esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Performance Evaluation
### Experiment 5: Round Trip Time  
As discussed in Section 2 and shown in Table 1, RTT is the most common performance evaluation metric for networks and also have been used in Thread-related literature. We also evaluate our network based on this metric for all three topologies as previously discussed. We analyzed the network response on these topologies using standard ICMPping. Figure 20 shows that the RTT increases with an increase in the number of hops and the lowest RTT is observed with one-hop communication, i.e., less than 20 ms.
## 4 Result and Discussion
### Model Training
#### 4.1.3 Determination of Optimal WAOA-XGBoost Models  
The multi-output XGBoost model has the same node splitting strategy as the multi-output RF model when constructing decision trees. The difference is that XGBoost takesinto account the bias of the previous decision tree when constructing a new decision tree, effectively reducing the variation in prediction performance between different outputs. From Figure 8e,f, the optimal fitness of the five WAOA-XGBoost models is distributed between \(1.6\times 10^{-3}\) and \(2.0\times 10^{-3}\) for the prediction of \(\theta\), and between \(1.7\times 10^{-3}\) and \(2.5\times 10^{-3}\) for the prediction of \(r_{b}/r\). Table 7 summarizes the prediction performance and ranking scores of the five WAOA-XGBoost models on the training and test sets. As can be seen from the table, the prediction performance and ranking scores of most WAOA-XGBoost models in \(\theta\) and \(r_{b}/r\) are relatively consistent. This can be observed more intuitively in Figure 9c, where the models with pop sizes of 10, 80 and 160 each have essentially the same scores on \(\theta\) and \(r_{b}/r\). This confirms the balanced consideration of potential links between different outputs and input features in the multi-output XGBoost model.  
It can also be seen from Figure 9c that the model with a pop size of 10 occupies the highest score for predicting \(\theta\). The model with a pop size of 80 (max_depth = 4, n_estimators = 374, learning_rate = 0.1626) occupies the highest score for predicting \(r_{b}/r\) as well as the highest combined score. In the subsequent study, the model with a pop size of 80 was chosen as a representative of WAOA-XGBoost.
# Isothermal Drying Process and its Effect on Compressive Strength of Concrete in Multiscale
## 3 Results
### Drying Deformation  
Figure 6a-c displays the deformations of cement paste, mortar, and concrete in the drying process (calculated by formula (2)). It should be noted that the deformation here refers to the average deformation in the specimen. Obviously, the deformation develops similarly at three scales, but is quite different in magnitude. The main findings were: (1) the deformation at three scales is characterized as expansion first and then shrinkage. The deformation shown in Figure 6 was the result of superimposition of expansion caused by heating and shrinkage due to water loss. (2) The deformation of cement paste is significantly larger than the deformation of mortar, which is larger than that of concrete. (3) When \(S_{\rm r}\) is high, the deformation is expansion, and when \(S_{\rm r}\) is low, the deformation is shrinkage. (4) The specimen recovers to its initial length when \(S_{\rm r}\) is reduced to a specific value, which is about 60%, 50%, and 15% for cement paste, mortar, and concrete, respectively. (5) The shrinkage of specimen increases significantly when \(S_{\rm r}\) is reduced below a specific value, which is about 30%, 25%, and 10% for cement paste, mortar, and concrete, respectively. From the above  
Figure 4: Relative compressive strength of cement paste, mortar, and concrete.  
Figure 5: (a) The typical pyramid failure mode of a saturated specimen, (b) the columnar failure mode of a dry specimen.  
analysis, it can be concluded that drying affects the deformation evolution of cement-based materials significantly. Moreover, Figure 6a-c indicates that this drying effect varies at three different scales. While the expansive deformations of cement paste, mortar, and concrete are comparable in magnitude, the deformation of cement paste develops most dramatically with the decrease of \(S_{\text{r}}\) and gains the maximum final shrinkage, mortar is next, and concrete is minimum.
## 2 Literature Review and Motivation of This Work
### Motivation of This Work  
This work considers user mobility while having optimum offloading decisions. To support service continuity, the host MEC should reallocate the task to the target MEC server. During the task offloading, if users move from the coverage region of a base station, it will cause handover that leads to additional cost. Hence, our problem formulation includes the handover cost. Below is a summary of the two employed algorithms.  
In the first algorithm, calculating the optimum BS for offloading is possible because the BS information is available. No handover will be presented, making the performance close to the optimal offline solution. However, we must ensure that users' total energy consumption will not exceed its budget as the energy budget is limited.  
In the second algorithm, the BS-side information is not available. This means that the channel gains and the available computing CPUs are unknown. Therefore, users must learn time and energy costs through observations. The optimum BS is determined based on these observations. However, sub-optimum BS selection may occur due to the variance of observation. Selecting sub-optimal BS will increase the total cost and number of handovers. Therefore, it is essential to select optimum controlling parameters in the optimisation function.  
The novelty and contribution of this manuscript can be summarized as follows:  
* Our proposed method is beneficial for the designers in the field of mobility management in MEC, as this approach considers three important parameters--energy consumption of UEs, handover delay, and task offloading--concurrently. This jointly consideration is missing in the reported literature;
* The proposed method is intelligent enough for finding the optimal value of alpha presented in Equation (18), resulting in average time and total energy costs near to the optimum offline solution;
* In our proposed methods, the offloading decision is user-centric, which is decided on the UE-side. Moreover, Algorithm 2 initially has neither base station nor network information. In this case, the learning process is used with optimized steps to reduce overall delay and improve energy performance.
## 2 UWB Signal Processing Steps  
### Data Cleansing and Clutter Rejection  
The received signal stream \(X\) comprises the reflections from physical obstacles within the radar range, and these reflected waves are corrupted with a noise resulting from the target's motions and the propagation environment. It can be approximated as:  
\[X\left[n\right]=E\left[n\right]+A\left[n\right]+N\left[n\right], \tag{1}\]  
where \(E[n]\) is the target signal modeled by the sparse matrix and \(A[n]\) is the clutter signal modeled by the Low rank matrix and \(N[n]\) represents the noise.  
By analyzing the amplitude spectrum, the waves reflected from targets, which are characterized by the dominant amplitude, have been located in the limited middle frequency interval. Other components of the raw signal will be related to noise and direct waves between Tx-Rx. Therefore, the noise and direct waves can be removed by one suitable band-pass filter. Figure 3 shows both the raw and filtered signal frames.  
After applying the bandpass filter, the remaining portion of the received signal beam supposedly consists of reflections from the static background and the moving target. The separation of these two components, also known as the background subtraction or clutter rejection, is a key step that aims at removing/minimizing the unwanted clutter signals through which the detection accuracy can be improved. There are a number of approaches for this core step, but they are all sensible to harsh conditions.
## 4 Information Fusion and Residual Life Estimation
### Residual Life Estimation
The point estimation and Bayes credible interval can be calculated as:  
\[\hat{\mu}_{\tau}=\frac{1}{S-M}\sum\limits_{j=M+1}^{S}\hat{\mu}_{\tau j}, \tag{66}\]  
\[\hat{\mu}_{L}=\frac{1}{S-M}\sum\limits_{j=M+1}^{S}\hat{\mu}_{Lj}, \tag{67}\]and  
\[\hat{\mu}_{H}=\frac{1}{S-M}\sum_{j=M+1}^{S}\hat{\mu}_{Hj}, \tag{68}\]  
where \(M\) is the burn-in period that can guarantee the stability of the sample. Then, the final results are the weighted sum of the results obtained by Equations (66)-(68). Drawing sample values \((\lambda_{v},\beta_{v})\) according to \(\pi_{i}(\lambda,\beta|D)\) is difficult and needs to be simplified. Taking the posterior distribution of expert information in Section 4.2.1 as an example, Equation (39) could be rewritten as  
\[\pi_{1}(\lambda,\beta|D)\propto\pi_{1}(\lambda|\beta,D)\pi_{1}(\beta|D), \tag{69}\]  
where  
\[\pi_{1}(\beta|D)=\frac{\tau^{a_{1}\beta}\beta^{\tau}M^{\beta-1}}{\left(N^{( \beta)}+b_{1}\tau^{\beta}\right)^{\tau+a_{1}}}, \tag{70}\]  
and  
\[\pi_{1}(\lambda|\beta,D)=\frac{\left(N^{(\beta)}+b_{1}\tau^{\beta}\right)^{ \tau+a_{1}}}{\Gamma(\tau+a_{1})}\lambda^{\tau+a_{1}-1}\exp\!\left[-\lambda(N^ {(\beta)}+b_{1}\tau^{\beta})\right]=\Gamma(\lambda;a_{1}+\sum_{i=1}^{n}\delta _{i},N^{(\beta)}+b_{1}), \tag{71}\]  
which means \(\pi_{1}(\lambda|\beta,D)\) is the gamma distribution. Therefore, in Step 1 of Algorithm 1, given \(\pi_{1}(\lambda,\beta|D)\), \(\beta_{v}\) can be generated using the Metropolis-Hasting (MH) sampling technique [47] according to Equation (70). Then \(\lambda_{v}\) can easily be drawn by Equation (71).
# A Nested UNet Based on Multi-Scale Feature Extraction for Mixed Gaussian-Impulse Removal
## 4 MSNUNet  
Most current mixed noise removal algorithms achieve good denoising results by utilizing local information or prior knowledge of the image. However, when the noise ratio increases or more complex noise, such as combined AWGN, SPIN, and RVIN, is encountered, the accuracy of these methods in estimating the noise distribution significantly decreases. This limitation hinders the model's ability to accurately model the noise and ultimately leads to the loss of image texture details. Additionally, although existing deep learning-based denoising models have shown promising results, the majority of these models perform denoising on low-resolution image blocks without considering the global information of the image. This limitation disrupts the overall structure and consistency of the image, thereby restricting the denoising performance of the model. In this paper, we propose MSNUNet for mixed noise removal. Firstly, the MSU-Subnet is introduced to enable the network to process high-resolution images more deeply and generate diverse receptive fields, capturing rich local and global features. Then, by integrating the MSCAM into the nested UNet architecture, MSUNNet is able to aggregate local and global features, which assist the model in preserving the overall structure and consistency of the image, resulting in improved denoising performance. Lastly, by introducing a channel attention block (CAB) in the MSCAM, the model enhances its ability to learn and extract important features, thereby preserving more image details.
## 4 Discussion
Specifically, non-photochemical energy dissipation relaxes slowly during the transition from high to low light [35], resulting in deviations in PAM parameters collected at lower light levels, such as abnormally low values of the photochemical yield of PSII. By adopting a low-to-high light intensity sequence, we aimed to improve the reliability of PAM observational data. Although this protocol increased the measurement time for light response curves due to repeated photosynthetic light induction at each intensity, it significantly enhanced the quality of PAM parameters while maintaining the quality of other measurements, such as \(A_{\text{net}}\) and \(\text{ChIF}_{\text{UP}}\) (Figure 3). Furthermore, the low-to-high light intensity sequence offered additional advantages for subsequent environmental response curve measurements (i.e., \(\text{CO}_{2}\) and temperature response curves), which require saturating light conditions. By concluding light response curve measurements under high light, this approach eliminated the need for re-induction, streamlining the transition between measurements. In addition to \(q_{\text{L}}\) modeling, the developed system offers significant potential for advancing the application of chlorophyll fluorescence in estimating carbon and water fluxes, as well as monitoring plant stress. Photosynthesis models following the Farquhar-von Caemmerer-Berry (FvCB) framework [36] rely on the maximum carboxylation rate (\(V_{\text{cmax}}\)) to simulate gas exchange fluxes associated with photosynthesis, serving as essential tools for evaluating ecosystem carbon and water dynamics. A major challenge in scaling these models to broader spatial domains is the accurate estimation of the key parameters (e.g., \(V_{\text{cmax}}\)), which remains a focal point of research [37]. Recent studies have highlighted the potential of SIF for estimating \(V_{\text{cmax}}\)[38; 39; 40]. The system developed here provides critical experimental data to support efforts aimed at utilizing chlorophyll fluorescence for such estimations. Moreover, chlorophyll fluorescence, predominantly driven by light intensity during the light reactions, occurs independently of the dark reactions that jointly govern photosynthesis [1]. Interestingly, our findings reveal that chlorophyll fluorescence can also reflect changes in dark reaction processes, such as variations in stomatal conductance induced by changes in \(\text{CO}_{2}\) concentration (Figure S3). These results underscore the system's capacity to enhance plant stress research through chlorophyll fluorescence-based methods [41; 42].
# Assessment of Close-Range Remote Sensing Methods for DTM Estimation in a Lowland Deciduous Forest
## 2 Materials and Methods
### Remote Sensing Data Collection
#### 2.2.3 Unmanned Aerial Vehicle Laser Scanning (ULS) Data  
The ULS data were collected on 26 April 2019, during the early leaf-on period using a custom-made Y6 copter based on Arducopter open-source software. The UAV was equipped with a triple return LS Nano M8 LiDAR sensor (LIDAR SWISS, city, Switzerland), whose primary components are OxTS xNAV250 INS (Oxford Technical Solutions Ltd., Oxfordshire, UK), Quangergy M8 (Quangergy Systems Inc., Sunnyvale, CA, USA) and an onboard camera. Quangergy M8 operates at a 905 nm wavelength, has a 360\({}^{\circ}\)\(\times\) 20\({}^{\circ}\) field of view with eight separate scan lines and a maximum range of 150 m with \(\pm\)3 cm accuracy at 50 m distance. The angular resolution of 3 mrad results in a 10 cm radius footprint in the nadir direction and ellipses with 0.5 m lateral axes on the far end of the scan line.  
Data were collected within a single 15 min flight at an altitude of 70 m and a flying speed of 5 m\(\cdot\)s\({}^{-1}\). The LiDAR sensor unit was set to operate at 420 Hz pulse and 20 Hz frame rate, with \(\pm\)50\({}^{\circ}\) off-nadir scan angles, and it resulted in an average of 285 points\(\cdot\)m\({}^{-2}\). The resulting point cloud consisted of 84.14% of first, 15.59% of second and 0.27% of third returns. The angle step size of the LiDAR sensor was not specified by the manufacturer, therefore, after manual assessment, it was determined that the distances between the subsequent in-line points and scan lines at ground level were 18 and 6 cm and, based on the footprint size, provided full coverage at the ground level, given that there were no obstructions on the laser beam path. The ULS trajectory was obtained by post-processing INS/GNSS data using NAVsolve software (Oxford Technical Solutions Ltd., Oxfordshire, UK) and CROPOS VRS, followed by georeferencing point cloud using LS GEO-LAS software. Plot point clouds resulted in an average of 0.5 million points per plot (\(\sim\)400 points\(\cdot\)m\({}^{-2}\)).
# Influence of the Tip Clearance on the Aeroelastic Characteristics of a Last Stage Steam Turbine
## 1 Introduction  
Flutter is a critical problem in modern steam turbines, as the trend for these blades is to be designed with high aerodynamic loading and a high aspect ratio to meet the demand for high efficiency. These design trends together with the low natural frequency of blades impair the turbine's aeroelastic stability and increase the susceptibility of the turbine blades to flutter. Physically, the majority of aerodynamic work during blade vibration is done in the near-tip region, which indicates that the tip clearance effects on the blade flutter characteristic can be remarkable. Therefore, researchers are concerned with the influence of tip clearance flow on the aeroelastic stability of turbomachinery, while most of the previous research focused on low speed and low stagger angle cascades.  
[PERSON] and [PERSON] [1] experimentally investigated the impact of tip gap height on the unsteady pressure during blade oscillation using a single-blade low-speed turbine model. The results showed that the influence of tip clearance flow on the pressure fluctuation during blade vibration is insignificant from 10-90% span when the blade-to-blade interference is excluded. Further experimental analysisby [PERSON] (2019) tested four different tip gap heights from 0-5% of the chord length on a multi-blade low-speed turbine model. A stabilization effect is revealed with a small tip gap height, which is located around the mid-chord on the suction side near the blade tip. With the increasing of tip gap height, the stabilization effect is primarily offset by the well-developed tip clearance vortex at about 80% chord of the blade. Therefore, the global aerodynamic damping at the least stable inter-blade phase angle (IBPA) first increases and then decreases with the increase of tip gap height.  
The influence on aeroelastic stability due to tip gap height was also investigated by numerical approaches. [PERSON] (2018) studied the flutter characteristic of the aeroelastic test rig (AETR) at KTH with subsonic boundary conditions. Numerical results indicated that when the tip gap height is in between 0% and 2.52% of chord length, the tip clearance flow had a stabilization effect at the least stable IBPA. [PERSON] (2018) extended the computations of the AETR to higher exit Mach numbers. A rise in overall aerodynamic damping at the least stable IBPA was presented in the models with tip clearance. Meanwhile, with a large tip gap equal to 5.8% of chord length, the tip clearance flow also produced a negative effect on blade stability at about 50% chord on the suction side near the tip. A recent analysis by [PERSON] (2018) investigated the flutter characteristics of a subsonic compressor vibrating in torsion mode. The investigation showed that the aerodynamic damping first increased with the tip gap till it equaled 2% of blade span, and then, the damping value dropped rapidly for larger tip clearances. Analysis of a transonic compressor model vibrating in bending mode G. L.
## 2 Related Work
### Anomaly Detection in One-Class Classification  
As mentioned earlier, anomalous behaviors occurring in enterprise networks or systems are typically very rare. Consequently, most research interprets anomaly detection as a one-class classification problem. [PERSON] et al. [4] proposed a temporal hierarchical one-class model and an end-to-end learning method for time-series anomaly detection. This model is designed with a dilated recurrent neural network and incorporates multi-scale clustering to better capture temporal dynamics. The cluster centers are encouraged to be orthogonal.  
[PERSON] et al. [5] developed a calibrated one-class classifier that learns a more refined normality boundary. The calibration of this model involves penalizing uncertain predictions and discriminating normal samples from simulated abnormal behaviors.  
[PERSON] et al. [16] focused on the representation of time-series data rather than designing a novel classifier. This study represented given time-series data based on their dissimilarities to a set of so-called prototypes. The study evaluated the Cartesian product of 12 dissimilarities and 8 prototypes and used a one-class nearest neighbor classifier to detect anomaly samples.  
[PERSON] and [PERSON] [17] explore the use of variational autoencoders (VAEs) combined with Mahalanobis distance for anomaly detection in time-series data. The study evaluates various deep learning architectures on the BATADAL challenge dataset, which focuses on detecting cyber-attacks in water distribution systems. The results indicatethat simpler VAE models using Mahalanobis distance can effectively detect anomalies, demonstrating significant promise in time-to-detection performance.
## 3 Experiments and Analysis
### Performance Testing and Comparative Analysis
#### 3.3.2 Subsubsection  
To validate the efficacy of integrating the RNN feature extraction method with the focal loss function, we contrasted the Encoder-Adaptive-Focal deep neural network capability evaluation model developed in this study against both the Adaptive-Focal and PCA-Adaptive-Focal models in separate tests. Given that neither the Adaptive-Focal nor the PCA-Adaptive-Focal models employed an RNN encoder for feature extraction, and thus couldn't procure time-series features, the capability features from the most recent instance were utilized for the sample features. Table 9 presents the performance metrics for each model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Value** \\ \hline _Layers_ & 5 \\ \(C_{l}\) & \(7,5,5,3,5\), respectively \\ \(g_{l}\) & Relu for the hidden layer and Softmax for the output layer \\ \(B\) & 64 \\ \(E\) & 300 \\ \(e\) & 0.005 \\ \(o\) & Adam \\ \(\rho\) & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Classifier hyperparameter settings for Encoder-DNN model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Value** \\ \hline _Layers_ & 5 \\ \(C_{l}\) & \(7,5,5,3,5\), respectively \\ \(g_{l}\) & Relu for the hidden layer and Softmax for the output layer \\ \(B\) & 64 \\ \(E\) & 300 \\ \(e\) & 0.005 \\ \(o\) & [PERSON] \\ \(\gamma\) & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Classifier hyperparameter settings for Encoder-Focal model.  
The Adaptive-Focal model lacks a feature extractor and solely uses the initial set of capability features (24 indicators) previously constructed as its input. The classifier's hyperparameters are as defined in Table 4.  
For the PCA-Adaptive-Focal model, the method of dimensionality reduction employed is PCA. The first three principal components from the most recent capability feature set are taken as the capability features for the sample, with their combined variance contribution exceeding 85%. The classifier's hyperparameters remain consistent with those listed in Table 4.  
As per the comparison results presented in Table 8, the RNN encoder dimensionality reduction method utilized in this study has proven to be more effective at enhancing model performance than the non-dimensioned and PCA methods. Notably, there's a 5% increase in accuracy metrics and an 11.4% rise in AUC compared to the PCA method. The accuracy and AUC metrics of the PCA-Adaptive-Focal are marginally lower than the Adaptive-Focal. This is because the PCA method not only neglects the temporal data features but also loses some information from the most recent period, leading to reduced classification accuracy.