esa-sceva/satcom-chunk-collection huggingface dataset:
# LogEDL: Log Anomaly Detection via Evidential Deep Learning
## 5 Experiment and Analysis
### Results and Analysis
#### 5.2.2 Ablation Studies  
The ablation experiments are shown in Table 5. Across all three datasets, the LogEDL model optimized with the ECE loss function demonstrates the best performance. ECE outperforms other methods regarding _precision_, _recall_, and \(F1\)_score_. Notably, on the BGL dataset, its _precision_ reaches an almost perfect 99.84%. Compared to LogBERT, the LogEDL model shows improvements in all cases, especially when using the \(ECE\) loss function, resulting in significant enhancements. Therefore, for similar log anomaly detection tasks, it is recommended to use the LogEDL model optimized with the evidence cross-entropy loss function to achieve the best performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Flops** & **Parameters** & **Time (s)** \\ \hline PCA & - & - & \(2.6\times 10^{-6}\) \\ iForest & - & - & \(2.3\times 10^{-6}\) \\ OCSVM & - & - & \(1.2\times 10^{-2}\) \\ LogCluster & - & - & \(3.2\times 10^{-5}\) \\ DeepLog & \(1.0\) M & \(0.1\) M & \(3.8\times 10^{-4}\) \\ LogAnomaly & \(2.0\) M & \(0.1\) M & \(1.3\times 10^{-3}\) \\ LogBERT & \(628.6\) M & \(2.1\) M & \(3.7\times 10^{-3}\) \\
**LogEDL (ours)** & \(1076.8\) M & \(2.1\) M & \(3.7\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: A comprehensive analysis of the computational efficiency of LogEDL (HDFS); all models tested with a sequence length of 512 and batch size of 1.
## 3 Results and Discussion
### Fourier-Transform Infrared Spectroscopy (FTIR) Analysis  
The obtained FT-IR spectrum for the Zn-Fe LDH is shown in Figure 4, with some peaks corresponding to functional groups. The broad band spectrum centered at the wavenumber of 3402 cm\({}^{-1}\) was attributed to the stretching vibration of the hydroxyl group (-OH) and was due to interlayer water molecules [24] present between the layers of the LDH and interacting the means of hydrogen bonds [25]. A weak band around 1608 cm\({}^{-1}\) could be assigned to the bending mode of interlayer water molecules [26] associated with anions by means of H-bonds [27; 28]. The peak at the wavenumber of 1384 cm\({}^{-1}\) was ascribed to the stretching vibration of nitrate (NO\({}^{-3}\)) groups attached to the LDH [25]. The peaks appearing at 901 cm\({}^{-1}\) and 826 cm\({}^{-1}\) were assigned to the lattice vibrations of the metal oxide groups M-O-H and O-M-O (M = Zn\({}^{2+}\) and Fe\({}^{3+}\)), respectively [27; 29].  
Figure 3: SEM image of the Zn–Fe LDH synthesized by a co-precipitation method.
# Learning Embedding for Signed Network in Social Media with Hierarchical Graph Pooling
## 4 Proposed Signed Network Embedding with Hierarchical Graph Pooling  
In this part, SHGP is proposed to learn signed network embeddings. Before formalizing the SHGP, we explain its construction. Firstly, traditional GCNs (with only positive links) generate a node representation through aggregate node local neighbors' information and then use the aggregation function to combine the original feature to update the feature of the current node. However, signed networks have valence signs (+, -, 0) on their edges, which carry different information from positive ones. Therefore, different aggregation functions are needed to aggregate features from different types of neighbors. Moreover, hierarchical graph pooling plays a crucial role in network embedding, which has been proven to boost numerous network analysis tasks such as link prediction. As shown in Figure 2, two different aggregators (the red one is the friend relation aggregator while the green one is the foe aggregator) are utilized to combine information from nodes in neighboring neighborhoods. The top of the diagram indicates the hierarchical graph pooling aggregation process, and the white nodes indicate the higher-order nodes involved in the aggregation. The global vector Zp together with the convolution vector Zc form the final node embedding vector Z. In addition, a pooling operation is utilized to aggregate hierarchical graph pooling from the signed graph, which computes the various significance coefficients of the neighbors (including high-order neighbors) and generates graph-level embeddings of the nodes. Finally, the signed link prediction results are obtained by the logistic regression classifier. The body of this section is structured as follows: we begin by discussing how to sample and propagate information from a signed network. Then, we explain in depth how hierarchical graph pooling may be utilized to manage the relevance of node embeddings. We conclude with an explanation of how to train this model and perform practical tasks in the signed networks.  
Figure 1: Balance and unbalance relations in a signed network.  
Figure 2: The framework of SHGP. Given a signed graph with positive and negative links, the lower part of the diagram shows the process of local feature convolution through two types of aggregators, red for the friend relationship aggregator and green for the enemy relationship aggregator.
## 3 Results and Discussion  
### Environmental Parameters  
Table 2 shows the outdoors weather conditions during the sample collection trials, in particular referring to temperature (T), humidity (RH), wind speed and direction. These data were retrieved from the regional environmental protection agency's database (ARPA Lombardia, 2020).  
In the farm considered, windows opening was automatized as they started opening when inside temperature and/or relative humidity (RH) was higher than 20 \({}^{\circ}\)C and 80%, respectively.  
Despite a large variation of outdoor temperature, the indoor temperature remained in a narrow interval, ranging from 21.2 \({}^{\circ}\)C to 16.5 \({}^{\circ}\)C, except for the 24 th November, when slurry pits were emptied and windows were manually opened, despite the outside temperature (+4 \({}^{\circ}\)C), to preserve animals and operators welfare.
## 4 Proposed Routing Protocol
### Routing Decision Learning Phase
#### 4.2.1 Markov Decision Process
\right) \tag{8}\]  
In accordance with the judgment matrix \(A\), we can obtain the eigenvector of matrix \(A\omega=(0.5,0.25,0.25)\), which correspond to the weights of the distance, the predicted energy, and the link stability, respectively. In accordance with \(A\omega=\lambda\omega\), the eigenvalue of matrix \(A\) is calculated as  
Figure 3: AHP for reward function weight computation. \[\lambda_{\max}=\sum_{i=1}^{n}\frac{[Aw]_{i}}{w_{i}}=0 \tag{9}\]  
Then, we should check the consistency to judge the rationality of the judgment matrix \(A\). \[CR=\frac{CI}{RI}=\frac{(\lambda-n)/(n-1)}{RI}=0<0.1 \tag{10}\]  
where \(n\) is the dimension of the matrix \(A\). \(CR\) is the consistency ratio, and \(RI\) is the random index. If \(CR\) is less than 0.1, it means the construction of matrix \(A\) is reasonable and satisfies the consistency condition. In accordance with the above reward function, as we can see, the weight of the distance is the largest, which means that when the link stability and the neighbors' predicted energy are the same, the shortest route is a priority, except for a void area situation. When nodes move slowly, the link stability has little effect on the reward function, but the energy factor mainly affects the reward function. When nodes move quickly, link stability is the dominant factor.
## 1 Introduction
Up to date, some light separation strategies using light property analysis were developed, including separation specular reflection using color analysis [3], examining chromaticity and intensity value of specular and diffuse pixels [16], employing polarization to separate reflection components [24] or using environmental structured illumination [25] techniques. Although these methods have been effective to some extents in separating diffuse from specular light, they basically assume either uniform surface reflectance or specific surface light reflectance conditions, which may not be applicable for many in-situ testing applications. Many semiconductor parts with variant reflectivities need to be inspected in in-situ manufacturing processes for strict dimensional compliance. These parts often comprise a body (such as epoxy and silicon), metal bumps, and pads (such as lead and solder), each of which may have complicated geometric shapes with different orientations with respect to the detecting optical sensor [26]. Moreover, the most challenging task is to overcome extremely variant wafer surface reflectivities, ranging widely from diffuse to specular conditions. In 3-D inspection, the 3-D surface profilometric technique of the tested objects often projects a structured light pattern with a desirable assumption that a non-absorbent and perfect diffusely scattered model of the surface under test (SUT) is observed. However, in reality, this assumption may not be true for many actual cases. In optical triangulation detection, viewing the measured surface from a sensing angle can pose a huge measuring uncertainty in ensuring detection of reflecting light due to variant surface reflectance properties. The extent of reflecting light detected by imaging sensors can significantly affect the success of surface measurement. Most existing techniques discussed above can resolve the light specular problem only for a surface with uniform reflectivity. For objects with non-uniform surface reflectivity, the captured image contrast becomes poor or even non-detectable. Therefore, it is of need to develop an effective technique that can overcome the complex reflectivity problem encountered in optical surface profilometry, especially for in-situ semiconductor wafer surface inspection. The article proposes a dual sensing strategy to overcome the 3-D surface profilometric problem encountered in semiconductor wafer surface. The rest of this paper is organized as follows. Section 2 presents the proposed method for 3-D profilometry using dual sensing. The experimental design and measured results are shown and analyzed in Section 3. Finally, the conclusions are summarized in Section 4.
## 2 Speech Recognition Scheme for Voice-Driven Multi-UAV Control  
### Conventional Speech Recognition Schemes  
Conventional speech recognition schemes are divided into two types according to the location of the speech recognition engine, i.e., device-embedded and server-centric schemes. In device-embedded speech recognition schemes, all processing modules (from front-end to post-processing) are embedded in user devices. In contrast, the server-centric scheme assigns overall processing to a remote server, e.g., a cloud server. These speech recognition schemes are compared in Figure 1 in Appendix A.  
Recent advances in deep learning techniques realized continuous speech recognition at stable performance [8; 9; 10]. However, the high computational intensity of deep learning algorithms requires high-performance hardware. Thus, most continuous speech recognition engines operate under the server-centric scheme.  
Conventional speech recognition schemes can be applied to voice-driven multi-UAV control, as shown in Figure 2. In the device-embedded scheme, a handheld device (e.g., smartphone, UAV controller) recognizes the user's speech and transfers the command to the UAVs. Thus, a single  
Figure 1: Procedure of standard speech recognition systems.  
device controls all UAVs via speech recognition processes. Although this direct control scheme facilitates rapid control, the device must be able to handle intensive processing burdens.  
The server-centric scheme depends on the server's hardware capacity. Here, the device transfers the user's input speech to a remote server. Then, the server performs the recognition process and returns the result to the user device. Finally, the device transmits the command to the UAVs. This scheme allows complex command formats with unlimited vocabulary to be processed because the server can handle huge computational loads. However, when multiple users submit speech commands to a single server simultaneously, the server may experience a huge burden, which would delay command transmission. In addition, the server-centric scheme manages indirect UAV control by performing three data transmission sequences: from a user device to the recognition server, from the server to the device, and from the device to UAVs. This indirect communication can incur communication costs, which may induce recognition errors or cause commands to be missed due to packet loss while a user device or UAV moves. Note that the packet loss problem may be more serious for special purpose UAVs (e.g., military UAVs) that move at high speeds.
# Multiple-Parallel Morphological Anti-Aliasing Algorithm Implemented in FPGA
## 3 Implementation of the Algorithm in FPGA
### Implementation of the Classification of the Aliasing Patterns  
The module Generate_Aliasing_Pattern in Figure 10 is used to classify the aliasing pattern. It can be divided into horizontal and vertical directions, and we take the horizontal direction as an example to introduce this module.  
After the module starts to work, we first determine whether there is a pixel above the left side of the current pixel according to its position. If it exists, we read the mask of the current pixel from BRAM4; otherwise, we can determine that there is no aliasing pattern at the current position. At this time, we can directly start the determination of the next pixel.  
After reading the mask of the current pixel, we need to determine whether the current pixel belongs to other aliasing patterns. If it does not belong, we read the edge of the current pixel and the pixel above it from BRAM2; otherwise, we can determine that the current pixel belongs to other aliasing patterns. At this time, we can also directly start the determination of the next pixel.  
After reading the edge of the current pixel and the pixel above it, we can determine the start pattern of the aliasing pattern at this position. After that, we continue to read the edge of the next pixel and the pixel above it from BRAM2 and determine whether the edge is the end pattern of the current aliasing pattern. If it is, we continue to determine the end pattern of the current aliasing pattern; otherwise, we can determine that the edge is not the end pattern of the current aliasing pattern. At this time, we increase the length of the current aliasing pattern by 1, set the mask of the current pixel in BRAM4 to 1, and continue to determine whether the edge of the next pixel is the end pattern of the current aliasing pattern.  
Figure 10: Flow diagram of the module Generate_Aliasing_Pattern (Horizon).  
After the determination of the end pattern of the current aliasing pattern is completed, we store the start pattern, length, and end pattern of the current aliasing pattern in BRAM6, and then we determine whether the current pixel is the last pixel of the image. If the current pixel is the last pixel of the image, it means that we have completed the classification of the aliasing patterns; otherwise, we need to continue the determination of the next pixel.
## 3 Detection of Cyber-Attacks with Supervised Machine Learning-Artificial Neural Networks
### Deployment of Supervised Machine Learning-Based Artificial Neural Networks
When comparing our model's performance, we find that the results--specifically related to the false positive rate and accuracy--demonstrate a lower false positive rate and higher overall accuracy. In evaluating criteria and performance, we aimed to incorporate additional metrics, such as precision and accuracy, for our model. The model's adaptability to dynamic attack detection provides an advantage over the static nature of traditional methods. For better illustration, the accuracy metric evaluates the model's overall performance. Precision measures the correctness of the model's positive predictions, while recall indicates the ratio of true positive data identified by the model. The F1 score reflects the model's overall effectiveness by harmonizing precision and recall. High accuracy implies generally good model performance, but it can be deceptive with unbalanced datasets. In cases where minimizing FP is essential, such as security systems, precision becomes critical to avoid false alarms. Conversely, recall is crucial in contexts where it is important to limit FN; for example, in security scenarios that cannot overlook potential attacks, high recall is imperative. The F1 score serves to balance precision and recall, meaning that if one metric is significantly low, the F1 score will likely follow suit. Table 3 displays the performance evaluation of all phases of the training process for ANN-I. \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Training Data** & **Validation Data** & **Test Data** & **All Data** \\ \hline
**Accuracy** & 99.98\% & 99.98\% & 99.98\% & 99.98\% \\
**Precision** & 99.997\% & 99.999\% & 99.998\% & 99.998\% \\
**Recall** & 99.96\% & 99.97\% & 99.96\% & 99.96\% \\
**F1 score** & 99.98\% & 99.98\% & 99.98\% & 99.98\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance evaluation of trained ANN-I for each phase. Figure 6: The best validation performance of the cross-entropy value for ANN-I during training until 1000 epochs.
## 4 Simulation Results and Discussions
### Multi-Objective OPF Problem
#### 4.2.1 IEEE 30-Bus System  
Two and three objective functions are simultaneously chosen as part of the objective functions in the IEEE 30-bus system for the MOOPF problems. The two-dimensional Pareto fronts for some pairs of objectives, which are cases 9-12, are depicted in Figure 4. For the three objectives, Figure 5 presents the three-dimensional Pareto fronts for cases 13-14. The Pareto fronts for each considered case are compared with those of PSO [83], PESA-II [84], SPEA2 [26], and SSA [85] where the setting parameters of the compared algorithms can be found in the provided references.  
To compare the Pareto fronts generated from all algorithms, hypervolume indicators for the fronts are calculated as presented in Table 11 where a higher value of the hypervolume indicates better generated Pareto fronts.  
The results of MOOPF problems in the IEEE 30-bus system expressed in Figures 4 and 5 show that MaMPA successfully generated Pareto fronts for all cases. Based on the hypervolume indicator values from Table 11, it is found that PSO provided the best Pareto fronts for all cases in this system, and MaMPA generated the second-best Pareto fronts for all cases. For cases 9-10, the Pareto fronts from all algorithms are very competitive while for cases 11-12, PSO and MaMPA obtained much greater Pareto fronts than those of PESA-II, SPEA2, and SSA. In cases 13 and 14, for the three objectives, PSO and MaMPA obtained the best and second-best Pareto fronts where the fronts from PSO and MaMPA have more diversity than those of PESA-II, SPEA2, and SSA, as is evident in Figure 5.  
Figure 4: _Cont._  
Figure 4: Two-dimensional Pareto fronts for: (**a**) case 9; (**b**) case 10; (**c**) case 11; (**d**) case 12.  
Figure 5: _Cont._
# Numerical Study on the Mechanical Behavior of Sand-Rubber Mixtures under True Triaxial Tests
## 2 Numerical Simulation Based on DEM
### Numerical Sample Preparation and Experimental Procedures  
An RSM is a typical binary mixture consisting of rubber particles and sand particles. Figure 2 gives the gradation curves of the numerical sand and rubber particles. The numerical rubber particles have a uniform particle size distribution of 4 mm\(\sim\)-5 mm. The rubber particle contents (RCs) are 0%, 10% and 30%. RC is defined as the percentage of the mass of rubber particles to the total mass of the mixture. The numerical model is illustrated in Figure 3. The numerical sample is a cuboid compression chamber composed of six frictionless rigid plates with initial dimensions of 40 mm in length, 40 mm in width and 80 mm in height.  
The preparation process of the numerical RSM sample is as follows. Spherical particles including rubber and sand particles are generated according to the particle gradation in the cuboid compression chamber. The porosity of the sample is the same as the test data in [PERSON] et al. [1]. A very small friction coefficient value is assigned to both the rubber and sand particles to prevent high internal forces and ensure the compactness and uniformity of the sample. After the preparation, the numerical samples are consolidated by moving six frictionless rigid plates by means of a servo system. In order to avoid the initial anisotropy caused by sample preparation, three-way isostatic stress is applied at the same time until the target confining pressure is reached. In the shearing stage, the upper and lower walls are used to load, and the servo mechanism is used to control the stress in the other two directions to keep the \(b\) value unchanged. The shear process will not be stopped until the major principal strain reaches 15%. In this study, the major principal strain refers to the principal strain in the major principal stress direction.  
Figure 2: Particle size distribution of SRM materials and numerical model.  
In this study, the RC values are set to 0%, 10% and 30%, and the confining pressures are \(\sigma_{c}\) = 100 kPa and 200 kPa. The intermediate principal stress ratios are \(b\) = 0.0, 0.2, 0.4, 0.6, 0.8 and 1.0. Under the control stress path of equal b and \(\sigma_{3}\), the stress path of the \(p\)-\(q\) stress space is a straight line. When \(\sigma_{3}\) is constant, the \(b\) value is the only simulated experiment variable. Therefore, the influence of the intermediate principal stress ratio b value on the strength of the RSM can be analyzed. Among them, the intermediate principal stress ratio \(b\), average stress \(p\) and generalized deviator stress \(q\) are defined as follows [27]:  
\[b=\frac{\sigma_{2}-\sigma_{3}}{\sigma_{1}-\sigma_{3}} \tag{8}\]  
\[p=\frac{\sigma_{1}+\sigma_{2}+\sigma_{3}}{3} \tag{9}\]  
\[q=\sqrt{\frac{\left(\sigma_{1}-\sigma_{2}\right)^{2}+\left(\sigma_{2}-\sigma _{3}\right)^{2}+\left(\sigma_{3}-\sigma_{1}\right)^{2}}{2}} \tag{10}\]
## 1 Introduction
Calibrating the system parameters is an important step in measuring 3D shape data accurately, including the distance between the reference plane and the liquid crystal device (LCD) screen, and the distance between the reference plane and the charge coupled device (CCD) camera. In order to calibrate the system parameters, the existing methods used a translating stage and a machine vision-based method. Therefore, the calibration accuracy, on one hand, depends on the accuracy of the stage, but on the other hand, it has a low accuracy due to the limited depth of field (DOF) of the lens. This paper presents a novel parameter calibration method that uses phase data to accurately determine the two system parameters. The phase information is calculated from sinusoidal fringe patterns, which are unaffected by the imaging lens being out of focus, so the calibrated system parameters have high accuracy. The following section briefly describes the DPMD technique. Section 3 explains the principle of using phase data to calibrate the system parameters. Section 4 describes the experiments used to verify the proposed calibration method and the results. Section 5 presents our conclusions.