esa-sceva/satcom-chunk-collection huggingface dataset:
# Visual Image Dehazing Using Polarimetric Atmospheric Light Estimation
## 2 Dark Channel Dehazing Theory (DCP)  
The dark channel prior dehazing method is based on the atmospheric scattering physical model proposed by [PERSON] [19] to suppress the impact of scattering effects on the imaging process. The model is illustrated in Figure 1[20] and is defined as shown in Equation (1).  
\[I(x)=J(x)t(x)+A(1-t(x)) \tag{1}\]  
In the equation, \(I(x)\) represents the hazy image obtained, \(x\) is the spatial coordinate of the pixel, \(J(x)\) is the haze-free image to be restored, \(A\) is the global atmospheric light component, and \(t(x)\) denotes the transmission rate. The expression for the transmission rate is defined by Equation (2):  
\[t(x)=e^{-nt(x)}\]  
Figure 1: Atmospheric degradation model.  
In the equation, \(r\) represents the atmospheric scattering coefficient, and \(d(x)\) is the light path between field and detector. The dark channel prior principle [6] suggests that in haze-free images, at least one pixel in the non-sky region has an intensity value close to 0. The dark channel of a haze-free image can be represented as follows:  
\[J^{dark}(x)=\underset{y\in\Omega(x)}{min}\bigg{(}\underset{c\in\{R,G,B\}}{ min}J^{c}(y)\bigg{)} \tag{3}\]  
Here, \(J^{c}\) represents each channel of the color image, and \(\Omega(x)\) denotes a window centered at pixel \(x\). The brightest 0.1% of the pixels' positions in the dark channel image are recorded, and these recorded pixel positions are mapped back to the hazy image \(I(x)\). Then, the value of the brightest point is considered an estimation of the atmospheric light \(A\). Using the estimated atmospheric light, Equation (1) is normalized as follows:  
\[\frac{I^{c}(x)}{A^{c}}=t(x)\frac{J^{c}(x)}{A^{c}}+1-t(x) \tag{4}\]  
By simplifying \(t(x)\) as a constant \(\widetilde{t}(x)\), we can obtain the following equation by taking the double minimum on both sides of Equation (1):  
\[\underset{y\in\Omega(x)}{min}\bigg{(}\underset{c}{min}\frac{I^{c}(y)}{A^{c}} \bigg{)}=\tilde{t}(x)\underset{y\in\Omega(x)}{min}\bigg{(}\underset{c}{min} \frac{I^{c}(y)}{A^{c}}\bigg{)}+1-\tilde{t}(x) \tag{5}\]  
The estimated value of the transmission rate \(\widetilde{t}(x)\) can be obtained by solving the equation in combination with the dark channel prior principle:  
\[\tilde{t}(x)=1-\underset{y\in\Omega(x)}{min}\bigg{(}\underset{c}{min}\frac{I ^{c}(y)}{A^{c}}\bigg{)} \tag{6}\]  
Considering the practical scenario, where particles in the air unavoidably affect the imaging system, to obtain a more natural restored image, we introduce a factor \(\omega\) with a value of 0.95 to retain some level of haze. The modified Equation (6) is as follows:  
\[\tilde{t}(x)=1-\omega\underset{y\in\Omega(x)}{min}\bigg{(}\underset{c}{min} \frac{I^{c}(y)}{A^{c}}\bigg{)} \tag{7}\]  
To avoid the restored image from becoming overly white due to very small transmission rate values, we introduce a threshold \(t_{0}=0.1\) for correction. The final restoration formula is as follows:  
\[J(x)=\frac{I(x)-A}{max(t(x),t_{0})}+A \tag{8}\]
# Effects of Toe-Out and Toe-In Gaits on Lower-Extremity Kinematics, Dynamics, and Electromyography
## 3 Results
### Ground Reaction Force  
The mean measured GRFs of 12 participants with 5 trials of the natural, toe-in, and toe-out gait patterns during the stance phase are presented in Figure 4, and statistical analysis of maximum and peak values are listed in Table 2. The second peak force in the Medial-Lateral (ML) direction was statistical significantly different among the three gait patterns (\(p\) = 0.04). [PERSON]'s test did not reveal significant differences between the natural gait and the toe-in gait. However, the toe-out increased the second peak value in the ML direction by 20% compared to the natural gait. GRF differences caused by the toe-in and toe-out gait patterns in the Anterior-Posterior (AP) and vertical directions were not significant.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Joint Angle (Degree)** & **Natural** & **Toe-In** & **Toe-Out** & \(p\)**-Value** \\ \hline Max hip flexion & 34.46 (2.94) & 36.73 (5.65) & 37.95 (4.45) & 0.13 \\ Max hip extension & 5.26 (3.63) & 4.6 (4.51) & 4.18 (4.90) & 0.21 \\ Max hip adduction & 7.17 (1.72) & 5.89 (3.35) \({}^{*}\) & 6.18 (1.71) \# & 0.04 \\ Max hip abduction & 5.96 (5.25) & 3.77 (2.41) \({}^{*}\) & 8.30 (3.26) \# & 0.01 \\ Max hip internal & 2.71 (5.88) & 10.54 (4.11) \({}^{*}\) & \(-\)2.70 (4.08) \# & 0.01 \\ Max hip external & 9.67 (9.66) & \(-\)3.02 (9.95) \({}^{*}\) & 19.15 (8.50) \# & 0.03 \\ Max knee flexion & 75.60 (8.86) & 67.43 (8.83) \({}^{*}\) & 63.42 (7.84) \# & 0.03 \\ Min knee flexion & 8.95 (0.92) & 10.93 (2.27) & 9.64 (0.69) & 0.07 \\ Max ankle dorsiflexion & 19.46 (18.90) & 19.01 (17.80) & 30.71 (2.69) & 0.29 \\ Max ankle plantar & 4.42 (14.93) & 2.52 (13.47) & \(-\)10.25 (7.46) & 0.07 \\ Max ankle adduction & 38.41 (4.37) & 34.33 (5.50) & 38.28 (3.84) & 0.06 \\ Max ankle internal & 17.16 (12.78) & 22.99 (15.73) \({}^{*}\) & 7.07 (7.24) \# & 0.01 \\ \hline \hline \end{tabular} \({}^{*}\) Significantly different from natural gait, # significantly different from toe-in gait.  
\end{table}
Table 1: Max joint angles of hip, knee, and ankle joints (mean \(\pm\) SD).  
Figure 4: Measured ground reaction forces (GRFs; mean and standard deviation) of the stance phase for the natural, toe-in, and toe-out gait patterns (12 participants and 5 trials for each gait patterns).
## 2 GNSS Signal Characteristics
### Cycle Slip  
Carrier phase cycle slip is generally due to a temporary loss-of-lock in the carrier tracking loop of a GNSS receiver, and is an unknown integer number of cycles varying from one to millions [20]. In general, cycle slips are caused by obstructions of satellite signal path, low carrier-to-noise density ratio, or failed receiver software [21]. [PERSON] et al. [12] showed the experiment of carrier phase cycle slip of the Xiaomi Mi 8; even with the duty cycle being switched off, the cycle slip percentage was close to 20%. According to the GNSS analysis app release notes by Google, the state of the \"Accumulated Delta Range\" can detect the carrier phase reset and loss-of-lock on a smartphone. The cycle slips detected by the Huawei Mate30 itself are shown in Figure 3.  
The cycle slip percentage of GPS, GLONASS, and BDS satellites are 1.85%, 4.04%, and 0.18% in 6000 epochs, respectively. However, it is not rigorous for cycle slip detection only depending on the smartphone itself. Therefore, the measurement-based polynomial fitting (MPF) method was used to detect cycle slip again. The basic principle of MPF is to conduct polynomial fitting for the phase observations of several epochs, and the \(k\)th epoch observation can be expressed as [22]:  
\[E(\widetilde{\varphi_{i}})=a_{0}+a_{1}k+a_{2}k^{2}+\cdots+a_{m}k^{m} \tag{1}\]  
where \(i=1,2,\cdots,n(n>m+1)\) and \(a_{0},a_{1},\cdots,a_{m}\) are the array coefficients, which are computed by the least squares method. The formal standard deviation (STD) of unit weight is computed as:  
\[\sigma=\sqrt{\frac{[V^{T}V]}{n-m-1}} \tag{2}\]  
where \(V\) is residual error. The number of extra GPS and BDS cycle slips detected by MPF is 25 and 19, respectively, and we mark them with red boxes in Figure 3. Surprisingly, thousands of extra GLONASS cycle slips are detected by MPF, and it is difficult to mark them in Figure 3a. We conducted extensive  
Figure 3: Huawei Mate30 carrier phase cycle slip detection of GNSS L1 band observations. (**a**) GPS and GLONASS; (**b**) BDS. The extra cycle slips of GPS and BDS satellites detected by measurement-based polynomial fitting (MPF) were marked with red boxes.  
experiments at a different time, and it is also occurred in the experiments conducted using the Huawei Mate20 and Huawei P30. Therefore, we believe that the cycle slip phenomenon of GLONASS seems to relate to the GNSS chip in smartphones. From Figures 3 and 2a, it can be found that the cycle slip rarely occurs when the C/N\({}_{0}\) value is above 40 dB-Hz, and it gradually occurs while C/N\({}_{0}\) value changes from 40 to 30 dB-Hz. With the C/N\({}_{0}\) value less than 30 dB-Hz, the number of carrier phase cycle slips increased significantly.
# Augmented Reality's Impact in Industry--A Scoping Review
## 4 Discussion
### RQ1--What Has Been the Publication Trend over the Years?  
Overall, a growing trend can be observed over the time interval established for this review. The trend analysis reveals a gradual increase in publications between 2015 and 2019, followed by a significant rise starting in 2020, when the number of publications almost doubled compared to the previous year. This sharp increase can be attributed to multiple factors, including the COVID-19 pandemic [36], which accelerated the adoption of new technologies to overcome operational challenges in the industry.  
The inherent human-centric nature of AR has contributed to its seamless integration into Industry X.0. Since its early adoption in 2011 in Industry 4.0, AR has been recognized as a valuable tool for enhancing industrial processes [37]. However, the introduction of the fifth industrial revolution in 2021 brought a renewed focus on personalization, sustainability, and the human factor, elevating the role of AR as a important technology in achieving these goals [16]. The transition from Industry X.0 represents a natural evolution, as AR aligns perfectly with the emphasis on human-centric innovation. This shift is reflected in the growing number of studies exploring AR applications designed not only to optimize processes but also to address societal and environmental needs, marking a broader and more inclusive role for the technology [38].  
The adoption of AR technology across various fields, coupled with the emergence of tools for its easy implementation has led to a continuous increase in publication trends. The development of such applications is becoming increasingly accessible due to developer forums and the simplification of development software [39; 40]. Additionally, the growing popularity of AI in different domains [41] has sparked interest in integrating these two technologies to enhance the overall performance of AR applications.  
A deeper exploration was conducted on publications from 2021 onwards to assess the influence of Industry 5.0 on publication trends. This analysis included the entire dataset prior to the first screening phase, allowing for a broader evaluation of publications addressing Industry 4.0 and/or Industry 5.0. Findings indicate that Industry 4.0 continues to dominate as the most referenced concept in terms of technology. Some studies incorporated principles of Industry 5.0, such as human-centric [42] or sustainable [43], without explicitly naming the concept. This observation underscores the intertwined nature of Industry X.0 within AR applications. Furthermore, studies rooted in Industry 4.0 cite Industry 5.0 as a framework for future research [44]. These findings highlight that while Industry 5.0 principles are being applied, the publication trend remains firmly grounded in Industry 4.0.
## 3 Numerical Simulation  
Commercial finite element simulation software ABAQUS is used for SPIF numerical simulation. The simulation was carried out using ABAQUS/Explicit. The thin sheet is set as a solid element. Forming tool, fixture and die are set as a rigid body. As shown in Figure 3 below, the stress-strain curve of Al-Li alloy at 200 \({}^{\circ}\)C is input into the material properties. Other material parameters are shown in Table 1. The coefficient of friction between the tool head and the sheet is set to 0.1. An eight-node linear hexahedral cell with reduced integration (C3D8R) was used for meshing. Grid sweep method is used for meshing the thin sheet, a global size of 2 is set and adaptive meshing is performed. The coordinate of the forming tool path is extracted according to the set trajectory route in Unigraphics NX software, the coordinates of the forming tool path are extracted and imported into ABAQUS software.  
In order to verify the accuracy of the model, a comparison of the wall thickness and contour accuracy between simulated truncated pyramid component and the experimentally formed pyramid cone component was conducted. As shown in Figure 4, the SPIF forming depth was set to 20 mm, the deformation temperature was at 200 \({}^{\circ}\)C, the forming angle was set as 50\({}^{\circ}\), the rotation speed was at 600 rpm, the feed rate was 1200 mm/min, and the forming tool head radius was 5 mm.  
Figure 2: (**a**) Truncated pyramid component mold; (**b**) 3D model of the component, the dashed lines represent the geometric profile measurement area; (**c**) the dimensions of the sheet (160 \(\times\) 80 mm) and forming area (130 \(\times\) 50 mm).  
From Figure 5, it can be seen that the contour accuracy and thickness distribution of experimentally formed truncated pyramid component is basically consistent with the simulation results. The main deviation is at the bottom area where material accumulates gradually during the SPIF process, leading to an increase in thickness and geometric errors.  
Figure 4: (a) Single point incremental forming components; (b) simulation results.  
Figure 5: Comparison of contour and thickness in the experimental results and simulation results of single point incremental forming.  
Figure 3: Stress-strain curves at different temperatures.
# A Blockchain Solution for the Internet of Vehicles with Better Filtering and Adaptive Capabilities
## 8 Potential Applications in Other Fields  
### Supply Chain Management  
In supply chain management, the integration of blockchain and consensus algorithms, such as AE-PBFT can address key challenges, including transparency, traceability, and trust among stakeholders. The AE-PBFT algorithm, with its dynamic consensus group and trust acceleration mechanism, is particularly well-suited for supply chains that involve diverse participants, such as manufacturers, distributors, retailers, and consumers. By leveraging AE-PBFT, the system can dynamically adjust the consensus group ratio to adapt to varying levels of trustworthiness among participants, especially in complex multi-tiered supply chains. Malicious actors--such as those introducing counterfeit goods--can be quickly identified and excluded based on their historical behavior. Additionally, AE-PBFT's efficient consensus mechanism ensures low latency and high throughput, making it feasible for real-time tracking of goods, reducing delays, and enhancing operational efficiency. This can be especially beneficial for industries like pharmaceuticals or food supply chains, where product authenticity and timely delivery are critical.
## 2 Materials and Methods  
### Excitation Modelling  
Section 2.1 presents the description of the proposed spatiotemporal mining-induced kinematic excitation model. It contains the theoretical background for the dynamic behavior of multiple-support structures under spatially varying ground motion, the conditional random field simulation of ground motions, and the empirical regression relationship of amplitude decay with an epicentral distance used in the excitation model. 1.1 Theoretical Background for Dynamic Behaviour of Multiple-Support Structures under Spatially Varying Ground Motion  
The equations of motion, characterizing the dynamic performance of a multi-degree-of-freedom system caused by ground vibrations, can be formulated as follows [8; 24]:  
\[ \begin{bmatrix}\mathbf{M}_{ss}&\mathbf{M}_{sg}\\ \mathbf{M}_{gs}&\mathbf{M}_{gg}\end{bmatrix} \cdot\begin{pmatrix}\ddot{\mathbf{ x}}_{s}\\ \ddot{\mathbf{x}}_{g}\end{bmatrix}+ \begin{bmatrix}\mathbf{C}_{ss}&\mathbf{C}_{ gs}\\ \mathbf{C}_{gs}&\mathbf{C}_{gg}\end{bmatrix} \cdot \begin{pmatrix}\dot{\mathbf{x}}_{s} \\ \dot{\mathbf{x}}_{g}\end{pmatrix} + \begin{bmatrix}\mathbf{K}_{ss}&\mathbf{K}_{sg} \\ \mathbf{K}_{gs}&\mathbf{K}_{gg}\end{bmatrix} \cdot \begin{pmatrix}\mathbf{x}_{s} \\ \mathbf{x}_{g}\end{pmatrix} = \begin{pmatrix}0\\ \mathbf{F}_{g}\end{pmatrix}  \tag{1}\]  
where: \(s\), \(g\)--degrees of freedom of structure and ground, respectively, \(\mathbf{M}\), \(\mathbf{C}\), \(\mathbf{K}\), mass, damping and stiffness matrices, \(\ddot{\mathbf{x}}_{s}\), \(\dot{\mathbf{x}}_{s}\), \(\mathbf{x}_{s}\), vectors of accelerations, velocities, and displacements for each DOF of the structure, \(\ddot{\mathbf{x}}_{g}\), \(\dot{\mathbf{x}}_{g}\), \(\mathbf{x}_{g}\), vectors of accelerations, velocities, and displacements for each DOF of the ground, \(\mathbf{F}_{g}\), reaction vector. The vector of total nodal displacements (\(x\)) consists of dynamic (\(x^{d}\)) and quasi-static (\(x^{d}\)) parts:  
\[\mathbf{x}= \begin{pmatrix}\mathbf{x}_{s}^{d}\\ 0\end{pmatrix} + \begin{pmatrix}\mathbf{x}_{s}^{d}\\ \mathbf{x}_{g}^{s}\end{pmatrix}  \tag{2}\]  
where:  
\(\mathbf{x}_{s}^{d}\)--sub-vector of dynamic nodal displacement of a structure,  
\(\mathbf{x}_{s}^{d}\)--sub-vector of quasi-static nodal displacement of a structure,  
\(\mathbf{x}_{g}^{s}\)--sub-vector of quasi-static nodal displacement of the ground. It must be emphasized that the dynamic contribution of the soil is neglected in Expression (2). This simplification is commonly used by authors; however, there are works concerning the non-synchronous excitation of bridges in which the dynamic effects of the soil-structure interaction are taken into account [25].
## 2 Related Works
Although relevant studies on device association or power control have been carried out in the above literature, there are few studies on the joint optimization of device association and power control for 5G and satellite converged networks oriented to power transmission and distribution scenarios. Therefore, inspired by the above work, in order to further improve the system performance and user experience, the joint optimization problem between user association and power control in heterogeneous networks also becomes particularly important. Inspired by the above work, under the premise of ensuring the minimum transmission rate requirement of PDs, we propose an online optimizationalgorithm that combines device association and power control to maximize the long-term total system energy efficiency.
# Prediction of Air Pollution Concentration Based on mRMR and Echo State Network
## 4 Results and Discussion
### Experimental Results and Analysis
Longitudinal comparison between Tables 2-4 shows that the longer the prediction time is, the larger the error will be, which is consistent with the basic characteristics of the chaotic time series. Comparing Tables 2-4, it can be found that the recurrent neural networks in three kinds of networks (SAE as the representative of deep learning, ELM and H-ELM as the representative of feedforward neural network and ESN as the representative of recurrent neural network) have more satisfactory prediction performance. This exactly demonstrates the validity of the structure of the reservoir in time series prediction. In order to further illustrate the performance of the proposed method, the running time of all comparison methods are shown in Table 5. The results show that the training time based on the ESN \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **RMSE** & **NRMSE** & **MAE** & **SMAPE** & **R** \\ \hline
**ESN** & 72.3723 & 0.2299 & 50.5928 & 0.5620 & 0.6858 \\
**LI-ESN** & 70.5700 & 0.2944 & 49.3628 & 0.5569 & 0.6895 \\
**ELM** & 71.6619 & 0.2429 & 47.1085 & 0.5385 & 0.6582 \\
**H-ELM** & 66.7932 & 0.2760 & 46.1529 & 0.5003 & 0.7053 \\
**SAE** & 71.7758 & 0.3631 & 49.7844 & 0.5617 & 0.6623 \\
**SLI-ESN** & 65.7108 & 0.1966 & 46.3633 & 0.5443 & 0.7314 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of ten-step (10 hours) prediction results. Figure 8: PM\({}_{2.5}\) concentration ten-step (10 hours) prediction curve. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **RMSE** & **NRMSE** & **MAE** & **SMAPE** & **R** \\ \hline
**ESN** & 43.1243 & 0.0677 & 29.3879 & 0.3777 & 0.8907 \\
**LI-ESN** & 41.1574 & 0.0646 & 27.6611 & 0.3577 & 0.9027 \\
**ELM** & 45.7617 & 0.0718 & 29.7892 & 0.3726 & 0.8678 \\
**H-ELM** & 49.9159 & 0.0783 & 32.1977 & 0.3974 & 0.8368 \\
**SAE** & 51.9847 & 0.0816 & 34.5487 & 0.4039 & 0.8394 \\
**SLI-ESN** & 37.6874 & 0.0591 & 25.5871 & 0.3392 & 0.9108 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of five-step (5 hours) prediction results. and ELM model is much smaller than the deep learning model. This is mainly because the training process of deep learning consumes a lot of time, and this time-consuming process is not required by the other neural networks. Moreover, SLI-ESN can complete training and testing within an acceptable time frame. It indicates that SLI-ESN can achieve good prediction results in both prediction accuracy and time consuming.
## 3 Methodology
### Feature Extraction Methods
#### 3.4.2 Deep Learning  
* AraBERT is a transformer-based model specifically designed for Arabic language understanding, developed by researchers from the University of Maryland and King Abdulaziz University. Utilizing the Bidirectional Encoder Representations from Transformers (BERT) architecture introduced by Google in 2018, AraBERT captures contextual relationships between words using self-attention mechanisms. Pre-trained on a vast corpus, including 1 billion tokens from the Open Source International Arabic News (OSLAN) corpus and 1.5 billion words from major news outlets, it effectively learns the linguistic features and statistical trends in Arabic. AraBERTv0.2 employs a segmented approach to word embeddings, breaking words into stems, prefixes, and suffixes to address lexical sparsity. This model generates robust word embeddingsby averaging activations from its layers, providing valuable input for various classification tasks. By leveraging the strengths of BERT's encoder, AraBERT delivers advanced, deep, and unsupervised bidirectional language representations, making it highly effective for ASA and other NLP tasks.
* Word2 Vec is a framework leveraging CBOW (Continuous Bag of Words) and SG (Skip-gram) models, which is pivotal for sentiment analysis in textual data processing. CBOW excels in generalization by predicting words based on context. At the same time, SG focuses on understanding the context of a given word, revealing syntactic and semantic patterns crucial for nuanced sentiment detection. Using the Skip-gram model, our research explores the effects of dimensionality and contextual scope, with window size (5) and vector dimension (300).
* The fastText model, leveraging subword features like infixes, suffixes, and prefixes, significantly enhances ASA by addressing out-of-vocabulary issues and the language's complex morphology [33]. Treating words as character n-grams, fastText captures morphological nuances valuable for agglutinative languages. We generate sentiment-focused word embeddings using CBOW and SG techniques, with SG models enhancing contextual understanding and CBOW models deepening language structure comprehension. Our research uses the Skip-gram model to examine the effects of dimensionality and contextual scope, with a window size of 40 and a vector dimension of 300.