esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study and Verification
### Analysis of Intelligent Transfer Diagnostic Method  
To verify the effectiveness of the proposed transfer learning model, the feature fusion network was used as the basic framework, and datasets A, B, and C were selected as the experimental datasets. Using one of datasets A, B, and C as the source domain and the others as the as the target domain, two sets of transfer experiments were transferred from A to B and A to C. Each group was randomly trained 10 times. The average identification accuracy was taken as the evaluation index. Six sets of transfer experiments were conducted.  
For Experiment 1, the CNN-LSTM network [30] was selected. For Experiment 2, the DCORAL method was adopted, using Coral as the metric function [27]. For Experiment 3,  
Figure 12: Confusion matrix of training and testing set. (**a**) Confusion matrix of training dataset. (**b**) Confusion matrix of testing dataset.  
Figure 13: The verification of fault diagnosis effectiveness under different noise interferences. (**a**) The accuracy and error of fault diagnosis for different input data under different noise interferences. (**b**) The accuracy and error of fault diagnosis for different neural network under different noise interferences.  
the dynamic distribution adaptation (DDA) method was adopted, using a Gaussian kernel MMD [31] as the metric function. For Experiment 4, the proposed intelligent transfer method was adopted, using MK-MMD as the metric function. The experimental results are shown in Table 3 and Figure 14. In Experiment 1, the transfer diagnostic displayed the poorest effectiveness. In Experiment 2, the accuracy of transfer diagnosis was 90.52%, and the error was 3.54%. In Experiment 3, the average accuracy was 94.23%, and error was 1.95%. In Experiment 4, using the multi-kernel MMD method, the average accuracy was 99.11%, and a negligible error was obtained. Moreover, the calculation time of the proposed method is slightly longer than other methods. Hence, the proposed method shows good robustness and is more effective than the other methods.
# RALR: Random Amplify Learning Rates for Training Neural Networks
## 4 Method
### Schedule of RALR  
RALR is conducted with a certain probability of \(p_{r}\) in the training procedure, and we name this probability factor \(p_{r}\) as the amplification probability factor. For a training epoch \(e\), the probability of it undergoing learning rate amplification is \(p_{r}\), and the probability of it being kept unchanged is \(1-p_{r}\). In this process, training epochs with RALR are generated.  
The procedure of RALR for training deep neural networks is shown in Algorithm 1. RALR usually sets a random boundary interval \([low,high]\), the amplification factor is set to \(p_{r}\). Assume our training procedure takes a new training epoch \(e\), \(e\) is a natural number, we randomly initialize the probability \(p_{e}\) for this epoch, if \(p_{e}<p_{r}\), we amplify the learning rate with a random numerical value \(random\_rate\in[low,high]\), i.e., the learning rate \(\eta_{e}\) of this epoch \(e\) is \(\eta_{e}=\eta_{o}\times random\_rate\), \(\eta_{o}\) is the global learning rate according to conventionally decaying way. Otherwise, we use the original global learning rate \(\eta_{o}\) in this epoch for training, i.e., \(\eta_{e}=\eta_{o}\).  
Figure 3: The first line shows the part of the original images of the Tiny-ImageNet test dataset. The second line shows the localized class-discriminative regions with ResNet-110 training with the traditional way. The third line shows the localized class-discriminative regions with ResNet-110 training with RALR.  
```
0: Model \(\theta\), \(N\) Training Samples \((x_{i},y_{i})_{i=1}^{N}\), Optimizer SGD, Momentum, Weight decay, Batch Size, Number of epochs \(e\), Decrease learning rate epochs \(e_{d}\), Loss Function \(f(\theta)\). Initial learning rate \(\eta_{0}\), Gamma \(\gamma\).
0: Random interval factor \([low,high]\), Amplification probability \(p_{r}\)
0: Initialize \(\theta,\eta_{0}\), best Loss \(L^{*}\longleftarrow f(\theta)\)
1:for\(i\in[1,e]\)do
2:if\(i\in e_{d}\)then
3:\(\eta_{0}*=\gamma\)
4:endif
5: Initialize random probability for each epoch \(p_{i}\)
6:if\(p_{i}\leq p_{r}\)then
7: Amplification rate \(random\_rate\in[low,high]\)
8: Amplify learning rate \(\eta=\eta_{0}\times random\_rate\)
9: Train for 1 epoch.
10: Evaluate new loss \(L\longleftarrow f(\theta)\)
11:else
12: learning rate \(\eta=\eta_{0}\)
13: Train for 1 epoch.
14: Evaluate new loss \(L\longleftarrow f(\theta)\)
15:endif
16:if\(L\leq L^{*}\)then
17: Update \(L^{*}=L\). Save checkpoint \(\theta\) and \(L^{*}\)
18:endif
19:endfor
```  
**Algorithm 1** Schedule of RALR based on epoch for training deep neural networks.
# A Fast Wavelet-Based Bridge Condition Assessment Approach Using Only Moving Vehicle Measurements
## 4 Numerically Verification
### Different Damage Severity  
In this section, different damage levels are considered in the VBI model. The higher velocity, i.e., \(v=10\) m/s, is used to detect the presence of bridge damage. The wavelet energy change ratio compared to the health condition of the bridge is calculated and the result is illustrated in Figure 18. It is obvious that the wavelet energy change rate increases with the damage degree, and this increase is roughly linear. For bridge damage with a 60% damage level, the change in wavelet energy is over 7%. The rate of change in wavelet energy can provide a reference for quantitative assessment of damage severity. The proposed approach has potential applications for realizing bridge SHM in level 3.
## 2 Materials and Methods
### Performance Evaluation  
The coefficient of determination (\(R^{2}\)) is often used to assess the prediction precision of the models, and an \(R^{2}\) value close to 1 indicates stronger prediction ability. The root-mean-square error (\(RMSE\)) can be used to further evaluate the prediction effect and precision of models. Determining the \(RMSE\) of the predicted values is a significant criterion since sometimes a relationship with a high \(R^{2}\) value may also exhibit a high \(RMSE\) value. Relative error (\(RE\)) can reflect the deviation between the predicted value and the actual value. In this study, \(R^{2}\) and \(RMSE\) were chosen to evaluate the predictive abilities of the models, and \(RE\) was used to comparatively assess the deviation between the predicted values of each model and the measured values. These evaluation indices were also used in [PERSON]'s research on the prediction of soil shear parameters [53]. The above three indices can be computed as follows [33; 53]:  
\[R^{2}=\frac{\left(\sum_{i=1}^{n}\left(f_{i}-\frac{1}{n}\sum_{i=1}^{n}f_{i} \right)\left(y_{i}-\frac{1}{n}\sum_{i=1}^{n}y_{i}\right)\right)^{2}}{\sum_{i=1} ^{n}\left(f_{i}-\frac{1}{n}\sum_{i=1}^{n}f_{i}\right)^{2}\sum_{i=1}^{n}\left(y_ {i}-\frac{1}{n}\sum_{i=1}^{n}y_{i}\right)^{2}} \tag{11}\]  
\[RMSE=\frac{\sqrt{\sum_{i=1}^{n}\left(f_{i}-y_{i}\right)^{2}}}{n} \tag{12}\]  
\[RE=\ \left(\left|f_{i}-y_{i}\right|\right)/y_{i}\ \ \ \ \ \ \left(i\leq n\right) \tag{13}\]  
where \(R^{2}\) is the coefficient of deterministic; \(RMSE\) is root-mean-square error; \(RE\) is the relative error; \(f_{i}\) is the predicted value; \(y_{i}\) is the measured value; \(n\) is the number of samples in the calibration set (or the validation set).  
The above three evaluation indicators are represented by \(R\)C\({}^{2}\), \(RMSE\), and \(RE_{C}\) for the calibration set prediction, and \(R\)\(V^{2}\), \(RMSE\), and \(RE_{V}\) were used for the validation set prediction. The data preprocessing and modeling involved in this paper were all completed using MATLAB (MathWorks, Natick, MA, USA).
# Prospects for the Use of Quasi-Mersen Numbers in the Design of Parallel-Serial Processors
## 1 Introduction
Mersenne codes 3, 7, and 11 provide greater noise immunity and an increased probability of correct detection in radar channels, offering a potential alternative to Barker codes for noise-resistant data transmission in radio channels [24]. Modified RSA algorithm, using fixed Mersenne prime numbers, improves encryption in medical ultrasound imaging while maintaining adequate elapsed time for image transfer [25]. From the perspective of performing calculations modulo an integer, these numbers are particularly convenient because the multiplication of a number \(a\) written in binary form by two modulo the Mersenne number is reduced to cyclic permutation of characters. For example, for calculations in the field \(GF(7)\), the next equality is true  
\[2\cdot a_{2}a_{1}a_{0}=_{(7)}a_{1}a_{0}a_{2} \tag{1}\]  
Relation (1) reflects the uniqueness of Mersenne primes. Quasi-Mersenne numbers are also unique, which is demonstrated in report [26]. Their uniqueness is due to the fact that the operation of digital logarithm, the study of which has recently received very serious attention [27; 28; 29], has a pronounced specificity in the case under consideration. Namely, the operation of digital logarithm allows us to reduce the operation of multiplication in a Galois field to the operation of addition. However, this operation is applicable only to non-zero elements of Galois fields. In Galois fields \(GF(p)\), corresponding to quasi-Mersenne numbers \(p=2^{k}+1\), the number of non-zero elements is obviously equal to \(2^{k}\), i.e., the multiplication operation (after using the digital logarithm operation) is de facto reduced not simply to an addition operation, but to an addition operation in terms of ordinary binary logic, easily implemented on the existing element base (addition modulo \(2^{k}\) is operationally reduced to discarding the most significant digits in the binary representation of a number). It is appropriate to emphasize that the uniqueness of quasi-Mersenne numbers has not previously received the attention that they deserve. Evidence for this is provided in our recent report [26], where it was shown that it is possible to implement a simple and convenient method of digital logarithm specifically for such fields. We also note that the approach using a specific Galois field (or varieties of such fields) is not fundamentally new. Modern binary computing technology is de facto built on the use of a specific field, GF(2). Consequently, the question of developing computing technology that uses the specifics of a particular type of Galois fields in a certain sense corresponds to the existing tradition.
# Microwave Staring Correlated Imaging Based on Unsteady Aerostat Platform
###### Abstract
Reference [23] focuses on sparsity-driven MSCI with array position error (APE) and propose two sparse auto-calibration imaging algorithms in sparse Bayesian learning framework to compensate the APE. [PERSON] et al. analyzes the target-motion-induced error and provides an applicable approach for MSCI in the presence of target-motion-induced error [24]. Hitherto, research on MSCI system error generally concentrated on gain-phase error, off-grid error, APE, and target-motion-induced error. There is no study on the imaging system error caused by instability of the platform which is an important issue in practice applications. Aiming at the above problems, this paper proposes a MSCI method based on unsteady aerostat platform. In the proposed method, the antenna array with multiple transmitters and one receiver is mounted on the aerostat platform combined with an adaptive variable suspension (AVS), and the position and orientation system (POS) located at the center of the array, controlling its antenna beam orientation to the target area and measuring dynamically its position and attitude during imaging process. The effects of antenna motion and dynamic beam coverage caused by instability of the platform are considered in imaging model to reduce the imaging model error. For antenna motion, the real-time position vectors of antenna are used in imaging model in place of static position vector. The calculation of real-time position vector of antenna depends on the translational speed and the rotational angular velocity of the array in each signal pulse, then based on the low-speed discrete POS data, a least square curve-fitting method is employed to estimate the accurate translational speed and rotational angular velocity of the array at every sampling time. For dynamic beam coverage, the selection matrix of beam coverage calculated by the position and the attitude of the array is introduced to indicate the illuminated area at each pulse. The rest of this paper is organized as follows. Section 2 presents the MSCI method based on unsteady aerostat platform. In Section 3, estimation of translational speed and rotational angular velocity of antenna array is given. In Section 4, serval simulations are demonstrated to show the effectiveness of the proposed method. Section 5 concludes this paper.
# Assessment of Self-Supervised Denoising Methods for
## 2 Materials and Methods  
### Only-Noisy Training  
In conventional speech-denoising tasks, it is common practice to use clean audio signals as a target for the training. However, acquiring such signals can be challenging due to their high cost or the need for specialized equipment or studio environments with strict requirements. To overcome this challenge, an end-to-end self-supervised method for speech denoising, called Only-Noisy-Training (ONT), was introduced [20]. This innovative approach aims to reduce reliance on clean signals during training. Unlike traditional methods, ONT utilizes only noisy audio signals for training, eliminating the need for additional conditions. The ONT framework comprises two key components: a module for generating training pairs and a module for speech enhancement. The first part employs a random audio sub-sampler to extract training pairs from each noisy audio signal. These pairs are then processed by a complex-valued speech denoising component, as illustrated in Figure 1.  
The denoising network incorporates state-of-the-art encoder and decoder modules based on the groundbreaking architecture of DCUNET-10.
# Impact of Road Traffic Tendency in Europe on Fatigue Assessment of Bridges
## 3 Fatigue Load Models
#### Fatigue Load Models LM2 and LM4 of Eurocode EN1991-2
It must be recalled that, in the framework of EN1991-2 pre-normative studies:  
* LM2 was calibrated referring to a kind of \"frequent\" value \(\Delta\sigma_{\max,f}\), representing the maximum stress range relevant for unlimited fatigue life assessment. This \"frequent\" value \(\Delta\sigma_{\max,f}\) was calculated according to two alternative definitions as:
* the stress range exceeded about \(10^{5}\) times during the notional design life of the structure; or, alternatively,
* the stress range corresponding to 99% of the total fatigue damage, calculated considering a \(S\)-\(N\) curve with constant slope \(m=5\), i.e., \[\Delta\sigma_{\max,f}\Big{|}\sum_{\Delta\sigma_{i}>\Delta\sigma_{\max,f}}\frac {n_{i}}{N_{i}}=0.01,\] (3) which means that damage produced by stress ranges \(\Delta\sigma_{i}>\Delta\sigma_{\max,f}\) is practically negligible; either way obtaining comparable values. * LM4 was calibrated disregarding the constant amplitude fatigue limit \(\Delta\sigma_{D}\) and considering a constant slope \(S\)-\(N\) curve, matching one of the two constant slope linear branches, \(m=3\) or \(m=5\), of the classical tri-linear curve for structural steel details given in EN1993-I-9 [23], better described in Section 4. Depending on the relevance of the route concerned and on the road category, in fatigue load model LM4 three different reference traffics are considered: long-distance; medium-distance; and local traffic, characterized by different traffic compositions. That accounts for the actual policy of optimal management of commercial lorries: in fact, bigger and heavier vehicles are increasingly used in long-distance traffic, preferably devoting lighter and quicker vehicles to medium-distance and local traffics. Of course, long distance traffic is more severe than medium distance traffic and medium distance traffic is more severe than local traffic composition. A rather obvious, but extremely important remark is that axle loads and total weights of standardized lorries are beyond the legal limits not only in LM2, but also in LM4; in fact:  
* the maximum axle load is 190 kN in LM2 and 270 kN in LM4; while the legal limit is 120 kN;* the maximum tandem axle load is 280 kN in LM2 and 240 kN in LM4, while the legal limit is 200 kN;
* the maximum tridem axle load is 360 kN in LM2 and 270 kN in LM4, while the legal limit is 240 kN;
* the maximum vehicle weight is 630 kN in LM2 and 490 kN, while the legal limit is 440 kN. Figure 4: Fatigue load model 4 (LM4) of EN1991-2—Set of equivalent lorries ([35]—adapted from [1]). Figure 3: Fatigue load model 2 (LM2) of EN1991-2—Set of frequent lorries” ([35]—adapted from [1]). Recalling the previous discussion about measured traffics, this is not surprising, being just a pragmatic acknowledgment of the real situation, but it further demonstrates the relevance of the subject.
# Improved Matching Algorithm with Anchor Argument for Rotate Target Detection
## 2 Related Work  
Recently, a lot of arbitrary oriented object detectors have been proposed for aerial images and scene text detection. The authors of [3] employed a rotated RPN to generate rotated region proposals based on the Faster R-CNN framework and further performed rotated bounding box regression. TextBoxes++ [6] adopted the framework of TextBoxes [7] and proposed an SSD-based oriented text detection method. An RRD [8] further improved TextBoxes++ and used different features for classification and regression tasks. The regression branch network extracts the rotation sensitive features, and the classification branch extracts the rotation invariant features by pooling the rotation sensitive features. Aerial object detection is a challenging task in computer vision as well. The authors of [9] proposed an RoI transformer that transforms the horizontal proposals to rotated ones and a rotation position-sensitive RoI align (RPS RoI align) model to extract rotation invariant features. In order to improve the sensitivity to small targets, the authors of [2] designed a sampling fusion network based on multi-layer features and effective anchor sampling with a supervised pixel attention network and channel attention network jointly to suppress the noise and enhance the features. A dynamic anchor learning [5] method was proposed, which uses the newly defined matching degree to comprehensively evaluate the localization potential of anchors and realizes a more effective label allocation process. The authors of [10] proposed an approximate skew loss to solve the problem that the calculation of skew loss cannot be deduced to obtain more accurate rotation estimations.  
For small targets that often appear in remote sensing target detection, [11; 12] proposed a small target detection method based on a multi-directional derivative weighted contrast measure and a new small target detection method based on a local hypergraph dissimilarity measure (LHDM). The authors of [13] proposed scale-aware rotated object detection. Deep learning has many applications in other scenarios as well [14; 15; 16; 17]. As we can see, most of the existing methods use the same anchor setting in both the training stage and the test stage.
applied sciences Article  
High-Efficiency Broadband Polarization-Independent Reflective Grating with Double-Layer Dielectric Rectangle Groove in Littrow Mounting  
[PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 1 and [PERSON] 1,*  
Footnote 11:  
reduce the number of stacks of films with different refractive indices, thereby reducing the mechanical stress between the films. In addition, metal mirrors can also increase the spectral tolerance and angular tolerance. [PERSON] et al. designed and manufactured a metal-mirror based multi-layer grating and the diffraction efficiency exceeded 97% with 200 nm bandwidth for TE polarization [19]. However, this grating is limited in some practical applications because of the polarization-dependent characteristics. Reference [20] proposed a polarization-independent mixed metal dielectric reflective grating. The \(-1\)st order diffraction efficiencies for TE and TM polarizations were greater than 90% at a bandwidth of more than 120 nm centered at 800 nm. However, the diffraction efficiency of the grating is not satisfactory, which limits the maximum output power of the optical system.  
In this paper, a high-efficiency, broadband and polarization-independent double-layer reflective grating with a double-layer dielectric rectangle groove is proposed. The physical mechanisms of the reflective grating can be explained by using the modal method and the parameters of the grating calculated and optimized by the RCWA algorithm [21] and the SA algorithm [22]. In CPA applications, when the short-pulse lasers used Nd: YLF as the gain medium, the center wavelength of the output laser was 1053 nm [23]. Therefore, the operating wavelength of the designed grating was centered at 1053 nm. The diffraction efficiencies of the designed grating, with optimization parameters of \(-1\)st diffraction order for TE and TM polarizations, exceeded 98%, ranging from 1015 nm to 1085 nm, with the Littrow incident angle of 46.35\({}^{\circ}\), and the average diffraction efficiency for the range from 1000 nm to 1100 nm was 98.92%, with an average PDL of 0.051 dB. Simulated by FEM, the electric field distributions of the designed grating for TE and TM polarizations clearly demonstrated the high efficiency of \(-1\)st diffraction order. Moreover, the fabrication tolerances and the dependence of diffraction efficiency on the incident angle are described here in detail. The results clearly demonstrated the feasibility of the designed grating in fabrication and application.
# Transport-Layer Limitations for NFV Orchestration in Resource-Constrained Aerial Networks
## 3 Orchestration in Intermittently Available Platforms
### Enhanced Policies for VNF Placement  
Recalling how VIM work in NFV orchestration, they usually take into account certain parameters for allocating VNFs on the NFVI nodes (e.g., CPU, memory usage, etc.). However, battery life is not included among those parameters, so VIMs do not take it into account when deploying/managing VNFs on NFVI nodes. Nonetheless, if a VIM were able to use this parameter in SUAV NFV environments, SUAVs could potentially improve its energy efficiency by assigning critical VNFs to healthier nodes, i.e., those aircraft with extended battery lifetime. Moreover, orchestration could also be improved by using this parameter, as knowing the remaining capacity is essential to trigger VNF migration (discussed in more detail in Section 3.1) and support re-allocation policies.  
Another aspect that needs to be addressed is the placement of SUAVs over an area. In traditional scenarios, NFVI node placement might not be as critical, as these nodes are placed in fixed locations interconnected through a cabled network. In this case, however, SUAVs can move around a delimited field, but its target position for their trajectories should be provided to the flight control engine running at the SUAV. The authors in [19] propose implementation of the flight control engine as a VNF on every unit, providing its flight trajectories using a VNFM for providing its flight trajectories as configuration parameters. Applying this method, these trajectories could be specified in the deployment to the NFVO, or even appear in the NS descriptor to be configured on the NS deployment.