esa-sceva/satcom-chunk-collection huggingface dataset:
# Bonding Performance of Steel Rebar Coated with Ultra-High-Performance Concrete
## 3 Results and Discussion
### Initial Stiffness Influence  
The test specimen (21-N) without the UHPC coating on the reinforcing bar's surface showed a large slip with a gradual rising bond stress, regardless of the type of steel fiber.  
Figure 10: Comparison of the maximum slip ratios at peak load: (**a**) uncoated rebars [32]; (**b**) UHPC-coated rebars.  
Figure 9: Comparison of the bond strength ratios: (**a**) uncoated rebars [32]; (**b**) UHPC-coated rebars.  
On the other hand, the test specimen (21-C) reinforced with UHPC between the reinforcing bar and the concrete body showed high initial stiffness due to the strong formation of bonding traction at the interface (Figure 3). In the test specimen reinforced with steel fiber, the overall initial stiffness was greater than that of the simple UHPC-coated rebar. In particular, compared to HSF, the fiber reinforcing effect was more pronounced in the test specimen containing CSF and CSSF, while the change in stiffness was also markedly improved due to the increase in the fiber mixing rate (Figures 5-7). On the other hand, in a study in which a concrete matrix was made with UHPC (\(\ell_{c}\) = 200 MPa) and then the rebar pullout test was conducted, the initial stiffness was very strong with zero slippage due to the strong chemical adhesion that occurred until the maximum load was reached [15,41]. In this study, the changes in slippage due to the initial stiffness during the pullout process of UHPC-coated steel rebars reinforced with steel fibers were judged to be greater than that of the specimen reinforced with the UHPC body.  
It is known that the fiber content does not affect the initial bond stiffness of the rebar because the fiber plays a role after the rebar has slipped to a certain extent [32]. Since the UHPC increases the bond strength of the rebar but has little effect on slippage, as shown in Figures 9 and 10, the effect of the UHPC coating on the initial bond strength is analyzed in this section. Figure 11 compares the initial bond stiffness ratios of uncoated rebars [32] and UHPC-coated rebars. The initial bond stiffness was calculated by dividing the load of each specimen by the slippage, which corresponded to 40% of the maximum slippage of the uncoated specimen. As shown in Figure 11a, the fiber content does not affect the initial bond stiffness, while the UHPC coating significantly increases the stiffness. Increasing the initial bond stiffness suppresses the bond cracking in the concrete.
## 7 Overview of Algorithms for Energy Optimization UAV-Based Cellular Networks
### Machine Learning Algorithms
#### 7.2.5 Reinforcement Learning  
Although it is not completely different from supervised and unsupervised learning methods, RL imitates humans' learning process. It shows how a system can perceive its environment and learn to make the right decisions in order to reach its goal. It differs from both supervised and unsupervised learning in that the agent is not given any prior knowledge of the environment, such as input data and output data, but gathers information about the environment by interacting with the environment and learning to take the right action in any given situation (for example, by repeated trial and error over a period). This method is frequently used in fields such as robotics, game programming, disease diagnosis, and factory automation [169; 170].  
RL approach consists of some basic elements. One of them is the state, which is a set, S, containing the state and environment of the agent. Another element is the set of actions, A, which constitutes the whole of actions that the agent can perform. Apart from the S and A sets, there are the principles that determine the agent's actions in the action process and the rules that describe the agent's observations. In this context, an agent interacts with the environment by performing an action. Evaluates the new state and the rewards obtained by observing the environment. Actions and observations follow each other in a cycle. In this cycle, each action gives feedback to the agent. The learning process is normally guided by a reward function which is a measure of how much the action that the agent takes in particular state contributes to archiving the desired goal. There are three main categories of RL, including policy-based, e.g., reinforce algorithm, value based, e.g., multi-armed bandit, Q-learning, and SARSA, and actor-critic RL [171].
# Additional Taxi-Out Time Prediction for Flights at Busy Airports by Fusing Flow Control Information
## 4 Experiment and Results  
### Experimental Setup  
To validate the effectiveness and superiority of the proposed NOA-XGBoost-based ATT prediction model, we compare its prediction accuracy with that of XGBoost, RF, and SVR. To study the impact of different types of features on the prediction of the ATT, we use ablation experiments to observe changes in prediction results after removing a certain type of feature. To validate the generalization ability of the proposed prediction model, we predict and analyze the ATT results for different partition pairing methods. To investigate the impact of different traffic indicator calculation nodes on prediction results, we use the calculation nodes in \(\Lambda\)CDM to compute traffic indicators and make predictions.  
We collected flight arrival and departure data from Pudong International Airport from 1 February 2018 to 28 February 2018. After preprocessing, the necessary dataset for the experiment was obtained and then split into a training set and a test set in a 4:1 ratio.  
We use five metrics to evaluate the performance of the prediction model: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Coefficient of Determination (R\({}^{2}\)), \(\pm\)3 min accuracy, \(\pm\)5 min accuracy, and (+5, \(-\)10) min accuracy. The expressions for the latter three metrics are shown in Equations (12)-(14). Here, \(y_{i}\) represents the actual value, and \(\hat{y}_{i}\) represents the predicted value. The (+5, \(-\)10) min accuracy is a metric established according to domestic airport standards, meaning that the predicted value can be slightly larger than the actual value under the premise of ensuring the flight taxiing.  
\[\left(\pm 3\;\text{min}\;\text{accuracy}\right) = \frac{\sum\limits_{i=1}^{n}L(\hat{y}_{i},y_{i})}{n},L(\hat{y}_{i},y_{i})=\begin{cases}0,|y_{i}-\hat{y}_{i}|>3\\ 1,|y_{i}-\hat{y}_{i}|\leq 3\end{cases} \tag{12}\] \[\left(\pm 5\;\text{min}\;\text{accuracy}\right) = \frac{\sum\limits_{i=1}^{n}L(\hat{y}_{i},y_{i})}{n},L(\hat{y}_{i},y_{i})=\begin{cases}0,|y_{i}-\hat{y}_{i}|>5\\ 1,|y_{i}-\hat{y}_{i}|\leq 5\end{cases}\] (13) \[\left(+5,\;\;-10\;\text{min}\;\text{accuracy}\right) = \frac{\sum\limits_{i=1}^{n}L(\hat{y}_{i},y_{i})}{n},L(\hat{y}_{i},y_{i})=\begin{cases}0,-10\leq y_{i}-\hat{y}_{i}\leq 5\\ 1,\;\;\text{otherwise}\end{cases} \tag{14}\]  
The initial parameters for the NOA and XGBoOST algorithms are shown in Table 4.
# Ciphered BCH Codes for PAPR Reduction in the OFDM in Underwater Acoustic Channels
## 5 Simulation Setup and Results
### BER Performance  
In this section, we demonstrate the BER performance of the proposed ciphered BCH codes and show that the error correcting capability of the original BCH codes remains preserved for the proposed system. Figure 11 compares the BER performance of the proposed system against the ordinary uncoded OFDM for a transmitter-receiver distance of 1500 m. It was observed that all the ciphered BCH codes improved the BER performance of the OFDM system, as would be expected. It is pertinent to mention that the BER results for ciphered BCH codes having the lowest \(k\) values showed significant improvement; however, the overhead is too high, and these codes are ideal for scenarios where a very reliable transmission of low data-rate information is required. The channel state information was assumed to be known and used in the zero-forcing equalizer. For BCH\((31,k)\), the lowest BER values were associated with \(k=6\). For the same codes, we did not report \(k\) values higher than 16, as there was a very insignificant improvement in BER performance and PAPR reduction due to the lack of unused combinations at higher \(k\) values. This holds true for BCH\((63,k)\) and BCH\((127,k)\) as well.  
Figure 10: PAPR performance of the proposed technique for ciphered BCH(127,\(k\)) systems. (**a**) Ciphered BCH(127,8) vs uncoded OFDM, (**b**) Ciphered BCH(127,8) vs original BCH(127,8), (**c**) Ciphered BCH(127,22) vs uncoded OFDM, (**d**) Ciphered BCH(127,22) vs original BCH(127,22), (**e**) Ciphered BCH(127,36) vs uncoded, (**f**) Ciphered BCH(127,36) vs original BCH(127,36), (**g**) Ciphered BCH(127,50) vs uncoded and (**h**) Ciphered BCH(127,50) vs original BCH(127,50).  
Figure 11: BER performance of C-BCH(31,\(\lambda\)), C-BCH(63,\(\lambda\)) and C-BCH(127,\(\lambda\)), (**a**) explanation, (**b**) explanation and (**c**) explanation.
# Towards a Fast, Open-Path Laser Hygrometer for Airborne Eddy Covariance Measurements
## 2 Materials and Methods
### Optics Design  
The optical setup of the dTLAS hygrometer is centered around the rotationally symmetric multipass cell shown in Figure 3a [26]. The laser beam passes the cell a total of 51 times in a star polygon pattern, resulting in a total pathlength close to 4 m. The optical measurement region is very planar and thin, has a maximum thickness of around 3 mm directly in front of the gold-coated mirror surface and decreases towards the center of the cell due to the confocal mirror geometry. To reduce the effects of stray light and to prevent interferences between neighboring reflections, an aperture mask with 51 elongated holes was designed and optimized and placed inside the multipass cell. In contrast to the commercially available cell, we designed an open-path configuration in order to take full advantage of the thin optical plane that is oriented perpendicular to the direction of the flow. This open-path circular cell configuration minimizes gas transport times and the effects of gas transition broadening while simultaneously avoiding delayed sensor response by gas sampling problems and, in particular, the H\({}_{2}\)O adsorption/desorption that is usually associated with sampling systems for atmospheric water vapor [6,34].  
Figure 3b shows the optical components of a new, \"angled\" laser-to-cell-coupling-setup located underneath the multipass cell. The beam is collimated using an off-axis parabolic mirror and focused into the cell with a concave, gold-coated mirror. A planar mirror (mirror 3 in Figure 3b) is used to reflect the beam upwards into the multipass cell and directs the outcoming beam from the cell onto the second concave mirror, which focuses it on the detector. This setup enables the optical plane of the secondary optics to be oriented at a 90\({}^{\circ}\) angle to the optical plane inside the multipass cell. This new geometry drastically reduces the surface area normal to the direction of the air flow, which is indispensable to minimizing the aerodynamic drag on the instrument when using it on an aircraft. All optical components are reflective, which reduces interference fringes caused by unwanted reflections on lenses. The two windows at the entrance of the multipass cell and in front of the detector are anti-reflection coated and oriented at an angle to avoid light reflections back into the optical path.  
Figure 2: **(a)** Two typical absorption signals at 1759 ppm and 7324 ppm. The model shown in Equation (2) is fitted to the raw signal, and the baseline polynomial and fit of the absorption signal are displayed. **(b)** The optical density (OD) with the fitted Voigt profile and the residual difference between the measured data and the fit below. With the standard deviation of the complete residual 1\(\sigma_{\text{total}}\), the local standard deviation inside the blue box 1\(\sigma_{\text{local}}\) and the maximum residual difference MRD values.
# Research on Learning Resource Recommendation Based on Knowledge Graph and Collaborative Filtering
## 2 Methods  
### Collaborative Filtering Algorithm  
Collaborative filtering algorithm is the most widely used recommendation technology [35], mainly divided into two methods: user-based collaborative filtering and item-based collaborative filtering. Item-based collaborative filtering focuses on the similarity between items. It analyzes user behavior data, calculates the similarity matrix between objects, and recommends other items that are similar to their past preferences to users. User-based collaborative filtering focuses on the similarity between users, searching for other users with similar preferences to the target user through their historical behavior. Based on the preferences of these similar users, recommend items that the target user may be interested in.  
In recommendation systems, user feedback data are commonly categorized into explicit data and implicit data [36; 37]. Explicit data are direct feedback provided by users, such as explicit ratings. On the other hand, implicit data are indirect behavioral signals generated by users in their daily activities, such as clicks, comments, and viewing history. Implicit feedback, when compared to its explicit counterpart, offers a more accurate reflection of users' actual behavior and yields richer user behavioral insights. These implicit feedback behaviors often convey users' item preferences, even in the absence of explicit ratings, enabling the inference of user interests and preferences.
# STDecoder-CD: How to Decode the Hierarchical Transformer in Change Detection Tasks
## 2 Materials and Methods
### A Brief Introduction to TransCNN  
Inheriting the merits of both transformers and CNNs is a trend in the design of state-of-the-art transformers, such as ConViT, CvT, CSWin [26], TransCNN, and so on. In this study, we opted to use TransCNN as the backbone for deep feature extraction, and the reason behind this is threefold: (1) TransCNN introduces a hierarchical framework for computing MHSA (namely, multi-head self-attention). (2) In order to decrease the computational/space complexity, the commonly used MHSA is replaced by H-MHSA, which has three advantages: (i) modeling global dependencies of the input directly; (ii) having the ability to process large input images with ease; and (iii) being more lightweight than traditional MHSA. (3) One can plug the flexible H-MHSA or the whole TransCNN as a backbone into CNNs instead of using an MLP (Multilayer Perceptron) after MHSA computation for feature enhancement, as in other transformers.  
The TransCNN backbone has four basic modules: Patch Embedding, H-MHSA, IRB, and TDB, and their architectures are displayed in Figure 1b-e. Of these modules, Patch Embedding, IRB, and TDB are all CNN-related; here, we focus on the inner workings of H-MHSA, which is a three-step procedure: Firstly, instead of computing attention across all patches/tokens, it is better to group patches into small grids and compute attention within each grid. This operation can capture local relationships and yields more discriminative local representations. Secondly, in order to capture feature relationships in larger regions,we need to merge these small grids into larger ones and compute attention within each new grid by viewing small grids in the preceding step as tokens. Thirdly, this process is iterated to gradually reduce the number of tokens.  
Throughout this procedure, we computed MHSA (refer to [27] for algorithmic details) in regions with increasing sizes step by step and naturally modeled the global relationship in a hierarchical manner. This strategy leads to better generalization results, and as each grid at each step only has a small number of tokens, the relevant computational/space complexity is greatly reduced. For example, according to [24], the computational complexity of MHSA is 3 NC\({}^{2}\) + 2N\({}^{2}\)C, where N and C are the number of tokens and the feature dimension of each token, respectively, while H-MHSA's complexity is only 3 NC\({}^{2}\) + 2 NG\({}^{2}\)C, where G is the size of the small grid, and G\({}^{2}\) is much smaller than N.  
Note that, here, we used the TransCNN_small model as the backbone; the dimensions of the four transformer-CNN blocks are 64, 128, 256, and 512, respectively; their grid sizes are 8, 8, 8, and 1, respectively; the depths (i.e., the number of repetitions) of each block are 3, 4, 8, and 3. Other hyperparameter settings of TransCNN_small are consistent with those in [24].
# Air Quality Predictions through Mathematical Modeling for Iron Ore Mine Project
## 3 Methodology
### Data Acquisition and Compilation  
Data for the study were acquired in two distinct phases: the construction and operation phases. The collection was carried out using the Respirable Dust Sampler (Envirotech APM-460, Envirotech Instruments Pvt. Ltd., New Delhi, India) and the Particulate Sampler (Envirotech APM-550 mini, Envirotech Instruments Pvt. Ltd.) at different locations in and around the mine (Figure 1b). The selected locations included Core Zone (CoZ, average distance of around 60 m from active mining area), Tiroda Village (TIV, distance 440 m from the active area of the mine), Nanos Village (NaV, distance 630 m), Shiroda Village (SiV, distance 1520 m), and Redi Village (ReV, distance 3170 m). In the operational phase, the mine areas, including the material loading point (MLP), waste dump point (WDP), haulage and transportation road (HTR), and mobile screen plant (MSP), were also selected for the sampling. The emission data during excavation and loading, along with the meteorological data, were used for the analysis in AQM. The data collection focused on the SPM and PM\({}_{10}\) levels during the dry summer season, ensuring no rainfall interference. Meteorological data, including wind speed, direction, temperature, and humidity, were also collected to aid in the modeling process. The data were grouped into pre-mining (PreMin), mining operation (PostMin), and along with the group of locations considered as the source of the particulate matter (OOPM) for comparative analysis.
## 2 System Architecture
### Energy Storage Option
\begin{table}
\begin{tabular}{l l l l} \hline
**Energy Storage** & **CAP-XX** & **Infinite Power Solutions** & \\
**Option/Specification** & **(GZ115F) [10]** & **(MEC125) [11]** & **Varta (V6 HR) [2]** \\ \hline Technology & Supercapacitor & Thin film lithium battery & NiMH battery \\ Nominal operating voltage & 2.3 V & 4.1 V & 1.2 V \\ Size & 20 \(\times\) 15 \(\times\) 1.25 mm\({}^{3}\) & 12.7 \(\times\) 12.7 \(\times\) 0.7 mm\({}^{3}\) & D: 6.8 mm, H: 2.15 mm \\ Energy capacity & 4 \(\upmu\)Ah & 200 \(\upmu\)Ah & 6200 \(\upmu\)Ah \\ Peak discharge current & 30 A & 7.5 mA & 18 mA \\ Cycle life & 30,000+ hours & 10,000 & 1000 \\ \hline \end{tabular}
\end{table} 
Table 1: Comparing energy storage options.
# Motion Compensation for SAA FMCW Radar Based on Specific Switching Scheme
###### Abstract  
Switch antenna array (SAA) frequency modulated continuous wave (FMCW) radar has been studied by many researchers due to its low cost and straightforward architecture. However, the motion-induced phase leads to incorrect direction-of-arrival (DoA) estimation, which is one of the main obstacles when it is applied to a moving scenario. To address this problem, we propose a motion compensation method based on a specific switching scheme in this paper. The effectiveness of this method is verified by simulation and measurement results. This approach only requires basic signal processing techniques and avoids complex optimization algorithms.  
switch antenna array (SAA); frequency modulated continuous wave (FMCW) radar; motion compensation +
Footnote †: journal: Applied Sciences
## 3 The Proposed Method
### Learnable Polar Transformation Module
#### 3.1.1 Polar Origin Detector  
When performing polar coordinate transformation, the polar origin is an important parameter that determines the final segmentation performance. In order to obtain a better polar representation, it is necessary to select an image center that is close to the segmented object and to then proceed to the next step based on the polar origin. Otherwise, the subsequent analysis in polar coordinates is likely to be inaccurate. Recent deep models [27] directly regress the coordinates of the target point through the fully connected layer of the network. However, these methods are not an optimal choice. Other methods predict heatmaps and take their argmax [28; 29].  
These are not the best positions because the backpropagation gradients are zero for all parts except the target point, which interferes with training. To be specific, we proceed as follows: According to [30], the green channel highlights the features of the OC. Thus, we take the green channel of the images and follow stepwise convolution to obtain a heat map. The polar origin detector consists of a sequence of standard convolution blocks followed by a 1\(\times\)1 convolution. The polar origin is the pixel coordinate point with the highest intensity calculated from the heatmap predicted in the last layer of the model.
A modification of the traditional kurtosis was proposed in [21], where the correlated kurtosis (CK) was proved to improve the sensitivity to the repetitive impulses typical of the bearing signature [22]. This technique is not completely blind, but requires the use of the periodicity of the impulses as an input. Other targeted methods which require assumptions about the periodicity of fault symptoms and exploit the relationship between cyclostationarity and envelope analysis are the envelope-based cyclic periodogram [23] and the log-cycligram [24], which was proved to be robust to highly impulsive noise [25]. A method which is blind but takes advantage of the cyclostationarity of the signal in a different way is the autogram. In [26], the kurtosis is not computed on the squared envelope itself, but on the unbiased autocorrelation of the squared envelope of the filtered signal. This allows it to overcome the lack of robustness to non-Gaussian noise of the traditional kurtogram. A computationally more efficient implementation is described in [27]. An analogous frequency domain approach is proposed in [28] where a modifiedprotrugram is found from the computation of the kurtosis of the autocorrelation of the envelope spectrum. Given the huge variety of algorithms presented in the literature survey, the present work sets two goals: to evaluate the general effectiveness of envelope demodulation and to assess the performance of common band-selection indicators against novel a posteriori indicators tailored for the purpose. The well-known Case Western Reserve University bearing dataset will be used as a test case.
# Stability Analysis of the Huasushu Slope Under the Coupling of Reservoir Level Decline and Rainfall
## 7 Discussion
### Limitations and Future Prospects  
This study uses numerical simulation as the main analytical tool, which can reveal landslide evolution laws. However, it still has the following limitations: (1) the model was not calibrated and verified through physical tests or long-term field monitoring data [40]; (2) the assumption that the soil body is homogeneous does not consider the non-homogeneity and structural complexity of the soil body in reality [41]; and (3) this study focuses on a short-term strong rainfall scenario and does not explore the impact of long-term rainfall on the stability of the slope with a lag or the hysteresis effect of long-term weak rainfall on slope stability [42].  
Future research can be strengthened in the following ways: combining field monitoring data and physical tests to improve simulation accuracy and extrapolation abilities; introducing soil heterogeneity parameters and exploring their influence on slope response mechanisms; and constructing dynamic early warning systems based on multi-source data (e.g., reservoir level, rainfall, and surface displacement) and combining it with weather prediction to achieve real-time hierarchical early warnings of landslide risk in reservoir areas, as well as proposals for emergency responses.