esa-sceva/satcom-chunk-collection huggingface dataset:
# Jet Noise in Airframe Integration and Shielding
### Jet--Flap Interactions  
The interaction of the exhaust jet and flaps can generate new noise sources, which is mainly known as jet-flap interaction noise. The principal mechanism is believed to be the impact of the downwash of the wing-flap on the jet flow [PERSON] and [PERSON] (1976); [PERSON] and [PERSON] (2018); [PERSON] and [PERSON] (2018). Figure 3 illustrates interaction between the jet and wing in the conventional under-the-wing configuration. Generally, the acoustic waves emanated from the jet hit the aerodynamic surfaces, and the reflection of such waves may influence the instability waves near the nozzle as they exit the shear layer and modify the turbulent structure of the jet. Such modifications in the flow field are referred to as new sources of noise that change the perceived noise. Since the real configuration of jet-wing-flap is rather complex, the effect of flaps on the jet is reviewed independently.  
noise to a range of parameters, such as the wing-flap settings, jet engine location, and pitching angle. The installation effect was reported to increase noise up to 6 dB. Following these experiments, some empirical prediction methods were developed to determine installation noise, such as [14; 16]. Since the noise levels were high, many studies focused on reduction technologies. [PERSON] et al. [17] investigated the effect of chevrons on installed engines. Their experimental results show that the installation effects of chevrons in conventional nozzles are reversed at approach and take-off. This trend is not observed in isolated nozzles. In addition, it was reported that certain azimuthally varying chevrons give larger total installed noise benefits at both conditions compared to conventional chevrons.  
Due to the complexity of the installation effects of the jet-flap interactions, some studies have investigated this configuration along with pylon effects. For example, [PERSON] et al. [18] performed experiments for a typical swept wing with an installed dual-stream nozzle and removable pylon. The effect of the flap deflection angle on jet-flap interaction noise was studied with and without the pylon for static and flight conditions. These experiments show that for both cases of with and without the pylon, jet-flap interaction noise remains qualitatively the same and is very sensitive to the flap deflection angle for static and flight conditions. However, the intensity of jet-flap interaction noise increases for the full configuration with the pylon installed.
# Image Deblurring Based on an Improved CNN-Transformer Combination Network
## 4 Experiments and Analysis
### Object Detection Performance  
As we mentioned above, blurry images are detrimental to subsequent vision tasks. In this section, we employed YOLOv4 [33] to analyze the influence of burry images in object-detection tasks and detect the restoration images of some deblurring models [2; 8; 12; 18] to compare the naturalness performance. We input blurry images and restoration images of these deblurring models to YOLOv4 successively and obtained the evaluation outputs. Figure 9 shows the comparison of the detection results. To observe the detection performance of these deblurring models more intuitively, Table 4 lists the detection evaluation results of each object: \"X\" means this object failed to be detected in the current restoration image.  
As shown in Table 4, the blurry image has unclear contours and only two objects can be detected. The restoration images of deblurring models all have better performance than blurry images. The restoration images of Restomer and our model can detect six objects using YOLOv4. However, the detection results of other restoration images miss some objects. This is probably because some detailed information is lost in the restoration process.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Blur** & **SRN [12]** & **MIMO-Unet+ [8]** & **MPRNet [2]** & **Restomer [18]** & **Ours** \\ \hline car(left) & 0.99 & 0.99 & 0.99 & 0.99 & 0.99 & 0.99 \\ plant & X & 0.78 & 0.56 & 0.74 & 0.75 & 0.66 \\ person & 0.88 & 0.76 & 0.86 & 0.77 & 0.86 & 0.89 \\ chair & X & X & 0.65 & X & 0.72 & 0.64 \\ umbrella & X & 0.54 & 0.51 & 0.58 & 0.59 & 0.54 \\ car(right) & X & X & X & X & 0.60 & 0.54 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The detection evaluation result of each object. “X” means this object cannot be detected in the restoration of the current deblurring model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **DeblurGAN-V2 [10]** & **MIMO-Unet+ [8]** & **MPRNet [2]** & **SDWNet [16]** & **Ours** \\ \hline Params (M) & 60.9 & 16.1 & 20.1 & 7.2 & 15.6 \\ Runtime (s) & 0.21 & 0.017 & 0.18 & 0.14 & 0.012 \\ PSNR (dB) & 29.55 & 32.45 & 32.66 & 31.26 & 32.68 \\ SSIM & 0.934 & 0.957 & 0.959 & 0.966 & 0.962 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters and runtimes comparison. The runtimes are the average testing time per image in the GoPro test dataset [7]. All models are tested with a single TITAN RTX GPU. The units of parameters and runtime are millions and seconds, respectively.  
Notably, the car on the right side of the image is inconspicuous; only Restomer [18] and our model can produce sharper images so that YOLOv4 can recognize it accurately. In conclusion, our model is capable of achieving sharp images, and the object-detection performance is satisfactory as well. Moreover, this experiment indicates that image deblurring is suitable for high-level computer vision tasks (e.g., object detection) as a pretreatment technique.
## 3 Materials and Methods
### Modified Part of Proposed FC-YOLOv4 Architecture  
In traditional YOLOv4, three layers are selected from the output of the backbone network. After passing through three convolutional layers, the input is routed via an SPP network for pooling, as shown in Figure 5a. Pooling layers enable the downsampling of feature maps by enumerating the features present in patches of the feature map. The design objective of our suggested solution is to improve the output characteristics of backbone networks. The higher the risk of overfitting as well as the error rate, the deeper its backbone layer. As a result, we performed research to improve both the quality of the shown feature map and the model's detection, as seen in Figure 5b. The architecture of such a Neck network can achieve specific, highly desirable properties. Figure 6 represents the overall network architecture of our proposed FC-YOLOv4 model.  
Selecting the appropriate activation function is also critical for optimizing YOLOv4 detection accuracy. Activation functions are non-linear point-wise functions that introduce nonlinearity into the linear transformed input at the layer between the backbone and the head of a neural network. We also need to employ the optimal activation function in our FC-YOLOv4 to address computer vision issues such as object identification, segmentation, and classification. The major purpose of the activation function is to determine the nonlinear relationship between both the input and output variables to solve complex problems. We used the Mish activation function instead of ReLU in our study because it significantly improves the deeper network of the model. The LeakyRelu activation function remains throughout the rest of the network. Mish and LeakyRelu activation functions can be expressed as follows.  
\[y_{mish}=x\ \tan h(\ln(1+e^{x}) \tag{2}\]  
\[y_{Leaky\ Relu}=\begin{cases}x,\ \ if\ x\ \geq 0\\ \lambda x,\ \ if\ x<0\end{cases} \tag{3}\]  
We employed max-pooling in SPP Block, a pooling process determining the maximum, or most significant, value in each patch of each feature map. We expanded the convolutional layer and executed the procedure first on the middle two feature layers. The PANet was chosen for our model because of its ability to reliably store spatial information, which aids in properly locating pixels for mask creation. This procedure significantly expands the effective receptive field. We used class label smoothing to transform soft labels during training, increasing the model's robustness. Regarding the bounding box regression loss function, we use the Complete-IoU [90] function because it results in a faster convergence rate and superior performance than the alternatives. CIoU loss is only effective when the anticipated bounding box overlaps with the target bounding box.  
Figure 5: Neck network structure (**a**) Traditional YOLOv4 (**b**) our proposed FC-YOLOv4.
## 2 Materials and Methods
### Algorithms
The maximum correlation coefficient between \(X^{T}w\) and \(Y^{T}v\) is expressed by:  
\[\rho=max_{w_{1},w_{3},v}\frac{E\big{[}\widetilde{x}\widetilde{y}^{T}\big{]}}{ \sqrt{E[\widetilde{x}\widetilde{x}^{T}]E[\widetilde{y}\widetilde{y}^{T}]}}. \tag{5}\]  
Then the optimized reference signal \(z\) can be calculated based on the optimal linear transformations \(\widetilde{w}_{1}\) and \(\widetilde{w}_{3}\), following the expression:  
\[z=x\times_{1}\widetilde{w}_{1}^{T}\times_{3}\widetilde{w}_{3}^{T}. \tag{6}\]On one hand, CCA can maximize the correlation between the experimental test data and the optimized reference signal to achieve classification; on the other hand, CCA can be used as a feature extraction method. Specifically, the obtained correlation coefficient calculated by CCA can be sent into the SVM classifier for further classification. The classification process is shown in Figure 2a. However, in MCCA, the optimized projection vector for each dimension is learned without regularization, and hence lack of sparsity that can provide greater interpretability for features. Therefore, the reference signal is further optimized through L1 regularization. The essence of regularization is to constrain the parameters to be optimized and prevent over-fitting. Since the scaling of \(w\) and \(v\) does not affect the correlation maximization, Equation (2) can be expressed as \(w^{T}XX^{T}w=v^{T}YY^{T}v=1\). \(w^{T}XX^{T}v\) is then maximized. We expressed the proposed MCCA as the following least-squares formulation:  
\[\begin{split} w_{1}\,,w_{3},v=\underset{w_{1},w_{3},v}{\text{ argmin}}\frac{1}{2}\parallel x\times_{1}w_{1}^{T}\times_{3}w_{3}^{T}-v^{T}Y \parallel_{2}^{2}\\ \text{s.t. }\parallel w_{1}\parallel_{  
In this study, a means combining L1-MCCA with PSO-optimized SVM was proposed to improve the classification accuracy. The experimental data was divided into a training set and a test set in a ratio of 7:3. PSO was used to optimize the parameters of SVM. The number of iterations was set to 200, the penalty parameter c and the kernel parameter g are selected as optimization variables, and the best accuracy rate is used as the fitness function. The optimized c and g parameters were transferred to SVM for training, and the test obtains the optimal accuracy rate. RBF function was used as the kernel function of the optimization variables. The optimization variables are optimized using the optimization variables. The optimization variables are optimized using the optimization variables. SVM. In order to further reduce the calculation errors and to improve the accuracy, the calculation was repeated ten times, and then the averaged data was obtained as the final accuracy. In addition, to testify the reliability and the performance of the proposed method, it was compared with the traditional CCA and PSD methods.
# On-Machine Precision Form Truing and In-Situ Measurement of Resin-Bonded Spherical Diamond Wheel
## 5 Conclusions  
In this paper, the coarse-grained diamond roller is applied to perform on-machine precision form truing of the resin-bonded spherical diamond wheel. The non-contact in-situ measurement based on a laser scan micrometer is proposed to achieve the measurement of form and alignment errors. The main conclusions include:  
(1) The diamond roller truing is an efficient and feasible method for conditioning the resin-bonded spherical diamond wheel. The grinding wheel surface after truing is flat and regular, and micro-breakage of the abrasive grains generated new sharp cutting edges. The form truing of a resin-bonded spherical diamond wheel with a radius of about 24 mm and particle size of D46 was conducted after alignment error compensation, and the obtained form-truing error is approximately 5 \(\upmu\)m.  
(2) The alignment error between the diamond roller and the spherical diamond wheel can be accurately calculated by applying the proposed biarc curve-fitting method. The spherical diamond wheel with good form accuracy can be obtained by introducing a \"truing-detection-compensation\" process to eliminate the alignment error.  
(3) The laser scan micrometer is used for non-contact in-situ measurement of the form-truing error of the spherical diamond wheel. The accuracy of the measurement results is validated by comparing with the results of the stylus profilometer. Besides, the actual form-trued diamond wheel has been employed in grinding a ZnS spherical surface, and the form accuracy is approximately 1.6 \(\upmu\)m without any compensation. The ground surface profile shared similar characteristics with the roller-trued diamond wheel profile, confirming that the diamond roller truing and in-situ measurements methods are accurate and feasible.  
Figure 12: Grinding results of the ZnS test workpiece: (**a**) grinding surface, (**b**) surface form error.  
**Author Contributions:** Conceptualization, J.W. and C.Z.; investigation, J.W.; methodology, Q.Z. and B.G.; resources, Q.Z.; software, C.Z.; validation, B.G.; writing--original draft, J.W.; writing--review and editing, J.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China [No. 51805484, 51875135, and 51575492], and the China Postdoctoral Science Foundation [No. 2019M652137].  
**Conflicts of Interest:** The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.
## 3 Results
### Distribution of Viewing Angles
In order to establish a reliable global Angular Distribution Model (ADM) to reconstruct the actual total outgoing radiation from TOA, it is necessary to acquire a mass of continuous data from the Earth disk with a diversity of configurations of viewing angles and a short data acquisition period. Therefore, the improvements provided by the Moon's halo orbits in these aspects may provide supplementary Earth disk data. Figure 6: Distributions of viewing elevation angle and azimuth angle during the period from 20 March 2024 to 20 March 2025 at four locations with different attitudes on the prime meridian. (**a**) Moon-based platform; (**b**) halo orbit with an amplitude of around 10,000 km; (**c**) halo orbit with an amplitude of around 20,000 km.
# A Hybrid User Mobility Prediction Approach for Handover Management in Mobile Networks
## 1 Introduction
We believe that the key factor for a successful mobility model is to properly discover the existing hidden correlations in a user's movement history. When the model captures the dependencies in the prior user movement trajectories, it can provide a better understanding of user mobility behaviour and can make a long-term prediction. In this paper, our main objective is to deploy user mobility prediction as an invaluable tool to optimize the handover signaling procedure. First, we propose a hybrid trajectory prediction method based on statistical and learning models. The proposed mobility model exploits the vector autoregression (VAR) model [6] and gated recurrent unit (GRU) [7] to further capture correlations in the user's past movement history as the input samples. Then, having predicted the future user trajectory, we optimize the handover signaling procedure in order to reduce the handover signaling costs. We observe that the predictor accuracy is of great important in obtaining an accurate and effective impact on handover signaling optimization. The major contributions of our work is as follows:  
* Proposing a mobility prediction technique to model user mobility behaviour. The proposed VAR-GRU model predicts the future trajectory (i.e., path) of a user. The core concept is to fully analyze the existing dependencies in a user's past trajectories and to extract general patterns in the data. * Investigating the impact of user mobility prediction on the conventional handover signaling procedure. Handover processing and transmission costs are evaluated to compare the predictive and non-predictive scenarios. * Conducting experiments on the user mobility data generated from real users to provide an in-depth analysis of the effectiveness of the proposed approach. The rest of this paper is organized as follows. Section 2 provides a short review of some of the recent related works in this field. Section 3 presents the proposed approach in two subsections: the proposed user mobility prediction technique and handover procedure analysis. Section 4 provides an analysis regarding HO signaling cost. In Section 5, we summarize the results of our experiments. Finally, the conclusion of our work is provided in Section 6.
## 3 Results and Discussion
### Influence of Persulfate Dose  
The initial persulfate concentration is an important parameter in the destruction of organic pollutants by UV/PS [24; 68]. For this approach, some experiments were done with various PS doses in the range of 17.70-70.80 mM using 0.1 mM TA at pH 9 and a temperature of 25 \({}^{\circ}\)C. The results are shown in Figure 5. As it can be seen in Figure 5a, high aromatics removals (>94%) were achieved independently of PS concentration. This indicates that persulfate concentrations higher than 17.70 mM are capable to degrade almost completely TA and its aromatic intermediates.  
However, TOC removal depends on PS concentration as demonstrated in Figure 5b. The increase of PS content from 17.70 to 53.10 mM led to increase the TOC removal from 32.97 to 54.41% after a 1 h treatment. PS is a source of sulfate and hydroxyl radicals in the UV/PS process, and more reactive radicals would be generated to degrade TA at higher PS doses. This indicates that an increase of PS dose up to 53.1 mM was able to mineralize more organics. A dose of PS higher than 53.10 mM led to the decrease in TOC removal. It seems that PS dose of 53.10 mM is considered optimal for the treatment of 0.1 mM TA aqueous solution by the UV/PS process. Increasing the persulfate dose above 53.10 mM (optimal dose) resulted in a decrease in TA degradation efficiency. This can be explained by the fact that excessive amount of persulfate (S\({}_{2}\)O\({}_{8}\)\({}^{2-}\)) in the solution can react with sulfate radicals (SO\({}_{4}\)\({}^{\bullet-}\)) to form less reactive species such as persulfate radicals [56; 61; 69; 70; 71]. This, in turn, reduces the efficiency of the process, as explained in Equations (9) and (15).  
\[\text{SO}_{4}\text{${}^{\bullet-}$}+\text{SO}_{4}\text{${}^{\bullet-}$} \rightarrow\text{S}_{2}\text{Os}\text{${}^{2-}$} \tag{15}\]  
These experimental results are similar to the results obtained by [PERSON] et al. [72] for the degradation of perfluorooctanoic acid (PFOA) using UV/PS process (50 mM persulfate were used).  
Figure 5: _Cont._
## 5 Test Results and Discussion
### Test Setups
Overall, BASPC can replace the original RPF model with a high level of accuracy. Another point worth discussing is that Figures 4-6 show that the distribution of ALSC varies across the three different scenarios, indicating that both penetration rate and correlation can influence the ALSC. However, BASPCE consistently maintains good accuracy under all scenarios. The computational speed is quantified by the total computation time \(T\), which can be divided into two components: the time spent on surrogate model construction \(T_{1}\) (absent for MCS-based methods) and the time spent on model evaluation \(T_{2}\) (for MCS-based methods, this refers to the original model, while for BASPCE-based methods, it refers to the surrogate model). By comparing SRS-BASPC \(10^{4}\) and LHS-BASPCE \(10^{4}\) with SRS-MCS \(10^{4}\) and LHS-MCS \(10^{4}\), it can be observed that, under the same sample size, the total computation time for BASPCE-based methods is significantly lower than that for MCS-based methods. Combined with the accuracy analysis above, this indicates that BASPCE-based methods require less time to achieve the same level of accuracy. Moreover, the total computation time for SRS-BASPCE \(10^{4}\) and LHS-BASPCE \(10^{4}\) is approximately the same as that of SRS-MCS \(10^{3}\) and LHS-MCS \(10^{3}\). However, as previously concluded, SRS-BASPCE \(10^{4}\) and LHS-BASPCE \(10^{4}\) demonstrate higher accuracy than SRS-MCS \(10^{3}\) and LHS-MCS \(10^{3}\). This highlights that, under the same computation time, BASPCE-based methods can achieve more accurate results. Overall, BASPCE can significantly improve computational speed while maintaining accuracy. The high computational efficiency of BASPCE is mainly due to its sparse modeling strategy and ability to achieve accurate results with fewer samples [41]. Notably, in BASPCE-based methods, \(T_{1}\) is significantly greater than \(T_{2}\), indicating that most of the computation time is spent on constructing the surrogate model, while the evaluation based on the surrogate model requires almost negligible time. Consequently, even when the sample size increases by an order of magnitude, the total computation time of BASPCE-based methods only increases by approximately 0.15 s. Thus, as the sample size grows or multiple rounds of MCS are required (e.g., in GSA), the speed advantage of BASPCE becomes more pronounced. Moreover, BASPCE-based methods can leverage an increased sample size to achieve higher accuracy without computational burden. In addition, according to Tables 2-4, the computational speed of RPF exhibits slight variations across different scenarios, which may be attributed to differences in the number of iterations required for convergence under each scenario.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 4 Advanced Theories and Methods for TD-NIRS
### Perturbation Theory  
Perturbation approaches have widely been employed for imaging inclusions with the optical properties different from those of the background homogeneous medium. The perturbation approach is a general concept applicable to any equations describing physical phenomena, and it is natural to apply the perturbation approach to the phenomenon of light propagation, or, photon migration, described by the TD-RTE, the TE, and the TD-DE. Actually, [PERSON] and [PERSON] [86] reported general formulations of the perturbation approach applied to the RTE. However, the perturbation approachusing the TD-RTE has difficulties in obtaining analytical solutions such as the Green's functions, and most studies of the perturbation approach are based on the TD-DE for which the Green's functions are available for simple geometries such as the infinite, semi-infinite, infinite slab, cylindrical, and spherical media [19].