esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Analysis of a Highly Dynamical Scene
### Experimental Results and Analysis
After the first maneuver, the tracking error of the DLB-AHCKF gradually converges to the normal range. However, the tracking error of the second maneuver fails to converge normally, and the trend of the divergence of the algorithm appears. The result of this experiment is determined by the fundamental principles of the DLB-AHCKF algorithm, and it has its own specific application background. On the contrary, as shown in Figure 6 and Table 1, the DLB-CKF algorithm is essentially proposed for the motion state mutation of the carrier or the inaccurate model. Thus, when the highly dynamical variable acceleration takes place twice, the DLB-STCKF shows good tracking performance similar to that of the DLB-RACKF. However, when the system model is accurate and the measurement noise exists, the tracking error of DLB-STCKF is always greater than the normal mean for the duration of abnormal noise, which is similar to DLB-RACKF. When the noise returns to normal, there is a convergence delay of about 1300 ms. In general, compared with DLB-AHCKF and DLB-STCKF, the DLB-RACKF algorithm can deal with abnormal measurement noise and inaccurate motion models simultaneously. Within the normal range, the precision of the three algorithms is roughly equivalent, and the DLB-RACKF is slightly better than the other two algorithms. Figure 6: Comparison of code phase tracking error among DLB-STCKF and DLB-RACKF. As shown in Table 2, the running time of the CKF algorithm is lower than that of the UKF under the same software operation conditions. The running time of the improved RACKF algorithm is about 1/2 of UKF. The computational efficiency of DLB-RACKF is better than that of DLB-AHCKF and DLB-STCKF, which is especially important for state estimation under highly dynamical conditions. Therefore, RACKF is an effective nonlinear state estimator.
## 3 Experiments and Results
### Results and Analysis of Datasets
#### 3.3.2 Results and Analysis of DeepGlobe Road Dataset
Most models confront road fragmentations here, especially within the internal alleys of housing areas. Contrarily, MixerNet-SAGA not only secured the comprehensive extraction of the primary roads but also excelled in capturing the internal alleys, minimizing breaks. Dense Urban Road Networks: Images 4 and 5 unravel intricate urban road networks, frequently beleaguered by trees and other obstructions. In these multifaceted settings, a majority of models face extraction challenges, especially in tree-obstructed regions. Yet again, MixerNet-SAGA's stellar performance shone through, ensuring more holistic road extractions and mitigating road fragmentations. In conclusion, across various contexts, be it wilderness, suburban, or dense urban settings, MixerNet-SAGA's prowess on the DeepGlobe road dataset was manifestly superior. Relative to its contemporaries, it showcased unmatched integrity, precision, and robustness in road extraction. Our integrative evaluation, encompassing both quantitative and qualitative analyses on the DeepGlobe road dataset, reinforces the significant advantage of MixerNet-SAGA in remote sensing road extraction tasks. Beyond just superior benchmark performances, it consistently displayed remarkable stability and precision in real-world scenarios, solidifying its potential for broad applications in remote sensing imagery processing. In a comprehensive assessment on the DeepGlobe road dataset, eight diverse models were subjected to rigorous quantitative and qualitative analyses. Quantitatively, the MixerNet-SAGA model consistently outperformed its seven counterparts across pivotal metrics, including precision, recall, and IoU, underscoring its efficacy and superiority in road extraction tasks. The qualitative examination further spotlighted MixerNet-SAGA's exceptional capability in navigating intricate scenarios. Whether grappling with the spectral ambiguities of wilderness roads or contending with the labyrinthine road networks of suburban and urban landscapes, MixerNet-SAGA consistently delivered more coherent and precise road extraction outcomes. Notably, in regions plagued by obstructions or confronted with spectral anomalies, the robustness of MixerNet-SAGA stood markedly above the rest. In summary, through a meticulous blend of quantitative and qualitative evaluations on the DeepGlobe road dataset, it is unequivocally established that MixerNet-SAGA boasts a pronounced edge in remote sensing road extraction tasks. Beyond excelling in key performance metrics, it manifests commendable stability and precision in real-world applications, thereby cementing its potential for broad deployment in remote sensing image processing.
# Performance Analysis of the IEEE 802.11p Multichannel MAC Protocol in Vehicular Ad Hoc Networks
## 4 Analytical Model and Performance Analysis
### Performance Analysis
#### 4.4.2. PRR for Safety Packets and WSA Packets  
**Definition 7** (_Packet Reception Rate (PRR)_).: _The PRR is the percentage of packets that successfully received to the number of packets transmitted [39]. To determine that the transmitted packets by the tagged vehicle are successfully received by any other vehicles within its transmission range, the following conditions must be met: (1) no higher ACs in the same node attempt to transmit; (2) when the tagged vehicle is transmitting, no other vehicles within its carrier-sensing range deliver packets at the same time slot; (3) when the tagged vehicle is transmitting, no hidden terminals start to transmit during the vulnerable period \(T_{vht}\); (4) no transmission errors occur during the packet transmission; and (5) no packets are dropped due to exceeding the retransmission limit. Combined with the above conditions, we can calculate the probability of successful reception of safety packets and WSA packets, \(PRR_{e}\) and \(PRR_{s}\), respectively, as_  
\[\left\{\begin{array}{l}PRR_{e}=(1-P_{e,pC})\times\sum_{k=0}^{\infty}{(1-\eta_{ total})^{k}\frac{(N_{cs}-1)^{k}}{k!}e^{-(N_{cs}-1)}}\\ \times\left[\sum_{k=0}^{\infty}{(1-\eta_{total})^{k}\frac{N_{\text{ int}}^{k}}{k!}e^{-N_{\text{ int}}}}\right]^{T_{\text{test}}/T_{\text{test}}}\\ \times(1-P_{e,err})\\ =(1-P_{e,pC})\times e^{-\eta_{total}(N_{cs}+N_{\text{int}}T_{\text{test}}/T_{ \text{test}}-1)}\\ \times(1-P_{e,err})\\ PRR_{s}=(1-P_{s,pC})\times\sum_{k=0}^{\infty}{(1-\eta_{total})^{k}\frac{(N_{cs }-1)^{k}}{k!}e^{-(N_{cs}-1)}}\\ \times\left[\sum_{k=0}^{\infty}{(1-\eta_{total})^{k}\frac{N_{\text{ int}}^{k}}{k!}e^{-N_{\text{int}}}}\right]^{T_{\text{test}}/T_{\text{test}}}\\ \times(1-P_{s,err})\times(1-P_{s,drop})\\ =(1-P_{s,pC})\times e^{-\eta_{total}(N_{cs}+N_{\text{int}}T_{\text{test}}/T_{ \text{test}}-1)}\\ \times(1-P_{s,err})\times(1-P_{s,drop})\end{array}\right. \tag{49}\]
# Evaluation of Disassembling Process Inference Based on Positional Relations Matrix
## 2 Disassembling Process Inference Based on Positional Relations Matrix
### Positional Relations Matrix  
The positional relations matrix adopted in the current study describes the impeding conditions among the parts. The matrix is expressed as  
\[\mathbf{M}=\left[\begin{array}{ccc}M_{11}&\cdots&M_{1N}\\ \vdots&\ddots&\vdots\\ M_{N1}&\cdots&M_{NN}\end{array}\right]\]  
for a system consisting of \(N\) parts. Matrix element \(M_{ij}\) is expressed as a binary number and denotes the impeding conditions of part \(i\) for part \(j\); the \(k\)th bit of \(M_{ij}\) expresses that part \(i\) impedes (1) or does not impede (0) the motion of part \(j\) in its corresponding direction. For the diagonal elements of \(\mathbf{M}\), accordingly \(M_{ii}=0\).  
In the case that the motion of a part to be disassembled is confined to the translational directions of the spatial (3D) coordinates, \(M_{ij}\) is expressed as a 6-bit binary number, where the bit 0 denotes the impeding condition in X-positive direction, the bit 1 corresponds to X-negative direction, the bit 2 corresponds to the Y-positive direction, and so on. For a system shown in Figure 1 that consists of 5 parts, the impeding conditions of part 1 for parts 2 \(\sim\) 5 are expressed as \(M_{12}=101000\), \(M_{13}=001000\), \(M_{14}=110011\), \(M_{15}=110011\), where the value of \(M_{12}\) expresses that part 1 impedes the motion of part 2 in Y-negative and Z-negative directions that correspond to bit 3 and bit 5, and so on.  
The positional relations matrix can be extended and generalized for any number of moving directions for disassembly. In addition, even curvilinear disassembling motions can be taken into account by adopting suitable curvilinear coordinates for such part motions, since the crucial point of the positional relations matrix is to describe the impeding conditions among the parts in an abstract sense. In the current study, we adopt two-dimensional disassembling problems for the computation example. In cases where the part motion is confined to the translational directions of the plane coordinates, each of the matrix element \(M_{ij}\) is expressed as a 4-bit binary number.
# Aggression Detection in Social Media from Textual Data Using Deep Learning Models
## 3 Methodology
### Evaluation Metrics  
We evaluate our models using the average _accuracy_, _recall_, _precision_, and _F1-score_. Calculation of these measures was done using true positive (\(TP\)), false positive (\(FP\)), true negative (\(TN\)), and false negative (\(FN\)). Correctly-classified cyber-aggressive tweets are called true positive(s) (TP), while FN is incorrectly classified as non-cyber-aggressive tweets. Tweets correctly classified as non-cyber-aggressive are named TN, and those incorrectly classified as cyber-aggressive are called FP.  
_Accuracy_ is the proportion of correctly classified aggressive and non-aggressive tweets.  
\[Accuracy=((TP+TN))/((TP+FN+TN+FP)) \tag{1}\]  
_Precision_ measures the number of correctly identified cyber aggression tweets among all tweets labeled as cyber-aggressive.  
\[Precision=TP/((TP+FP)) \tag{2}\]  
The _recall_ is the number of aggressive tweets among all of the tweets in the dataset.  
\[Recall=TP/((TP+FN)) \tag{3}\]\(F1\)-_score_ is a measure of how well your classifier balances precision and _recall_.  
\[F1\text{-}score=(2*(P*R))/((P+R)) \tag{4}\]
# Optical Flow Estimation with Occlusion Detection
## 1 Introduction
Then three kinds of information are used to obtain occlusion detection respectively. Next, our fusion strategy is adopted to obtain robust occlusion detection results. Finally, the whole image is divided into occlusion and non-occlusion and estimated optical flow separately. occlusion areas, but this method depends on the quality of optical flow estimation. The occlusion only appears on the moving edges, and the moving edges of the two images surround the candidate area of occlusion. We fuse the above three kinds of information into an initial occlusion detection result. Because occlusion has regional consistency, we propose a regional-based optimization strategy. Since the occluded edge is composed of the moving edges of two images, we first fuse the two images, and then optimize the occlusion detection results by using the regional consistency. For optical flow estimation, we use two optimization strategies for different areas. Using the occlusion detection results obtained from the above work, the whole image is divided into occlusion areas and non-occlusion areas. We use a modified energy function for non-occlusion area. Due to the lack of effective information, neighborhood smoothing are often used for optical flow in the occlusion areas. Based on the discrimination of occlusion and non-occlusion areas, we not only improve the efficiency and accuracy of the optimization function, but also reduce the influence of smoothing operation on the results, thus obtaining better optical flow estimation results. The remainder of this paper is organized as follows. We discuss the related work in Section 2. Our occlusion detection strategy are given in Section 3. Our occlusion-aware optical flow estimation are given in Section 4. Experimental results and analyses are presented in Section 5. Our conclusion and future work are summarized in Section 6.
###### Abstract  
The increasing incorporation of Industrial Internet of Things (IIoT) devices into critical industrial operations and critical infrastructures necessitates robust security measures to safeguard confidential information and ensure dependable connectivity. Particularly in Cyber Physical Systems (CPSs), IIoT system security becomes critical as systems become more interconnected and digital. This paper introduces a novel Lightweight Industrial IoT Authentication (LIZA) method as a solution to address security concerns in the industrial sector and smart city infrastructure. Mutual authentication, authenticated message integrity, key agreement, soundness, forward secrecy, resistance to a variety of assaults, and minimal resource consumption are all features offered by LI2A. Critical to CPS operations, the approach prevents impersonation, man-in-the-middle, replay, eavesdropping, and modification assaults, according to a security study. The method proposed herein ensures the integrity of CPS networks by verifying communication reliability, identifying unauthorized message modifications, establishing a shared session key between users and IIoT devices, and periodically updating keys to ensure sustained security. A comprehensive assessment of performance takes into account each aspect of storage, communication, and computation. The communication and computing capabilities of LI2A, which are critical for the operation of CPS infrastructure, are demonstrated through comparisons with state-of-the-art systems from the literature. LI2A can be implemented in resource-constrained IIoT devices found in CPS and industrial environments, according to the results. By integrating IIoT devices into critical processes in CPS, it is possible to enhance security while also promoting urban digitalization and sustainability.  
authentication; intelligent sensors; industrial internet of things (IIoT); forward secrecy (FS); symmetric key; cybersecurity; SCADA +
Footnote †: journal:  
0000-0001-5000-0000][PERSON]
###### Abstract  
After nearly two decades of development, transient electromagnetic (TEM) 3D forward modeling technology has significantly improved both numerical precision and computational efficiency, primarily through advancements in mesh generation and the optimization of linear equation solvers. However, the dominant approach still relies on direct solvers, which require substantial memory and complicate the modeling of electromagnetic responses in large-scale models. This paper proposes a new method for solving large-scale TEM responses, building on previous studies. The TEM response is expressed as a matrix exponential function with an analytic initial field for a step-off source, which can be efficiently solved using the Shift-and-Invert Krylov (SAI-Krylov) subspace method. The Arnoldi algorithm is used to construct the orthogonal basis for the Krylov subspace, and the preconditioned conjugate gradient (PCG) method is applied to solve large-scale linear equations. The paper further explores how dividing the off-time and optimizing parameters for each time interval can enhance computational efficiency. The numerical results show that this parameter optimization strategy reduces the iteration count of the PCG method, improving efficiency by a factor of 5 compared to conventional iterative methods. Additionally, the proposed method outperforms direct solvers for large-scale model calculations. Conventional approaches require numerous matrix factorizations and thousands of back-substitutions, whereas the proposed method only solves about 300 linear equations. The accuracy of the approach is validated using 1D and 3D models, and the propagation characteristics of the TEM field are studied in large-scale models.  
Keywords:TEM; large-scale models; time-segmented method; SAI-Krylov subspace method; PCG method +
Footnote †: journal: applied sciences
# _Applied_
## 4 Evaluation and Platforms
### Dataset Preprocessing: Windowing  
The BDLib dataset contains 1800 audio files saved as wav files, ESC-10 has 400 ogg audio files and UrbanSound 1302 audio files. The files of BDLib and ESC-10 are 10 s long, the ones of UrbanSound are of variable length, between 1 s and several minutes. Furthermore, the audio files in the UrbanSound dataset have different file formats such as wav,.mp3,.ogg,...To facilitate the experiments, all audio files in the UrbanSound  
Figure 2: Evaluation flow detailing the steps performed for a fair comparison of classical ML-based approaches and a DL-based approach.  
dataset are first converted to wav. This file format is used because most of the audio files in UrbanSound are already stored in this format and it is lossless as opposed to mp3 format.  
To generate more data from these audio files, a windowing technique is used. Figure 3 depicts how the windowing process works. An audio file is loaded and the first 4 s are used for the windowing process. These 4 s audio are then divided into seven audio frames of 1 s, with a 50% overlap. These frames are only saved temporarily in an array, the time that features are extracted out of them. After that the next audio file of the dataset is loaded and the process is repeated. This way, seven times more feature groups are generated and assure enough training data. For example, for the ESC-10 dataset, 2800 feature groups are obtained with windowing instead of 400 without. It should be noted that windowing is only used to generate more training samples, and multiple feature groups are not to combined together to generate an averaged feature group per audio file.  
The audio files of the UrbanSound dataset are different from those of the other datasets, in the sense that they are not exactly 10 s long, they can be shorter or longer, and the relevant audio does not always start at the beginning of the audio file. Therefore, every audio file comes with a CSV file containing the metadata of the associated audio file, including the start time. That start time is then used as offset to define where the 4 s used for windowing will start. For example if the start time of audio file X is 2 s, then for seconds 2 to 6 the windowing process will be used.  
One feature group is generated per frame. The feature groups coming from the first 4 s of the audio file are used for the training and the testing of the classifiers, except for the testing of the MosAlc approach. This is due to the fact that all the feature groups coming from the first 4 s of the audio files, are used to train the ten binary classifiers which are used for the MosAlc approach. So in order to avoid using the same data for the training and the testing, another part of the audio files is used as test data for the MosAlc approach: a 4 s long audio frame starting after the fourth second of the audio file (plus a potential offset if working with the UrbanSound dataset). This is also shown in Figure 3.  
Figure 3: Visual representation of the windowing process to extract the seven audio frames from the four seconds of the audio file used for training.
## 2 The Role of Phytochemical in Oxidative Stress
### Case Study: Antioxidant Activity of Flavonoids
#### 2.2.2 Metal-Ion Chelating  
Metal ions, such as Fe\({}^{2+}\) and Cu\({}^{+}\), are considered to be primary triggers of in vivo ROS generation. These redox-active metal ions can react with H\({}_{2}\)O\({}_{2}\), and the result is OH\({}^{-}\), which damages DNA by strand breakage or base damage, leading to genetic mutations, cancer, or even cell death [43; 44].  
Some flavonoids can chelate these metal ions, decreasing one of the factors for the development of free radicals. The proposed binding sites for metal ions in flavonoid structure are the catechol moiety in the B ring, the 3-hydroxyl and 4-oxo groups in the C ring, and the 4-oxo and 5-hydroxyl groups between the C and A rings [45]. For the iron-chelating mechanism of flavonoids, the stability constants for flavonoid-iron interactions have been measured, providing insight into their antioxidant behavior [46].
# A Multi-Objective Evolutionary Algorithm Model for Product Form Design Based on Improved SPEA2
## 4 Discussion
### Complete PFDMOEAM and Entropy-TOPSIS  
As the last module of the complete PFDMOEAM, the main task of the OSS module is to use the MADM method to evaluate and rank Pareto design solutions according to multi-objective calculated scores. Based on the superiorities of innovation and accuracy gained in the DA and MOO modules, the goal of OSS module is to consolidate these superiorities and get accurate MADM results. Therefore, an objective, accurate and operable MADM method is needed in this module. Compared with other subjective MADM methods, the entropy-TOPSIS method has better objectivity, accuracy and operability, so it can achieve the goal of the OSS module more effectively. The consistency of the results of Tables 15 and 16 demonstrates that the entropy-TOPSIS method has good applicability to PFDMOEAM and has been integrated with the other main methods to provide strong support for the comprehensive performance benefits of the model.  
Based on the above methods, the automatic simulation system of the model was developed by using MATLAB software. The system provides a visual interface that allows designers to carry on designing and observing the results intuitively and efficiently (Figure 12). The implementation and operation of simulation system proves that the proposed model can be used as a universal Kansei engineering design expert system with great innovation and application value.  
Note that 45 representative samples were used in the case study for test, but the proposed PFDMOEAM can increase or decrease the sample size according to actual needs. Since the PCA in the EFA method requires a certain number of samples, the sample size should not be too small. In theory, the larger the sample size, the more reliable the result will be.