esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Attention-Guided Encoder-Decoder Convolutional Neural Network (AED-CNN)
### Training
#### 3.5.2 Loss Function  
Equation (3) shows our overall loss function, which is a weighted summation of the two branches, one is the confidence loss (_conf_) of the softmax classifier, and the other is the localization loss (_loc_) of the bounding box regression.  
\[L(\{p_{i}\},\{t_{i}\})=\frac{1}{N}\big{(}\big{(}L_{conf}\big{(}p_{i},p_{i}^{*} \big{)}\big{)}+\lambda L_{loc}\big{(}t_{i},t_{i}^{*}\big{)}\big{)} \tag{3}\]  
where \(p_{i}^{*}\) is the ground truth label of an anchor \(i\) in a mini-batch, and the value of \(p_{i}\) is the probability of an anchor \(i\) being a pedestrian. The ground truth location of the anchor \(i\) is denoted by \(t_{i}^{*}\) and  
Figure 4: Visualization of feature maps from decoder module (**top**) and visualization of feature maps from the attention module (**bottom**).  
the predicted bounding box location of the anchor \(i\) is represented by \(t_{i}^{*}\). \(N\) denotes the number of positive anchors. Notably, if \(N=0\), the overall loss \(L=0\). Currently, we set weight term \(\lambda\) to 1 via cross-validation. The classification loss \(L_{conf}\) is the cross-entropy loss over two classes (pedestrian vs. non-pedestrian). As in Fast R-CNN [14], the smooth \(L_{1}\) loss is adopted as our regression loss \(L_{loc}\).  
\[L_{loc}\Big{(}t_{i},t_{j}^{*}\Big{)}=\sum\
olimits_{j\in\{x,y,w,h\}}\text{ smooth}_{L_{1}}\Big{(}t_{j},t_{j}^{*}\Big{)} \tag{4}\]  
where \(\text{smooth}_{L_{1}}\) is defined as:  
\[\text{smooth}_{L_{1}}(x)=\left\{\begin{array}{cc}0.5x^{2}&\quad if\ |x|<1\\ |x|-0.5&\quad otherwise,\end{array}\right. \tag{5}\]
## Experimental Section
### Synthesis
#### 3.2.3 Measurements
Optical microscopy: Observations were performed with an optical reflection microscope (Zeiss axioplan) equipped with a differential interference contrast (DIC) module. The micrograph was acquired with an Axio Cam digital camera. Ellipsometry: Layer thicknesses were determined using a MM-SPEL-VIS ellipsometer from OMT. The silicon or Glass substrates were examined with a spectral method in the wavelength range from 450 to 900 nm. The azimuthal angle was kept at 15 degrees. The integration time was dependent on the layer thickness and the resulting signal intensity. The main source for systematic errors during the measurements is the correct position of the sample. This results in uncertainties in both the angle of incidence and the azimuthal angle. This error has been minimized by measuring all samples within one session right after another with exactly the same geometry of the device and positioning of the sample holder. Each single measurement averaged over the area of 3 \(\times\) 5 mm. In addition, to further reduce systematic errors in the data collection, always one merely cleaned substrate was measured as reference in a series of experiments. Statistic errors are thus small but have, however been evaluated by measuring 5 different areas on each sample. The presented data are the average values of each sample and statistic errors for all samples were \(<\)1 nm  
Sessile drop method: The contact angle measurements were carried out on a sessile drop G40 instrument (Kruss GmbH). With double distilled water as the wetting liquid, droplets were deposited onto substrates and the contact angle values were averaged over 10 droplets. IR-Spectroscopy: Photodegradation kinetic of the protective layer was followed by FT-IR-spectroscopy. Spectra of the coated SCG samples were collected on a Nexus 470 spectrometer (Thermo Nicolet) using the transmission mode. As the spectral range below 2000 cm-1 is obscured by the very strong absorption of the glass only the C-H-stretching vibrations of the polymer around 2900 cm-1 could be observed. The band area was determined by drawing a baseline and integratingbetween 3040 and 2745 cm-1. For background subtraction we recorded spectra of the SCG without polymer coating. Photodegradation: The rate was estimated by measuring the rate of decrease in the integrated IR absorbance associated with the C-H stretching vibration of a solution cast film of poly(acrylic acid-_co_-allyacrylate) during sequential UV irradiation with light intensity of 5 mW/cm2 and wavelength range 290-800 nm. The photochemical process associated with UV irradiation was measured on coated conventional Planilux glass (TiO2 free) and found to be marginal with respect to the photocatalytic degradation.
## 6 Discussion
### Effects of Porosity Change on Radionuclides Transport
These uncorrected porosity values consider interlayer water as pore water, reflect the total water distribution in the system, and overestimate the actual intergranular porosity [43]. Recently, several relevant studies have been conducted on porosity change with the bentonite hydration expansion behavior, including porosity investigation of compacted bentonite using XRD profile modeling [60], A dual-porosity model has been established for the study of chemical effects on the swelling behavior of MX-80 bentonite [61], porosity changes due to the hydration of compacted bentonite [62], and a review of porosity and diffusion in bentonite [63]. Various results have been obtained in these studies, and many models of porosity change in bentonite have been developed. In the future, smectite dehydration reactions could be included into the chemical thermodynamics database of geochemical models. A database comprising chemical thermodynamic equilibrium and nonequilibrium kinetic reactions of dehydration was established. It was combined with the chemical thermodynamic data of mineral formation or nuclide adsorption reactions in the bentonite. These complete chemical thermodynamic databases were incorporated into the coupling model of the geochemical and reactive chemical transport. Even the thermal, pressure, and stress fields were coupled with the reactive chemical transport model [64]. Further, the test data of the underground laboratory from multiple years (such as the status of the field FEBEX test after 18 years: heterogeneous bentonite barrier [65]) or those of other montmorillonite materials as the buffer (such as the alkylammonium-modified montmorillonite [66]) were employed. Thus, a comprehensive safety assessment of the radioactive waste disposal site could be effectively conducted.
# Onboard Centralized ISL-Building Planning for LEO Satellite Constellation Networks
## 5 Performance Evaluation  
### Simulation Setup  
To compare the performance of the method proposed in this paper and several common methods used in related works, we developed a simulation scenario involving which is a Walker Delta \(32/4/1\) constellation; that is, there are four planes in the constellation, with eight satellites per plane. The orbital elements of one of the satellites in the constellation are stated in Table 1. The simulation period was 7800 s, approximately the constellation's repeat cycle.  
Furthermore, there were four ISL terminals in each satellite. The installation direction and turntable rotation half-angle of each ISL terminal are stated in Table 2.  
The preliminary simulation indicates that, in the constellation, all satellites on the same plane can use ISL terminal I and ISL terminal II to build ISLs continuously and form a ring. Nevertheless, on the other hand, the ISLs between adjacent planes need to be switched.  
Furthermore, to verify the applicability of the proposed link-building planning method, we developed a large-scale constellation scenario, as described in Section 5.4 below.  
All the simulations were performed on a computer typically used on satellites, having a 50 MHz CPU and 512 MB RAM.
## 3 Problem Description and Multiple Operation Procedures Design for U-Shaped Area  
### Problem Description  
The apron U-shaped area is a three-sided closed airfield area enclosed by two adjacent finger corridors and the main body of the terminal building, with parking stands and taxiways in this area. If the entrance to the U-shaped area is considered as a runway node, the apron U-shaped area can be further considered as a miniature airport surface system. The apron U-shaped area is widely used in medium and large airports at home and abroad because of its high apron utilization rate and aircraft leaning rate. According to the number of accesses, the apron U-shaped area can be divided into single-access U-shaped area, double-access U-shaped area, and multi-access U-shaped area. The focus of this paper is on the narrow double-access U-shaped area, which is the most common in the civil aviation system.  
Figure 1 shows the general distribution of aprons at Wuhan Tianhe International Airport. The main passenger apron at this airport contains 5 U-shaped areas (aprons 1-5 shown in the figure) and one semi-U-shaped area (apron 6 shown in the figure), and the number of parking stands involved accounts for 70% of the main passenger security stands and 54% of the corridor stands. According to the current operating rules of Wuhan Airport [30; 31], only one aircraft is allowed to operate in any one U-shaped area at the same time, resulting in extremely inefficient operations.  
To this end, according to regulation documents [32; 33], three types of U-shaped area operating procedures are designed in this section, namely exclusive operation procedure, partition-shared operation procedure and global-shared operation procedure. Differentiated arrival and departure aircraft operation rules are also proposed for the different types of apron operating procedures.
## 4 Case Study for SU Allocation in 0.4 kV PTLs According to the Developed Methodology
### Effect of Sectionalization and Automatic Reclosing in the Event of Stable Failure  
If a failure turned out to be stable, the main change in PS restoration time would be associated with a reduction in the duration of obtaining and recognizing information. In the considered case of failure in Mezenka village, the time to obtain information about it will be reduced up to time required for message transmission from SU to a dispatcher and its analysis. According to [43], this constituents can be also disaggregated into the constituents:  
\[\text{t}_{\text{obt.infor.}}\ =\ \text{t}_{\text{obt.infor.1}}+\text{t}_{\text{obt.infor.2}}+ \text{t}_{\text{obt.infor.3}}, \tag{15}\]  
where  
\(\text{t}_{\text{obt.infor.1}}\) is time interval to obtain information about a failure by a sensor;  
\(\text{t}_{\text{obt.infor.2}}\) is time interval to obtain information about a failure by a signaling unit of dispatcher room;  
and \(\text{t}_{\text{obt.infor.3}}\) is time interval to obtain information about a failure by a dispatcher.  
Time intervals \(\text{t}_{\text{obt.infor.1}}\) and \(\text{t}_{\text{obt.infor.2}}\) depend on the applied data transmission method, for example, for the GPRS data transmission technology, together, they will be less than one second (0.0002 h) [44]. The time interval \(\text{t}_{\text{obt.infor.3}}\) depends on the mode of information display to a dispatcher and includes the time required for a dispatcher to notice a message and read it. According to study [45], when equipping a dispatcher room with a SCADA system, this time interval is no more than 15 s (0.0042 h). Hence, time for receiving information about failure in the considered case would be 0.0044 h, and therefore, the power supply restoration time would be:  
\[\text{t}_{\text{restor.st.dam.MCUSU}}\ =\ 0.0044+1.2+1+0.1\ =\ 2.3044\ \text{h}\]  
The reduction in PS restoration time would be:  
\[\text{t}_{\text{resotor.st.dam.MCUSU}}\big{/}_{\text{resotor.st.dam.MCUSU}} \ =\ 3.31\big{/}2.3044\ =\ 1.44\ \text{times}\]  
Therefore, in the case of the stable failure, the application of SU with automatic reclosing function would enable to reduce PS restoration time over than 1.44 times. The damage would decrease for consumers S30-S43 up to USD 26.58, whereas the liquidated damage for consumers S1-S29 would amount to USD 127,61.
## 2 Materials and Experimental Methodology
### Multiple Stress Crep and Recovery (MSCR) Examination  
The MSCR test is designed to replicate the high-stress conditions road surfaces endure, particularly in the early stages of their service life when rutting is prevalent. This test precisely replicates the first field aging process using asphalt binders aged via RTFO. Thus, the MSCR test provides a better estimate of the rutting resistance of a binder.  
The MSCR test data yield two fundamental parameters: % recovery, and non-recoverable creep compliance (J\({}_{nr}\)) (3 samples). Equation (1) shows the J\({}_{nr}\), which, by means of the ratio of the unrecoverable shear strain to the applied stress, gauges the binder's sensitivity to permanent deformation under continuous load. Computed as the percentage change in unrecovered strain from peak strain, percent recovery (rec%) measures the binder's capacity for recovery following loading. Equation (2) details this [36].  
\[J_{nr}=\frac{\textit{Unrecoverable shear strain}}{\textit{Applied Stress}} \tag{1}\]  
\[\text{rec }(\%)=100\ \times\ \frac{\textit{Peak strain}-\textit{Unrecovered strain}}{\textit{ Peak strain}} \tag{2}\]  
Twenty stress cycles at 0.1 kPa and then ten cycles at 3.2 kPa comprise the MSCR test. Every cycle consists of a 1 s shear creep load then a 9 s recovery period. Whereas the higher stress level of 3.2 kPa evaluates the binder's performance under non-linear conditions, the lower stress level of 0.1 kPa assesses its behavior within the linear viscoelastic range. Predicting long-term pavement performance depends on a thorough knowledge of the binder's behavior over several stress levels, which this method guarantees. Equations (3)-(6) help us to determine the average values of J\({}_{nr}\) and rec% [37].  
\[J_{nr(0.1)}=\frac{\sum_{n=11}^{20}\left[J_{nr}0.1n\right]}{10} \tag{3}\]  
\[J_{nr(3.2)}=\frac{\sum_{n=21}^{30}\left[J_{nr}3.2n\right]}{10} \tag{4}\]  
\[rec_{0.1}=\frac{\sum_{n=11}^{20}\left[rec_{0.1}n\right]}{10} \tag{5}\]\[rec_{3.2}\ =\ \frac{\sum_{n=21}^{30}\left[rec_{3.2}n\right]}{10} \tag{6}\]  
Based on expected traffic loads, these criteria define the appropriate grade of high-temperature performance for bituminous binders. Jnr3.2 thresholds of 4.5 kPa\({}^{-1}\), 2.0 kPa\({}^{-1}\), 1.0 kPa\({}^{-1}\), and 0.5 kPa\({}^{-1}\) define the PG classifications: Standard (S), Heavy (H), Very Heavy (V), and Extremely Heavy (E). Furthermore, the difference in Jnr diff between the 0.1 kPa and 3.2 kPa stress levels cannot be more than 75%, thus maintaining the stress sensitivity of the binder within reasonable limits.  
By ensuring the choice of bituminous binders with enough resilience and stability under various stress levels, this strong assessment system guarantees the lifetime and durability of pavement constructions are improved [37].
## 1 Introduction
Instead of utilizing typical random training data, the extensive training scheme uses a set of data that is properly chosen according to the physical nature of the fiber-induced signal distortions. In this paper, we propose a more efficient ANN equalizer for mitigating the nonlinear electrical impairments induced in the IMDD short-reach optical-fiber communication system. The proposed equalizer consists of a basic single-hidden-layer ANN that minimizes the overhead computational complexity required for the signal recovery at the receiver end. An advanced training method is also introduced to significantly improve the bit error rate (BER) performance of the equalizer, compared to the ANN equalizer introduced in [21], with the same computational cost per symbol. The proposed training scheme considers multiple noisy sets of all possible data combinations that could be transmitted through the optical channel. This is contrasted with the extensive training scheme introduced in [21], which utilizes only noise-free data during the training process. The robustness, performance, and efficiency of the proposed ANN equalizer are demonstrated through applying it to mitigate the distortions of 10- and 28-Gbaud short-reach optical-fiber communication scenarios with the IMDD system. Thanks to the advanced training scheme, the compensation performance of the proposed ANN equalizer outperforms the BER performance obtained using the robust but computationally expensive MLSE equalizer. The remainder of the paper is organized as follows. Section 2 introduces the operation principles of the proposed ANN-based equalization technique. A full description of the introduced training scheme is also provided. In Section 3, we present system setups, results, and discussions on the evaluation of system BER performance, computational complexity, and efficiency. Finally, conclusions of the work are drawn in Section 4.
### Gist of the Proposed Framework  
Our proposed framework utilized the Emotion Robustly Optimized Bidirectional Encoder Representations from Transformers (BERT) Pre-training Approach (Emorberta) [20] technique to extract the emotions from the RUW tweets. Emorberta is an amalgamation of the NLP's state-of-the-art Roberta technique [21] and GoEmotions [22]. Both of these techniques relied on the popular BERT model developed by Google [23]. Studies such as [22] have found that ROBERTA had outperformed the BERT model and became the leading pre-trained model to utilize for text classification tasks [24]. Moreover, in opposition to existing emotion detection techniques proposed by [25], which classify the text into six emotion categories (joy, anger, fear, sadness, disgust, and surprise), Emorberta classifies the text into 28 emotion categories (admitration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, surprise + neutral) [20; 22; 26]. Furthermore, according to [27; 28], a framework is a set of different components that fosters the process to resolve a problem. Our proposed way of extracting and classifying societal emotions comprises many interleaved components (i.e., data collection and preprocessing, Emorberta, labeling, feature extraction, classification, and evaluations) that together work to fulfill the objective of this work. Thus, we are addressing our approach as a framework.  
Due to the growing availability of electronic documents from many sources, supervised ML research is becoming more and more important [29]. Numerous issues may be resolved with supervised ML [30]. ML has several advantages for text classification tasks such as efficacy, interdomain data insight transfer, and cost savings on expert labor [31]. ML techniques are required because of the growth of data-related tasks such as text classification, where the quantity of unlabeled samples frequently outweighs the number of labeled samples [32]. Specifically, supervised ML techniques need to be labeled datasets to train efficiently [33]. Such techniques therefore rely on the labeled data that is usually not available. Mostly unlabeled datasets are available that requires further manual labeling through expert services like Amazon Mechanical Turk. This makes data labeling tasks costly and time extensive [34]. Our proposed framework demonstrates the automated way of labeling the unlabeled data for the ML model's training.
# Analysis of the Failure Area of the Slope Using the Slip Line Method
## 3 Analysis of Failure Area
### Analysis of Failure Area of Heterogeneous Ground Slope
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c} \hline \hline
**Property** & **Grouped** & **Case 1** & & & **Case 2** & & & **Case 3** & & & & & & & & & & & & \\ \hline Slope angle (\(\tau\)) & 10 & 20 & 30 & 10 & 20 & 30 & 10 & 20 & 30 & 10 & 20 & 30 & 10 & 20 & 30 & 10 & 20 & 30 \\ Failure area along (m) & 6.29 & 6.6 & 7.89 & 6.29 & 6.62 & 7.90 & 6.3 & 6.68 & 7.90 & 5.77 & 5.18 & 6.00 & 5.51 & 5.85 & 7.08 & 5.53 & 8.58 & 7.08 \\ Failure area along (m) & 17.1 & 15.6 & 13.7 & 17.1 & 15.6 & 13.7 & 17.2 & 15.6 & 13.7 & 14.7 & 11.9 & 11.4 & 14.4 & 13.5 & 12.3 & 14.5 & 13.5 & 12.3 \\ internal stress & 4967.36 & 3306.2 & 2287.53 & 5007.52 & 3423.49 & 2282.87 & 5078.84 & 3572.64 & 2330.81 & 2910.29 & 906.70 & 605.46 & 2807.84 & 1776.37 & 1183.25 & 2621.87 & 1801.17 & 1203.34 \\ (Q.P.) & & & & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Analysis result of plastic area of heterogeneous ground analyzed by slip line method. As a result of the failure area analysis, both analysis methods confirmed that the shape of the failure surface was discontinuously connected at the boundary of the heterogeneous layer (see Figures 14-19). This is because of the difference in ground properties between the upper and lower ground. The features revealed in the analysis results of the slip line method are as follows:  
1. The failure surface of the upper and lower ground changed according to the internal friction angle of each ground. The green line is the failure surface according to the internal friction angle of the upper ground, and the red line is the failure surface according to the internal friction angle of the lower ground. 2. There was no significant difference in the failure area, because there is small variation in the internal friction angle according to each analysis condition. However, it could be confirmed that the difference in internal stress was different because of the difference in other ground properties (cohesion, unit weight) (see Table 3). The features revealed in the analysis of the finite element limit analysis are as follows:  
1. The plastic area appeared over the entire slope when the difference in ground properties between the upper and lower ground was small, or the slope angle was large. 2. Case 1, in which the difference in ground properties between the upper and lower ground was the largest, showed a plastic area limited to the upper ground. This is because the upper ground, which had the lowest ground properties, failed before the lower ground, because the strength reduction method was applied without external load. 3. As with the analysis result of the single ground, there was no stress singularity, such as an external load. Therefore, the failure area was formed at the start and end points of the slope, which were the other stress singular points. 4. As with the analysis results of the slip line method, it can be seen that the failure surface was discontinuously connected to the ground boundary layer.