esa-sceva/satcom-chunk-collection huggingface dataset:
# New Approach of Blind Adaptive Equalizer Based on Genetic Algorithms
## 3 Methods
### Concepts of Linear Programming Under Uncertainty  
Linear programming (LP) is a technique used to optimize linear objective functions, subject to linear equality and linear inequality constraints. A LP can be expressed in canonical form as  
\[\begin{array}{ll}\text{Optimize}&v^{T}w=z\\ \text{subject to}&Uw=b\ \ (Uw\leq b\text{ or }Uw\geq b),\ U:m\ \times\ n,\\ &w\geq 0.\end{array} \tag{1}\]  
where \(w\in\mathbb{R}^{n}\) represents the decision variables vector, and \(v\), \(U\) and \(b\) are known data associated to the problem. The optimization operation can denote either a maximization or a minimization transformation. The mathematical properties of an LP imply in certain assumptions that must be satisfied in relation to the activities of the problem. Thus, these assumptions are defined by [39] as follows:  
* Proportionality assumption: The contribution of each activity to the value of the objective function \(z\) is proportional to the level of the activity \(w_{j}\), as represented by the \(v_{j}w_{j}\) term in the objective function;
* Additivity assumption: Every function in a linear programming model is the sum of the individual contributions of the respective activities;
* Divisibility assumption: Decision variables in a linear programming model are allowed to have any values, including non-integer values, that satisfy the functional and non-negativity constraints;
* Certainty assumption: The value assigned to each parameter of a linear programming model is assumed to be a known constant.  
In real applications, the certainty assumption is seldom satisfied precisely. Linear programming models usually are formulated to select some future course of action. Therefore, the parameter values used would be based on a prediction of future conditions, which inevitably introduces some degree of uncertainty [39]. Thus, a LP model under uncertainty (or a Stochastic Linear Programming) can be defined in a simplified way as  
\[\begin{array}{ll}\text{Optimize}&v^{T}w+E\Big{[}Q(w,\zeta)\Big{]}=z\\ \text{subject to}&Uw=b\ \ (Uw\leq b\text{ or }Uw\geq b),\ U:m\ \times\ n,\\ &w\geq 0.\end{array} \tag{2}\]  
Figure 1: A simplified scheme of the evolutionary process of a GA.  
where \(\xi\) represents the uncertain data and \(Q(w,\xi)\) represents the optimal value for the uncertain (stochastic) parameters.
# Theoretical Optimization of Trapped-Bubble-Based Acoustic Metamaterial Performance
## 3 Results and Discussion
### Reflector Effect  
We will now apply the technique developed above to solve the same problems in the case of presence of reflector in a vicinity of metatamaterial. Let us consider a case of a single interface first. Assuming that \(hk\ll 1\), \(\Omega_{i}\) read as:  
\[\Omega_{1,2}=\Gamma_{3}\pm\sqrt{\Delta_{3}^{2}+\delta^{2}}, \tag{30}\]  
where \(\Gamma_{3}\) and \(\Delta_{3}\) are given by:  
\[\Gamma_{3}=\frac{B(-1+r_{0}^{2})}{2r_{0}},\quad\Delta_{3}=\frac{B(1+r_{0})^{2} }{2r_{0}} \tag{31}\]  
Afterwards, the resonant frequencies of the system read as:  
\[\omega_{01}=\frac{\omega_{b}}{\sqrt{1-A+\Gamma_{3}+\sqrt{\Delta_{3}^{2}+\delta ^{2}}}},\quad\omega_{02}=\frac{\omega_{b}}{\sqrt{1-A+\Gamma_{3}-\sqrt{\Delta_{ 3}^{2}+\delta^{2}}}} \tag{32}\]  
If both \(hk\ll 1\) and \(\delta\ll 1\) the solution reduces to:  
\[\Omega_{1,2}=\Gamma_{3}\pm\Delta_{3} \tag{33}\]  
Subsequently, the resonant frequencies of the system are:  
\[\omega_{01}=\frac{\omega_{b}}{\sqrt{1-A+\Gamma_{3}+\Delta_{3}}},\quad\omega_{ 02}=\frac{\omega_{b}}{\sqrt{1-A+\Gamma_{3}-\Delta_{3}}} \tag{34}\]  
or in more explicit form:  
\[\omega_{01}=\frac{\omega_{b}}{\sqrt{1-A+B(1+r_{0})}},\quad\omega_{02}=\frac{ \omega_{b}}{\sqrt{1-A-B\Big{(}1+\frac{1}{r_{0}}\Big{)}}} \tag{35}\]  
Analogous to this, in case of two interfaces near the bubble metascreen for \(hk\ll 1\), we obtain:  
\[\Omega_{1,2}=\Gamma_{4}\pm\sqrt{\Delta_{4}^{2}+\delta^{2}}, \tag{36}\]  
where \(\Gamma_{4}\) and \(\Delta_{4}\) are given by:  
\[\Gamma_{4}=\frac{B(-1+r_{0}^{2})}{4r_{0}},\quad\Delta_{4}=\frac{B(1+r_{0})^{2} }{4r_{0}} \tag{37}\]  
Subsequently, the resonant frequencies of the system read as:  
\[\omega_{01}=\frac{\omega_{b}}{\sqrt{1-A+\Gamma_{4}+\sqrt{\Delta_{4}^{2}+\delta ^{2}}}},\quad\omega_{02}=\frac{\omega_{b}}{\sqrt{1-A+\Gamma_{4}-\sqrt{\Delta_ {4}^{2}+\delta^{2}}}} \tag{38}\]  
If both \(hk\ll 1\) and \(\delta\ll 1\) the solution reduces to:  
\[\Omega_{1,2}=\Gamma_{4}\pm\Delta_{4} \tag{39}\]  
Afterwards, the resonant frequencies of the system are:  
\[\omega_{01}=\frac{\omega_{b}}{\sqrt{1-A+\Gamma_{4}+\Delta_{4}}},\quad\omega_{ 02}=\frac{\omega_{b}}{\sqrt{1-A+\Gamma_{4}-\Delta_{4}}} \tag{40}\]or in more explicit form:  
\[\omega_{01}=\frac{\omega_{b}}{\sqrt{1-A+\frac{B}{2}(1+r_{0})}},\quad\omega_{02}= \frac{\omega_{b}}{\sqrt{1-A-\frac{B}{2}\big{(}1+\frac{1}{r_{0}}\big{)}}} \tag{41}\]
###### Abstract
Thus, TOPSIS has gained a wider usage in flood risk assessment by assessing the relative risks based on attribute value information and thus facilitating the identification of more potential flood risks [53; 54; 61; 62; 63; 64]. According to this method, a distance measure is required to compute the distance from the PIS and NIS for each alternative. Thus, distance metrics offer a basis for assessing the difference or similarity between data points, enabling meaningful comprehension to be obtained from data. Therefore, they affect the accuracy of the results. In the literature, there are various distance measures that can be grouped under various hypotheses and behaviors. For instance, Euclidean, Manhattan, and Chebyshev are grouped in [PERSON]'s metrics, while Jaccard and cosine similarity is a geometry-based metric and is grouped under the inner product family. Some metrics, such as Pearson, are derived from correlation metrics, while some metrics, such as Soergel, can be coherently clustered [65; 66]. The choice of a distance measure to be used is crucial since it directly affects the prediction capability of the model. The most widely used distance measure in flood-related studies is the Euclidean distance [67; 68]. The literature review showed that there is a very limited number of studies on the performance evaluation of the distance measures [69; 70], but none on flood susceptibility mapping to the best knowledge of the authors. Since ranking depends on this computed distance, it is crucial to integrate the most appropriate distance measure considering the problem to be solved and the MCDM approach to be used. [PERSON] et al. [71] used the Euclidean distance in the k-Nearest Neighbor model and noted that other distance measures, such as Manhattan, Chebyshev, and Hamming, could be more suitable for other model settings with no detailed explanation. So, the main goal of this study is to evaluate the performance of different distance measures chosen according to their wide usage or their origins, such as Manhattan, Euclidean, Chebyshev, Jaccard, and Soergel distances, on the generated flood susceptibility maps by using the TOPSIS method in order to further improve the capability of the model outcomes. The frequency ratio (FR) and the weight of evidence (WoE) values are used for the hybridization of the conditioning factors for the creation of a decision-making matrix in the TOPSIS method. After the calculation of closeness coefficient values, flood susceptibility maps were generated. Furthermore, the performance of the predicted maps was evaluated by the application of the Receiver Operating Characteristic (ROC) curve analysis. Flood hazard potential classes were estimated using the four classification methods named equal, quantile, natural break, and geometric intervals to evaluate the outputs by the Seed Cell Area Index (SCAI) analysis.
# Green Synthesis Method and Application of NaP
## Results and Discussion
### XRD Result Analysis  
#### Influence of SiO\({}_{2}\)/Na\({}_{2}\)O Molar Ratio  
Figure 2a is an XRD spectrum with an initial gel composition of 5.2 SiO\({}_{2}\):1.0 Al\({}_{2}\)O\({}_{3}\):xNa\({}_{2}\)O:100H\({}_{2}\)O, in which the molar ratio of SiO\({}_{2}\)/Na\({}_{2}\)O is controlled to be 1.00-1.50 by adjusting the amount of NaOH added. When the molar ratio of SiO\({}_{2}\)/Na\({}_{2}\)O is increased from 1.00 to 1.04, the product changes from an amorphous phase to a characteristic peak of NaP zeolite. As the molar ratio further increases to 1.13, the crystallinity of the NaP zeolite gradually increases. Appropriate OH\({}^{-}\) groups can promote the dissolution of amorphous gel, shorten the induction period, and accelerate the growth of NaP zeolite crystals [18; 19]. When the SiO\({}_{2}\)/Na\({}_{2}\)O molar ratio reaches 1.50, the characteristic peak of the NaP zeolite is still significant, but the crystallinity is slightly decreased, which is related to the large negative charge density in the skeleton structure [20].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Parameter** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **CaO** & **K\({}_{2}\)O** & **MgO** & **Na\({}_{2}\)O** & **Other** & **LOI** \\ \hline CGCS & 53.44 & 17.21 & 11.23 & 10.12 & 2.27 & 1.92 & 1.33 & 2.48 & 12.55 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition of coal gasification crude slag (CGCS) by XRF analysis (wt%) LOI is loss on ignition.
## 3 The Role of Organizational Theories in Blockchain Literature in the Context of LSCM
### Transaction Cost Analysis  
The theory of transaction costs analysis, or transaction costs economics, is based on the assumption that a company's decision is determined, not only by the price, but also its transaction costs [63]. The most economically efficient transaction, i.e. that minimizes the cost of the transaction, and maximizes the value for the parties, is then the adequate mode for a transaction. The adoption ofblockchain may affect companies inside and outside transaction costs simultaneously, and reduce or expand the company's transaction cost boundaries [1]. For example, a company may discontinue business with companies whose services can be substituted by blockchain technology (e.g., clearing, trade settlement or even mandatory reporting) [64], or new market players offering services needed for blockchain to emerge.  
From a blockchain perspective, eight articles specifically use transaction costs analysis as a foundation to investigate blockchain technology in LSCM. For example, [PERSON] and [PERSON] [47] investigate the potential of blockchain to reduce transaction costs. Having generated six theses, they conclude that blockchain potentially involves a reduction in transactions costs, as well as a more market-oriented governance structure for buyer-supplier transactions by the limitation of opportunistic behavior as well as uncertainty of environment and behavior. Similarly, [PERSON], [PERSON] and [PERSON] [48] examined the potential of blockchain technology to reduce or even avoid cost, and thereby define nine effects of blockchains on supply chain transactions under the three enablers: transparency, trust and disintermediation.  
[PERSON] et al. [65] discuss how blockchain can help startups to deal with their financing issues from a transaction cost perspective, and thereby build a model to prove the effectiveness of blockchain technology for this application. Using smart contracts, [PERSON], [PERSON], [PERSON] and [PERSON] [46] investigated how blockchain alters transaction costs and affects firm boundaries, while [PERSON], [PERSON] and [PERSON] [29] showed that blockchain-based systems can also reduce opportunistic behavior from both trading parties, since verifiable smart contracts are immutably recorded on the blockchain.  
Overall, transaction costs analysis can help to determine the effect of transaction costs changes through blockchain technology, which subsequently affects organizational structures and practices. Thus, transaction costs economics can be used to explain how certain elements of blockchain technology change the design of contract agreements, and how automated smart contracts may reduce transaction costs significantly.
## 2 Related Work
### Vanishing Points Analysis  
Geometrically, a vanishing point on a spatial line is formed by intersecting the image plane with a ray parallel to the line and passing through the center of the camera [31]. [PERSON] et al. searched for line features based on vanishing points before fusing parallel 3D straight lines and constructed parallel line residuals by removing anomalies during the clustering of multi-view lines [20]. In the camera calibration method [32] proposed by [PERSON] et al., multiple vanishing points are utilized to estimate the camera's internal parameters, simplifying the calculations associated with calibration. [PERSON] et al. proposed a two-module approach for estimating vanishing points, where the two modules are used to track clustered line features and generate a rich set of candidate points, respectively [33]. [PERSON] et al. detected vanishing points based on the inertial measurement and constantly updated it by the least squares method, which provided a global rotation observation around the gravity axis, thus correcting the yaw angle drift in pose estimation [34]. [PERSON] et al. projected the direction of the vanishing point of the 3D line and plane normal onto the tangent plane of the unit sphere, which estimates the rotation matrix of each image frame [35]. [PERSON] et al. involved the vanishing point in a deep multi-task learning algorithm, which was used to realize the adjustment of the camera, and then went through three sub-tasks to improve the detection accuracy of the vanishing point, and finally followed the track segmentation to determine the perimeter of the intruded area [36]. The analysis of vanishing points is summarized in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline
**Line Feature Analysis** & **Characteristics** \\ \hline
[23] & Combining line features with point features using the LSD algorithm. \\
[19] & Modifying the parameters of the LSD algorithm for real-time \\  & extraction of line features. \\
[20] & Fusing line features and corner features using positional similarity. \\
[26] & Detection of line features using the ED algorithm to improve \\  & extraction speed. \\
[27] & An algorithm capable of extracting line features with geometric \\  & constraints is proposed. \\
[28] & Improving line segment detection efficiency with ELSED with a local \\  & line segment growth algorithm. \\
[29] & Setting an adaptive threshold algorithm for the line segment \\  & extraction process. \\
[30] & Proposed probabilistic clustering of line segments using descriptors \\  & based on grid computing. \\ \hline \end{tabular}
\end{table}
Table 1: Summary of line feature analysis.
# Visual-Saliency-Based Abnormality Detection for MRI Brain Images--Alzheimer's Disease Analysis
## 2 Proposed Methodology
### Generation of Saliency Maps
#### 2.3.1 Top-Down Saliency Maps (\(S_{t}\))  
In practice, a physician with some expertise can find the most atrophic brain areas in MRI images. According to neurodegeneration cell pattern, many brain areas are responsible for AD. Hence, the visual assessment of MRI images depends solely on brain shrinkage. This is accounted for in terms of tissue property variations.  
In MRI analysis, brain deterioration is viewed as a variation of tissues in the gray matter or white matter. Tissue density variations in cell degeneration are the major differences between normal and Alzheimer's disease brains. If cell density is reduced, it reflects the reduced volume in the structure of gray matter and white matter. This top-down knowledge was added to the saliency map. Loss of hippocampal volume differentiates the brain of a person suffering from Alzheimer's disease from the brain of a normal person [26; 27]. Normally, top-down saliency maps incorporate high-level knowledge denoted by brain MR volumes. Each MRI consisted of three tissues--gray matter, white matter, and cerebrospinal fluid.  
To obtain the domain knowledge of each tissue, initially, a probability map was generated. A Gaussian distribution cluster analysis was used to segment the tissues. This was identified through the voxel intensity distribution of the brain tissue. This represents the distribution of tissues in the brain, which is calculated using a statistical parametric mapping approach. Probability map values range from 0 to 1. These maps highlight the spatial distribution of brain tissues. The intensity is proportional to the tissue volume before warping.  
The probability map of a voxel at \((x,y)\) is \(m_{0}m(x,y)\). It belongs to the \(set=\{g,m,c\}\) and it is represented by \(set=P(m(x,y)/set)\). If \(P(m(x,y)/g)\) is the probability of a voxel being gray matter [28].  
Steps for estimating the top-down saliency map (\(S_{T}\))  
1. Build probability map;
2. \(m_{0}m(x,y)\leftarrow\mathrm{T}(m_{0}m(id))\)_for all id_;
3. Regularize into a fixed range (0...1);
4. If probability (g)> = 0.5 then
5. \(S_{T}\gets m_{0}m(x,y)\)
6. else
7. \(S_{T}\gets 0\);
8. \(end\)\(if\)\(S_{T}\gets S_{T}\times\ \delta\).  
Top-down saliency maps mostly depend on domain knowledge. According to the top-down saliency map construction steps, the saliency map is calculated by considering only visible voxels (the probability of gray matter being greater than 0.5). In the proposed method, top-down saliency was generated to identify whether the gray matter tissues of patients with AD varied from those of normal control patients. This is achieved through the rejection of irrelevant features from both cases. Min- max-margin discrimination is used to classify features between two classes, namely, the Alzheimer's disease class and the normal control class [20]. It is an optimization task to classify each feature in the brain volume.
## 5 Analysis of Key Factors Influencing the Cyclic Operation Time  
For the time for cyclic operation for intelligent solid backfilling, the influencing factors include geological conditions, working face layout, filling equipment and processes used, backfill material, and intelligence level.  
When the integrity of the roof strata in the working face is poor or causes the rear beam of the SBMHS to rotate and sink, and the caving of roof rocks into the goat reduces the space available for backfilling operations, it will lead to increased interference between the tamping mechanism and the backfilling scraper conveyor, resulting in an increased number of connections for the tamping cylinder. In addition, an uneven roof will increase the time for the hydraulic support adjusting process [30].  
With the constant geological conditions, working face layout, and filling equipment, we can shorten the time of the operation cycle by upgrading the intelligence level and optimizing the processes. We change the parameters of the filling technology to analyze the influence of upgrading the intelligence level and optimizing the processes on the backfilling efficiency. The filling process parameters include the following: time gap in cylinder actions, number of tappings, number of supports in parallel operation, and intelligence index.  
Univariate analysis was performed, and the values of the parameters were preset as follows: length of the working face of 58 m, distance between support centers of 1.5 m, number of supports of 39, and time gap in cylinder actions of 5.0 s; the number of tappings required to achieve a satisfactory filling effect was 10; the number of supports in parallel operation was 3; and the intelligence indices of the mechanized, automated, and intelligent operations were 1, 2, and \(\infty\), respectively.
###### Abstract  
This paper presents two power amplifiers designed for 5G NR n77 and n78 bands. These power amplifiers were fabricated using WIN(tm) Semiconductors' 0.25 \(\upmu\)m GaN/SiC technology and GaAs IPD technology. To achieve a reduction in costs, GaAs IPD technology was incorporated in the design, leading to the realization of a quasi-monolithic microwave integrated circuit design. To ensure high power, high efficiency, and broadband operation, a continuous Class-J mode output matching network was utilized. The power amplifier with split chip-on-board wire-bond assembly had a power gain of 21.7 dB, a 3 dB power bandwidth ranging from 2.85 GHz to 4.48 GHz, a saturation power of 40.3 dBm, and a peak power-added efficiency of 39.5%. On the other hand, the power amplifier with stack chip-on-board wire-bond assembly had a power gain of 21.7 dB, a 3 dB power bandwidth ranging from 2.84 GHz to 4.47 GHz, a saturation power of 40 dBm, and a peak power-added efficiency of 36.5%. For a 5G NR FR1 256-QAM 100-MHz bandwidth modulated signal with a frequency range of 3.3 GHz to 4.2 GHz, both the split and stack chip-on-board wire-bond assembly power amplifiers achieved average output powers of 29.6 dBm and 28.3 dBm, respectively. These output powers were measured under an error vector magnitude requirement of 3.5%.  
5G; broadband; continuous Class-J mode; GaN/SiC; GaAs; IPD; power amplifier (PA); quasi-monolithic microwave integrated circuit (quasi-MMIC)
# A Comprehensive Review of Mechanisms, Predictive Techniques, and Control Strategies of Rockburst
## 3 Technologies and Methods for Rockburst Research
### Numerical Simulation Techniques
#### 3.4.1 Continuum Approaches  
Continuum approaches, which include the finite element method (FEM), boundary element method (BEM), and finite difference method, are based on classical continuum mechanics and thermodynamics theory (FDM). The stress-strain relationship of materials is introduced using the principle of conservation of energy, momentum, and mass based on the deformation and stress continuity in the object. The static or dynamic problems are transformed into partial differential equation (PDE) initial and boundary value problems, which are then discretized into linear, nonlinear, or time-history integral equations for numerical solutions. [PERSON] claimed that FDM is better suited for rockburst simulation than FEM because FLAC can simulate local dynamic processes without causing the entire model to fail [111]. This is due to FLAC's use of an iterative oscillating sequence that boosts out-of-balance forces within narrow grid boundaries. [PERSON] reported similar results when comparing FLAC and Phase2 simulation results, pointing out that the difference in analysis results was due to explicit and implicit solvers, with one tool handling dynamic unloading by default while the other tool could not [112]. As a result, an explicit and dynamic solution scheme is better suited for rockburst simulation because the explicit numerical scheme is stable when the physical system is unstable [113].