esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study of OWA Operators Learned in Convolutional Neural Networks
###### Abstract
In our previous work [13], we already showed how the OWA layer added new knowledge to a mid-size CNN (based on the VGG13 architecture) on a relatively large dataset. In this work, instead, we are interested in the specific operators learned, and we decide to choose a reduced architecture to facilitate the study. In particular, we employ a minimal two-layer CNN and train different configurations on a small image classification dataset, CIFAR100 [24]. This small dataset and the smaller network architecture enables us to do several repetitions of each configuration, letting us aggregate the final weighting matrices for the OWA layer to extract more meaningful conclusions. Although we acknowledge that considering an experimental framework with a single dataset is a limitation of this work, it also enables us to carry out an in-depth study on the characteristics of the OWAs learned, which would not be straightforward considering a variety of scenarios. The findings presented are meant to serve as a foundation for guiding further lines of work, where the OWA layer should be properly analyzed in larger networks and new datasets to corroborate the presented results. The remainder of this work is as follows. Section 2 introduces some preliminaries on CNNs and OWA operators. In Section 3, we present the literature relevant to our proposal. Section 4 defines our proposed layer architecture and its design. Then, Section 5 presents the experimental implementation that we have chosen and the specific experiments that we use to test the new layer. Section 6 analyzes the results of the experiments, including a detailed analysis of the OWA weighting matrices. Finally, Section 7 summarizes the conclusions of the work and proposes some future research work.
## 3 Results
### Simulation Results
time on average when another vehicle is traveling at a higher speed and a shorter distance, such as in situations 1 and 3 with a time to intersection (TTI) of less than 0.9 s or scenario 2 with a TTI of less than 1.3 s. The automobile can go ahead slowly before another vehicle \(j\) has completely past the intersection in scenario 5 with a smaller TTI, while in scenario 4 the car is even capable of moving after vehicle \(j\), being more aggressive than earlier planning algorithms and closer to an experienced human driver. In the first three scenarios, other vehicles travel in a straight line, while the middle three scenarios involve left turns. In scenario 6, the car behaves more cautiously than in scenarios 4 and 5, at least until vehicle \(j\) completely crosses the car's lane line, at which point the car begins to accelerate and passes the intersection. Comparing the behavioral decision model to the TTI method reveals that the former significantly increases passage efficiency while maintaining safety, with the latter showing relative improvements of 16.3%, 23.7%, 29.6%, 19.2%, 29.2%, 20.0%, 22.7%, 4.5%, and 20.9% for the nine aforementioned scenarios, respectively. The TTI method's comparatively straightforward risk estimate in the various unit scenarios resulted in comparatively mechanical behavioral choices. In order to ensure traffic safety and increase traffic efficiency, driver operational risk field models offer an edge over TTI method.
# Defining the Ideal Phenological Stage for Estimating Corn Yield Using Multispectral Images
## 3 Results
### Spatialization of the Estimated Variable  
Results of the maps generated as a function of the yield estimate for Areas 1 and 2, are shown in (Figure 8A,B), respectively.  
Points measured in the field (Figure 8A) and the result of the spatialization of the yield variable, which is calculated using the SVM model (Figure 8B) are shown. It is possible to identify the differences between the measured and estimated scenarios by analyzing the images. The primary difference is the number of values in the interval below 3000 kg, which is indicated in red. The second map presents a significant reduction in the area comprised in this interval. It is possible to verify the presence of vertical \"lines\" in both maps, which may be due to the difference of treatments used in the area.  
Figure 9 presents the normalized difference between the estimated and observed values for each study area.  
Figure 8: Spatial distribution of yield in the study area based on the data measured in the field (**A**) and spatial distribution of yield in the study area based on the SVM algorithm applied to the bands/indices (**B**) from the Planet satellite.  
The study area presents a greater difference in the lower portion of the plot and certain strips in the left and right extremities. The most significant proportion of values is located between the interval ranging from \(-0.5\) to \(-0.25\), with \(53.98\%\) of the areas centered in this class. These results corroborate those found in Table 5, although they are numerically different. In this context, the results are more quantitative and are closer to the values observed in the field.
## 2 Methods
### Thermal Health Monitoring  
To overcome the limitations of vibration-based thermography as a stationary ground-based system the shaker and the IR-camera can be replaced by PWAS and temperature sensors. For the practical application of this novel method in large sandwich structures, several options to monitor the temperature of the structure are feasible. Temperature monitoring can be realized by an array of local surface temperature sensors, temperature sensitive thin films or fiber optical sensor (FOS) [10; 11]. Nanocomposite thin films, such as carbon nanotube (CNT) thin films, which are very sensitive to temperature deviations, could be used. In addition, the combination of CNT thin films and the electrical impedance tomography (EIT) enables the thermal imaging of surfaces [29; 30]. The presented method would be generally applicable to curved structures typically used in aerospace applications. The array of local surface temperature sensors used in this experimental investigation, or the alternative sensors mentioned previously, would also be applicable to curved surfaces. In contrast, the PWAS used for excitation in this investigation are not suitable for mounting on highly curved surfaces, instead piezoceramic patch transducers that are flexible and bendable could be used in practical aerospace applications.
## 3 Characteristics of Passenger Tolerance to Approaching Pedestrians
### Collection and Analysis of a Novel Experiment Dataset
#### 3.2.3 Experimental Results  
It was clarified that two factors influence passenger tolerance, as shown in Figures 9 and 10. The relationship between the predicted passing distance \(h\) and the tolerance metric \(q\prime\) showed a similar trend with the previous study's dataset (Figure 9). A larger \(h\) allowed the passengers to tolerate the pedestrians invading their PS. By contrast, the relationship between curvature radius \(R\) and \(q\prime\), as illustrated in Figure 10, showed an opposite tendency. A larger \(R\) (i.e., a straighter pedestrian trajectory) makes the passenger tolerate the pedestrian's approach. This result suggests that, when pedestrians move in a straight line, it is easier for the passengers to judge their moving directions and predict their future trajectories relative to themselves. If the pedestrians are not predicted to come close, the concerns about potential collisions with them can be reduced.  
Based on Sections 3.1 and 3.2 analysis results, the characteristics of PMV passenger tolerance to approaching pedestrians in crowded pedestrian spaces can be summarized as follows:  
* When the predicted passing distance \(h\) is larger, the passenger tolerates the pedestrian's approach.
* When the vehicle does not perform active avoidance behavior, the passenger tolerates the pedestrian's approach when the curvature radius \(R\) is larger.
## 4 Systematic Experimental Process and Results Analysis
### Evaluation Metrics  
The evaluation metrics for this experiment are accuracy \(P\), recall \(R\), and \(F1\) value. The accuracy rate represents the ratio of the number of samples correctly classified to the total number of samples on the test dataset. Recall represents the ratio of the number of correct predictions to the number of all correct predictions in the test dataset. The \(F1\) value represents the summed average between \(P\) and \(R\). The formulae are shown in Equations (9)-(11):  
\[P=N^{\prime}/N \tag{9}\]  
\[R=N^{\prime}/M \tag{10}\]  
\[F1=\frac{(\alpha*\alpha+1)}{(P+R)\alpha*\alpha}P*R \tag{11}\]  
where \(N^{\prime}\) denotes the number of sentiment words correctly identified by the model, and \(N\) and \(M\) denote the number of sentiment words that are all predicted to be correct and the number of sentiment words that are all actually correct, respectively. The recall rate has more influence on the \(F1\) value when \(\alpha>1\) and the accuracy rate when \(0<\alpha<1\).
## 1 Introduction
Their numerical and experimental results demonstrated that the CP method achieved precise and reliable modal identification, and it could provide effective approximation to non-diagonalized high damping modes even in the case of close modes, as well as high damping modes excited by a nonstationary environment. In addition, generalized eigen decomposition is a simple BSS method based on second-order statistics [28; 29; 30]. Its basic idea is that the two correlation matrices of observed signals are composed of matrix bundles and decomposed into generalized eigenvector matrices; then, the transposition of generalized eigenvector matrices is exactly an estimation of the separation matrix. Compared with the ICA, SOBI, and CP algorithms, the GED algorithm does not need an iterative optimization calculation process, it can directly separate the source signal by solving a generalized eigenvalue problem. Thus, the GED algorithm has the benefits of rapid convergence, simplicity, and easy implementation. However, at present, these two algorithms have not been widely used; the CP algorithm has only been applied to assess the modal parameters of a basic steel frame structure, and the GED algorithm is still in the theoretical research stage. Therefore, considering the complexity of civil structures and their operation environment, utilizing the CP and GED algorithms as effective tools for modal identification of large civil structures remains a challenge. This paper investigates the evaluation of system identification algorithms for modal parameter identification of civil structures (a suspension bridge and an overhead transmission line-crossing frame) based on ambient vibration measurement. Taking into account that the measured response is only an ambient response, which is also a random vibration signal, the signals separated by the CP and GED algorithms cannot be directly used to evaluate the damping ratios of the structures. For solving this problem, the random decrement technique (RDT) and Hilbert transform (HT) are introduced into the BSS algorithms; then, a joint system identification algorithm that relies upon output measurements is obtained. This paper is structured as follows: Section 2 describes two novel BSS learning rules termed complexity pursuit and generalized eigen decomposition, intending to realize the modal parameter identification of structures; Section 3 demonstrates the ability of two proposed algorithms, CP and GED, for output-only modal identification, through using the simulation data of a suspension bridge mock-up; Section 4 focuses on verifying the effectiveness of the mentioned methods under a laboratory test environment; Section 5 presents the application of the two BSS methods in an overhead transmission line-crossing frame; lastly, Section 6 summarizes the findings and conclusions of the study.
# Enhanced Dual Reversible Data Hiding Using Combined Approaches
## 2 Preliminaries
### Examples  
The process of hiding data for the block given in Equation (12) is briefly explained with an example (Figure 2). Assume the secret data \(m=\{1\ 0\ 0\ 1\ 0\ 0\ \ldots\ 0\ 1\ 1\}\).  
\[B_{i}^{1}=B_{2}^{2}=\begin{bmatrix}162&162&161&\cdots&155\\ 162&162&161&\cdots&155\\ 162&162&161&\cdots&155\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ 159&159&160&\cdots&157\\ \end{bmatrix} \tag{12}\]  
1. The values \(x_{j}^{1}\) and \(x_{j}^{2}\) read from the first block (Figure 2) are both assigned as 162. Using these values, the codeword \(P\) is constructed first. The binary representation of pixels \(x_{j}^{1}\) and \(x_{j}^{2}\) is \((10100010)\), and the codeword \(P\) is generated by concatenating the upper 4 bits of \(x_{j}^{1}\) with the upper 3 bits of \(x_{j}^{2}\); that is, \(P=(b_{j,7}^{1},b_{j,6}^{1},b_{j,8}^{1},b_{j,4}^{1},b_{j,7}^{2},b_{j,6}^{2},b_{j,5}^{2})\) = \((1010101)\).
2. For the constructed codeword \(P\), calculate the syndrome of the HC, i.e., \(S=H\cdot P^{T}=(0\ 0\ 1)\). Here, \(H\) is the HC checker matrix. Perform an XOR operation between this syndrome \(S\) and the secret bit \(m=(1\ 0\ 0)\) to calculate a new syndrome, i.e., \(S^{\prime}=S\oplus m_{k}=(0\ 0\ 1)\oplus(1\ 0\ 0)=(1\ 0\ 1)\). Add the syndrome \(S^{\prime}\) to the array \(M\); that is, \(M(j:j+2)=(101),j=j+3\).
3. After completing steps (1) and (2) for all the pixels in the block, the array \(M\) will contain data hiding information \(16\times 16\times 3\) in size. Since the amount of information is too large to be hidden in the LSB without degrading the image quality, this data are compressed using arithmetic coding; that is, \(D_{comp}=A(M)\), and the compressed data are hidden in \(D_{comp}\), with their length being \(L\) (Figure 2). For example, if \(M=\{101\ldots 100\}\), the compression result is \(D_{comp}=\{0\underline{0}11\ldots 1100\}\) and \(L=64\).
4. Hide \(D_{comp}\) in the LSBs of the pixels that make up blocks \(B_{i}^{1}\) and \(B_{i}^{2}\). In this process, data hiding is performed by applying Equations (5) and (6). Specifically, since \(x_{i}^{1}=162\) and \(x_{i}^{2}=162\), we have \(f=(162+162\times 2)\bmod 5=1\). Therefore,  
Figure 2: Example illustration of the proposed scheme.  
\(pos_{j}=d_{k}-f=0-1=-1=4\). According to Equation (6), the two pixels \(x_{i}^{1}\) and \(x_{i}^{2}\) become 163 and 161, respectively (Figure 2(b)).
5. Data hiding is completed by applying the process iteratively to the pixels within the block for the length \(L\). Subsequently, the next blocks are read in order, and the same process is repeated. As a result, two cover images, \(I_{1}^{\prime}\) and \(I_{2}^{\prime}\), are produced. The data extraction process can be performed by reversing these steps.
## 4 Results and Discussion
### Performance Comparison  
In this paper, the prediction accuracy of the proposed method is verified by comparing the recognition accuracy with several state-of-the-art supervised methods, including BP neural network, Bayesian network and support vector machine (SVM). The verification is achieved based on the dataset of Test II. In order to realize quantitative analysis of prediction accuracy, the accuracy [29] of different models is calculated by comparing the predicted tool wear states with the actual tool wear states. As it is known that the training process is necessary in supervised algorithms, to compare the experimental results more objectively, three groups of data under cutting parameters A, B and C in test II are utilized as the training data, respectively. Correspondingly, the other two groups of data are taken as the test data. For each of the above supervised methods, optimum parameters are selected after many tests. The obtained results are shown in Tables 5-7, while the unsupervised method proposed in this paper can directly obtain the prediction results without any training, and the prediction accuracy is shown in Table 8. It can be seen that the proposed method outperforms the state-of-the-art supervised methods. For each test dataset in Tables 5-7, prediction accuracy varies with the training dataset, demonstrating that supervised monitoring methods are unstable with respect to cutting parameters. In addition, the prediction accuracy of our method for Test I and Test III is 94% and 100%, respectively. However, it is evident that worse results would be obtained by supervised methods, when taking the data of Test II as the training dataset to process the data of Test I or Test III.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cutting** & **VB (mm)** & **The Actual Tool** & **Clustering** & **Cluster** & **War** & **The Predicted Tool** \\
**Number** & **VB (mm)** & **The Actual Tool** & **Clearing** & **Label** & **Density** & **War Condition** \\ \hline NO. 1 & 0 & Initial wear & I & 12.25 & 0 & Initial wear \\ NO. 2 & 0.06 & Initial wear & II & 8.08 & 0.09 & Initial wear \\ NO. 3 & 0.28 & Middle wear & III & 5.90 & 0.19 & Middle wear \\ NO. 4 & 0.29 & Middle wear & III & 5.90 & 0.19 & Middle wear \\ NO. 5 & 0.34 & Severe wear & IV & 4.27 & 0.34 & Severe wear \\ \hline \hline \end{tabular}
\end{table}
Table 4: Tool wear condition monitoring results of Test III.  
Figure 18: Clustering results of Test III.
## 5 Performance Evaluation
### Evaluation Metrics  
We have used the following metrics to evaluate our detector and assess the vulnerability to adversarial evasion attacks.  
* Accuracy (\(ACC\)) represents the percentage of the test samples accurately classified by the detector to the total number of samples in the test dataset. It is calculated as follows: \[ACC(\%)=\frac{TP+TN}{TP+TN+FP+FN}\times 100.\] (7)
* False Alarm (\(FA\)), known also as the false positive rate (\(FPR\)), represents the percentage of the false positive samples out of the total number of negative samples. It is calculated as follows: \[FA(\%)=\frac{FP}{FP+TN}\times 100.\] (8)
* Detection Rate (\(DR\)), known also as true positive rate (\(TPR\)) and recall, represents the percentage of the true positive samples out of the total number of positive samples. It is calculated as follows: \[DR(\%)=\frac{TP}{TP+FN}\times 100.\] (9)
* Precision (\(PR\)) represents the percentage of true positive samples out of the total number of samples identified by the detector as positive. It is calculated as follows: \[PR(\%)=\frac{TP}{TP+FP}\times 100.\] (10)
* F1-score (\(F1\)) represents a statistical measure of both the precision and the detection rate. It is calculated as follows: \[F1(\%)=\frac{2*PR*DR}{PR+DR}\times 100.\] (11)
* Highest difference (\(HD\))represents the difference between the detection rate and the false alarm. It is calculated as follows: \[HD(\%)=DR(\%)-FA(\%).\] (12)  
Equations (7)-(12) are derived from the values within the confusion matrix (\(TP\), \(TN\), \(FN\), and \(FP\)). \(TP\) represents the count of correctly classified malicious samples, known as true positive. \(TN\) is the count of correctly classified benign samples, known as true  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**DAE** & **FFNN** & **CNN** \\ \hline Input, 48, Linear & Input, 48, Linear & Input, 48, Linear \\ Dense, 200, Tanh/ReLU & Dense, 96, Linear & Conv1D, 128, ReLU \\ Dense, 100, Tanh/ReLU & Dense, 192, ReLU & Dense, 256, ReLU \\ Dense, 50, Tanh/ReLU & Dense, 387, ReLU & Dense, 128, ReLU \\ Dense, 32, Tanh/ReLU & Dense, 768, ReLU & Dense, 64, Sigmoid \\ Dense, 50, Tanh/ReLU & Dense, 192, ReLU & Output, 2, Softmax \\ Dense, 100, Tanh/ReLU & Dense, 200, ReLU & \\ Dense, 200, Tanh/ReLU & Output, 2, Softmax & \\ Output, 48, Linear & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The optimal hyper-parameters for the DAE defense detector and the FFNN and CNN attacker detectors.  
negative. \(FN\) is the count of malicious samples incorrectly classified as benign, known as false negative. \(FP\) is the count of benign samples incorrectly classified as malicious, known as false positive.
# Spot Market Cloud Orchestration Using Task-Based Redundancy and Dynamic Costing
## 4 Our Proposed Approach
### Quantifying the Cost of Failure  
The task of achieving a reliable system is, in its essence, a trade-off between the cost of redundancy and the expected cost of failure. We define the expected cost of failure as the financial cost of a catastrophic system failure multiplied by the probability of that failure occurring. The addition of redundant components increases the cost of production (or usage, in the cloud context) while decreasing the probability of the failure occurring.  
Applied to our proposed model in Section 3, we can define the expected cost of failure with respect to some task \(T\) before time \(t\), \(E_{T}(t)\), in terms of \(\Omega\), the financial cost of failure, and \(F_{T}(t)\), the failure function of task \(T\)[6].  
\[E_{T}(t)=\Omega F_{T}(t) \tag{7}\]  
In closed systems, particularly electronic and physical systems, changing the level of component redundancy is often not possible or feasible after production (e.g., adding an additional engine to an existing airplane). Thus, in the design phase, a particular level of reliability is determined, possibly by contract stipulation, and the system is designed accordingly. Should some externality arise that varies the probability of failure, or indeed the cost of a catastrophic failure, it is not possible to take advantage of the change or mitigate it, as the case may be, by changing the level of redundancy.  
However, reconfiguration of software systems post-deployment is much more feasible, with stateless cloud microservice containers being particularly suitable for this. Supposing the probability of failure to remain unchanged, if the cost of a catastrophic failure can be quantified at a reasonable frequency, the cloud system can be dynamically reconfigured to add or remove stateless microservice containers, keeping the expected cost of failure to an acceptable level and reducing the cost of idle redundancy.  
In [6], we proposed a cloud orchestrator that added new microservice containers to the cloud until it reached an optimum configuration where the marginal cost of adding a new microservice container outweighed the benefit to the expected cost of failure. We showed empirically that this approach achieved a lower overall cost (the sum of the running cost and cost of catastrophic failures) than a fixed reliability approach [6]. However, this assumed a fixed model in which both the cost of catastrophic failure and the marginal cost of adding another microservice were constant. In this research, we propose a dynamic SM model based on free market forces which varies both these parameters.