esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method  
Figure 1 shows the basic EACNN framework, including its endmember network (EN) and unmixing network (UN). First, through an effective clustering method, the required pseudo-pure endmember bundles are obtained by aggregating the HSI feature information. Next, the EN maps the pseudo-pure endmember bundles to the network layer, which obtains the global spatial and spectral information in the HSI through an efficient convolutional block attention module (ECBAM). The UN obtains the spectral information in the HSI that is useful for learning via effective channel attention (ECA) [49]. Finally, the EACNN uses a parameter-sharing strategy to make the two networks communicate closely, allowing the EN to embed the inherent physical properties of the endmembers into the UN, the UN to feed back its information to guide the EN, and the two sides to promote each other in such a manner as to make the whole network learn in a more accurate and reliable way.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Advantages** & **Disadvantages** \\ \hline FCLSU & The addition of the \(\ell_{2}\)-norm as a regularization term & The obtained solution tends toward a local \\  & effectively improves the computational efficiency & optimum solution \\  & Constructs the nearest-neighbor graph of the local & Global feature information is lost \\ GLNMF & window to represent the structural characteristics & \\  & of data & Global feature information is lost \\ GraphL & Introduces a normalized graph Laplacian to improve & Improved efficiency but insufficient use of HSI \\  & the efficiency & feature information \\  & Graph total variation (gtv) regularization is introduced & Regularization schemes are designed only for \\ gtvMBO & to capture the similarity between spectra and & endmembers, leading to biased estimates of \\  & improve scalability & the abundance \\ DAEU & Spectral features are obtained via a stacked & During dimensionality reduction, feature information \\  & non-negative sparse autoencoder & is lost \\ EGU-pw & Shared network weights improve the unmixing & Using the AE network, feature information is lost in \\  & performance and make it more interpretable & the dimensionality reduction process and the captured \\ \hline \hline \end{tabular}
\end{table}
Table 1: Advantages and disadvantages of different unmixing algorithms.  
Next, the proposed EACNN framework is described in detail.
## 3 Background  
### Memory-Based Algorithm  
As illustrated in Figure 2, current recommendation algorithms can be classified into four types: CB, CF, KB, and hybrid [43], CF is the most common technique amongst them and is either memory-based or model-based [3]. We analyzed the memory-based approach based on early user ratings for an item arranged in a rating matrix. The neighbor-based algorithm is the most well-known algorithm of the memory-based process. It predicts ratings based on users' ratings that are similar to the active user. There are two neighbor-based algorithms: users-based CF [43] and item-based CF [8]. In this paper, we analyzed the user-based CF class. Ordinarily, the two essential steps in CF algorithms are: selecting proper neighbors according to a similarity measure and predicting the rating to generate recommendations for an active user [44]. There are various similarity measures, such as PCC [8] and mean-squared difference [45]. As an output, the similarity method produces a user similarity matrix, determining the similarity or correlation between user pairs. Thus, building a similarity between users forms the necessary neighborhood. Then, a rating of unrated items is predicted using various procedures [46; 47]. The technique used in this paper is explained in Section 3.2.
## 3 Results and Discussion
### Characteristic of GE-EP@OIMs
#### 3.1.3 Thermostability and Inhibitor Loading Content of GE-EP@OIMs
Given that the degradation temperature of BaSO\({}_{4}\) exceeds 1500 \({}^{\circ}\)C, it follows that the degradation process  
Figure 6: TGA curves of pure OIMs, GE-EP, and GE-EP@OIMs. of GE-EP@OIMs will not induce the decomposition of BaSO4 as per the findings of [31]. The loading proportion of OIMs within GE-EP is approximately 34.75%.
## 4 Nonlinear Static Analysis  
To evaluate the seismic behavior of the bridge, pushover analysis, or nonlinear static analysis, was first performed to assess the capacity of the pier. The bottom of the pier, in which a plastic hinge could occur due to ground motion, was subdivided into fine elements. The yield and ultimate displacement capacities were then derived from the shear force and displacement curve obtained from the pushover analysis for each pier.  
In general, the yield displacement of a pier can be determined using either the equivalent energy or effective stiffness approximation method [44; 45; 46]. The equivalent energy approximation method generates a bilinear energy capacity curve from the shear force and displacement relationship and then determines the intersection of the bilinear curve, at which the energy capacity becomes equal at each part of the bilinear curve, as the yield displacement of the pier. The effective stiffness approximation method defines the yield displacement using the bilinear force and displacement curve obtained using the initial and post-yielding stiffness. The initial stiffness is defined as 0.6 times the effective yield load. The yield displacement is defined by intersecting the lines of the initial stiffness and ultimate load, which balance the area of the force and displacement before and after the yield point.  
Figure 5: Numerical modeling of the bridge.  
Figure 6: Stress–strain curves for the inelastic material model: (**a**) [PERSON] et al. (1988) model; (**b**) [PERSON] and [PERSON] (1973) model.  
In this study, the effective stiffness approximation method was used to obtain the yield displacement of the piers. The ultimate displacement is usually defined at a point where a 15% reduction in the force capacity occurs in the force-displacement relationship. When the force reduction was insufficient, a 15% decrease in stress in the critical section of a pier was used to define the ultimate displacement using the strain corresponding to the stress reduction. Table 4 lists the yield and ultimate displacements for each pier according to the pushover analysis results. The yield and ultimate displacements increased, and the shear force decreased with increasing height of the pier. The increase in the yield displacement could be mainly attributed to the decrease in the initial stiffness of the pier. The increase in the ultimate displacement was due to the increase in the corresponding displacement to the constant stress-strain relationship of the material.
# A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks
## 4 Network Traffic Forecasting Based on ARIMA-BPNN
### IGWO-Based BPNN Parameter Optimization
#### 4.2.2 Improvement of the GWO Algorithm  
* OBL-Based Population Initialization  
In the standard GWO algorithm, \((\alpha,\beta,\delta,\omega)\) are initialized randomly, which tends to result in the uneven distribution of the initial population and affects the convergence performance. In order to enable individuals in the initial population to make the best possible use of the solution space information, we introduce the opposition-based learning (OBL) strategy for population initialization. The essence of OBL is to evaluate the optimal solution obtained from the current search while considering the opposite of the optimal solutions, as shown in Figure 6.  
The OBL strategy is specifically defined as follows  
\[\widetilde{X}_{ij}^{t}=\left\{\begin{array}{l}C_{j}+(C_{j}-X_{ij}^{t})\cdot r_{ 3},\ X_{ij}^{t}<C_{j}\\ C_{j}-(C_{j}-X_{ij}^{t})\cdot r_{3},\ X_{ij}^{t}\geq C_{j}\end{array}\right. \tag{20}\]  
\[C_{j}=\frac{\overline{X}_{j}+\underline{X}_{j}}{2} \tag{21}\]  
where \(\widetilde{X}_{ij}^{t}\) denotes the reverse point of the \(i\)-th individual in the \(j\)-th dimension in the \(t\)-th iteration, \(\overline{X}_{j}\underline{X}_{j}\) denote the upper and lower boundaries of the \(j\)-th dimension, respectively, and \(r_{3}\) is randomly distributed within [0, 1].  
* Cosine non-linear convergence factor  
As a swarm intelligent optimization algorithm, it is difficult for GWO to strike a reasonable balance between a local search and a global search, i.e., it is easy for the standard GWO to converge prematurely. The convergence factors \(a\in[0,2]\) in Equation (13) to Equation (15) linearly decrease, making it difficult to reasonably describe the actual non-linear convergence process of GWO. In this paper, a cosine non-linear convergence factor is introduced to describe the convergence process, i.e.,  
\[a=2\cos\left(\frac{\pi}{2}\left(\frac{t}{T_{\text{max}}}\right)^{2}\right) \tag{22}\]  
where \(t\) represents the current iterations and \(T_{\text{max}}\) is the maximum number of iterations. The IGWO algorithm is obtained by improving the GWO with the above two strategies.
# Power Amplifier Predistortion Using Reduced Sampling Rates in the Forward and Feedback Paths
## 4 Experimental Validation  
In this section, the experimental setup used in this work is first presented along with the procedure followed during the experimental validation. Two case studies are then reported. In the first case study, the proposed predistorter is validated using a 20 MHz test signal. In the second case study, the performance of the proposed predistorter is evaluated using a 40 MHz test signal. In each case study, the performance of the proposed predistorter is assessed for several sampling rates in the forward and feedback paths and is benchmarked against state of the art models.
Further investigation is warranted to establish their security, effectiveness, and mode of action in various skin conditions [13]. The paraprobiotics (non-viable cells; intact or broken) and postbiotics (non-viable bacterial products or metabolic byproducts of probiotics) have attracted attention from the food and pharmacological industries recently [21]. Several questions, including the bioactive components of postbiotics, the processes through which they confer health benefits [22; 23], and the immunological aspects of postbiotics, still need to be answered completely [21]. However, many researchers have accelerated their research, implementing postbiotics to identify its benefits in areas including the food industry, pharmaceutics, and cosmetics. Recently, [PERSON] et al. reported that the _L. plantarum_ K8 strain isolated from kimchi could improve skin moisture by regulating the expression of hyaluronic acid synthase-2 and aquaporin-3 in HaCaT cells. The results showed that paraprobiotics of _L. plantarum_ K8 might be used in moisturizing cosmetic products [24]. The studies on paraprobiotics are very limited, and the proficiency of paraprobiotics in skin care products has not been elucidated in detail. Studies on human subjects are necessary to confirm the activity of the paraprobiotics-containing moisturizing product. Thus, the study's objective was to analyze the impact of paraprobiotics (_Bifidobacterium lactis_ (_B. lactis_) and _Lactobacillus plantarum_ (_L. plantarum_)-containing moisturizer on the skin hydration and skin microbiome of healthy subjects.
# Concealed Conduit Routing in Building Slabs
## 2 Materials and Methods
### Pipe Path Routing
#### 2.2.3 Feasible Space Creation  
Once the pipes are sorted, they are to be routed one by one. When routing a pipe, we can optimize its costs by searching the entire workspace and selecting optimal positions to shorten its path and reduce the number of bends. However, this will lead to slow computation and unsafe pipe paths since we have to perform an exhaustive search in the workspace but do not take safety regulations into consideration.  
To overcome these problems, the proposed piping method establishes a _feasible space_ in the bottom-level representation before routing the target pipe. The feasible space covers only a small portion of the workspace, and all its voxels are collision-free and satisfy the safety regulations. Hence, the computation of path-finding is sped up, and the resultant pipe path meets the geometrical constraints and safety regulations.  
* Usage of the distance field  
To establish the feasible space, we retrieve the slabs of the abstract path of this pipe first. These slabs form a subspace, which can be used to lay out the pipe, but we refine it further to reduce the scope of this subspace and enforce the safety regulations and geometrical constraints upon its voxels. To do so, we scan the ordinary voxels of these slabs and collect those ordinary voxels whose distance values \(d(i,j,k)\) meet the following conditions:  
\[\begin{array}{l}r+\varepsilon_{1}\leq d(i,j,k)\leq 2r+\varepsilon_{1}+ \varepsilon_{2},\\ 0<\varepsilon_{1},\ 0<\varepsilon_{2}.\end{array} \tag{1}\]  
where \((i,j,k)\) denotes the indices of an individual voxel, \(r\) is the radius of the pipe, and \(\varepsilon_{1}\) and \(\varepsilon_{2}\) are user-defined parameters for controlling the spaces between the voxel and the obstacles. The collected voxels form a smaller subspace. This smaller subspace is separate from all obstacles and potential hazards by at least \(r+\varepsilon_{1}\) units. Its width is limited by \(r+\varepsilon_{2}\) units. As a result, the subspace is safe for tracing the pipe path while its scope is much smaller than the entire workspace.
* Connectivity of the terminals and safety gaps This subspace cannot be used to route the pipe unless it contains all the terminals of the pipe. To ensure that, we examine all the terminals of the pipe and identify those who are outside this subspace. If an outsider is found, a breath-first search (_bf_s) [9] is triggered from that terminal and is expanded outwards until reaching the current subspace. Then, those ordinary voxels visited by the _bf_s are included in this subspace. Once all the terminals are inside this subspace, the creation of the feasible space is completed.  
The routing of the pipe will be carried out within the feasible space. Since the voxels of the feasible space are screened by Equation (1), the searching space is greatly reduced. Furthermore, by adjusting \(\varepsilon_{1}\), we can create safe gaps between the feasible space and the obstacles or potential hazards. Thus, the resultant pipe path will not violate any safety regulations.
# Social Media and Digital Inequity: Reducing Health Inequities by Closing the Digital Divide
## 5 Actions Needed: Reducing Health Inequities by Reducing Digital Inequalities
### Making Digital Access and Use Affordable  
The digital divide is driven not just by availability but also by affordability. Nearly a third of Americans attribute their lack of broadband to high costs [29]. While these households often have the necessary infrastructure, they cannot afford to pay for broadband services. One U.S. report stated that the affordability gap is the biggest driver of the digital divide in 43 American states [39].  
Affordability is a notable barrier globally, too. The cheapest internet-enabled handset devices cost an average of 19% of the monthly income in low- or middle-income countries [40].  
Several initiatives are in place to make digital access more affordable, at least for some parts of the world. For example, so-called lightweight operating systems--stripped-down smartphones that are still functional but far less expensive to manufacture and operate--significantly lower the cost of internet usage. Increasing offerings of second-hand and refurbished devices also increases the likelihood of low-cost access. Flexible payment models are useful among low- or middle-income populations, where there are more likely to be unpredictable variations in income [40]. Service-providing companies can also implement alternative payment models for such individuals. For example, research from the Brookings Institute indicates that waiving data caps for people who cannot pay for more costly data plans helps to reduce the affordability gap [41].
## 1 Introduction
Nonetheless, the traditional machine-learning-based method requires manual feature extraction and selection, which can be time-consuming and labor-intensive. Considering one of the main advantages of deep learning is its automated feature learning capability on raw data [11], adopting a deep-learning-based approach could circumvent manual feature extraction and selection. Furthermore, when a deep-learning-based approach is fed with handcrafted features, knowledge can be distilled from those features to improve performance [12]. Therefore, in this paper, we introduced a novel deep-learning-based classification framework that analyzed the acceleration data from a single right-ankle sensor for the automated detection of irregular walking surfaces. The acceleration data collected from the right-ankle sensors of 12 subjects from our previous experiment were labelled and applied to our proposed approach. Concretely, because the acceleration data were collected in a time series manner, we proposed using a deep LSTM network. Three different input modalities--raw accelerometer data, single-stride and multi-stride hand-crafted accelerometer-based gait features--were explored, and their effects on the classification performance of the proposed framework were compared and analyzed. To verify the effectiveness and feasibility of our approach, the classification performance of our proposed approach was compared to the traditional machine-learning-based method from our previous study. This paper provided the following contributions:  
1. We presented a novel classification framework using a deep LSTM network to distinguish good and irregular walking surfaces with a single wearable sensor placed on the right ankle. To the best of our knowledge, no existing studies in the walkability assessment domain have proposed a framework that combines deep learning and gait analysis techniques to analyze wearable sensor data for the purpose of conducting sidewalk surface assessments. 2. We compared the performance of three different input modalities for the LSTM-based framework and identified the most suitable modality. It was shown that the LSTM networks that took gait feature modalities as the input were able to achieve convergence and robust performance with limited samples compared to the LSTM networks fed with raw data modalities. 3. We demonstrated that the LSTM networks with gait feature modalities could outperform conventional machine-learning-based methods in the problem domain. 4. We showed that post-processing on several consecutive per-stride predictions of LSTM networks fed with single-stride gait feature modalities to generate final predictions on a larger segment could improve classification performance. The rest of the paper is organized in the following manner: We first discuss a review of related works. Next, we present the research methodology utilized in this work, followed by the experiments section where the experimental setup and results are presented. Lastly, a discussion on the findings and implications of this study is provided. This paper was based on Chapter 4 of the first author's master's thesis [13].
# A Convenient Way to Determine the Optimum Angle of Incidence of Fizeau Interferometer
## 1 Introduction
In this paper, using the second model, firstly, the influence of parallel plate on spatial interference intensity modulation when parallel beam is incident on Fizeau interferometer system is studied theoretically. It is proposed that if the thickness of the parallel plate behind the wedge cavity cannot be ignored, the spatial interference intensity distribution will have an intensity reproduction along the longitudinal direction from the rear surface of the parallel plate. In particular, based on predecessors, an improved method was proposed for finding the optimum incident angle by finding the extreme point of the relationship between the  
Figure 1: Optical layout of Fizeau spectrometer: Based on the configuration of an intentionally inclined afocal system and Fizeau interference system. sum of amplitude vector of the point and the incident angle. On this basis, a more exact expression formula of the optimum incidence angle is also derived. By simply substituting the value of the wedge angle and the reflectivity of the wedge cavity, the accurate value of the optimum incidence angle can be obtained immediately. Thus, it eliminates the tedious and complex process of finding the optimum incident angle by graphing method and makes the formula method the most straightforward choice for finding the optimum incident angle. This method of determining the optimal incident angle is expected to be used to evaluate the optimal laser exposure angle position [16; 17]. The structure of this paper is as follows: In the second section, we analyze the modulation effect of the thickness of the parallel plate behind the wedge cavity on the spatial light intensity distribution and theoretically analyze the influence of the parallel plate on the value of the optimum incident angle. In the third section, we introduce the influence of various factors on the interference intensity and the reasons for the appearance of interference intensity sidelobes. An improved graphing method is proposed to correct the distortion interference intensity by calculating the pole of the relationship between the sum of vector amplitude and incident angle. Based on this method, a more accurate estimation expression of the optimum incident angle is obtained. In Section 4, the optimum incidence angle obtained by the formula is compared with that obtained by the graphing method and [PERSON]'s formula. It is found that the results obtained by the formula method are in good agreement with those obtained by the graphing method.
## 3 Results and Discussion
### Electrical Properties of Nano-Modified Mortars  
The effect of salt spray (fog) corrosion test on the mortars' electrical properties was investigated by measurement of surface electrical resistivity at successive depths inside the ground mortars, with a step of 10 mm, as shown in Figure 11. The depicted changes in electrical resistivity at successive depths below the exposed surface are indicative of physical and chemical changes taking place into the material [33]. Specimens with 0%, 0.2% and 0.4% nano-inclusion concentrations, demonstrate a gradual increase in surface electrical resistivity with interrogation depth and reach, at a depth of 60 mm, a practically common value. This indicates the absence of chloride ions at such depths, independently of the concentration of nano-inclusions in the compositions. On the other hand, resistivity in samples with 0.6% and 0.8% nano-inclusion concentrations differences at the surface with significantly lower values than in the previous case, and follows a gradually increasing trend with depth, until it catches up with the behavior of the lower CNT concentrations. This may be attributed to the electrical percolation threshold of the nano-modified mortars wherein a conductive filler concentration of 0.6% wt. cement CNTs is already sufficient for the creation of a conductive electrical path [2]. Electrical resistivity appears to increase with interrogation depth below the exposed surface, which indicates that chloride concentration decreases with depth. On the contrary, porosity is one of the main parameters affecting the electrical resistivity in a cement-based material. The lower porosity values of specimens with 0.6% and 0.8% wt. to cement carbon nanotubes (Table 2), may indicate that the electrical current takes the path of least resistance inside the specific structures.  
Figure 10: Chloride diffusion coefficient a function of mortar porosity.