esa-sceva/satcom-chunk-collection huggingface dataset:
# Convolution-Transformer Adaptive Fusion Network for Hyperspectral Image Classification
## 2 Datasets and Methods
### Method
#### 2.2.1 CTAFNet Architecture Overview  
The overall architecture of the proposed CTAFNet for HSI classification is presented in Figure 5a. CTAFNet has a CNN-Transformer hybrid architecture, which mainly composed of encoder and decoder. The encoder follows the hierarchical pyramid architecture equipped with a CTAFK in each stage, which is used to capture local-global feature representations at different levels. The decoder uses bilinear interpolation for up-sampling to recover the spatial resolution of the feature map and concatenates the feature map from the previous and the current stage of encoders. The encoder-decoder framework enables CTAFNet to effectively utilize the local and global information extracted at each stage to improve HSI classification accuracy. The output feature size for each stage is shown in the Table 4.
## 3 Materials and Methods
### Proposed Architecture
#### 3.4.1 LSTM Layer  
LSTM is a specialized variant of the recurrent neural network (RNN), and it was introduced by [PERSON] et al. [46]. Unlike a traditional neural network, which is incapable of capturing temporal information as the nodes in each layer are disconnected, the RNN compensates for that limitation by mapping input sequences to output sequences. However, the RNN has the disadvantage of recognizing data for only short periods of time due to the vanishing/exploding gradient [47]. The LSTM solves the problem of the RNN with special memory cells that can solve complex, artificial, long-time-lag tasks, which resulted in it being an appropriate solution for our problem, since acceleration data are time series data [46]. The LSTM network introduced a new mechanism called a memory cell.  
A memory cell is regulated with three main gates: an output gate, an input gate and a forget gate. The output gate protects other neurons from being perturbed by a currently irrelevant memory cell state, while the input gate determines what input can alter the memory cell state [46]. Lastly, the forget gate is responsible for gathering prior data and what past information to forget from the cell's previous state [46].  
A LSTM memory cell would take in an input \(x_{t}\) at time \(t\), the hidden state \(h_{t-1}\) and the cell state \(c_{t-1}\) from the previous LSTM memory cell, and would output the current hidden state \(h_{t}\) plus the memory cell state \(c_{t}\). \(U\), \(W\) and \(V\) denote the weight matrices,  
Figure 8: Stacked LSTM architecture.  
while \(b\) represents the bias vector. The forward pass of the LSTM unit could be expressed as follows:  
\[f_{t}=\sigma(W_{f}x_{t}+U_{f}h_{t-1}+b_{f}) \tag{1}\]  
\[i_{t}=\sigma(W_{i}x_{t}+U_{i}h_{t-1}+b_{i}) \tag{2}\]  
\[\tilde{c}_{t}=tanh(W_{c}x_{t}+U_{c}h_{t-1}+b_{c}) \tag{3}\]  
\[c_{t}=i_{t}\circ\tilde{c}_{t}+f_{t}\circ c_{t-1} \tag{4}\]  
\[o_{t}=\sigma(W_{o}x_{t}+U_{b}h_{t-1}+V_{o}c_{t}+b_{o}) \tag{5}\]  
\[h_{t}=o_{t}\circ tanh(c_{t}) \tag{6}\]  
where \(\circ\) represents the element-wise multiplication, while \(f_{t}\), \(i_{t}\), \(\tilde{c}_{t}\) and \(o_{t}\) represent the forget, input, cell, output gates' outputs, respectively. \(c_{t}\) and \(h_{t}\) are passed on to the next time step.  
As mentioned above, the LSTM-Raw network had 224 memory cells for each LSTM layer and would take in a three-dimensional input vector \(x_{t}\) per time-step. Each of the LSTM layers would produce a hidden state output vector \(h_{t}\) with 224 dimensions. As for the LSTM-Features networks, it would take in an input vector \(x_{t}\) with 40 dimensions per time step. As each LSTM layer had 100 memory cells, each layer would generate a hidden state output vector \(h_{t}\) with 100 dimensions.
## 2 Materials and Methods
### Material Properties
#### 2.1.1 Mechanical Tests  
Compression tests were performed with a Shimadzu AGS-X equipped with a 10 kN load cell. A constant compression speed of 1.3 mm min\({}^{-1}\) was used (ASTM D1621 [17]) and the compression modulus E\({}_{\text{c}}\) was determined from the stress-strain curves in the 3-6% deformation range. Tests were performed on five specimens with an average diameter of 19.5 mm and an average height of 17.0 mm.
# A New Defect Diagnosis Method for Wire Rope Based on CNN-Transformer and Transfer Learning
## 3 Methodology  
### Data Preprocessing  
Non-destructive testing technology based on MFL is widely used in the research of wire rope diagnosis activity. Due to harsh working conditions and different structures of wire rope, such as aggressive movement, electromagnetic interference, the pole tip effect, and movement friction, MFL signals are inevitably mixed with various noises that have an adverse impact on the defect diagnosis result. All of these factors contribute to the difficulty of extracting useful features from the original signals. Therefore, it is necessary to conduct the data preprocessing step, prior to further operations of feature extraction and defect detection.  
As one typical MFL signal commonly used in practical applications, the LMA signal was selected to analyze the condition of the wire rope. Furthermore, a large number of studies have proved the validity of the EMD [30] method for processing nonlinear or nonstationary signals [31]. In this paper, the EMD method was used to preprocess the original LMA signals and realize the function of denoising. As described in Equation (5), the original signal (\(x(t)\)) can be divided into several intrinsic mode function (IMF) signals (\(s(t)\)) and one residual signal (\(r(t)\)). According to the principle of EMD, each IMF signal reflects different frequency components and signal features. By recombining the useful IMFs, the purpose of eliminating noise signals can be achieved. Specifically, the constraints on the IMF signal are as follows:  
1. For each IMF signal, the number of extreme points should be equivalent to zero crossing points, or the difference cannot be greater than one.
2. The upper envelope formed by maxima and the lower envelope formed by minima should be symmetrical and the average values must be zero at any moment. \[x(t)=\sum_{i=1}^{n}S_{i}(t)+r(t)\] (5)  
According to the EMD method, ten different IMF signals and one residual signal can be obtained, these are presented in Figure 2. When these IMF signals are observed and compared to the original LMA signal, it can be determined that most of these noise-interference signals can be divided into different IMFs, which can be obviously separated from the useful information. Thus, the signals presented in Figure 2, from (g) to (l), can be understood as noise because of the pole tip effect or strand wave structure, which should be removed. Similarly, the signals, from (b) to (f), containing defect information should be reconstructed to complete the data preprocess and feature refinement stages. After EMD decomposition and reconstruction, the refined signals are presented in Figure 3b, which exhibits improved clarity and stability results, and the defect signal features are more obvious. This is beneficial to further feature extraction and defect diagnosis steps.
## 1 Introduction
Calibration curves were developed for Cr, C, Si, Cu, Mn, Ni, Mo, and V using the dominant factor-based partial least square regression (PLSR) method with and without spectral standardization. Average absolute measurement errors of 0.019, 0.039, 0.013, and 0.001% were achieved respectively for Si, Cr, Mn, and Ni. [PERSON] et al. [18] used a second generation B&W Tek (Newark, DE, USA) _NanoLIBS_ instrument to qualitatively analyse pig iron in open air. The emission lines of Si, Mn, and Fe were measured within the 280-315 nm spectral window, with results comparing favourably to those achieved using a typical LIBS benchtop apparatus. In particular, the Si emission line of interest could be rapidly identified with a high degree of confidence. Very recently, [PERSON] et al. [19] improved substantially the analytical performance of a Bruker _EOS 500 h_LIBS instrument (Billerica, MA, USA) for steel analysis using a nonlinear calibration approach based on a set of artificial neural networks (ANNs), one optimized for the determination of major elements in the alloy and the others specialized for the analysis of minor components. Although tests performed on steel samples used for instrument internal calibration achieved an accuracy comparable with results of ANN treatments, the latter ones yielded much more accurate results when unknown samples were tested. The present work aimed to evaluate the performance of _h_LIBS supported by two different calibration models, i.e., univariate calibration and multivariate PLS calibration, in the identification and the quantification of the elements C, Mn, P, Si, and Ti in a number of blast furnace pig iron samples, in comparison to that of X-ray fluorescence (XRF) analysis. The predictive capability of each calibration model was evaluated by calculating the Pearson's correlation coefficient (R), root mean square error (RMSE), and mean absolute prediction error (MAPE) and LODs, whereas the leave one out cross validation (LOO-CV) method was used to perform external validation.
# Prism-Based Spatial Heterodyne Spectrometer with a Fixed Fringe Localization Plane
## 2 Principles and Designs
### Preliminary Optical Design
An added advantage of this system is that the outgoing beams in the two different directions are not backtracking toward the source. The two outgoing beams can be detected by two detectors to increase the luminous flux. This advantage remains if the prisms are replaced by gratings, which is not discussed further in this paper.
# Ultra-High-Performance Concrete (UHPC) Piles and Splicing Options
## 4 UHPC Pile Splices
### Previous Work on UHPC Pile Splice  
[PERSON] et al. [41] developed a splice detail for UHPC piles involving a steel embedment in the ends of each pile, shown in Figure 10[41], with field welding of the embedment on both sides to create the splice. The splice was designed to develop the full moment and shear capacity of the UHPC pile section. They adopted the welded pile splice option, which is typically common to steel H-pile splice, so there were no concerns with field welding being a new practice for splicing UHPC piles.  
[PERSON] et al. [41] tested the developed splice detail in direct tension, flexure, and shear. The specimen failed in the initial direct tension test at a load of 103 kips (458 kN), 27.4% of the unspliced pile tension capacity, toward one of the supports (outside of the splice region).  
The failure of the test specimens occurred due to the fracture of the weld between the corner angle and the 0.5-inch (12.7 mm) end plate of the pile splice, shown in Figure 11[41]. It was concluded that the shop welding at the precast plant had a shorter length between the corner angle and end plate than specified, which led to the failure of the splice. They recommend a full penetration weld between the edge angles and end plate.  
At the time of writing, this is the sole published work on the welded splice method for UHPC pile.  
Figure 10: UHPC pile splice embedment developed by [PERSON] et al. [41].
# Middleware for Plug and Play Integration of Heterogeneous Sensor Resources into the Sensor Web
## 3 SWE-Based Acquisition Chain
### SWE Bridge  
The SWE Bridge is a universal acquisition middleware, aimed to be deployed in any acquisition platform, regardless of its software and hardware architecture. It interprets SDFs, automatically configuring itself to connect to and operate a sensor. Its implementation of the OGC PUCK protocol allows to automatically retrieve a SDF from the sensor itself, enhancing interoperability and providing a plug and play mechanism. When interfacing with a COTS a local SDF has to be used. The acquired data is stored in standard O&M files, encoded in XML or EXI format in order to reduce their size [17].  
The observation platform's communication link to a shore station can be critical in terms of bandwidth as well as in terms of power consumption. Therefore the SWE Bridge does not directly send the O&M files to the server, but relies on the platform's operator to setup for this transmission. The platform operator can decide under which conditions is desirable to transmit these files (i.e., stopping the transmission when the battery is low). Thus the platform operator still has full control of the platform.  
The SWE Bridge has its own SensorML description, the SWE Bridge Model, where all its functionalities, parameters and built-in functions are described in a standard manner. This model allows an automated process to understand the role and the capabilities of the SWE Bridge within the acquisition chain.
# ICENET: An Information Centric Protocol for Big Data Wireless Sensor Networks
## 5 Simulations and Results
### Simulation Setup  
To run the simulations we considered randomly-generated topologies with the number of nodes \(N\in\{100,200,300\}\). For each \(N\), 18 simulations, divided into three groups of six rounds, were performed. Each simulation group assumed a different sampling rate \(\mathrm{SR}\in\{5,10,30\}\) s. The deployment region \(R\) presented three levels: \(\{\)A, A\(\backslash\)A1, A\(\backslash\)A1\(\backslash\)A1.1\(\}\). During a simulation round, one single query asking for one physical parameter for 300 s, was injected in the network. The same injected query was processed by both ICENET and CoAP protocols. Four physical parameters were monitored by the network, but each node monitored only one parameter. Inside the simulation groups, the injected queries were evenly distributed among the three \(R\) levels.
## 4 Results and Discussion  
### Validation  
Here, the present LESs are validated by comparing them with the experimental results. The experimental data were acquired using the same facility at the Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency (JAXA), utilized in the previous study [30]. The test section size of the wind tunnel was 100 mm in width, 400 mm in height, and 700 mm in length. The turbulence intensity at the center of the tunnel was verified to be approximately 0.08% at a freestream velocity of 6.6 m/s. A two-dimensional NACA0015 wing model with a chord length of 100 mm and a span length of 100 mm was used. The model surface had a total of 29 pressure ports, and the time-averaged pressure measurements were conducted, but the pressure around \(x/c=0.05\) could not be measured due to the PA installation on the airfoil surface. The freestream velocity was set to 10 m/s, corresponding to the Reynolds number of approximately 63,000, based on the chord length and the freestream velocity, which is the same as the present LES. The applied voltage of PA was 3.5 kV\({}_{\text{PP}}\) (peak-to-peak voltage). The details may be found in the literature [30]. It should be noted that the experimental results include the effect of the wind tunnel's side walls and the freestream disturbance.  
Figure 5 compares pressure coefficient (C\({}_{\text{p}}\)) distributions at \(\alpha=6\), 8, and 10 obtained by the LESs and experiments. The shape of the NACA0015 airfoil is shown in gray on the background. Table 2 shows mean absolute errors (MAE) between the C\({}_{\text{p}}\) distributions obtained by the LESs and the experiments.  
In the PA-OFF case at \(\alpha=10\) (Figure 5a) and the Burst cases (Figure 5c), the LES results show quantitative agreement with the experimental results at all angles of attack. The MAE values are relatively low in these case. On the other hand, in the PA-OFF case at \(\alpha=6\) and \(8\) and the Continuous case, the LESs overestimate the plateau region of \(C_{\rm P}\) distribution. This discrepancy between the LESs and experiments is probably because of a tripping effect of the PA electrodes on the airfoil surface and a freestream disturbance [30]. These disturbances would make the Continuous case's flow of experiments close to that of the Burst case. The parameter \(D_{\rm c}\) uncertainty, which is set to match the maximum value of the PA-inducing velocity, also might affect the computational results. Although a discussion of the Continuous case needs to be conducted carefully; we consider that the LES results are reliable enough to discuss the effectiveness of PA because the LESs can predict the quantitative agreement of the Burst cases and the qualitative tendency that the plateau region in each case becomes smaller as the angle of attack increases. In addition, the computational method and the PA model used in the present study have been validated even at the angle of attack after stall [8; 23].
## 2 Antenna Design and Performance
### Single P-Band Dipole Antenna Design and Performance
Figure 9e shows the simulated E-field on the E-plane at 667 MHz when \(d_{1}\) is  
Figure 8: Orthogonal feeding structure of a single dipole antenna: (**a**) geometry of antenna, (**b**) simulated reflection coefficient performances depending on feeding structures, and (**c**) simulated radiation patterns at 580 MHz with orthogonal feeding structure. 20 mm and \(d_{2}\) is 6 mm. A strong E-field is induced at the edge of the lower half bow-tie dipole arms due to the electromagnetic coupling caused by the optimized spacing between the upper and lower half bow-tie dipole arms. Therefore, the induced E-field of the lower half bow-tie dipole arms contributes to the resonance around 445.67 MHz. Figure 9f shows the simulated radiation performances (gain and FBR) versus frequency when \(d_{1}\) is 20 mm and \(d_{2}\) is 6 mm. Figure 9g-i show the simulated radiation patterns at 450, 580, and 700 MHz, respectively. The simulated radiation performances are summarized in Table 4. The radiation characteristics of the dipole antenna are maintained over a wide frequency band. Figure 9: _Cont._
## 4 Conclusions  
In this paper, the pavement defects were detected by using the Mask R-CNN and transfer learning, and during the experiment, the performance of the Mask R-CNN and Faster R-CNN in pavement defect detection were compared. The following points could be concluded:  
1. By using transfer learning, the performance results of the Faster R-CNN and Mask R-CNN combined with six backbone structures, it could be seen that the detection accuracy of the two algorithms was close, and the AP under each backbone structure of the Mask R-CNN was higher than that of the corresponding Faster R-CNN. The models containing FPN were faster than others in testing time.
2. Through comparing the performance of the Mask R-CNN under three different LRs, the result could be obtained that the M-R101-FPN was the relatively optimal model when the learning rate was 0.0001, and the AP could reach 92.1%, and the detection effect was best.
3. The testing results of M-R101-FPN showed that the model had certain capabilities in the detection of multiple defects of complex pavements, and the quantification error was within a certain acceptable range.  
The conclusions show that the optimal model in this paper can solve the problem of pavement defect detection under complex backgrounds, and the quantitative results of the defects can provide a reference for pavement maintenance personnel. This paper established a dataset of 1200 pavement images, which was still insufficient compared with other types of well-known datasets, which also leads to an ideal AP. On the other hand, the dataset in this paper was collected by the driving recorder, which also made it impossible to predict the quantification of the real size and area of the defect. Therefore, future work needs to be carried out from three aspects: image acquisition perspective, augmenting the datasets, and trying more algorithms to further improve the detection effect of pavement defects.  
Project administration, J.Z. and S.T.; Writing--original draft, Y.H. and Z.J.; Writing--review & editing, J.Z., S.T., G.C., X.S. and F.C. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the Project of Guangdong Province High Level University Construction for Guangdong University of Technology (Grant No. 262519003).  
Not applicable.  
Some or all data, models, or code generated or used during the study are available from the corresponding author by request.  
The authors declare no conflict of interest.
Figure A1: Loss curves of the training process: (**a**) F-R50-C4 and M-R50-C4, (**b**) F-R101-C4 and M-R101-C4.
## 3 Results and Discussion
### Comparison of Optimal Extraction Conditions with the Lowest UAE Treatment  
In addition, the optimal results obtained in this study were compared to those obtained with the lowest extraction time (0.5 min) of UAE at the optimal temperature and pH of each side stream (Figure 5). As can be seen in the table, the percentage of protein recovered was very similar for head and viscera side streams, compared to the optimal condition for time of extraction with the lowest one (0.5 min). However, a higher protein recovery was obtained for skin and bone, reaching 33.7 and 54.2%, respectively, under the optimal condition. The antioxidant activity obtained (measured with ABTS and ORAC values) was higher for all side streams under the optimal condition. Moreover, in general, better results were observed by increasing the treatment time.
## 4 Advanced Strategies for eNO\({}_{3}\)R Toward Ammonia and Nitrogen  
eNO\({}_{3}\)R process has been extensively studied using a wide range of materials, including platinoids which have been used several times for nitrate reduction such as Pt [112; 113; 114; 115], Ru [116], Pb [117], Rh [118; 119], Ir [120]. Thus there are other materials that have been studied such as Sn [121; 122], Zn [117], Ni [115; 117]. Additionally, many researchers have studied alloys containing at least two components and having to contain at least one metal, such as platinoid alloys including Pt-Ir [120], Pt-Sn [123; 124; 125], Pd-Sn [125; 126; 127], or Pt-Rh [128]. Furthermore, non-metallic materials, including graphene, graphite, [129; 130], carbon fiber [131], Boron doped diamond (BDD) [132; 133], and so on, have emerged as promising catalysts for eNO\({}_{3}\)R, offering advantages such as low-cost, durability, and tunable electronic structures. In the search for efficient eNO\({}_{3}\)R catalysts, researchers' efforts diverge to synthesize materials effective in reducing nitrates to nitrogen and ammonium. Figure 10 and Table 4 showed that copper-based catalysts have demonstrated interesting results in yield rates and faradaic efficiency (FE) compared to the value required for practical application (\(\sim\)57 mg cm\({}^{-2}\) h\({}^{-1}\), faradaic efficiency FE > 90%) [134]. Various strategies have been explored to enhance Cu-based catalysts including facet manipulation, defect engineering, geometric regulation, fabricating copper composites with other metals or non-metals, bimetallic catalysts, encapsulation of active species, nanostructuring, and so on. Therefore, in the following section, the strategies used to convert nitrate to non-toxic gaseous nitrogen, in particular with copper and iron nanomaterials, will be outlined. As a matter of fact, these latter have yielded interesting results, as shown in Table 4, with the emphasis on their advantages. The engineering of zero-valent iron to advance the process is particularly addressed. Additionally, the strategy to convert ammonia generated through indirect oxidation by chloride-electrochemical advanced oxidation is discussed. Figure 10: Comparison between the performance of different transition metal-based electrocatalysts for electrochemical nitrate reduction to ammonia (yellow zone is required for application). \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Cathode**} & \multicolumn{2}{c}{**Ammonia Production**} & \multicolumn{2}{c}{**Nitrogen Selectivity**} & \multicolumn{1}{c}{**Refs.