esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results  
To evaluate the proposed RSR method, experiments were carried out on two designed tasks: a UR5 robot manipulation task and TurtleBot navigation task, as shown in Figure 4, respectively. The first task was learning a skill policy to control the robot gripper to reach a target object, avoiding obstacles in 3D space. The second one was to train the TurtleBot to navigate from a random starting position to a random goal position in 2D space, without obstacle collision as well. A real-world RGB-D camera was visually calibrated to match the position and orientation of the simulated camera for each task respectively.  
Figure 4: Two designated tasks are evaluated for our method. (**a**) Manipulation task. A policy is trained to control a UR5 robot gripper to reach the target object in 3D space and avoid obstacles (yellow box). (**b**) Navigation task. Learning a policy navigates the TurtleBot to the target position without obstacle collision.
# A New Coding Paradigm for the Primitive Relay Channel +
## 2 Existing Upper and Lower Bounds
### Cut-Set Upper Bound  
For the _general relay channel_, the cut-set upper bound on the achievable rate \(R\) is given by ([17] Theorem 16.1)  
\[R\leq\max_{p_{X_{\text{s}},X_{\text{R}}}}\min\{I(X_{\text{s}},X_{\text{s}};Y_ {\text{sD}});I(X_{\text{s}};Y_{\text{sD}}|X_{\text{s}})\}. \tag{1}\]  
For the case of the _primitive relay channel_, the cut-set bound specializes to ([26] Proposition 1)  
\[R\leq\max_{p_{X_{\text{s}}}}\min\{I(X_{\text{s}};Y_{\text{sD}})+C_{\text{sD}} ;I(X_{\text{s}};Y_{\text{sD}},Y_{\text{sD}})\}. \tag{2}\]  
For the special case of the _erasure relay channel_, the cut-set bound can be rewritten as  
\[R\leq\min\{1-\varepsilon_{\text{sD}}+C_{\text{sD}};1-\varepsilon_{\text{ss}} \varepsilon_{\text{sD}}\}. \tag{3}\]  
Figure 3: The erasure relay channel: primitive relay channel in which the link from source to relay is a BEC(\(\varepsilon_{\text{sR}}\)) and the link from source to destination is a BEC(\(\varepsilon_{\text{sD}}\)).
## 3 Data Processing  
In the present investigation, it is very difficult to derive precise predictions of the bearing condition based on signal-processing methods due to the noisy environment. Therefore, intelligent methods are used. A convolutional neural network (CNN) is tested for this purpose. Since the CNN processes image data, spectrograms are generated from the raw signals in the first step. This is explained in more detail in Section 3.1. The general functionality and the structure of the used CNN is presented in Section 3.2. Furthermore, since many classifiers show the overfitting and drawbacks of imbalanced datasets, suitable evaluation methods are identified in Section 3.3 to enable a reliable evaluation of the results.
# Infrared Image Superpixel Segmentation Based on Seed Strategy of Contour Encoding
## 4 Experiment
### Benchmark  
As mentioned in various superpixel studies [14; 38], boundary adherence is the primary property in the superpixel evaluation system. To quantitatively evaluate the performance of the superpixel segmentation algorithms, two commonly used evaluation metrics were taken into account: boundary recall (\(BR\)) and under-segmentation error (\(USE\)).  
\(BR\) is used to measure the degree of overlap between the boundary of the superpixel map and the boundary of an artificially labeled result. Better superpixel generation algorithms should lead to effective adherence to object boundaries. \(BR\) is expressed as follows:  
\[BR=\frac{\sum\limits_{p_{i}\in AL_{b}}IF(p_{i})}{|AL_{b}|}, \tag{21}\]  
\[IF(p_{i})=\left\{\begin{array}{cc}0&\Omega(p_{i})\cap SM_{b}=\varnothing\\ 1&\Omega(p_{i})\cap SM_{b}\
eq\varnothing\end{array}\right., \tag{22}\]  
where \(AL_{b}\) and \(SM_{b}\) represent the boundary of the artificially labeled map and the superpixel map, respectively; \(|AL_{b}|\) represents the total number of pixels in boundary \(AL_{b}\); \(\Omega(p_{i})\) is a region with a radius of two pixels centered at \(p_{i}\); and \(IF(\cdot)\) is an indicator function: if there is a point on the edge of the superpixel falling within \(\Omega(p_{i})\), it returns 1; otherwise, it returns 0. The larger the value of \(BR\), the stronger the boundary adherence capability.  
For each superpixel \(S_{j}\) generated by the segmentation algorithm, the internal pixels should belong to the same object, and under-segmentation will occur when there are two objects within it. To measure the \"leakage\" of the superpixel over the artificially labeled region \(G_{i}\), the \(USE\) metric is introduced, as follows:  
\[USE=\frac{1}{N}\left[\sum\limits_{i=1}^{M}\left(\sum\limits_{S_{j}|S_{j}\cap G _{i}>B}|S_{j}|\right)-N\right], \tag{23}\]  
where \(N\) is the total number of image pixels, \(M\) represents the number of artificially labeled segments, and \(B\) is the tolerance factor of \(S_{j}\) and \(G_{i}\), which is set to 5% of \(|S_{j}|\) in this paper. A smaller value of \(USE\) indicates better overlapping between each superpixel and the artificially labeled regions. A value of \(USE\) close to zero indicates accurate segmentation.
# Frequency-Shifted Optical Feedback Measurement Technologies Using a Solid-State Microchip Laser
## 4 Applications of the Laser Confocal Feedback Tomography
### Profilometry  
In Section 4.1, only the amplitude information of the light is demodulated and utilized. When the system is applied to a surface measurement, the phase information can be demodulated, together  
Figure 19: (**a**) The normalized defocusing curve, and (**b**) the calibrated linear range with different objective lenses. I, NA = 0.65, II, NA = 0.55, III, NA = 0.3. (Figure reproduced from Ref. [82]).  
Figure 20: (**a**) Physical maps of the micro-gyroscope and the rotor. (**b**) Measurement results of the lateral scanning. (Figure reproduced from Ref. [82]).  
with the amplitude information, to reveal the micro-nano structure [38; 87]. The system is shown in Figure 21. Both the intensity modulations \(I_{m}(2\Omega)\) and \(I_{r}(\Omega)\) can be obtained from the PD. By demodulating \(I_{m}(2\Omega)\), not only the amplitude (A), but also the phase (P\({}_{\rm m}\)) can be obtained. At the same time, the phase information (P\({}_{\rm r}\)) from \(I_{r}(\Omega)\) is used to compensate the P\({}_{\rm m}\) to achieve high accuracy in phase measurement, and thereby result in the high axial accuracy with nanometer resolution.  
When the profilometry is conducted, the objective lens is scanned in the longitudinal direction to obtain the defocus response curve, locating the sample surface. After that, scanning the sample in the horizontal direction and simultaneously detecting the feedback light intensity. When measuring the height difference \(\Delta h\) of two lateral positions on the sample surface, it obtains the integral number \(n\) of \(\lambda/2\) contained in \(\Delta h\) based on the amplitude (A) variation of the laser intensity with the range of 10 \(\upmu\)m, and the fractional number \(s\) of \(\lambda/2\) using the phase measurement with a resolution of \(\approx\)2 nm. Finally, the height difference is computed as:  
\[\Delta h=\zeta\times(n+s)\times\lambda/2 \tag{16}\]  
where \(\zeta\) is a calibrated coefficient to take into consideration the influence of the high NA of the objective, whose value is carefully calibrated to be 0.93.
# Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder +
## 5 Experiments
### Compared Methods  
The following baseline methods were compared with our proposed SCSA:  
* The standard classifier without unsupervised domain adaptation technique; we introduced support vector machine (SVM) in the experiments.
* Transfer component analysis (TCA) [13], which aims to project the original data into the common latent feature space via dimension reduction for unsupervised domain adaptation.
* Marginalized stacked denoising autoencoders (mSDA) [19], which are elaborated to learn more abstract and invasive feature representations so that domain integration can be carried out.
* Transfer learning with deep autoencoders (TLDA) [14]. The dual-level autoencoder is designed to learn more transferable features for domain adaptation.
* Transfer learning with manifold regularized autoencoders (TLMRA) [2]. To obtain more abstract representations, the method combines manifold regularization and softmax weight regression.
* Semi-supervised representation learning framework via dual autoencoders (SSRLDA) [1]. The mSDA with adaptation distributions and multi-class marginalized denoising autoencoder are applied to obtain global and local features for unsupervised domain adaptation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Domain1** & **Domain2** & **Domain3** & **Domain4** \\ \hline Number of Positive Instances & 1000 & 1000 & 1000 & 1000 \\ \hline Number of Negative Instances & 1000 & 1000 & 1000 & 1000 \\ \hline Feature & 900 & 900 & 900 & 900 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the ImageNet dataset used in our experiment.
## 3 Proposed IMCKD
### Imckd
#### 3.2.1 The Index Teager Envelope Spectral Kurtosis (TEK)  
The Teager energy operator (TEO) is a nonlinear operator proposed by [PERSON] that can effectively extract the energy of the signal, which can effectively highlight the transient characteristics of the impact component [44]. The Teager energy operator is used to track the instantaneous energy of signal \(y(t)\), which is defined as follows [45]:  
\[\overline{y_{n}}(t)=\psi[y_{n}(t)]=\dot{y}_{n}^{2}(t)-y_{n}(t)\ddot{y}_{n}(t) \tag{12}\]  
where, \(\dot{y}(t)\) and \(\ddot{y}(t)\) are the first and second derivatives of the signal \(y(t)\).  
We simulated the bearing single point fault signal and added random noise with Signal to Noise Ratio (SNR) of -16, -12, -8, -4, 0, 4, 8, 12 (dB) respectively. Then we calculated the Teager energy operator signal using Equation (11), and the results are shown in Figure 3.  
Figure 3: _Cont._The blue line represents the original signal and the red line represents the Teager energy operator signal, and **A**, **B** represent the partial enlargements of SNR = 0 and SNR = 8 dB, respectively. It can be seen from Figure 3 that TEO can track the instantaneous energy of the signal and better reflect the impact characteristics from the time-domain partial map.  
Then, we calculated their envelope spectrum by Hilbert transform by the following equation:  
\[E(t)=\sqrt{\overline{y}_{n}^{2}(t)+\left(\frac{1}{\pi}\int\limits_{-\infty}^{ \infty}\frac{\overline{y}_{n}(\tau)}{t-\tau}d\tau\right)^{2}} \tag{13}\]  
and calculated their envelope spectral kurtosis (TEK):  
\[TEK=\frac{1/N\sum\limits_{i=1}^{N}\left(E_{i}(t)-u_{E}\right)^{4}}{\left[1/N \sum\limits_{i=1}^{N}\left(E_{i}(t)-u_{E}\right)^{2}\right]^{2}} \tag{14}\]  
where \(u_{E}\) is the mean of the signal and \(N\) is the signal length. The corresponding TEK values for different SNRs are shown in the Figure 4.  
It can be seen from the trend graph that as the SNR of the signal increases, the TEK value tends to increase. Therefore, the index TEK can be used to measure the noise reduction effect of the signal.  
Figure 4: The Teager envelope spectral kurtosis (TEK) values for different SNRs.  
Figure 3: The diagram of Teager energy operator under different SNRs, (**a**) \(-\)16 dB, (**b**) \(-\)12 dB, (**c**) \(-\)8 dB, (**d**) \(-\)4 dB, (**e**) 0 dB, (**f**) 4 dB, (**g**) 8 dB, (**h**) 12 dB. A—the partial enlargements of (**e**); B—the partial enlargements of (**f**).
## 4 Results
### Pontotoc, Mississippi  
UAV remote sensing was conducted in 2017, 2018, and 2019 for cover crop studies at Pontotoc, Mississippi. Among the studies, a soybean field of 14K m\({}^{2}\) in the research farm area was focused on with UAV remote sensing. For the soybean field study, in 2017, five days were successful for flyovers, 24 July, 8 August, 17 August, 5 September, and 18 September. In 2018, nine days were successful, 1 May, 22 May, 20 June, 3 July, 10 July, 16 July, 1 August, 8 August, and 22 August. In 2019, four days were successful, 27 March, 11 June, 6 August, and 21 August. Figure 9 shows the UAV NDVI images from 2018 of the soybean field in the research farm area at Pontotoc, Mississippi, calculated from the acquired multispectral images. These time series of NDVI images indicate the phenological difference of the soybean planted one month apart in two different field plot areas with overall highest vegetation vigor indicated on 3 July.  
Figure 10 shows the multispectral imagery of Landsat 8, Sentinel 2A, and PlanetScope over the research farm area at Pontotoc, Mississippi in 2018. Similar to the case at Brooksville, Mississippi, Landsat 8 imagery is limited for cloudless data, as shown in the figure, and the 30 m spatial resolution of the images is not sufficient to clearly delineate the boundary of the field. The imagery of Sentinel-2 is more available for cloudless data, and with the 10 m spatial resolution, the Sentinel-2 images can clearly delineate the boundaries of the fields within the farm area, but not the rows within the fields. Additionally, there are even more PlanetScope images available to clearly cover the fields in the research farm area, and with 3 m spatial resolution, the images can delineate the boundaries of the fields and identify features and variations over the fields, although the rows still cannot be differentiated within the field, which may be good enough to discriminate the plot treatments of the experiment in conjunction to the high-resolution UAV images. In the concerned soybean field area, a study has been conducted with soybeans planted one month apart in  
Figure 8: Satellite multispectral imagery of the research farm area at Brooksville, Mississippi: Landsat 8 (16 June 2018), Sentinel 2A (12 July 2018), and PlanetScope 4-band (4 July 2018).  
two different field plot areas with the treatments of five cover crops, three fertilizers, and four replications for two different planting dates.  
Figure 9: UAV NDVI images of the soybean field in the research farm area at Pontotoc, Mississippi in 2018.
###### Abstract  
In this research paper, the acoustic emission technique and a deep learning framework based on two types of pre-trained CNN models (alexNet and squeezeNet) and a new model are proposed to characterize and classify the mechanical behavior of AlSi10 Mg components. Specimens are built in a Selective Laser Melting machine with different bed orientations along X, Y, Z, and 45 degrees. Tensile tests are performed, and AE signals are recorded from these tests. To characterize the elastic and plastic deformation stages, a time-frequency domain analysis was performed using CWT-based spectrograms. Three different categories of damage classification strategies were implemented, and CNN models were trained for each strategy. CNN models including AlexNet, SqueezeNet, and the new model were used. Several training modes were performed to determine the CNN model that can accurately classify AE data. Understanding the minimum set of AE signals needed to train the CNN while having 100% accuracy and understanding the parameters affecting the accuracy of a CNN and the training time for the efficient classification of AE signals are the main objectives of this work. The results obtained demonstrated that the new simplified CNN model proposed can accurately classify the AE signals in a short time compared to AlexNet and SqueezeNet.  
AlSi10 Mg; SLM; acoustic emission; CWT; deep learning; CNN Article
## 3 Proposed Model
### Optimal Feature Selection
#### 3.5.1 Hybrid Feature Selection Model  
The feature selection algorithm consists of two parts. The first part is a filter-based approach. It uses sequential forward floating search (SFFS) where the feature subset evaluation process does not depend on the classification algorithm. The second part is a wrapper-based approach. This part takes the feature sets derived from the filter-based approach as input and evaluates the best feature sets among them.  
The whole process of optimal feature selection is shown in Figure 6. The filter-based feature analysis is an iterative approach that is conducted by SFFS. Initially, 25 users' first and second datasets are applied for the training and validation respectively. The dataset is again randomly splitted into two equal parts for the training and testing process. The first equal part is then again divided into two equal parts to use in the filter-based approach. For testing in the wrapper approach, the second equal part is used. SFFS is applied in the first equal part where the process iterates five times and brings out the 10 sub-optimal feature sets. This process is repeated for the rest of the sensor data. Then the selected optimal feature sets are forwarded to the wrapper method. The wrapper method analyzes different optimal feature sets by using different machine learning algorithms to evaluate the best features.  
Figure 6: Block diagram of the optimal feature selection process.  
```
Input: The set of all features \(X=\{x_{1},x_{2},\ldots,x_{n}\}\) Output: A subset of features \(Y=\{y_{i}\,|\,i=1,2,3,\ldots,n;\,y_{i}\,e\,X\}\) Where, \(n=\{0,1,2,\ldots,m\}\) Steps: 1. \(X_{0}=\{\varnothing\}\) 2. Select the best feature \(Y^{+}\) Update: \(X_{N+1}=X_{N}+Y^{+}=+1\) 3. Select the best feature \(Y^{-}\) 4. If \(I(X_{N}-Y^{-})>I(X_{N});I(Y)=criterion function] Then, \(X_{N+1}=X_{N}-Y^{-};N=N+1\) Go to step 3.
```  
**Algorithm 1****SFS Algorithm**  
For training and testing of the second equal part of the dataset, the SVM algorithm is applied in the wrapper-based approach. Then the 10 sub-optimal features which are obtained from the filter-based approach are forwarded to the SVM machine learning algorithm to find the best feature sets in the wrapper approach. To show the stability of our proposed model of writer identification, we have done the feature selection procedure and the classification process using two additional machine learning algorithms as logistic regression (LR) and random forest (RF) classifier. For that, we repeated the above procedure for feature selection with LR and RF, and also the evaluation is done using the same classifier algorithm. From the experimental evaluation, it is observed that the performance of our proposed model with LR and RF is also satisfactory which makes our claim stronger. However, among the 3 machine learning algorithms (SVM, LR, and RF), SVM gives us the best feature sets from the 10 optimal feature sets in terms of classification accuracy.
## 1 Introduction  
The burning of fossil fuel is a primary reason for the increase of greenhouse gas CO\({}_{2}\) concentration in the atmosphere and the exacerbation of global warming. To reduce CO\({}_{2}\) emissions, a mitigation strategy that consists of CO\({}_{2}\) capture and sequestration (CCS) is employed. Currently, three types of geological formations that have received extensive consideration for the geological sequestration of CO\({}_{2}\) are deep saline formations, oil and gas reservoirs, and deep unmineable coal seams [1; 2; 3; 4]. CO\({}_{2}\) Enhanced Coalbed Methane recovery (CO\({}_{2}\)-ECBM) is a technique that combines CO\({}_{2}\) sequestration and enhanced coalbed methane recovery. The implementation of this technique and its commercial value have been studied via different projects over the past 20 years [5; 6].  
According to the results of previous studies [7; 8], coal structure is characterized as a dual pore system that includes the primary porosity consisting of microspores, transitional pores, and mesopores in the coal matrix, and the second porosity composed of non-uniformly distributed macropores and microfractures. During the process of CO\({}_{2}\)-ECBM, CO\({}_{2}\) is distributed into the coal seam through the coal clear system and then stored within the coal matrix due to the stronger affinity between CO\({}_{2}\) and the coal substance [9; 10; 11]. Previous studies have also shown that there are various physical and geochemical interactions between CO\({}_{2}\) and coals, such as gas adsorption on the coal internal surface [9; 10; 11; 12; 13; 14], coal matrix swelling due to gas adsorption [15; 16; 17], and CO\({}_{2}\) reaction with minerals [18; 19; 20].  
In most cases, the depths of the coal seams used for CO\({}_{2}\)-ECBM are greater than 800 m, and the temperature and the pressure of coal seams are greater than the supercritical state of CO\({}_{2}\) (\(T_{\rm c}\) = 31.1 \({}^{\circ}\)C, \(P_{\rm c}\) = 7.38 MPa). Hence, CO\({}_{2}\) is injected into coal seams in its supercritical state. At this stage, Sc-CO\({}_{2}\) fluid can extract small organic matters from the coal matrix to make the coal properties change. The components of organic matters of Sc-CO\({}_{2}\) extraction have been tested from geochemistry [21; 22]; however, few studies have focus on examining the effect of supercritical CO\({}_{2}\) (Sc-CO\({}_{2}\)) extraction on the physical structure change of coal and its influence on the CO\({}_{2}\) sequestration in coal seams. Therefore, in this work, we focus on changes in the pore structure and the connectivity of five undeformed coals before and after the second coalification jump when they are exposed to Sc-CO\({}_{2}\) environments that are typical conditions of CO\({}_{2}\)-ECBM.
## 3 Permute-Scatter-Gather Implementation
### Implementation
#### 3.3.1 Challenging Issue  
As described above, we can easily integrate the Permute-SG technique into the OpenSSL library, thus adaptability, one of our design goals, is trivially achieved. However, it is not trivial to achieve the other two design goals together when implementing the technique. That is, evaluating P with an index idx is a time consuming operation and it occurs at every scatter and gather step. This may lead to the significant performance degradation. The optimal solution is to implement the evaluation procedure using a permutation table. By looking up the table with idx, the value of P(idx) can be retrieved just within a few CPU cycles. For security perspective, however, the lookup operation with the permutation table is subject to the fine-grained cache attack. This is because the memory access to the table reveals the index of the used multiplier during exponentiation. Therefore, implementing the permutation with regard to efficiency and security is a challenging problem.
## 2 Materials and Methods
### Validation of the Modelling Tool
#### 2.3.2 Results of the Comparison Trials  
Figure 2C shows the DA values obtained from virtual model simulation and test cell measurements, both for 100 and 500 lx illuminance thresholds. The highest maximum deviations between DA values from simulations and measurements are of 7.1% and 7.4% with the 100 and 500 lx thresholds, with divergences under 10% in both cases. The bias error values for DA\({}_{100\mathrm{lx}}\) and DA\({}_{500\mathrm{lx}}\) are 5.42% and 3.08% respectively, while the standard deviations (95% reliability) are 2.55% for 100 lx and 8.03% for 500 lx, which are below the 10% of deviation and therefore acceptable.  
These results, as well those previously published [8; 24; 38], show that DIVA-for-Rhino can calculate DA dynamic metric with accurate results for indoor spaces with similar size and boundary conditions, so it can provide a reliable calculation for DAp metric.  
Figure 2: (**A**) Size of the test cell and distribution of illuminance-meters—(**B**) Inner view of the test cell—(**C**) DA results calculated both from illuminance measurements and simulations, including Relative Difference (RD) between them.