esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on 3D Digital Technological Process of Construction Products with Model-Based Definition
## 4 Application Examples and Discussion
### Infill Wall Masonry Process Information Expression  
#### 4.2.1 Infill Wall Masonry Process Information Processing  
1. Combing quality and technical control points  
The quality and technical points of each construction process of the infill wall masonry are presented in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline
**Serial** & **Name of the** & & \\
**Number** & **Material** & **Specification** & **Remark** \\ \hline \multirow{2}{*}{1} & Steel & & \\  & reinforcement & & \\ \hline \multirow{4}{*}{2} & Vapor & & Strength grade A3.5, block \\  & compression & B06: 600 \(\times\) 240 \(\times\) 200 mm & density \(\leq\)6.5 KN/m\({}^{3}\) \\  & concrete block & B06: 215 \(\times\) 240 \(\times\) 200 mm & Product age greater than \\  & & concrete block & 28 days, moisture content \\  & & & less than 30\% \\ \hline \multirow{2}{*}{3} & Autoclaved & & \\  & sand-line brick & MU10: 200 \(\times\) 95 \(\times\) 53 mm & / \\ \hline \multirow{2}{*}{4} & Cement & Grade 32.5 ordinary & \\  & & portland cement & / \\ \hline \multirow{2}{*}{5} & Sand & Medium sand & 5 mm sieve \\ \hline \multirow{2}{*}{6} & Additive & Finely ground raw lime & Raw lime powder ripening \\  & & powder and fly ash & no less than 2 days \\ \hline \end{tabular}
\end{table} 
Table 1: Case infill wall material table. \begin{table}
\begin{tabular}{c c c c} \hline
**Construction Process** & & **Technical Essential** & **Quality Problems** \\ \hline \multirow{4}{*}{Preparation for construction} & \(\bullet\) & Watering wet 1–2 days in advance to & \\  & ensure the moisture content of masonry. & \(\bullet\) & Bricks are not wet enough; mortar bond \\  & \(\bullet\) & Draw brick layout, site brick. & strength is not enough. \\  & \(\bullet\) & The strength grade of block and masonry & \(\bullet\) & Site disorder. \\  & mortar meets the design requirements. & & \\ \hline \multirow{4}{*}{Measurement and discharge line} & \(\bullet\) & Fixed base point, hanging head-tail & \(\bullet\) & The whole wall is unevenly bricked. \\  & & large-angle line. & \(\bullet\) & The position deviation of each window \\ \cline{1-1}  & \(\bullet\) & On the benchmark level line, set each & \(\bullet\) & Wireless bit control of the first row \\ \cline{1-1}  & \(\bullet\) & Bullet height control line, beam position & of bricks. \\ \cline{1-1}  & \(\bullet\) & line, etc. & \(\bullet\) & The verticality of the wall has no control line. \\ \hline \end{tabular}
\end{table} 
Table 2: The common problems and technical points table of infilled wall masonry construction quality. 2. Technological process refinement  
Based on the typical technological process and the process information of infill wall masonry, the important processes were refined, as shown in Figure 2.
## 4 Experiment and Analyses  
To compare the performance of the proposed multi-exposure fused method, 12 multi-exposure source image sequences, shot in different scenes with rich details and high brightness contrast, were used. Figure 6 shows two images of different exposures in each image sequence, as used in [PERSON] et al.'s method [14], [PERSON]'s method [15], and other databases. Additionally, more details of each multi-exposure source image sequence are described in Figure 6, including name, spatial resolution and number of source images. The experiment was run on a PC platform with an Intel(r) Core(tm) i5 processor with 4-GB memory.  
Figure 6: Multi-exposure source image sequences used in the experiments: (**a**) “balloons”, \(512\times 339\)\(\times\) 9 (the sequence contains a total of nine images, and the spatial resolution of each is \(512\times 339\)); (**b**) “recorder”, \(1024\times 768\times 13\); (**c**) “Belgium House”, \(512\times 384\times 9\); (**d**) “mask”, \(1200\times 800\times 3\); (**e**) “StudioEV”, \(1200\times 800\times 5\); (**f**) “landscape”, \(512\times 341\times 3\); (**g**) “lighthouse”, \(512\times 340\times 3\); (**h**) “Kluki”, \(512\times 341\times 3\); (**i**) “desk”, \(640\times 427\times 7\); (**j**) “farm house”, \(512\times 341\times 3\); (**k**) “office”, \(512\times 340\times 6\); and (**l**) “Madison Capitol”, \(512\times 384\times 30\).  
In all of the following experiments, unless otherwise indicated, the parameters of the proposed method were as follows: the patch size, \(n\), was fixed to \(9\times 9\); the global error was \(\varepsilon=0.1\); the threshold of exposure quality, \(\eta\), was set to 0.15; another threshold of exposure quality, \(\alpha\), was set to 0.05; and the parameter of the recursive filter was set to the optimal value, as in [PERSON]'s method [12].  
Five representative multi-exposure fused methods (i.e., Liu15 method [12], Ma18 method [14], Mertens10 method [15], Vonikakis11 method [16], Li13 method [18]) were employed to make comparisons via subjective and objective evaluation methods. The code of these fusion methods is available on the corresponding websites [32; 33; 34; 35; 36], and default parameters were used in the comparisons.
## 2 Materials and Methods
### Background
#### 2.2.2 Iva  
IVA extends ICA to multiple datasets by exploiting the additional information from the statistical dependence across multiple datasets [21]. Given \(K\) datasets, each containing \(M\) observations and \(V\) samples, \(\mathbf{X}^{[k]}\), the generative model for IVA assumes that each dataset is a linear mixture of \(M\) independent sources,  
\[\mathbf{X}^{[k]}=\mathbf{A}^{[k]}\mathbf{S}^{[k]},\ 1\leq k\leq K, \tag{4}\]  
where \(\mathbf{A}^{[k]}\in\mathbb{R}^{M\times M}\) denotes the \(k\)th mixing matrix and \(\mathbf{S}^{[k]}\in\mathbb{R}^{M\times V}\) denotes the set of independent sources. IVA jointly estimates \(K\) demixing matrices, \(\mathbf{W}^{[k]}\), to compute the estimated sources of each dataset,  
\[\mathbf{\hat{S}}^{[k]}=\mathbf{W}^{[k]}\mathbf{X}^{[k]},\ 1\leq k\leq K, \tag{5}\]  
by minimizing the cost function given as  
\[\mathcal{J}_{\text{IVA}}(\mathbf{W})=\sum_{m=1}^{M}\left[\sum_{k=1}^{K} \mathcal{H}\Big{(}\hat{s}_{m}^{[k]}\Big{)}-\mathcal{I}(\mathbf{\hat{s}}_{m}) \right]-\sum_{k=1}^{K}\log\Bigl{|}\det\mathbf{W}^{[k]}\Bigr{|}, \tag{6}\]  
where \(\mathcal{H}\Big{(}\hat{s}_{m}^{[k]}\Big{)}\) denotes the entropy of the \(m\)th source estimate for the \(k\)th dataset [21]. \(\mathcal{I}(\mathbf{\hat{s}}_{m})\) denotes the mutual information within the \(m\)th source component vector (SCV), defined as \(\mathbf{\hat{s}}_{m}=[\hat{s}_{m}^{[1]},\hat{s}_{m}^{[2]},\ldots,\hat{s}_{m}^{ [K]}]^{\intercal}\in\mathbb{R}^{K\times V}\), where \(\hat{s}_{m}^{[k]}\in\mathbb{R}^{V}\) is the \(m\)th source from the \(k\)th dataset and the \(m\)th SCV is formed by concatenating the \(m\)th component from all the \(K\) datasets [24]. Thus, the maximization of the mutual information within the SCV enables IVA to exploit dependence across datasets. The estimated mixing matrix for the \(k\)th dataset is calculated as \(\mathbf{\hat{A}}^{[k]}=(\mathbf{W}^{[k]})^{-1}\). The \(c\)th column of \(\mathbf{\hat{A}}^{[k]}\) contains the weights for the \(c\)th row of the \(\mathbf{\hat{S}}^{[k]}\) (\(c\)th source) across different rows of \(\mathbf{X}^{[k]}\). Since the \(c\)th source is a part of the \(c\)th SCV, the \(c\)th column of \(\mathbf{\hat{A}}^{[k]}\) can be used to identify which observation makes the highest contribution to the \(c\)th SCV; i.e., which observation makes the highest contribution to the dependence across different modalities. These identified observations from \(K\) modalities enable the discovery of associations across different modalities.
## 3 Current Developments
### Contributor Analysis
#### 3.2.2 Areal Distribution  
The areal distribution of the active OSM community members shows a similar heterogeneous pattern as the aforementioned data quality and quantity analyses. Since an OSM member does not have to provide his or her location information when registering to the project, [PERSON] [125] and [PERSON] and [PERSON] [71] localized the members by utilizing different approaches. [PERSON] [125] analyzed the number of added Nodes per country for each contributor, whereas [PERSON] and [PERSON] [71] focused on the first edit of a contributor or the area which shows the most activity, to identify the origin of the project members. Both studies showed similar results for the years 2010 and 2012 in which three-quarters of the contributors were located in Europe. The remaining quarter was distributed over North America and Asia. South America, Africa and Oceania proved to have only a small contributor number. When considering the population density of the different countries, it is surprising to see that the USA, China or India only show relatively small project contributor numbers, which can be caused by a number of reasons, such as other freely available datasets, such as the TIGER/Line data for the U.S., or governmental restrictions that make the collection of geodata illegal in certain countries. Additionally, [PERSON] _et al_. [114] illustrated that other factors next to population density or income must have an influence on contributor growth. The highest concentration of active contributors in OSM can be found in Germany. Out of the 2500 daily contributors, around 550 members (22%) edited data in Germany [130]. Thus, it is not surprising to see that the German OSM dataset also shows a higher quality. Figure 4 illustrates the distribution of active OSM contributors per day related to population in millions (a) and area (b) for each country, highlighting the strong concentration of OSM contributors in Europe (b).  
The OSM contributors, however, are not just limiting their data collection efforts to their home regions. [PERSON] [125] and [PERSON] and [PERSON] [71] revealed that a smaller number of highly active OSM members collect data in at least two or more countries.  
Figure 4: Distribution of active OSM contributors per day and per population in million (**a**) and per area (1000 km\({}^{2}\)) (**b**) (1 August–31 October 2013).
## 3 Antwerp Parks Survey Campaign
### Soundscape Questionnaire  
A questionnaire (in Dutch) was created based on previous research [6; 29] and a survey was conducted amongst park visitors. The interviewers aimed to collect at least 80 completed questionnaires in the central area of each park. The questionnaire included questions about the visitors' general profile, their use of the park, their evaluation of the soundscape along a number of perceptual dimensions [6], their perception of the environmental quality of the park and other factors.  
In particular, the questionnaire inquired about the noticing of sound sources during the park visit. The three groups of sounds were listed as \"human sounds (talking people, playing children,...)\", \"natural sounds (wind in the leafs, birds, water,...)\" and \"mechanical sounds (traffic, airplanes, machines, small electronic accessories,...)\". Participants were asked to rank hearing sounds in these categories on a 5-point ordinal category scale with categories: \"not at all\", \"a little\", \"moderately\", \"much\" and \"very much\".  
Figure 1: Locations of the eight parks in the city of Antwerp studied in the survey campaign.  
Another question examined the participants' agreement with the statement \"the sounds in this park are the sounds that one expects to hear in a park\". The answers were listed on a 5-point ordinal category scale with values: \"completely agree\", \"agree\", \"neither agree/nor disagree\", \"disagree\" and \"completely disagree\". Additionally, participants were asked on the overall quality of the sonic environment during their visit. Their opinion was marked on the 5-point ordinal category scale with labels: \"very bad\", \"bad\", \"neither good/nor bad\", \"good\" and \"very good\".  
Finally, participants were asked, in a direct question, on their opinion about the purpose of a park in general. Corresponding to the tranquility viewpoint groups: \"Natural sound sources\", \"Silence\" and \"Social relationships\" (Section 2.2), three mutually exclusive options were provided for park as a: \"place for appreciating nature\", \"place for appreciating silence\" and \"place for meeting other people\", and the participants were asked to select only one option.
# Privacy Auditing in Differential Private Machine Learning:
## 3 Privacy Attacks
### Type of Attacks
#### 3.3.4 Model Extraction Attack  
Model extraction attacks (also known as reconstruction attacks) aim to steal the functionality, replicate ML models, and expose sensitive information of well-trained ML models [146]. The attacker can approach or replicate a target model by sending numerous queries to infer model parameters or hyperparameters and observing its response. In model extraction attacks [95; 147; 148], in the context of DP auditing, attackers attempt to derive a victim model by extensively querying model parameters or training data in order to train a surrogate model. The attacker learns a model to try to extract information and possibly fully reconstruct a target model by creating a new duplicate model that resembles the target model in a way that behaves very similarly to the attacked model. This type of attack only targets the model itself and not the training data.  
This type of attack can be categorized into two classes [149]: (i) _accuracy extraction attacks_, i.e., focusing on replacing the target model; and (ii) _fidelity extraction attacks_, i.e., aim to closely match the prediction of target model.  
This threat can increase the privacy risk as a successful model extraction can enable a subsequent next threat, such as a model inversion. There are two approaches to creating a surrogate model [62; 147]. First, the surrogate model fits the target model to a set of input points that are not necessarily related to the learning task. Second, create a surrogate model that matches the accuracy of the target model on the test set that is related to the learning task and comes from the distribution of the input data.  
Model extraction attacks pose a significant security threat to ML models, especially those provided via cloud services or public APIs. In these attacks, an attacker repeatedly queries a target model to train a surrogate model that mimics the functionality of the target's model.
# Effects of Drying Methods and Temperatures on the Quality of Chestnut Flows
## 3 Results and Discussion
### Aflatoxin Determination  
The aflatoxin content might help to obtain a general picture of the quality of a product. Several studies reported the presence of _Aspergillus flavus_ and the contamination from aflatoxin in chestnuts and derivates [3; 10; 21]. Drying temperature and storage can affect aflatoxin production by mycotoxic fungi, as demonstrated by the study on hazelnuts where aflat were not recorded at higher drying temperatures (above 40 \({}^{\circ}\)C), even after storage at 25 \({}^{\circ}\)C [50]. The total aflat are largely within the legal content in each of the analyzed flour. For B2, G1, and G2 aflat, the values are below the limit of detection (LOD), namely, they are less than 0.5 \(\upmu\)g/kg.  
Concerning B1 aflatoxin, all the flours are within the allowed limits. Hence, there is no correlation between the defective chestnuts and toxicity in terms of aflat content. In fact, in the case of hot air drying at 40 \({}^{\circ}\)C in the first year, the content of B1 is higher in non-defective flours with respect to defective ones.
## 1 Introduction
The N\({}_{2}\)O emissions tend to decrease sharply with increasing stem height, suggesting that tree stems rather than leaves are the main N\({}_{2}\)O emitters [21; 24; 26; 27]. Black adler is known to grow in symbiosis with the N\({}_{2}\)-fixing actinomycetes _Frankia_ sp. [28; 29]. Due to this symbiosis, black adler forests are significant nitrogen (N)-fixing ecosystems. The decomposition of adler litter improves the C:N ratio in the soil, which alters soil microbial activity and affects the production of N\({}_{2}\)O [30]. Since this GHG is mainly produced during N mineralization in aerobic peat soils, rewetting can reduce the N\({}_{2}\)O emissions to levels close to those of undrained, natural peatlands [31]. As with CO\({}_{2}\) and CH\({}_{4}\), N\({}_{2}\)O can also be taken up by roots from deeper soil layers and, as it is further transported through the tree's vascular tissues, released from the stem surface [12]. It is known that plant tissues can also produce N\({}_{2}\)O through nitrate uptake, which requires optimal light intensity, temperature, water and nutrient availability [32; 33]. Emissions from tree stems have not been studied efficiently due to the complexity of the process and the factors affecting it. The aim of this study is to evaluate the effects of groundwater level and soil temperature, as well as general soil chemistry on GHG emissions from the surface of tree stems in deciduous stands with organic soil in Latvia. This study assessed the emissions of CO\({}_{2}\), CH\({}_{4}\) and N\({}_{2}\)O.
# Hysteresis Compensation and Trajectory Tracking Control Model for Pneumatic Artificial Muscles
## 2 Hysteresis Model and Inverse Model of PAM  
Similar to most flexible actuators, PAM actuators exhibit significant hysteresis characteristics between displacement and pressure. This hysteresis effect impacts the accuracy of the PAM position modeling and its application in bionics. Therefore, it is necessary to establish a high-precision hysteresis mathematical model that is widely applicable under different input conditions to comprehensively and accurately describe this complex hysteresis behavior. To improve the applicability and accuracy of the PAM hysteresis model, this section proposes an FN-QUPI model to establish the hysteresis relationship between pneumatic pressure and displacement in PAMs. First, the QUPI (quadratic unparallel Prandtl-Ishlinskii) operator is introduced based on the UPI operator, where the QUPI operator replaces the linear envelope function with a quadratic function to enhance modeling accuracy. Then, the QUPI operator is incorporated into the nonlinear autoregressive moving average model to describe the complex hysteresis of the PAM, and a feedforward neural network is used to build the nonlinear function relationship of the NARMAX (nonlinear autoregressive moving average) model, forming the FN-QUPI model. Since the model reduces the number of unknown parameters, the particle swarm optimization algorithm is employed to solve all model parameters, enabling the model to adapt to dynamic system changes and achieve higher accuracy. Finally, the inverse model of QUPI is introduced.
## 1 Introduction  
Shear modulus degradation (\(\mathrm{G/G_{max}}\)) and damping (\(\mathrm{D}\)) curves are the most important properties that need to be considered when simulating dynamic problems, such as seismic wave propagation in the ground, or dynamic soil-structure interactions (SSIs) [1; 2; 3; 4]. Previous studies have indicated that good modeling of the nonlinear hysteretic properties of soil, including shear modulus degradation and damping, could enable wave propagation and soil-structure interaction to be accurately simulated [2; 5].  
Among famous commercial programs, such as ABAQUS, ANSYS, LS-DYNA, FLAC, and PLAXIS, ABAQUS has commonly been used for dynamic problems and SSI investigation, because of its capacity and convenience with numerous materials and elements for soil, structure, composite, and porous media. However, constitutive models with the implementation of \(\mathrm{G/G_{max}}\) and damping curves for soils and rocks in ABAQUS have still been quite limited. The simplified nonlinear kinematic hardening (SNKH) model was implemented in ABAQUS to simulate the strain-dependent properties of soil [5; 6; 7]. Although the procedure for estimating SNKH model parameters was proposed by [PERSON] and [PERSON] [5], with a single calibrated parameter based on the ultimate shear strength of material, the kinematic hardening parameters were varied to fit with \(\mathrm{G/G_{max}}\) obtained from experiments, without changing the shear strength at extremely large shear strains. Furthermore, the SNKH model did not accurately simulate the nonlinear dynamic properties of soil, due to the use of a simple nonlinear kinematic hardening rule. Several user-defined materials (e.g., UMAT and VUMAT) imply that more complicated hardening rules had been developed [4; 8; 9; 10]. However, the implementation of these UMAT materialsby engineers has been limited due to their complexity and difficulties, as well as the application of the nonlinear kinematic hardening model (NKH) in geotechnical engineering being limited due to the difficulty in estimating its parameters.  
In this study, a semi-automated procedure implemented in MATLAB was proposed to predict NKH model parameters, with seven input values, for fitting to target G/Gmax and D curves obtained from experimental results. Three soils were used with the nonlinear G/Gmax and damping curves obtained from the resonant column test (RCT). The NKH model parameters were estimated and compared with the SNKH model results. The procedure was also used for the rock materials, proving the application of the NKH model and the semi-automated procedure for both soils and rocks.