esa-sceva/satcom-chunk-collection huggingface dataset:
# Variable Delayed Dual-Core Lockstep (VDCLS) Processor for Safety and Security Applications
## 5 Failure Analysis
### Fault Coverage  
The aim of the lockstepping technique is to maximize the fault coverage, one of the most important factors that determine reliability. Altogether, 98% mitigation of bit-flips injected into the register file has been reported in [27], while the experimental results presented in [37] indicate 99.63% error coverage. Moreover, the authors of the system demonstrated in [38] claim coverage up to 99.3%. All these results were obtained using a combination of hardware/software approach exploiting checkpointing without full synchronization. On the other hand, [23] exploits two processor pipelines executing two virtual cores. The comparison between both pipeline stages occurs every clock cycle resulting in 100% fault coverage. This is the value expected from the classic delayed dual-core lockstep processor, as well as from the presented VDCLS technique.  
To evaluate and confirm fault coverage of VDCLS, we adopted the fault injection technique used in [37]. We prepared a matrix multiplication benchmark that uses random generated 16 \(\times\) 16 matrices of 32-bit integer elements. During PC simulation, after matrices initialization, an error is injected to a random position of randomly selected register file entry or PC register in the master core. We run the processor design under test (DUT) PC simulation in parallel with golden reference model for covering errors undetected by the Checker Module. We injected 14,068 faults resulting in 7233 (51.41%) errors. Figure 20 shows the percentage of errors caused by the particular registers. Some registers never caused error. This is due to the fact that the compiler does not use all registers, and in some cases, registers are overwritten by the software before a fault activates. Table 3 shows that as expected VDCLS achieves 100% fault coverage.
# Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
## 3 Materials and Methods
### Overall Structure of the Network
#### 3.2.4 Lightweight Image Feature Extraction Module  
To ensure that image features can be correspondingly fused with point cloud features, we designed the lightweight feature extraction module as a U-shaped encoder-decoder network, as shown in Figure 5. The network structure is based on the lightweight MobileNetV2 [47] network. Each downsampling layer features an inverted residual module and depthwise separable convolution, and the downsampling layer structure contains two consecutive bottlenecks. The encoder part includes four downsampling layers, ultimately generating a feature map of size (H/16,W/16,1024). Correspondingly, the decoder symmetrically uses four consecutive upsampling layers to restore the feature map to a feature map of size (H,W,64). The upsampling layers are based on the upsampling method in DenseFusion and PSPNet [48], with each upsampling layer consisting of bilinear interpolation and a 2D convolution layer.  
Figure 5: Structure of the image feature extraction module.  
The core of the network is the inverted residual module, which is used multiple times during downsampling. As shown in Figure 6, in addition to the depthwise separable structure, this module also employs Expansion and Projection layers. The Expansion layer uses a \(1\times 1\) network structure to map the low-dimensional space to a high-dimensional space. The Projection layer also uses a \(1\times 1\) network structure but functions in the opposite way, mapping the high-dimensional space back to a low-dimensional space. These two layers are positioned at either end of the inverted residual module, with a \(3\times 3\) depthwise separable convolution in between, giving the module a spindle-like structure--narrow at the ends and wide in the middle. This design facilitates smoother gradient propagation, helping to improve the model's convergence speed. Additionally, the presence of skip connections in the inverted residual module allows the original input to be directly passed to subsequent layers, making gradient propagation through the network easier. This helps alleviate the vanishing gradient problem, making the network easier to train.
## 4 Discussion  
### Experimental Setup  
The entire experiment was carried out on Google Colaboratory using a Python3 engine, a GPU as a backend, and a system RAM of 12 GB. TensorFlow 1.x was employed. For image processing, we loaded the picture as a color image with three RGB channels and transformed it into a NumPy array. On the CXR dataset, we trained all three transfer learning models with an identical set of hyperparameters. During the entry procedure, the images were scaled to 224 \(\times\) 224 pixels with various dimensions. For training purposes, we settle learning rate to \(2\times 10^{-4}\) with 60 epochs, which are small enough to prevent the fitting problem of the transfer learning models. An Adam optimizer was used as a compilation, and FCL with ReLU 98 was employed as the activation function for feature extraction.
# Parametric Analysis of Rotational Effects in Seismic Design of Tall Structures
## 2 The Response Parameter Defining the Contribution of Rotational Seismic Component  
The ratio of maximal seismic effects on a structure, taking into account the translational and rotational components of the earthquake motion, can be expressed as  
\[\Delta=\frac{\sqrt{S_{u}^{2}+S_{rot}^{2}}}{S_{u}}, \tag{1}\]  
where  
\(S_{u}\) is the seismic effect assuming only translational components;  
\(S_{rot}\) is the seismic effect assuming only rotational components;  
\(\sqrt{S_{u}^{2}+S_{rot}^{2}}\) is the SRSS combination of translational and rotational components.  
This ratio can as well be expressed as the ratio of the maximal design moment in the end fixity, when the structure of the mast, tower, chimney, or high-rise building is approximately considered as a cantilever  
\[\Delta=\frac{\sqrt{\left(\sum_{i}h_{i}F_{i,u}\right)^{2}+\left(\sum_{i}h_{i}F_ {i,rot}\right)^{2}}}{\sum{h_{i}F_{i,u}}} \tag{2}\]  
where  
\(F_{i,u}\) is the inertial force from translational effects at story \(i\);  
\(F_{i,rot}\) is the inertial force from rotational effects at story \(i\);  
\(h_{i}\) is the elevation of the story from the foundation.  
\(\Delta\) is a dimensionless parameter. We assume the rough estimate of the importance of the rotational effect, namely, if \(\Delta\) is up to 1.1.
## 3 Discrete-Time Adaptive Delay Compensator Based on Kalman Filter
### Parameter Estimation  
Although the mathematical model of the control plant can be established using the first principle modeling method, it is sometimes unable to suitably represent the actual relationship between the displacement command and measurement. Hence, it is necessary to use the actual testing data to revise the system model parameters.  
It can be seen that Equation (4a) is a linear difference equation and [PERSON] et al. (2018) and [PERSON] et al. (2018) employed the least-square method to estimate the system model parameter. However, the convergence of the least-square method cannot be guaranteed, and it is sometimes time consuming. To overcome the previously mentioned problems, a promising estimator, the KF is used to estimate the system model parameter. In this study, the RTHS system is assumed to be a slow time-varying system, and the state and observed equation for this estimation problem can hence be given by  
\[\mathbf{x}_{k+1}=\mathbf{x}_{k}, \tag{5}\]  
\[y_{k}=\mathbf{H}_{k}\mathbf{x}_{k}+v_{k}, \tag{6}\]  
where \(\mathbf{H}_{k}=\boldsymbol{\varphi_{k}}^{\mathrm{T}}\), \(y_{k}\) is the commanded displacement at the \(k\)-th time step, and \(v\) is the measurement noise that satisfies  
\[\mathrm{E}v_{k}=0,\mathrm{E}v_{k}v_{k}^{\mathrm{T}}=R, \tag{7}\]  
where \(R\) is the measurement noise covariance. The KF estimates the system model parameter vector using  
\[\overset{\boldsymbol{\wedge}}{\mathbf{x}}_{k+1}=\overset{\boldsymbol{\wedge }}{\mathbf{x}}_{k}+\mathbf{G}_{k}\Big{(}\mathbf{y}_{k}-\mathbf{H}_{k}\overset {\boldsymbol{\wedge}}{\mathbf{x}}_{k}\Big{)}, \tag{8}\]  
where \(\overset{\boldsymbol{\wedge}}{\mathbf{x}}\) denotes the estimate of \(\mathbf{x}\), and \(\mathbf{G}_{k}\) is the Kalman gain, which is calculated by  
\[\mathbf{G}_{k}=\mathbf{P}_{k-1}\mathbf{H}_{k}^{\mathrm{T}}\Big{(}\mathbf{H}_{ k}\mathbf{P}_{k-1}\mathbf{H}_{k}^{\mathrm{T}}+R\Big{)}^{-1}, \tag{9}\]  
where \(\mathbf{P}\) is the estimate error covariance, which is given by  
\[\mathbf{P}_{k}=(\mathbf{I}-\mathbf{G}_{k}\mathbf{H}_{k})\mathbf{P}_{k-1}. \tag{10}\]
# Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model
## 4 Results and Discussion
### xLSTM Results for Wind Energy Forecasting
#### 4.1.2 xLSTM Results
The distribution shows a more substantial probability mass in the intermediate power ranges (500-1000 kW), indicating more frequent operation at partial load conditions. The autumn distribution exhibits distinctive features that differentiate it from other seasons. A substantial probability density at near-zero output (0-200 kW) appears similar to spring conditions but with a slightly lower magnitude (\(\approx\)0.0019). A moderate secondary peak exists at rated power, with a density of approximately 0.00065. The power distribution shows an increased probability mass in the range of 2000-3000 kW relative to other seasons, which indicates more variable power generation. Figure 4: _Cont._
# Precise Feature Removal Method Based on Semantic and Geometric Dual Masks in Dynamic SLAM
## 2 Related Work
### Semantic Segmentation-Based Dynamic SLAM  
Dynamic SLAM methods based on semantic segmentation utilize deep learning networks to semantically parse images, thereby eliminating potential dynamic targets to enhance localization accuracy and map quality [12; 14; 25; 26; 27]. DS-SLAM [28] combines ORB-SLAM2 with a segmentation network for instance segmentation and verifies the motion consistency of feature points using epipolar constraints; however, it is limited to predefined dynamic categories (e.g., pedestrians). DynaSLAM [29] employs Mask R-CNN for instance segmentation, which it uses along with geometric constraints to identify dynamic regions in RGB-D images. This approach significantly improves robustness in dynamic scenes, albeit with a high computational cost that limits real-time performance due to inference times exceeding 200 ms. VDO-SLAM [30] uses dense optical flows to track object motion and enhances mapping capabilities for multi-target dynamic scenes through joint optimization of camera and object poses. However, it is primarily suitable for offline processing. For greater efficiency and performance balance, methods such as Detect-SLAM [31] and RDS-SLAM [32] focus on semantic processing of keyframes. This approach performs instance segmentation on keyframes to reduce computation, but relies heavily on keyframe quality and lacks dynamic frame selection strategies, limiting system adaptability in complex scenes. Overall, while semantic-based dynamic SLAM has made progress in dynamic target perception, it still faces challenges such as computational complexity, erroneous elimination of static feature points, and over-reliance on keyframes, making it difficult to balance real-time performance with accuracy [33]. To address these issues, our paper proposes a dual-mask strategy that integrates instance segmentation with outlier residual information. The proposed approach first employs YOLOv11 to perform coarse segmentation of potential dynamic objects, then refines the detection by leveraging geometric residuals to further eliminate anomalous regions. This method effectively reduces the system's dependence on keyframes and enables precise removal of dynamic features within local areas. By improving the system's tolerance to erroneous rejection of static points, the proposed strategy enhances the overall robustness and stability of the system while maintaining real-time performance.
# CNN-Based Defect Inspection for Injection Molding Using Edge Computing and Industrial IoT Systems
## 3 CNN-Based Defect Inspection for Injection Molding  
### System Architecture  
The proposed model of overall architecture is composed as shown in Figure 1. Image data are acquired by means of a vision camera that scans the photographing unit and sends it to the edge box. The defect inspection is done in the edge box. If a defect is detected, the number of the defective cell is transmitted to the PLC, which plays the role of removing defective products from the PLC.  
Image data are acquired using lighting and a GigE vision camera. Lighting minimizes how much the difference between day and night affects quality inspection. It is configured in the form of a conveyor belt that connects the rails in a cylinder. The advantage of these rails is that the product to be inspected is rotated while the product is being inspected so that one can inspect the quality of all surfaces of the object rather than one. In system design, the product is inspected twice, improving the existing deep learning inspection method by means of CNN. Figure 2 is a picture of the product taken by the vision sensor on the rails.  
The algorithms done in the edge box are summarized in Table 1. First, when a raw image comes by means of the vision sensor, it is cropped as an image of a product for defect detection. Then, it does defect detection and finds out how many times the product in the cell was defective. These data would be transferred to the database via the cloud. Finally, it communicates with the PLC. If the time from the \(n\)-th cell to the discharge port is calculated and transmitted to the PLC, the defective product is discharged in the final quality inspection. We designed the automated system.  
Figure 1: System Architecture.  
Figure 2: Products on the Rail photographed with a Vision Sensor.
## 2 Methodology
### An Optimal Energy Resources Allocation Method Based on Clustering Optimization
Firstly, all sensors in the sub-network are divided into several clusters, and then a node in each cluster is selected as the cluster head, which is responsible for collecting the data of the remaining sensor nodes in the cluster and forwarding the data to the corresponding sink node. Therefore, the communication load and corresponding energy consumption of cluster head nodes are much higher than those of other non-cluster head nodes. In order to ensure the balance of energy consumption of each node and avoid the failure of cluster head nodes due to excessive energy consumption, it is necessary to re-select and rotate the cluster head before each round of communication. The data between cluster head and cluster members is transmitted by single-hop forwarding, and the data transmission between cluster head and sink node is also carried out by single-hop forwarding. The structure of cluster-based energy optimization routing protocol for sub-network is shown in Figure 4.  
The structure of cluster-based energy optimization routing protocol consists of three layers: Data input layer, clustering optimization strategy layer, and data output layer. The data input layer is mainly responsible for inputting the initial data of each sensor node in the monitoring sub-network into the target model for optimizing the processing. The input data mainly includes the number of sensors located around the track, energy storage, and location information. In clustering optimization strategy layer, sensor networks and their nodes aim at minimizing and balancing overall energy consumption, constantly updating the selection of all clusters and their cluster heads, so as to enhance the life cycle of railway status monitoring systems. In this paper, an efficient optimization model to optimize the cluster generation and cluster head selection is proposed; it can ensure that the sub-network always works in the mode of the highest energy efficiency. In the process of cluster head selection and rotation, a comprehensive index considering the candidate probability, residual energy, and predicted energy consumption of candidate cluster head nodes is proposed. Then, a multi-objective optimization model is constructed, and genetic algorithm is used to optimize and update the formation of clusters. Figure 4: Structure of cluster-based energy optimization routing protocol for sub-network. In the data output layer, all sensor nodes in the sub-network are allocated to different clusters for data transmission in an energy-efficient optimization way, which would improve the life cycle of the monitoring sub-network.
## 2 Materials and Methods
### Error Indicators of Static Measurement  
For comparison purposes, static measurements of the RTK receivers were performed. During these measurements, the same conditions were maintained as in the dynamic measurements of the four RTK receivers: measuring environment, measuring period in three same days and setting of the RTK receivers, including reception of the correction signal and logging of NMEA messages. These static measurements were performed according to the methodology described in reference [17]. Receiver A was selected to determine the reference points by calculating the average coordinates from the \"FIX\" period of each of the three measuring days, such as in the dynamic one. The same error indicators (\(\mu_{err}\), \(\sigma_{err}\) and \(RMS_{err}\)) were used to calculate the error indicators for the static measurements as for the dynamic measurements in Evaluation (2) and Evaluation (3). The \(RMS_{\sigma\tau}\) indicator from the \"FIX\" period was suitable as a value to be compared with the values in the manufacturers' catalogues (Table 1).
# A Non-Smooth Numerical Optimization Approach to the Three-Point Dubins Problem (3 PDP)
## 3 Derivation of Dubins Path with Trigonometry
### Case CCC
,\]  
where \(\tilde{\alpha}=\alpha-s\ell_{2}\) and \(\tilde{\beta}=\beta+s\ell_{2}\). In conclusion, we can summarize the results for the CCC case as follows:  
\[t^{\text{CCC}}=\frac{3+\cos(\alpha-\beta)-s\,d\,S^{-}}{4}-\frac{d^{2}}{8}, \tag{31}\]  
If \(t^{\text{CCC}}\in[-1,1]\), then build  
\[\ell_{2}=\arccos(t^{\text{CCC}})\quad\text{and}\quad\ell_{2}=2\pi-\arccos(t^{ \text{CCC}}); \tag{32}\]  
solve the linear system (30) and compute  
\[ \begin{split}\ell_{1}&=\,\text{atan2}(\sin\ell_{1},\cos \ell_{1})\mod\quad 2\pi,\\ \ell_{3}&=\,\text{atan2}(\sin\ell_{3},\cos\ell_{3})\mod\quad 2\pi. \end{split}  \tag{33}\]  
Discard the solutions that do not satisfy \(\alpha+s(\ell_{1}^{\text{CCC}}-\ell_{2}^{\text{CCC}}+\ell_{3}^{\text{CCC}})= \beta\mod 2\pi\). Range of the Solution  
The solution exists only when condition (29) is satisfied. Introducing new variables \(X\) and \(Y\), in the same fashion as before, defined from the angles \(\alpha=2\arctan X\) and \(\beta=2\arctan Y\), and substituting them in the inequality (29), we obtain four quadratic polynomials:  
\[ \begin{split} p&=\,s\,d\sin\beta-\frac{d^{2}}{2}, \qquad q=2(\sin\beta-s\,d)\\ P_{\text{CCC},1}(X;\beta)&=\,(p-\cos\beta-1)X^{2}+qX+p +\cos\beta-1\\ P_{\text{CCC},2}(X;\beta)&=\,(p-\cos\beta+7)X^{2}+qX+p +\cos\beta+7\end{split}  \tag{34}\]  
and  
\[ \begin{split} p&=\,-s\,d\sin\alpha-\frac{d^{2}}{2}, \qquad q=2(\sin\alpha+s\,d)\\ P_{\text{CCC},3}(X;\alpha)&=\,(p-\cos\alpha-1)X^{2}+qX+p +\cos\alpha-1\\ P_{\text{CCC},4}(X;\alpha)&=\,(p-\cos\alpha+7)X^{2}+qX+p +\cos\alpha+7.\end{split}  \tag{35}\]  
The real roots of the polynomials return the border of the interval range where \(\alpha\) can run, to yield a solution for the \(CCC\) problem with \(\beta\) fixed. The same holds for \(P_{\text{CCC},2/3}(X;\alpha)\) for the interval range of \(\beta\). The following section uses the results derived so far to compute the derivatives of the optimal lengths so that an algorithm for the 3 PDP can be constructed.
## 5 Conclusions and Future Works  
This paper implemented fake news classification using machine learning, deep learning and natural language processing. NLP methods, such as Tokenize, TF-IDF and Word2 vec, were applied to increase the accuracy. Traditional ML models implement four algorithms--binomial logistic regression, naive Bayes classifier, support vector machines and random forest. The neural network models utilized three algorithms--CNN with GlobalMaxpool, CNN with DeepNetwork and LSTM. The novel empirical studies from previous works were to classify tagged false news, and NLP is integrated with classical machine learning to create an AI model. On an open public dataset based on content-level attributes, we evaluate seven models to discover the best model between classical ML and neural network approaches. From the experiments, we can see that the random forest model achieves excellent accuracy compared to the other traditional ML models. CNN with GlobalMaxpool is the best neural network model among the other two models. By implementing NLP methods, all the models have a greater than 85% accuracy. All the neural network models have a greater than 90% accuracy.  
In the future, various models and methods can be improved, such as using bidirectional encoder representations from transformers (BERT), applying LSTM sequences to sequences, implementing bigrams and trigrams in training traditional ML and neural network models.  
Conceptualization, C.-M.L. and M.-H.C.; methodology, C.-T.Y. and V.K.V.; software, E.K.; validation, V.K.V. and C.-T.Y.; formal analysis, C.-M.L. and M.-H.C.; data curation, E.K.; writing--original draft preparation, E.K.; writing--review and editing, C.-T.Y., C.-M.L. and M.-H.C. All authors have read and agreed to the published version of the manuscript.  
Figure 32: The comparison of model Accuracy and RMSE.  
**Funding:** This research was supported by the Ministry of Science and Technology, Taiwan, under grants number 110-2221-E-029-020-MY3, 110-2621-M-029-003, 110-2622-E-029-003, 110-2222-E-029-001, and 110-2222-E-029-001.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interest or personal relationships that could have appeared to influence the work reported in this paper.