esa-sceva/satcom-chunk-collection huggingface dataset:
# A Two-Stage Method to Test the Robustness of the Generalized Approximate Message Passing Algorithm
## 1 Introduction  
Compressed sensing (CS) has been a research focus in recent years [1]. Given the support of an unknown \(K\)-sparse vector \(\mathbf{x}\in\mathbb{R}^{N}\) as \(S=\{j:j\in\{1,\ldots,N\},x_{j}\
eq 0\}\), we consider an under-determined noisy linear system  
\[\mathbf{y}=\mathbf{A}\mathbf{x}+\mathbf{n} \tag{1}\]  
where \(\mathbf{y}\in\mathbb{R}^{M}\) is a compressed sampling observation, \(\mathbf{n}\in\mathbb{R}^{M}\) is an additive Gaussian noise, \(\mathbf{A}\in\mathbb{R}^{M\times N}\) is the sensing matrix. The object is to reconstruct \(\mathbf{x}\) from \(\mathbf{y}\) and \(\mathbf{A}\). There are many reconstruction algorithms to solve the CS problem. The message passing based methods, including approximate message passing (AMP) algorithm [2] and generalized approximate message passing (GAMP) algorithm [3], have remained the most efficient ever since they were proposed. They work very well in the zero-mean Gaussian sensing matrix case, but become unstable and divergent in the more general sensing matrix case. For example, although an improvement to the non-zero mean Gaussian sensing matrix case has in a sense been proposed [4], the highly columns-correlated non-Gaussian sensing matrix case is still a problem. We propose a new GAMP-like algorithm, termed Bernoulli-Gaussian Pursuit GAMP (BGP-GAMP), to raise the robustness of standard GAMP algorithm. Our algorithm firstly utilizes the marginal posterior probability of \(\mathbf{x}\) to sequentially find the support \(S\), and then estimates the amplitudes on \(S\) by a tiny revised GAMP algorithm, termed Fixed Support GAMP (FS-GAMP) which has been proposed in our previous work [5]. A recent work has been proposed by [PERSON] [6], which is similar to our work, but gives another viewpoint from a neural network statistical model. The numerical experiments verify our idea and show a good performance and robustness.  
The rest of this paper is organized as follows. Firstly, we review the standard GAMP, especially Bernoulli-Gaussian prior GAMP, algorithm in Section 2. Secondly, we propose the FsGAMP and BGP-GAMP algorithms in Sections 3 and 4, respectively. Thirdly, experiments are conducted in Section 5. Finally, we draw a conclusion in the last section.
### Microclimate and Human Comfort  
One of the most critical aspects of resilience to consider within the \"gentrification nodes\" is managing climate-related risks and adapting to those risks, to be specific, considering the human heat stress. Welfare and social well-being are essential for climate resilience [11] and fulfilling the \"sustainable development goals\" (SDGs) developed by the United Nations [12]. Open spaces and outdoor activities promote better living standardsand increase the context's land value. Thus, it is essential to consider the outdoor microclimate design and its impact on indoor well-being [11]. Historically, cities' outdoor spaces were well-known for their well-being and moderate temperature ranges. A large percentage of green areas, well-designed plazas, houses with courtyards, and good-ventilated urban streets existed, with passive cooling design strategies to promote better microclimates for indoor and outdoor activities [13].  
Designing spaces mounts a challenge [14] due to the complexity of the microclimate [15] of outdoor spaces. Environmental and artificial parameters shape microclimate and human comfort of a specific context in addition to personal and behavioral parameters [16]. A human's comfort in outdoor spaces is governed by multiple complex parameters differently than the indoor spaces where the environment is controlled [17]. Humans experience interaction with the surrounding's parameters, including:  
* The context parameters where buildings vary in their geometry, clusters, spacing, height, orientation, and surface roughness [18];
* Radiative temperature, the albedo of the materials, and heat exerted [19];
* Local context climate parameters: air temperature, humidity, relative air velocity and direction, solar radiation, and local wind character [20];
* Different vegetation; water patterns that vary in type, size, shape, arrangement, type, and color [21]; and water elements, if they exist;
* Metabolic rate (MET) defines the personal activity of the human sitting, running, and walking [19];
* Clothes insulation factor (Clo) [22].  
Several thermal comfort indices were developed that reflect the human energy balance and quantify the effects of the surrounding environment on the human thermal comfort in outdoor spaces. These developed indices mainly consider metrological parameters, the surface albedo of the materials of the surrounding environment [23], and human parameters [24]. Various indices were developed, including outdoor PMV, PET, UTCI, and many more [23]. PET and UTCI are widely used because mean radiant temperature (Tmrt) is integrated with the calculation formula [24]. This index is easy to interpret and read through a scaled bar range from extremes of heat to extremes of cold, which is why we will use this index in our study.
## 3 Results and Discussion
### Natural Charge Analysis  
The calculated natural charges of Ag and ligand groups for the [Ag(pyridine-3-carboxaldhyde)\({}_{2}\)NO\({}_{3}\)]; (**1**), [Ag\({}_{3}\)(2-pyridone)\({}_{3}\)(NO\({}_{3}\))\({}_{3}\)]; (**2**), and [Ag(3-hydroxypyridine)\({}_{2}\)]NO\({}_{3}\); (**3**) complex units are listed in Table 1. It is clear that the net charge of the nitrate group is close to \(-1\) in complex **3** which comprised an ionic nitrate, which is weakly coordinated to Ag(I) ion. The amount of electrons transferred to Ag(I) in this case is only 0.0358 e. In contrast, the amount of electrons transferred from the nitrate group to Ag(I) ion in complexes **1** and **2** are higher. The amounts of electron density transferred are 0.1635 and 0.1936 e, respectively indicating strongly coordinated nitrate to the silver ion. The pyridine ligand groups transferred 0.1071, 0.0762, and 0.1395 e in complexes **1**-3, respectively. As a result, the charges at silver atom were reduced to 0.6222, 0.7284, and 0.6852, respectively.
## 2 Multi-Scenario Generation Based on Wind Power Forecasting Errors
### Statistical Analysis of Wind Power Forecasting Errors  
To characterize the forecasting error characteristics of wind power output, statistical analysis is first conducted based on historical data from 144 time intervals per day over a one-month period. Let the forecasted wind power at time interval t of a given day be denoted as \(\hat{p}(t)\), and the actual wind power be denoted as \(p(t)\). The wind power forecasting error is then defined as follows:  
\[e(t)=p(t)-\hat{p}(t),t=1,2,\ldots,144 \tag{1}\]  
Based on the historical data from the first 29 days, the forecasting errors are grouped by time interval to form an error sample set for each time period, denoted as \(e_{i}(t)\), where \(i=1,2,\ldots,29\).  
To describe the distribution characteristics of wind power forecasting errors at different time intervals, statistical analysis is further conducted on the error samples of each interval  
Figure 1: Comparison between forecasted and actual wind power outputs.  
to compute the sample mean \(\mu(t)\) and sample standard deviation \(\sigma(t)\). The corresponding formulas are as follows:  
\[\mu(t)=\frac{1}{29}\sum_{i=1}^{29}e_{i}(t) \tag{2}\]  
\[\sigma(t)=\sqrt{\frac{1}{29}\sum_{i=1}^{29}\left(e_{i}(t)-\mu(t)\right)^{2}} \tag{3}\]
# Removal of Methylene Blue from Aqueous Solution by Bone Char
## 2 Experimental
### Kinetic and Isotherm Models
#### 2.3.3 Adsorption Isotherm Models  
The adsorption process is described by Langmuir, Freundlich, and Temkin models, respectively. These isotherm equations can be represented respectively as below [16; 17]:  
The Freundlich isotherm equation can be represented as a linear form:  
\[\text{log}q_{e}=\text{log}k+1/n\text{log}c_{e} \tag{5}\]  
where \(k\) and \(n\) represent the adsorption capacity (mg g\({}^{-1}\)) and adsorption intensity, respectively. When the \(n\) value is between 2 and 10, it is thought that adsorption occurs readily. When the \(n\) value is less than 0.5, the occurrence of adsorption becomes more difficult.  
The Langmuir isotherm equation is represented as follows:  
\[1/q_{e}=1/q_{max}bc_{e}+1/q_{max} \tag{6}\]  
where \(q_{max}\) stands for the maximum adsorption amount (mg g\({}^{-1}\)) and \(b\) represents the adsorption equilibrium constant (L mg\({}^{-1}\)).  
The Temkin isotherm can be described as follows:  
\[q_{e}=B\text{ln}K_{t}+B\text{ln}c_{e} \tag{7}\]where \(B=RT\ b^{-1}\). The units of adsorption temperature \(T\) and the universal gas constant \(R\) are K and J mol\({}^{-1}\) K\({}^{-1}\), respectively. The binding constant \(K_{t}\) is representative of the maximum binding energy, and \(B\) is a constant relevant to the adsorption heat.  
In adsorption studies, the Freundlich, Langmuir, and Temkin isotherms usually are used to describe monolayer adsorption and cannot tell the mechanisms and energy of adsorption; it is the Dubinin-Kaganer-Radushkevich (DKR) isotherm that can give the adsorption mechanism and energy of adsorption process, which is expressed as a linear form [4]:  
\[\ln q_{e}=\ln q_{m}-\beta\varepsilon^{2} \tag{8}\]  
where \(q_{e}\) is the adsorption quantity of methylene blue on bone char (mmol g\({}^{-1}\)). \(q_{m}\), \(\beta\), and \(\varepsilon\) are the DKR single-layer adsorption capacity (mmol g\({}^{-1}\)), adsorption energy constant (mol\({}^{2}\) kJ\({}^{-2}\)), and Polanyi potential (kJ mol\({}^{-1}\)), respectively. \(\varepsilon\) can be represented as follows:  
\[\varepsilon=RT\ln(1+1/c_{e})/1000 \tag{9}\]  
where \(c_{e}\) is the concentration of methylene blue at adsorption equilibrium time (mmol L\({}^{-1}\)). \(\beta\) and \(q_{m}\) can be derived from the plot of ln\(q_{e}\) vs. \(\varepsilon^{2}\). The adsorption energy (\(E\), kJ mol\({}^{-1}\)) can be obtained by the following equation:  
\[E=(2\beta)^{-0.5} \tag{10}\]  
The types of adsorption processes can be estimated by the ranges of \(E\), and the \(E\) values of physical and ion-exchange adsorption are below 8 kJ mol\({}^{-1}\) and among 8\(\sim\)16 kJ mol\({}^{-1}\), respectively [31; 32].
## 4 Numerical Modeling
### Simulation Results and Analysis  
#### 4.5.1 Effect of the Depth of the Vibration Isolation Trench  
Figure 15 shows the trends of PGA and PGV with measurement distance under different depths of isolation trenches. The analysis of soil response behind the isolation trench show that the depth of the isolation trench has a significant effect on its isolation effectiveness. As the depth of the isolation trench increases, the attenuation of PGV and PGA in all directions also increases, indicating that the isolation effect of the isolation trench becomes more significant. The isolation effect changes significantly when the depth of the isolation trench increases from 1 m to 3 m. However, when the depth of the isolation trench increases from 3 m to 4 m, the isolation effect hardly changes. These results indicate that there is a critical depth for
# Bit Independence Criterion Extended to Stream Ciphers
## 5 Conclusions  
An algorithm was proposed to extend the application of the Bit Independence Criterion (BIC) to stream ciphers. This algorithm detects the existence of statistical dependence between the inputs and outputs of a stream cipher. The effectiveness of the BIC test was experimentally confirmed when applied on random matrices, in which it does not detect dependence, and on the RC4 cipher, detecting statistical dependencies between the inputs and outputs of this cipher that are related with previously reported.  
The algorithm depends on the number \(n\) of bits of the inputs, the number \(m\) of bits of the outputs, and the number \(l\) of inputs used. These parameters determine its complexity. The results achieved confirm the importance of varying the \(n\) and \(m\) parameters to apply the BIC criteria in the evaluation of stream ciphers. For RC4 the effectiveness of the criterion is a growing function of the \(n\) and \(m\) parameters.  
It is recommended to guarantee the effectiveness of the proposed BIC test by selecting the values of the parameters greater than the minimum value estimated in the article. From that minimum, increase the values depending on the available computing power, estimating the time using the complexity expressions that were presented from the algorithm.  
The BIC statistical test complements the classical statistical tests of randomness as it allows expanding the evaluation of the stream ciphers, by measuring the degree of independence present between the input of the cipher and its outputs, thus measuring other statistical characteristics that are not only the evaluation of randomness of their output sequences.  
In future work, it is planned to apply this test to other stream ciphers, investigate the optimal choice of the \(m\) and \(n\) parameters and compare the effectiveness of the criterion taking into account the mutual information, the Pearson's coefficient, with the transformation mentioned, and the criteria applied in this work. The behavior of the proposal will be experimentally verified when the sample size increases. It will be investigated in an implementation variant using parallelism for Algorithm 3.  
**Author Contributions:** Conceptualization, E.J.M.-C., G.S.-G. and C.M.L.-P.; methodology, E.J.M.-C., G.S.-G. and C.M.L.-P.; software, E.J.M.-C., G.S.-G. and C.M.L.-P.; validation, E.J.M.-C., R.S.-L. and O.R.; formal analysis, E.J.M.-C., G.S.-G., O.R., R.S.-L. and C.M.L.-P.; investigation, E.J.M.-C., G.S.-G., O.R., R.S.-L. and C.M.L.-P.; writing--original draft preparation, E.J.M.-C., G.S.-G., O.R., R.S.-L. and C.M.L.-P.; writing--review and editing.  
E.J.M.-C., G.S.-G., O.R., R.S.-L. and C.M.L.-P.; supervision, E.J.M.-C., G.S.-G., O.R., R.S.-L. and C.M.L.-P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.
However, there is little research on the shear behavior of the soil around the pile strengthened by permeation grouting outside the pile-soil interface, and the strengthening mechanism of this soil under permeation grouting is not well understood. Most of the present studies about the shear properties of post-grouting cast-in-place bored piles are focused on the soil-pile interface, which is the first type of soil around piles. The bearing capacity of piles is affected by both the pile-cemented soil interface and the strength of the cement soil around piles [29]. In order to explore the shear property of the soil around piles with permeation grouting, analyze its strengthening mechanism, and provide guidance for specific engineering construction, this paper intends to conduct research through indoor tests. In this article, the soil around permeation grouting piles was simulated by homemade cement-soil samples with different cement content to carry out the shear strength test, aiming to study the shear property of the soil around piles from the following investigations. Direct shear tests were carried out, including the evolution rule of the shear stress-shear displacement curve of the soil around the pile under different cement content and the influence of different cement content on the shear strength parameters. A composite exponential model describing the shear stress and shear displacement of permeation grouting silty clay was established. A microstructure test was carried out to investigate the variation rules of the structural characteristic parameters of the soil around piles with permeation grouting, and the strength formation process with the increase of cement content was analyzed. The macro mechanical behavior and microstructural characteristics of grouting soil were investigated to reveal the strengthening mechanism of soil around piles by permeation grouting. The results are of great significance to the optimization of grouting parameters and the evaluation of shear properties of soil around piles.
## 3 Results and Discussion
### Materials Characterization
#### 3.1.3 FT-IR Analysis  
The Fourier transform infrared spectrum (FT-IR) of the MFA@PDMS is shown in Figure 3. The absorption peak at 2964 cm\({}^{-1}\) corresponds to -CH\({}_{3}\) stretching vibrations; that at 1262 cm\({}^{-1}\) was attributed to the sharp symmetric deformation vibration peak of Si-CH\({}_{3}\); that at 793 cm\({}^{-1}\) corresponded to that Si-C tensile vibration peak [23]; that near 1083 cm\({}^{-1}\) was attributed to the asymmetric stretching vibrations of Si-O-Si bond and Si-O-Al bonds, which was caused by the polymerization of the fly ash aluminosilicate or silicon-oxygen tetrahedron structure. Finally, a crystal lattice absorption peak of Fe\({}_{3}\)O\({}_{4}\) appeared at 599 cm\({}^{-1}\), indicating that the MFA@PDMS material had been prepared successfully.  
Figure 2: SEM of (**a**,**b**) PDMS and (**c**,**d**) MFA@PDMS.
## 1 Introduction  
Recently, the construction industry has benefited from the increased application of smart construction led by the core technologies of the fourth industrial revolution, such as Building Information Modeling (BIM), Artificial Intelligence (AI), modular construction, and Augmented Reality (AR)/Virtual Reality (VR), which enhance productivity and work efficiency. In addition, the importance of \"Off-Site Construction (OSC)\", a factory-based production method, is being highlighted as modular construction increases in the domestic construction market as a means of productivity enhancement [1; 2; 3; 4].  
OSC involves factory-producing framework members, components, and pre-assembly parts in advance and transporting them to the building construction site where they will be assembled and installed [5; 6; 7]. Precast concrete (PC) members produced at the factory and brought to the site require a thorough quality check since damage or defects may occur during transportation. However, although accurate quality inspection is performed on PC members prior to leaving the factory, the same level of quality checking is lacking at the site where the members are transported to. The existing on-site inspection of PC members relies on visual checking using one's naked eye and is limited to sample inspection due to a shortage of qualified manpower, thus compromising the accuracy of quality inspection as well as inspection efficiency.  
In order to enhance the efficiency and accuracy of member damage or defect detection, research on automated defect detection that uses Convolutional Neural Network (CNN), one of the deep learning models, and object detection techniques applied to image recognition for concrete cracks or surface defects of electronic components, has gained traction recently [8; 9; 10; 11]. Additionally, several studies have focused on automated quality inspection systems using mobile devices, such as smartphones and tablet PCs, that support field workers in conducting efficient quality checks and management of structures and construction work [12; 13; 14; 15].  
With the goal of improving the accuracy and efficiency of quality inspection practices, this study proposes an AI-based smart PC member quality inspection system that minimizes reliance on the inspector's subjective experience in conducting quality inspection work. Through the proposed system, it is possible to reduce the inspection preparation time as well as the overall inspection work time while securing the work quality of the OSC project, thereby building a smart work environment.
## 1 Introduction  
Aerogels are a class of porous solids first synthesized in 1931 by [PERSON] [1]. Aerogels refer to wet gels in which the liquid is later replaced with air resulting in a solid with little shrinkage. Application fields of aerogels include catalysis, thermal and electric insulation, water filtering and many more [2]. Although the Polymer Derived Ceramic (PDC) route has been extensively used for synthesizing Si-based ceramics it was not until recently that SiC, SiCN and SiOC aerogels were synthesized using the PDC route [3; 4]. In this process, first a polymer aerogel is synthesized and then converted, through a pyrolysis process in inert atmosphere, into the corresponding PDC aerogel. The polymer aerogel is synthesized via hydrosilylation of a silicon-based polymer bearing Si-H bonds with molecules bearing C=C moieties [5]. The cross-linking is performed in highly diluted conditions.  
PDC aerogels have been synthesized and characterized for gas sensing [6], anodes for Li-ion batteries [7], water purification [8], and electromagnetic adsorbers [9]. For example, SiCO aerogels were found to show good sensing response towards N\({}_{2}\) at 300 \({}^{\circ}\)C and H\({}_{2}\) at 500 \({}^{\circ}\)C [6]. In a separate study, SiCO aerogels showed high specific reversible capacity of more than 900 mAh/g with 360 mAh/g (10C) charging rate [7].  
In this study we explore the role of the solvent in the synthesis of polymer aerogels made from poly(methylhydrosiloxane) and divinylbenzene (PMHS/DVB) aerogels. Accordingly, four different types of organic solvent (n-hexane, cyclo-hexane, tetrahydrofurane, and acetone) have been used while keeping all the other synthesis parameters fixed.