esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Results of Time Interval Determination  
When predicting metro flow, data with different historical time intervals can lead to different results. Shorter time intervals might not provide enough input data for the model to capture time-related patterns, while longer intervals could incorporate irrelevant inputs. Thus, to determine the optimal number of historical time intervals for superior predictive performance, this study incorporates historical data with different time intervals into the model to forecast entry and exit flows for the next time interval. The entry and exit metro flow prediction results of different historical time intervals for the two selected stations are illustrated in Table 2.  
Figure 2: Total normalized mutual information matrix. (**a**) Mutual information calculated by entry flow. (**b**) Mutual information calculated by exit flow.  
The results indicate that the optimal historical time intervals for predicting entry and exit flow at Zhonglou Station are 48 and 56, respectively. Similarly, the optimal historical time intervals for predicting entry and exit flow at Hangtiancheng Station are 52 and 58, respectively. Taking Zhonglou Station's entry flow prediction as an example, when the input time interval was set to 48, the RMSE, MAE, and MAPE were 71.27, 46.85, and 10.35%, respectively. Notably, the MAPE exhibited the most significant decrease compared to other time intervals. The model demonstrated the best predictive performance under these specific input time intervals. Therefore, this time interval configuration was adopted for subsequent experiments.
## 2 Method
### Feature Extraction  
CNNs can automatically generate high-level features (i.e., weights and thresholds) through training. First, the sample was sent to the network for training, the input vector was obtained, and the loss function was compared with the given target vector:  
\[L=\frac{1}{2}\sum_{k=0}^{n-1}(d_{k}-y_{k})^{2} \tag{4}\]  
where \(L\) is the loss function (standard deviation), \(y_{k}\) is the output vector, and \(d_{k}\) is the target vector. The weight and threshold values are updated according to \(L\), and the update step can be expressed as follows:  
\[\Delta W_{jk}(n)=\frac{\alpha}{1+l}\times\Big{(}\Delta W_{jk}(n-1)+1\Big{)} \times\delta_{k}\times h_{j} \tag{5}\]  
where \(\alpha\) represents the learning rate, \(j\) represents the neural units of the hidden layer, k represents the output layer unit, \(M\) represents the number of output neuron units, \(h_{j}\) represents the output vector of the hidden layer, \(W\) represents the adjusted weight, and \(\delta\) is the threshold to be adjusted.  
\[\delta k=h_{j}\big{(}1-h_{j}\big{)}\sum_{k=1}^{M-1}\delta_{k}W_{jk} \tag{6}\]  
The feature extraction process is shown below.  
Step 1: The ECG-SPP-net was initialized by setting the weight \(W\) as a random number within [0,1]. The threshold value \(\delta\) was set to be 0 and the learning rate \(\alpha\) was defined as 0.1. Finally, the training epochs was set to be 60.  
Step 2: The heartbeat from the training set was sent into ECG-SPP-net. The network was trained with one sample for each round due to various sizes of different heartbeats, and the target output vector was set to be \(d_{k}\).  
Step 3: Calculate the actual output vector \(y_{k}\) with Equations (1)-(3) and conducted the pooling with the proposed SPP algorithm in Figure 2. Then, the cost function was calculated with Equation (4).  
Step 4: The weight W and threshold value \(\delta\) were updated according to Equations (5) and (6).  
Steps 2-4 were repeated 60 times and the values of \(W\) and \(\delta\) were obtained as the high-level features extracted automatically by the ECG-SPP-net. Such high-level features and heartbeat signals from the test sets were then sent to the ECG-SPP-net for testing before sending the results to the classifier.  
Figure 5: Comparison between the original signal and the de-noising signal.
# Image Quality Assessment of Augmented Reality Glasses as Medical Display Devices (HoloLens 2)
## 4 Discussion
### Evaluation of Luminance and Luminance-to-Grayscale Conformance  
With a maximum luminance of 2366 cd/m\({}^{2}\) and a luminance ratio of 1478.75, HoloLens 2 clearly surpasses the requirements for primary and secondary display devices. However, HoloLens 2 lacks an absolute black point owing to its laser beam technology, which can present significant challenges for diagnostic applications.  
Moreover, the contrast response of HoloLens 2 occasionally showed unacceptable deviations from the GSDF, potentially impairing the perception of subtle contrast differences and thereby complicating diagnostic tasks.
# Diatomaceous Soils and Advances in Geotechnical Engineering--Part I
## 2 Evolution of Concepts Surrounding Diatomaceous Soils
### Taphonomic Processes of Diatomaceous Soils  
Taphonomy studies the processes in which organic remains pass from the biosphere to the lithosphere, considering the moment of death of an organism or the detachment of its parts. Such a change occurs through decomposition, burial, or preservation in the form of fossils or other types of biostable material. Only a tiny fraction of the organisms that inhabited the planet are preserved as fossils, while organic remains are relatively abundant. Taphonomy is essential for interpreting the meaning of the fossil record [77].  
The microfossils or micro-remnants present in DSs, particularly in Japanese marine soils, are usually less than 1 mm in diameter. Of these, both plant and animal origins are reported. Their evolution is due to diagenetic changes technically defined as taphonomic processes [8].  
Taphonomic processes alter diatom assemblages in sedimentary deposits depending on the climate, available space, time, and species. Silica is an element concentrated in the photic zone of most marine environments, leading to a higher productivity rate of diatoms. This element is necessary for the growth of frustules and generates ballast for subsequent sedimentation [49]. Beyond the above, very little is known about the evolution of silicification processes in diatom frustules [50]. High oxygen contents and low silica concentrations in a diatom indicate that the diatom developed in a planktonic community in deep freshwater environments. Conversely, high silica concentrations suggest that diatoms developed in benthic environments and shallow lakes [66].  
The skeletons of diatoms and radiolarians are composed of amorphous silica (Opal), which is very soluble in seawater. Hence, the preservation of frustules is only possible for larger species. Less than 5% of diatom skeletons reach the seafloor without being dissolved. Larger proportions could accumulate if sedimentary basins were shallow or if the sedimentation rate were higher [8].  
Diatoms have not been eliminated in the last few tens of millions of years because of the multilayered porous structures of their frustules, which present unique mechanical properties, such as a high elastic modulus (22.4 GPa), which gives it restorative properties under the imposition of loads and strains [76; 78].
# Fine-Scale Population Estimation Based on Building Classifications: A Case Study in Wuhan
## 1 Introduction
However, light images are affected by problems such as overflow and saturation, and the spatial resolution of the populations is low because of the limited resolution of night lighting data [27]. [PERSON] et al. used IKONOS-2 orthophotos, light detection, and lidar data to determine the volume of single-family houses and high-rise residences in order to estimate urban populations [28]. Given the close relationship between the residential population and number of residential units, geometric attributes such as the number, area, and volume of residential buildings can be employed to establish a population estimation model [29]. The relationships between the attributes of an urban fine-scale population estimation model can be assessed, by analyzing the population, residential building area, and layer number [30; 31]. Population estimation is a complex scientific task that involves human geography, demography, resource and environmental science, and other disciplines. Various achievements in the field of population estimation have provided the foundations for urban planning and resource planning. However, the following problems still affect the current popular estimation methods. (1) There has been a lack of fine-scale urban population distribution research and most population estimation research focused on the city and county scales at a low spatial resolution, thereby failing to meet the needs of fine urban population analysis. (2) In building-based estimation, the building classifications based on land classification data and manual interpretations lack efficiency and the costs are high. (3) The importance of the variables employed was not analyzed and models were built directly using the variables selected for population estimation research. This paper aims at fine-scale population estimation in the context of rapid population movement. In summary, the following contributions are made in this paper. 1. This paper proposes a building classification method with _POI_ data. The text classification method, frequency-inverse document frequency (_TF-IDF_), is applied to calculate the _POI_ data to obtain functional classifications of buildings (Section 2). 2. Based on the results of building classification, this paper establishes a population estimation model at a subdistrict scale. Firstly, this paper makes statistics on the relevant characteristics of buildings. Secondly, by calculating the importance of each building feature, a certain number of variables are selected, in turn, to establish a fine-scale population estimation model (Section 2). 3. This paper conducts experiments on the real dataset in Wuhan. The experimental results show that our approach achieves a lower error rate, which provides a new solution to the estimation for fine-scale populations (Section 3).
# The Suitability of PlanetScope Imagery for Mapping Rubber Plantations
## 2 Materials and Methods
### Data
#### 2.2.2 PlanetScope Imagery Data  
PlanetScope images were acquired by Planet Inc., the world's largest satellite constellation, with more than 170 orbiting satellites; together, this constellation can monitor the whole world once per day. PlanetScope satellites are the world's only remote sensing satellites with high-resolution, high-frequency, and full coverage capabilities globally. In the study, we selected PlanetScope images that were acquired on the 27 th of February and the 8 th of June, 2019, and were clear and cloud free. The spatial resolution of the satellite images was 3 m, and each image included four spectral bands: the blue, green, red and near-infrared bands. The PlanetScope images used in this study were ortho-grade (3B) data products; these imageries have undergone a series of processes (including sensor correction, radiation correction, atmospheric correction, and geometric correction) to obtain true and reliable surface reflectance images. The main parameters of the PlanetScope satellites are shown in Table 1.
# Global GNSS-RO Electron Density in the Lower Ionosphere
## 3 Results
### Solar-Cycle Variations  
Characterizing the long-term \(D\)/\(E\)-region \(N_{e}\) variations requires stable satellite operation and uniform sampling/quality in GNSS-RO observations. Because of the strong diurnal variability in the \(D\)/\(E\)-region \(N_{e}\), a non-uniform or variable sampling of local times would make the observed \(N_{e}\) variation difficult to interpret and prone to the sampling error from aliasing to the diurnal variation. Although COSMIC-1 had a full diurnal coverage during the early period of mission, its sampling degraded over time as several of its operating satellites stopped working. As a result, the COSMIC-1 data are not ideal for studying the solar-cycle variation. On the other hand, the MetOp observations have been stable with the longest record at two Sun-synchronous local times around 8-10 a.m. and 8-10 p.m. Since the routine GNSS-RO acquisition from MetOp goes up to \(\sim\)90 km, this long record is only available for the \(D\)-region \(N_{e}\). To fill in the early period (2007-2008) of the MetOp record, we added the SACC data when they had the sampling at the similar equator-crossing time (see Appendix B).  
The daytime and nighttime solar-cycle variations at 70 km from the MetOp/SACC observations are highlighted in Figure 13 as a function of magnetic latitude. As expected for the sunlight-driven ionization in the \(D\)-region, the \(D\)-region \(N_{e}\) correlates well with the Ly-\(\alpha\) flux in terms of the solar-cycle variation. In addition, there is a strong semiannual modulation in the magnetic tropics from both daytime and nighttime time series. Because of the presence of the dominating solar-cycle and semiannual variations, it is not straightforward to discern a quasi-biennial oscillation (QBO) modulation in the \(D\)-region \(N_{e}\), which was reported in some of the early studies [76; 77].
###### Abstract  
Multi-view information fusion can provide more accurate, complete and reliable data descriptions for monitoring objects, effectively improve the limitations and unreliability of single-view data. Existing multi-view information fusion based on deep learning mostly focuses on the feature level and decision level, with large information loss, and does not distinguish the view weight in the fusion process. To this end, a multi-view data level information fusion model CAM_MCFCNN with view weight was proposed based on a channel attention mechanism and convolutional neural network. The model used the channel characteristics to implement multi-view information fusion at the data level stage, which made the fusion position and mode more natural and reduced the loss of information. A multi-channel fusion convolutional neural network was used for feature learning. In addition, the channel attention mechanism was used to learn the view weight, so that the algorithm could pay more attention to the views that contribute more to the fault identification task during the training process, and more reasonably integrate the information of different views. The proposed method was verified by the data of the planetary gearbox experimental platform. The multi-view data and single-view data were used as the input of the CAM_MCFCNN model and single-channel CNN model respectively for comparison. The average accuracy of CAM_MCFCNN on three constant-speed datasets reached 99.95%, 99.87% and 99.92%, which was an improvement of 0.95%, 2.25%, and 0.04%, compared with the single view with the highest diagnostic accuracy, respectively. When facing limited samples, CAM_MCFCNN had similar performance. Finally, compared with different multi-view information fusion algorithms, CAM_MCFCNN showed better stability and higher accuracy. The experimental results showed that the proposed method had better performance, higher diagnostic accuracy and was more reliable, compared with other methods.  
fault diagnosis; multi-view information fusion; convolutional neural network; attention mechanism; planetary gearbox +
Footnote †: journal: Applied Sciences
#### 3.1.5 Energy Consumption  
Finally, the last factor considered serves as a proxy for the environmental impacts within the sustainability framework: energy consumption. In the context of energy efficiency criteria (Table 9), the mechanical ventilation system with centralized ventilation exhibits the highest energy consumption. This is attributed to the need for regulating air properties such as temperature and humidity, in addition to the requirement for air circulation throughout all exit points within the building. Furthermore, these systems commonly incorporate automatic control systems that contribute to energy consumption.  
Conversely, natural ventilation systems are situated at the opposite end of the spectrum, characterized by negligible energy consumption, except in cases where electrical components or similar devices are employed for the operation of doors and windows, which is not prevalent in the majority of the analyzed facilities. As for hybrid systems, their energy consumption is moderately higher, as it necessitates the operation of fans or equivalent mechanisms.  
Taking the aforementioned factors into consideration, the relative energy efficiency scores among all-natural systems are denoted as 1. In comparison, the scores for hybrid  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **N-SSV** & **N-CRV** & **N-AAC** & **H-NMR** & **M-CVS** & **Results** \\ \hline
**N-SSV** & 1 & 3 & 3 & 3 & 1/3 & 0.200 \\ \hline
**N-CRV** & 1/3 & 1 & 1 & 1 & 1/9 & 0.067 \\ \hline
**N-AAC** & 1/3 & 1 & 1 & 1 & 1/9 & 0.067 \\ \hline
**H-NMR** & 1/3 & 1 & 1 & 1 & 1/9 & 0.067 \\ \hline
**M-CVS** & 3 & 9 & 9 & 9 & 1 & 0.600 \\ \hline \hline \end{tabular}
\end{table}
Table 8: AHP Matrix for Thermal Comfort in Classrooms in Winter.  
and mechanical solutions are 2 and 9, respectively, when juxtaposed with the natural ventilation systems.
# Study on Seismic Performance of Steel Fiber Reinforced Concrete Pier Under Bending-Torsion Coupling
## 4 Parametric Analysis of Seismic Performance
### Influence of SFRC Height  
Keeping the torsion-bending ratio constant, the skeleton curves of the piers with different heights of SFRC in the plastic hinge region were compared, as shown in Figure 14a-e. To quantitatively analyze the influence of the SFRC height on the maximum bearing capacity of the specimens, the improvement in the maximum bearing capacity of the specimens with different heights of SFRC compared to the RC pier was determined, as listed in Table 8.  
It can be observed that, when the torsion-bending ratio is the same, the maximum bearing capacity increases with the increase in the height of the SFRC in the plastic hinge region. In the early stage of loading, there is no significant attenuation in the stiffness of the piers. The maximum bearing capacities of the piers with SFRC are all larger than those of RC pairs. After the peak load, the skeleton curves of RC piers decrease more steeply, and the larger the torsion-bending ratio, the more obvious the decrease in the bearing capacity. Due to the addition of steel fibers, the bearing capacities of the piers with SFRC decrease relatively slowly.  
When the torsion-bending ratio does not exceed 0.17, the addition of SFRC improves the maximum bearing capacity of the pier to a certain extent, and the maximum increase ratio is about 23%. When the torsion-bending ratio exceeds 0.17, the maximum bearing capacity of the SFRC pier is significantly improved.  
Considering the presence of torque, the bending-torsion coupling has a great influence on the bearing capacity of the pier. The larger the torsion-bending ratio, the more obviously the maximum bearing capacity of the SFRC pier is improved. The addition of SFRC material at the bottom of the pier increases its tensile strength and toughness, delays crack development, and significantly improves the maximum bearing capacity of the bridge pier. Under the common bending-torsion coupling effect (the torsion-bending ratio is less than 0.3), when the height of SFRC is 600 mm (half of the clear height of the pier),  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Torsion–Bending** & \multicolumn{4}{c}{**Reduction Ratio of Bearing Capacity/\%**} \\ \cline{2-6}
**Ratio** & **RC** & **SFRC300** & **SFRC600** & **SFRC900** & **SFRC1200** \\ \hline
0 & — & — & — & — & — \\
0.09 & 0.21 & 0.74 & 2.56 & 1.1 & 0.44 \\
0.17 & 6.52 & 3.73 & 3.92 & 3.54 & 3.67 \\
0.27 & 22.47 & 25.24 & 9.12 & 9.61 & 8.95 \\
0.36 & 28.62 & 27.12 & 23.26 & 15.95 & 15.93 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The reduction ratio of the maximum bearing capacity compared to no-torsion specimens.  
the pier already has a high bearing capacity, and the material can be fully utilized. If the height of SFRC continues to increase, the impact on the maximum bearing capacity is not significant. Under the action of a higher torsion-bending ratio, there will be a certain benefit when further increasing the height of the SFRC.
# Cross-Domain Access Control Model in Industrial IoT Environment
## 2 Related Work
### Blockchain Technology  
Traditional cross-domain access models [13; 14] usually use centralized systems. Authentication and access to all devices need to go through a centralized server. However, this centralized scheme causes a single point of failure and raises issues regarding the availability of the device.  
Blockchain, a decentralized and distributed technology, offers a new solution for IoT sharing across domains. Individual local IIoT devices form relatively independent trust domains in a distributed environment, making it easy to meet local needs. However, it is difficult for a single trust domain to meet the needs of industrial products; instead, multiple domains need to collaborate. To address this issue, [PERSON] et al. [15] proposed a blockchain-based cross-domain access control framework for IoT. However, it was not modeled or validated. [PERSON] et al. [16] combined blockchain and edge computing concepts and proposed a cross-domain trust model based on the Consortium chain, which reduces the traditional cross-domain access process. With the increase in IIoT devices and domains, there are higher requirements for system scalability, computing power, and storage capacity. [PERSON] et al. [21] proposed a cross-domain access control approach based on a master-slave chain. However, this study did not address the reliability of the master chain nodes to ensure the security and reliability of the master chains.  
The above models have made some contributions to the reliability of blockchain technology. However, most of the literature is not focused on the scalability of the system in access control systems. Therefore, this paper proposes a master-slave chain structure and demonstrates the system's scalability by continuously adding slave chains. In this study, the master chain nodes were selected by evaluating the reputation, thus providing security and reusability of the master chain. Therefore, the security and reliability of the whole system are also ensured.