esa-sceva/satcom-chunk-collection huggingface dataset:
# A Deep Learning Approach for MIMO-NOMA Downlink Signal Detection
## 5 Conclusions  
The application of deep learning in MIMO-NOMA communication systems is a promising approach to address the shortcomings of the SIC method. Instead of the complicated algorithm design and interference cancellation process, the deep learning approach can search for the optimal solution of the hyperparameters of the multilayer neural network with machine learning.  
In this paper, we designed an MIMO-NOMA-DL signal-detection system to perform signal recovery. The proposed technique can simultaneously complete the processes of channel estimation and MIMO-NOMA signal detection. The detailed construction and learning algorithm have been provided. We first compared the SER performance of the proposed method and the SIC algorithm via simulations. The highest performance gain reached 3.6 dB. Then, the impact of the crucial parameters, including the modulation type and power allocation, were studied. Numerical results showed that the MIMO-NOMA-DL method had powerful detection performance. Finally, mini-batch gradient descent simulations were conducted to accelerate the training step of the MIMO-NOMA-DL algorithm. The results indicate that the mini-batch size is a key parameter for balancing the convergence speed and loss precision.  
Future works will explore the DL-based approach to detect other types of NOMA signals, such as the sparse code multiple access (SCMA), multi-user shared access (MUSA), and pattern-division multiple access (PDMA). Moreover, we also consider an extension assessing the performance under different channel situations and the multiple clusters situation. Additionally, detecting the communication signal with memory using RNNs will be explored. CNNs, another advanced DL approach, could be deeply developed in terms of their potential in signal detection as our following work.  
Conceptualization, C.L. and Q.C.; methodology, C.L.; software, C.L.; validation, Q.C. and X.L; formal analysis, C.L. and Q.C.; investigation, C.L.; resources, C.L. and Q.C.; data curation, C.L.; writing--original draft preparation, C.L.; writing--review and editing, C.L. and Q.C.; visualization, C.L.; supervision, Q.C.; project administration, Q.C.; funding acquisition, Q.C., C.L., and X.L.  
This research was funded by the National Natural Science Foundation of China, grant number 61471021.  
Figure 15: Loss function for different mini-batch sizes in the MIMO-NOMA-DL method.  
**Acknowledgments:** We would like to thank [PERSON] for his significant discuss during this research. In addition, we thank [PERSON] for his direction on mathematical calculation.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 6 Experimental Results
### Experiment A  
The initial experiment considered the original dataset with GBM against the other algorithms used in the benchmark, with and without the PCA and FS reduction. Table 2 tabulates the best outcomes for Dataset A, i.e., the original set of the Greek tokens only. These scores correspond to the PCA transformation, keeping 30% of the original variance, i.e., approximately 250 principal components. The best numbers per performance metric are shown in boldface.  
As observed, the original set displays quite satisfactory results using almost any algorithm. GBM is the most powerful method, outperforming all other methods in precision and recall of both classes, followed by SVM. GBM was found to outperform all others in all metrics by a significant difference that reached almost 12% when compared to deep learning. For reasons of space, we have not included the correspondent table when utilizing feature selection; however, we must note that FS was quite close to PCA and, thus, not proficient at significantly improving the performance of the classifiers. When incorporating FS, the results were slightly worse than the ones found in Table 2 by a varying factor of 1.5% to 6%, for various top-k settings. Table 3 presents the optimal parameters of the classification algorithms for the aforementioned results of Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset A: Original** & **Accuracy** & **Precision (+)** & **Recall (+)** & **Precision (-)** & **Recall (-)** \\ \hline Decision Trees & 68.42\% & 59.12\% & 62.86\% & 75.76\% & 79.54\% \\ SVM & 77.82\% & 80.10\% & 67.60\% & 79.20\% & 87.40\% \\ Naive Bayes & 69.12\% & 58.40\% & 64.30\% & 76.30\% & 72.50\% \\ Deep Learning & 72.00\% & 67.50\% & 74.80\% & 82.40\% & 78.00\% \\ GBM & **84.20\%** & **82.40\%** & **77.90\%** & **86.30\%** & **88.30\%** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of GBM and other benchmarking methods for Dataset A: original. GBM, Gradient Boosting Machine.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Parameters** \\ \hline Decision Trees & Criterion: Gini Index; Maximal Depth: 60; Confidence: 0.3; Minimal Leaf Size: 12 \\ SVM & Kernel Type: polynomial; Degree: 3; C: 1.35; Epsilon: 10\({}^{-4}\) \\ Naive Bayes & _No parameters to optimize_ \\ Deep Learning & Activation: Tanh; Hidden Layers: 3; Loss Function: Quadratic; Epochs: 30; \\ GBM & Loss Function: Ada-boost; Number of Trees: 120; Max-depth: 4; Learning rate: 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter values, upon the completion of the parameter tuning process for Experiment A.
# Energy Concepts and Critical Plane for Fatigue Assessment of Ti-6 Al-4V Notched Specimens
## 2 Theoretical Framework of Strain-Based Multiaxial Fatigue Criterion
### Step I: Verification Point Position  
The fatigue lifetime is computed at point \(P\) which is on the notch bisector at a certain distance, \(r\), from the notch surface [25; 26] (Figure 1). The expression of the above distance is obtained by means of a best fitting procedure (details are provided in Reference [24]) and is given by [22]:  
\[r=\ -(0.221)^{\lambda-1.484}\cdot R_{m}\ +\ 11.3R_{m} \tag{1}\]  
where \(\lambda\) is the ratio between the amplitude of the remote shear stress and the amplitude of the remote normal stress (named biaxiality ratio) and \(R_{m}\) is computed as the mean value of \(R_{1}\) (control volume radius under Mode I loading) and \(R_{3}\) (control volume radius under Mode III loading). The last ones are calculated by the SED criterion, and are functions of notch stress intensity factor (NSIF) ranges (\(\Delta K_{1A}\), \(\Delta K_{3A}\)), high-cycle fatigue strengths of smooth specimens (\(\Delta\sigma_{1A}\), \(\Delta\tau_{3A}\)) and the notch geometry [33]. More precisely, the above radii are computed according to the following equations [33]:  
\[R_{1}=\left(\sqrt{2\mathrm{e}_{1}}\cdot\frac{\Delta K_{1A}}{\Delta\sigma_{1A}}\right) ^{\frac{1}{1-\lambda_{1}}} \tag{2a}\]  
\[R_{3}=\left(\sqrt{\frac{\mathrm{e}_{3}}{1+\
u_{e}}}\cdot\frac{\Delta K_{3A}}{ \Delta\tau_{3A}}\right)^{\frac{1}{1-\lambda_{3}}} \tag{2b}\]where \(\rm e_{1}\) and \(\rm e_{3}\) are two parameters depending on the V-notch geometry, \(v_{e}\) is the elastic Poisson ratio, and \(\lambda_{1}\) and \(\lambda_{3}\) are the eigenvalues for Mode I and Mode III, respectively, calculated by means of finite element analysis, as is discussed in Reference [33].  
A finite element model is employed in order to numerically compute the strain state at verification point \(P\)[24]. In particular, linear transient dynamic analyses are performed on notched specimens through the Commercial Package Strauss7(r)[34], by adopting both 6- and 8-node tridimensional finite elements. Only one half of each specimen is modeled, taking advantage of the geometric symmetry. Moreover, the adopted discretization is shown in Figure 2, where the finite element mesh is that adopted after a convergence analysis, being the minimum finite element size equal to about 0.25 times the value of the notch root radius.
# Improving Robotic Bin-Picking Performances through Human-Robot Collaboration
## 5 Improving a Bin-Picking Workcell through Human-Robot Collaboration
### Human-Robot Collaboration in Bin-Picking Failures
#### 5.5.2 Human-Robot Collaboration during Object Detection Failures  
Due to the reflectance properties of hexagon nuts, with changes in lightening conditions, perception failures may also occur. An example of perception failure is depicted in Figure 8a: the circled nut is not recognized, although it must be picked before the green nut underneath (which, instead, has been correctly recognized). In Figure 8a also, a magnification of the acquired 3D point map is reported (3D points are represented in light blue): it can be seen that the map has too few points, and therefore the computer vision algorithms cannot recognize the part. In this situation, the robot seeks help by turning on the red light installed on the robot arm. The human operator then proceeds to manually move the robot above the grasping pose, exploiting the manual guided teaching function. Once this is done, the operator notices the robot that the fault has been cleared. This is also done by simply touching the robot.  
Figure 6: Setup of collision avoidance through _Fanuc Interference Avoidance_.
# A Method for Translating Automotive Body-Related CAN Messages Based on Labeled Bits
## 3 Methodology
### Data Collection  
In the data collection phase, the approach described in this paper uses a combo cable to acquire the CAN traces of the target vehicle, as shown in Figure 3a. This combo cable consists of an OBD-II to DB9 diagnostic cable and a PCAN-USB FD adapter [28]. As shown in Figure 3b, the cable connects the vehicle's OBD-II port to a USB port on the computer side, which allows the computer to read and write CAN bus information to enable the experiment to proceed. It is not necessary to use the entire CAN message in our approach. Only the message ID, data segment, and timestamp must be captured. In this phase, the raw data include three data sets: the stationary (\(\Omega_{s}\)), the motion (\(\Omega_{m}\)), and the body operation (\(\Omega_{b}\)) data sets. To avoid blindness when collecting data, we previously analyzed a portion of the available DBC files and summarized the possible car body behavior described by CAN messages. The results are shown in Table 1.  
Figure 3: Data collection equipment and environment. (**a**) Combination cable for data collection; (**b**) In-vehicle data collection environment.  
Figure 2: Overall flow of the translating method.  
* \(\Omega_{s}\) records the CAN traces without operation when the car is stationary after ignition;
* \(\Omega_{m}\) contains the CAN messages of the driver during everyday driving of the car on a regular road;
* \(\Omega_{b}\) records CAN traffic during the execution of body operations in place after the car is started. The operations are shown in Table 1.
# Two-Stage Intelligent Model for Detecting Malicious DDoS Behavior
## 6 Conclusions  
For DDoS attacks caused by 5G, we proposed a two-stage intelligent detection DDoS attack mechanism that combines similarity-based prior knowledge and CNN-based attention to detect multiple types of DDoS attacks. We simulated a variety of attacks and benign traffic in the virtual platform, constructed a DDoS dataset, and applied the detection method to this dataset. The experiments showed that the similarity detection method can distinguish between benign and abnormal traffic. The CNN-based attention model deeply learns packet payload and header statistical features. It can effectively detect multiple types of DDoS attacks. Furthermore, we defined a metric including malicious detection capability. Compared with existing statistical and neural network methods, the proposed model has the shortest response time, the highest detection rate, and an adequate malicious traffic detection capability.  
However, the detection performance for specific types is insufficient. In the future, the following four research directions are worth exploring:  
1. We will select representative features for each attack type, establish the neural network model suitable for this attack type, and improve the multi-classification performance by optimizing parameters.
2. To verify the effectiveness of the generated self-generated dataset, we must further verify the effectiveness of the detection method on the existing benchmark dataset.
3. We will introduce research on the interpretability of the deep learning model, and visualize the neural network training process. Furthermore, we will analyze the relationship between model weights and detection results.
4. We will analyze the attention layer parameters and further adjust and optimize the model structure, which can enhance the model's detection capability.  
Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing--original draft preparation, visualization, M.L.; project administration, funding acquisition, H.Z.; writing--review and editing, M.L. and H.Z.; supervision, M.L., H.Z. and Y.Q. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Key R&D Program of China under Grant No. 2018 YFA0701604.  
Not applicable.  
The dataset created in this article can be found on github: [[https://github.com/liliMpro/source_dataset](https://github.com/liliMpro/source_dataset)]([https://github.com/liliMpro/source_dataset](https://github.com/liliMpro/source_dataset)) (accessed on 22 February 2022).  
The authors declare no conflict of interest.
# Tabular Data Models for Predicting Art Auction Results
## 3 Results
### K-Nearest Neighbors  
KNN performed significantly better than LinearModel, with its sMAPE scores ranging between 60.38 and 62.68% (presented in Figure 1), indicating that it struggled with the complexity of the auction datasets.  
KNN is a distance-based algorithm that predicts the target variable based on the average of the nearest neighbors' target values. It is defined as  
\[\hat{y}=\frac{1}{k}\sum_{i\in N_{k}(x)}y_{i} \tag{4}\]  
where \(N_{k}(x)\) represents the \(k\) nearest neighbors of \(x\).  
Figure 1: Bar chart comparing the sMAPE metric of KNN model across the datasets.  
We observe that KNN performed relatively consistently across the auction datasets, showing minor improvements when image-derived features were introduced. This indicates that dimensionality alone was not the key factor in KNN's underperformance compared to Random Forest. Auction datasets, characterized by volatility due to external market factors and the subjective nature of art pricing, often contain outliers, such as unusually high or low auction prices. KNN's reliance on simple averaging of the nearest neighbors makes it highly sensitive to these outliers, which leads to less accurate predictions.
# A Comparative Study on Traffic Modeling Techniques for Predicting and Simulating Traffic Behavior
## 4 Spatiotemporal Traffic Prediction Models
### Summary of Spatiotemporal Traffic Prediction Models
The output of the individual ANN predictors is combined to enhance the prediction accuracy using an adaptive updating scheme that allows the predictors to be dynamic. Moreover, this type of combined model can capture the non-stationary traffic characteristics, as it considers prediction at different time scales so that the predicted values can be applied to the congestion control schemes. This approach outperformed the parametric autoregressive (AR) model, as the combination of ANN predictors enhanced the prediction accuracy [52]. The use of ANNs overcomes many failingsrelated to traditional methods for the prediction of a congested freeway's traffic status, as most data prediction techniques highly depend on the accuracy of the stochastic processes governing the freeway [42]. The freeway modeling process is not mandatory for ANNs because the multilayer perceptron (MLP) type of ANN requires only an input training set along with appropriate outputs for prediction. As a result, this ANN architecture can be applied generally since it is not dependent on the particular geometry of a freeway section. Artificial neural networks are relatively insensitive to missing data for predicting traffic conditions and faulty data. In addition, ANNs can deal with nonlinear systems to handle highly dynamic traffic data. However, for traffic speed prediction problems, ANN models are time-consuming to train with high-dimensional data. Therefore, dimension reduction through proper feature selection would help to improve the modeling accuracy [53].
# Concrete Cracks Detection Based on FCN with Dilated Convolution
## 2 Related Work
### The Methods Based on Image Patch Classification  
The main idea of this kind of algorithm is to build an image classifier with excellent performance, which can classify image patches well. [PERSON] et al. [35] apply CNN to detect pavement cracks and use Principal Component Analysis (PCA) to analyze the skeleton of crack. [PERSON] et al. [36] proposed a deep learning framework called NB-CNN based on CNN and Naive Bayes data fusion scheme, which can analyze a single video frame and detect cracks. [PERSON] et al. [37] construct a CNN architecture with 8 layers and use pictures taken under uncontrolled conditions to train. This classifier has high precision in both training set and verification set. [PERSON] et al. [38] proposed an automatic detection technique for crack surface morphology of concrete surfaces based on CNN, which is evaluated by processing images taken in the field and real-time video frames taken by an unmanned aerial vehicle. Because of the automatic learning ability of CNN, [PERSON] et al. [4] constructed a deep CNN architecture to detect concrete cracks. The proposed method combined with sliding window can detect cracks on images with pixel size greater than \(256\times 256\). [PERSON] et al [39] proposed a two-stage crack detection method based on convolution neural network. Specifically, the predictor of small receptive field is used in the first detection stage, and a larger receptive field predictor is used in the second stage to improve the detection result. This kind of algorithm divides the image into patches by sliding window, and then classifies the image patches. The position of the sliding window is taken as the position of the crack. These patches often contain a large amount of non-crack information, so for the whole image, the obtained crack position is relatively rough.
## 4 Discussion  
In this paper, we proposed a new method for the inverse QSAR/QSPR to rank-2 chemical graphs by significantly enhancing the framework due to [PERSON] et al. (2018), [PERSON] et al. (2019), and [PERSON] et al. (2019), and implemented it for inferring rank-2 chemical graphs using the algorithms for enumerating rank-2 chemical graphs due to [PERSON] et al. (2015) and [PERSON] et al. (2016). From the results on some computational experiments, we observe that the proposed method runs efficiently for an instance with \(n^{*}\leq 30\) non-hydrogen atoms up to Step 4 and an instance with \(n^{*}\leq 15\) non-hydrogen atoms up to Step 5. Due to this development, the ratio of chemical compounds covered in the PubChem database increased from 16.26% to 44.5%. It is left as future work to apply our new method for the inverse QSAR/QSPR to a wider class of graphs. The ratio of the number of chemical graphs with rank at most 3 (resp., 4) to the number of all chemical graphs in database PubChem is 68.8% (resp., 84.7%). Among rank-4 chemical compounds, Remdesivir \(\text{C}_{27}\text{H}_{35}\text{N}_{6}\text{N}_{6}\text{N}\)P, an antiviral medication, which is being studied as a possible post-infection treatment for COVID-19, has a chemical graph \(G\) with \(\text{r}(G)=4\), \(n(G)=42\), \(\text{cs}(G)=24\), and \(\text{ch}(G)=8\). The number of polymer topologies with rank 3 (resp., 4) such that the maximum degree is at most 4 is 12 (resp., 73). Our MILP formulation can be easily extended to the case of rank 3 or 4 by replacing the current set of constraints for the scheme graph with a set of those for a new scheme graph that is designed for rank-3 or -4 polymer topologies.  
Conceptualization, H.N. and T.A.; methodology, H.N.; software, J.Z., C.W., and A.S.; validation, J.Z., C.W., A.S., and H.N.; formal analysis, H.N.; data resources, H.N. and T.A.; writing--original draft preparation, H.N.; writing--review and editing, T.A.; project administration, H.N.; funding acquisition, T.A. All authors have read and agreed to the published version of the manuscript.  
H.N. and T.A. were partially supported by the Japan Society for the Promotion of Science, Japan, under Grant #18H04113.  
The authors declare no conflict of interest.
# Design of a Noise Mitigation System Using Lightweight Graded Micro-Porous Material
## 1 Introduction
### Objective of This Work  
The overall objective of this work is to design, develop, and validate a micro-structurally controlled graded (varying pore sizes) micro-porous hollow bubble (\(\upmu\)PHB) composite material aimed at aircraft engine acoustic liners, measuring 50 mm in thickness. This material is intended to possess optimal acoustic properties, with an absorption coefficient \(\geq\)0.50 and transmission loss \(\geq\)20 dB across a broad range of sound frequencies exceeding 500 Hz, pertinent to aircraft engine environments.  
The specific objectives of this work include developing and establishing an inverse method for calculating flow properties, validating acoustic predictions (absorption coefficient and transmission loss) through experimentation involving polydisperse spherical beads (model materials); selecting lightweight Conospheres and pre-processing the particle-size distribution; the processing and fabrication of the \(\upmu\)PHB composite specimens using methods pre-established by the authors; conducting normal incidence impedance tests; the calculation of the flow properties of the \(\upmu\)PHB composite material using the introduced inverse method in acoustic predictive models in MATELYS AlphaCell; the validation of the acoustic response predictions by a normal incidence impedance test; and then the design, development, and validating of a graded micro-porous material that exhibits the desired combination of sound absorption and transmission loss.
## 3 Design Optimization
### \(h_{\rm t}\)  
When \(h_{\rm b}\) = 180 nm, \(h\) = 100 nm, Re(\(N_{\rm eff}\)) and Re(\(\Delta N_{\rm eff}\)), the MD and PL of two modes in the DBOSW modulator versus the top layer thickness \(h_{\rm t}\) are investigated. As shown in Figure 8a, Re(\(N_{\rm eff}\)) of the TM mode decreases with the increment of \(h_{\rm t}\) at \(\mu_{\rm c}\) = 0 and 1 eV. When \(h_{\rm t}\) is 110 nm, Re(\(N_{\rm eff}\)) of the complex mode exhibits a minimum of 1.6722 and 1.6469 at \(\mu_{\rm c}\) = 0 and 1 eV, respectively. It is noteworthy that both modes share the same Re(\(N_{\rm eff}\)) when \(h_{\rm t}\) is 200 nm and \(\mu_{\rm c}\) is 1 eV. This scenario also can be found when \(h_{\rm t}\) is 210 nm and \(\mu_{\rm c}\) is 0 eV. In Figure 8b, a maximum Re(\(\Delta N_{\rm eff}\)) is first presented with the increase of \(h_{\rm t}\) as \(\mu_{\rm c}\) = 0 and 1 eV. Then, a minimum Re(\(\Delta N_{\rm eff}\)) of 6 \(\times\) 10\({}^{-4}\) (\(\mu_{\rm c}\) = 1 eV) appears when \(h_{\rm t}\) is 200 nm. A minimum Re(\(\Delta N_{\rm eff}\)) of 1 \(\times\) 10\({}^{-4}\) (\(\mu_{\rm c}\) = 0 eV) can be observed when \(h_{\rm t}\) is 210 nm. The different behaviors of Re(\(N_{\rm eff}\)) and Re(\(\Delta N_{\rm eff}\)) in Figures 8a and 8b can be explained by the change of fringing field distribution as well as the variation in the slot region. This phenomenon is similar to what happens to the MIM waveguide [20]. As shown in Figure 8c, the PLs of both the TM mode and the complex mode decrease till \(h_{\rm t}\) rises up to 200 nm. As \(h_{\rm t}\) increases from 50 nm to 200 nm, the MD of the complex mode decreases by 57.1%, while the MD of the TM mode only reduces by 7.0%. Obviously, the change of \(h_{\rm t}\) has a more remarkable impact on the complex mode than that on the TM mode. This can be explained by the field distribution difference that can be observed from the illustration in Figure 8c. With comprehensive consideration, a balanced \(h_{\rm t}\) of 200 nm is chosen.