determinism make the output cryptographic key significantly more secure than that obtained by applying only traditional PRNG and XOR techniques. Finally, from a scalability point of view, with regard to the multiscale sieve, it is clear that by shifting the complexity from computing power to memory, broad scalability is achieved. The limitation is more related to the quantum-inspired aspects, as these can be considered examples of the strength of a hybrid algorithm. However, it should be added that, given the modularity of the conceptual architecture, it lends itself both to accommodating different algorithms and to finding application in new-generation hybrid and post-quantum solutions.