esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Health Risk Assessment
#### 3.2.2 Carcinogenic Analysis  
The values of cancer risk are presented in Table 4. The interpretation and description of this parameter is shows in Table 5. The results obtained in case of Pb were in a tolerable range, while Cd values are tolerable, due to low detection in the samples. Regarding Cr, 11% of samples were in a tolerable range and the rest of the samples were tolerable. Concerning Ni, 89% of results were in tolerable range and 11% were classed as intolerable. This means that, after repeated exposure to intolerable samples, Ni can produce some carcinogenic side effects. The ascendent trend of cancer risk, estimated for the 4 studied elements, is as following Cd \(>\) Cr \(>\) Ni \(>\) Pb. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multicolumn{4}{c}{**Heavy Metals Levels (ug/L)**} & \multirow{2}{*}{**Detection**} & \multirow{2}{*}{**Law No.**} & \multirow{2}{*}{**Directive EU**} \\ \cline{2-2} \cline{5-8}  & & **Min** & & & & **Max** & & **Rate (\%)** & **311/2004** & 2020/2184 \\ \hline Ba & \(<\)0.09 & \(1.68\times 10^{1}\) \(\pm\) 0.32 & 3.87 \(\pm\) 0.19 & 68.42 & - & - \\ Co & \(<\)0.10 & \(2.50\times 10^{-1}\) \(\pm\) 0.01 & \(7.0\times 10^{-2}\) \(\pm\) 0.01 & 47.37 & - & - \\ Cu & \(<\)0.09 & \(1.73\pm 0.03\) & \(9.60\times 10^{-1}\) \(\pm\) 0.02 & 100 & 100 & 2000 \\ Zn & \(9.60\times 10^{-1}\) \(\pm\) 0.07 & \(4.47\pm 0.22\) & \(2.04\pm 0.07\) & 100 & 5000 & - \\ Mn & \(<\)0.09 & \(4.17\pm 0.20\) & \(3.50\times 10^{-1}\) \(\pm\) 0.04 & 42.11 & 50 & 50 \\ Ni & \(3.10\times 10^{-1}\) \(\pm\) 0.01 & \(2.25\pm 0.06\) & 1.02 \(\pm\) 0.04 & 100 & 20 & 20 \\ Li & \(<\)0.11 & \(7.09\pm 0.43\) & \(1.69\pm 0.08\) & 84.21 & - & - \\ Fe & \(6.24\times 10^{1}\) \(\pm\) 6.13 & \(1.69\times 10^{3}\) \(\pm\) 3.92 \(\times 10^{4}\) & \(4.95\times 10^{1}\) \(\pm\) 1.82 \(\times 10^{4}\) & 100 & 200 & 200 \\ Pb & \(1.10\times 10^{-1}\) \(\pm\) 0.01 & \(1.79\pm 0.02\) & \(4.30\times 10^{-1}\) \(\pm\) 1.0 \(\times 10^{-2}\) & 100 & 10 & 5 \\ Cd & \(<\)0.09 & \(<\) 0.09 & - & 0 & 5 & 5 \\ Cr & \(<\)0.10 & \(1.60\times 10^{-1}\) \(\pm\) 0.01 & \(1.0\times 10^{-2}\) 0.00 & 10.53 & 50 & 25 \\ Sb & \(<\)0.07 & \(1.30\times 10^{-1}\) \(\pm\) 0.00 & \(3.0\times 10^{-2}\) \(\pm\) 0.00 & 31.58 & 5 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Potentially toxic elements levels in baby drinking water samples.
## 4 Discussion
### Biophysical Performance of Vegetation Indices for the Sugarcane Plantations
In the above publication, the LUE\({}_{\text{AGB}}\) or LUE\({}_{\text{GPP}}\) parameter at two sugarcane sites in Hawaii, USA, were \(0.88\pm 0.02\) g C M\({}^{-1}\) (\(0.43\pm 0.01\) g C mol\({}^{-1}\) PPFD) or \(1.24\pm 0.22\) g C M\({}^{-1}\) (\(0.61\pm 0.11\) g C mol\({}^{-1}\) PPFD) at the site located at a low altitude and \(0.75\pm 0.01\) g C M\({}^{-1}\) (\(0.37\pm 0.01\) g C mol\({}^{-1}\) PPFD) or \(1.15\pm 0.15\) g C MJ\({}^{-1}\) (\(0.56\pm 0.07\) g Cmol\({}^{-1}\) PPFD) at the site located at a high altitude. Different LUE definitions should be considered a source of errors associated with satellite-based GPP estimates. In our study, the maximum LUE parameter at the Brazil site and the USA site was set to \(0.9\) g Cmol\({}^{-1}\) PPFD, which was slightly lower than the maize crop (\(0.92\) g Cmol\({}^{-1}\) PPFD), another C4 crop in China [79]. The range value of the maximum LUE parameter for sugarcane cropland indicates the need for the evaluation of the maximum LUE parameter over diverse sugarcane croplands in the world under different climate conditions and management practices.
## 2 Proposed Method
### Data Collection  
Since there has been no publicly available dataset for insulator detection in UAV aerial images, the 'InST_detection' dataset is constructed to validate the performances of the proposed network, as shown in Table 2. The 'InST_detection' dataset consists of 4031 images that contain different surroundings, and the filming distances and angles are varied in every image. These aerial images are collected from the power company's database and captured by an on-UAV camera. In this work, first, all the images in 'InST_detection' dataset are normalized to the same size of 416 pixel \(\times\) 416 pixel.  
Then, we label the ground-truth for each insulator string using LabelImg tool [58]. Finally, we get 9609 insulator string with their ground-truth in total.  
To verify the proposed insulator faults detection method, it is necessary to construct an insulator faults dataset. However, although insulator faults detection is an important task in high-voltage transmission line inspection, it is well known that the main factor for limiting the development of insulator fault detection methods is that there are few insulator faults aerial images which can be collected, and we share a common limitation with the previous works in that we only obtained 42 images with one fault or multi-fault at the beginning. To address this problem, a novel data augmentation method is proposed in this work. First, based on the analysis of insulator fault aerial images, it is found that although the backgrounds of different regions in an image are different, the background patches in a local region still have similar features. Second, it is conceivable that the background behind the normal insulator should be similar to the pixels around it. Third, we learn that if there are four insulator faults in an insulator string, it should be repaired within 24 h. Otherwise, the electrical performance of the insulator string will be greatly reduced, which will further affect the stability of the power grid operation. Based on the above facts, Photoshop software [59] is used to erase the normal insulator regions and replace them with their nearby pixels. Finally, a dataset containing 120 insulator fault images was created, and there were a total of 228 insulator faults (detail is shown in Table 3). Specifically, 60 images contain insulator multi-fault while the other 60 images only contain insulator one fault. The number of insulator faults in each insulator string is at most four in our dataset. Our data augmentation method is simple but effective: it reduces the time and the cost of insulator fault images collection, which is meaningful for future deep-learning-based methods that need plenty of insulator fault aerial images to train.
# Prediction of Ground Subsidence Risk in Urban Centers Using Underground Characteristics Information
## 2 Research Methods and Data
### Data
#### 2.2.4 Height  
Ground subsidence is primarily attributed to damage in sewer pipelines, the majority of which use a natural flow configuration. Thus, a lower height increases the likelihood of prolonged wastewater retention, potentially leading to ground subsidence. The height of the target area ranged from 5.61 m to 541.15 m, and the median of the grid was selected and extracted as a representative value.
###### Abstract  
A constitutive model able to describe both tensile damage and plastic deformation under confinement is a prerequisite to numerically simulate the behaviour of sandstone rock under an impact loading induced in a percussive drilling process. Therefore, model identification under both tensile and high confinement states is needed. In the present work, an experimental investigation was carried out in order to determine the mechanical properties of a sandstone rock for the purpose of advanced constitutive model identification. Different testing methods were used in quasistatic and dynamic loading regimes. This first part of the study is dedicated to static experiments, whereby three-point bend tests were first performed to evaluate the quasistatic tensile strength of the rock and its distribution by employing the Weibull statistics. Secondly, direct compression tests were conducted to evaluate the stiffness and strength in an unconfined condition. Afterwards, quasioedometric compression (QOC) tests were carried out in order to obtain the deviatoric and volumetric behaviours of the material as a function of the hydrostatic pressure (up to 375 MPa). In these QOC tests, the metallic confinement cell was instrumented with strain gauges to deduce the state of the stress and strain within the sample. A linear volumetric response along with a continuous increase of strength with the level of hydrostatic pressure was observed. This experimental work points out that, under unconfined loading (three-point bending and uniaxial compression), pre-existing structural defects play a major role leading to a highly scattered behaviour in terms of sample stiffness and ultimate applied load. On the other hand, under high confinement levels (QOC tests), beyond the nonlinear response of the curve foot, the influence from structural defects was observed to be small.  
s +
Footnote †: journal: Applied sciences  
Article Investigation of the Mechanical Behaviour of Lingulid Sandstone Emphasising the Influence from Pre-Existing Structural Defects, Part 1: Model Identification Based on Static Experiments
## 2 Materials and Methods
### Statistical Analysis  
In Tables 2 and 3 and Figures 3-5 mean \(\pm\) SEM values are shown. All statistical analysis was carried out using GraphPad Prism 8. Significant differences were identified using unpaired t test. Significant difference was assumed at \(p\leq\) 0.05.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Hsa-miRNARNA**} & **Predicted Target** & **Hsa-miRNA Plus** & **Scrambled** & **Hsa-miRNA No** & **No hsa-miRNA** \\  & **hsa-mRNA Mean** & **hsa-mRNA** & **hsa-mRNA** & **mRNA** & **hsa-mRNA** \\  & & \(\pm\) SEM & **Mean \(\pm\) SEM & **Mean \(\pm\) SEM** & **Mean \(\pm\) SEM** \\ \hline Hsa-miR-486-3p & Ca\({}_{\text{v}}\)3.1 & 0.511 \(\pm\) 0.111 & 1.188 \(\pm\) 0.281 & 0.596 \(\pm\) 0.344 & 0.567 \(\pm\) 0.318 \\  & Ca\({}_{\text{v}}\)1.3 & 0.211 \(\pm\) 0.039 & 0.449 \(\pm\) 0.019 & 0.057 \(\pm\) 0.020 & 0.083 \(\pm\) 0.026 \\  & TPSAB1 & 1.049 \(\pm\) 0.256 & 2.240 \(\pm\) 0.032 & 0.593 \(\pm\) 0.344 & 0.580 \(\pm\) 0.313 \\ \hline Hsa-miR-938 & Ca\({}_{\text{v}}\)1.3 & 0.159 \(\pm\) 0.025 & 0.449 \(\pm\) 0.019 & 0.074 \(\pm\) 0.020 & 0.087 \(\pm\) 0.020 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Significant downregulation of hsa-mRNAs by hsa-miRNAs in H9C2 cells, observed following luciferase assay experiments. Mean \(\pm\) SEM values are shown. Red represents significantly more expressed mRNA in the SN vs. right atrium, whereas green represents significantly less expressed miRNA in the SN vs. right atrium, as shown in our previous publications [4,10]. \(p\) values are shown in Figures 3–5.  
Figure 4: Luciferase bioluminescence after transfection of H9C2 cells with hsa-miR-938 and hsa-Ca\({}_{\text{v}}\)1.3. bioluminescence was recorded 24 h after transfecting H9C2 cells with 0.25 \(\upmu\)g of hsa-Ca\({}_{\text{v}}\)1.3–3′-untranslated region-containing plasmid and 1.25 \(\upmu\)L hsa-miR-938 or scrambled miRNA. Two control groups did not include the luciferase reporter gene, and cells were transfected with only hsa-miR-938 or remained untransfected (i.e., contained an equivalent volume of culture medium to replace the miRNAs or Ca\({}_{\text{v}}\)1.3). Data are shown as mean \(\pm\) SEM shown (\(n\) = 3 batches of experiments; *** \(p\) \(\leq\) 0.0010).  
Figure 3: Luciferase bioluminescence after transfection of H9C2 cells with hsa-miR-486-3p and plasmids. Bioluminescence was recorded 24 h after transfecting H9C2 cells with 0.25 \(\upmu\)g of (**A**) Ca\({}_{\text{v}}\)1.3′- or (**B**) Ca\({}_{\text{v}}\)3.1—3′-untranslated region-containing plasmid and 1.25 \(\upmu\)L hsa-miR-486-3p or scrambled hsa-miRNA. Two control groups did not include the luciferase reporter gene, and cells were transfected with only hsa-miR-486-3p or remained untransfected (i.e., contained an equivalent volume of culture medium to replace the hsa-miRNAs or 3′-untranslated region-containing plasmid). Data are shown as mean \(\pm\) SEM (\(n\) = 3 batches of experiments were plotted for Ca\({}_{\text{v}}\)1.3; \(n\) = 6 batches of experiments were plotted for Ca\({}_{\text{v}}\)3.1; \({}^{*}\)\(p\)\(\leq\) 0.05, **\(\thickapprox\)**\(p\)\(\leq\) 0.005).
## 7 Module III: Temporal Privacy
### Simple Global Anti Temporal Scheme (SGAT)
#### 7.1.1 Security Analysis  
The adversary sees every SN sending a message at a fixed data rate at any one time. It also cannot distinguish any message from the rest of the messages in the network since none has similar ID. If we have N nodes in the WSN, the probability that one adversary can locate the sending node is:  
\[P_{r}=\frac{1}{N} \tag{40}\]We can calculate anonymity as:  
\[\textbf{A}=\textbf{1}-\textbf{P}_{\textbf{r}} \tag{41}\]
# LDPC Coded Massive MIMO Systems
## 1 Introduction
#### Notations  
* \(n_{T}\): Number of transmit antennas.
* \(n_{R}\): Number of receive antennas.
* \(N_{ch}\): Total number of channel uses required to transmit all symbols.
* \(\mathcal{C}\): Complex number.
* \(\mathcal{R}\): Real number.
* \(\mathbb{A}\): Set of values for complex transmit symbols.
* \(\bar{\mathbb{A}}\): Set of values for real-valued transmit symbols.
* \(M_{o}\): Modulation order of complex transmit symbols, where \(M_{o}\) is the cardinality of \(\mathbb{A}\).
* \(\mathbf{x}^{(l)}\): Transmit symbol vector at the \(l\)th channel use, \(l=1,2,\cdots,N_{ch}\), where \(\mathbf{x}^{(l)}\in\mathbb{A}^{n_{T}\times 1}\).
* \(\mathbf{y}^{(l)}\): Received signal vector at the \(l\)th channel use, where \(\mathbf{y}^{(l)}\in\mathcal{C}^{n_{R}\times 1}\).
* \(\mathbf{w}^{(l)}\): Additive noise vector at the \(l\)th channel use, where \(\mathbf{w}^{(l)}\in\mathcal{C}^{n_{R}\times 1}\).
* \(\mathbf{H}^{(l)}\): MIMO channel gain matrix at the \(l\)th channel use, where \(\mathbf{H}^{(l)}\in\mathcal{C}^{n_{R}\times n_{T}}\).
* \(\bar{\mathbf{x}}^{(l)}\), \(\bar{\mathbf{y}}^{(l)}\), \(\bar{\mathbf{w}}^{(l)}\), \(\bar{\mathbf{H}}^{(l)}\): Real-valued representations of \(\mathbf{x}^{(l)}\), \(\mathbf{y}^{(l)}\), \(\mathbf{w}^{(l)}\) and \(\mathbf{H}^{(l)}\), respectively, where \(\bar{\mathbf{x}}^{(l)}\in\bar{\mathbb{A}}^{2n_{T}\times 1}\), \(\bar{\mathbf{y}}^{(l)}\in\mathcal{R}^{2n_{R}\times 1}\), \(\bar{\mathbf{w}}^{(l)}\in\mathcal{R}^{2n_{R}\times 1}\), \(\bar{\mathbf{H}}^{(l)}\in\mathcal{R}^{2n_{R}\times 2n_{T}}\).
* \(\bar{x}^{(l)}_{i}\), \(\bar{g}^{(l)}_{i}\), \(\bar{w}^{(l)}_{i}\): The \(i\)th entry of \(\bar{\mathbf{x}}^{(l)}\), \(\bar{\mathbf{y}}^{(l)}\), \(\bar{\mathbf{w}}^{(l)}\), respectively.
* \(\bar{h}^{(l)}_{ij}\): The \((i,j)\)th entries of \(\bar{\mathbf{H}}^{(l)}\).
* \(\Re\{\cdot\}\): Real part of a complex value.
* \(\Im\{\cdot\}\): Imaginary part of a complex value.
* \(\mathrm{E}\{\cdot\}\): Expectation.
* \(\mathrm{Var}\{\cdot\}\): Variance.
* \(\mathrm{DET}_{l}\): Detector node corresponding to the \(l\)th channel use.
* \(\mathbf{y}_{i}\): Vector obtained by excluding the \(i\)th entry of \(\mathbf{y}\).
* \(N_{g}\): Number of global JDD iterations.
* \(N_{det}\): Number of detection iterations in one global iteration.
* \(N_{dec}\): Number of decoding iterations in one global iteration.
* \(I(U;X)\): Mutual information between \(U\) and \(X\).
# Adaptive Bat Algorithm Optimization Strategy for Observation Matrix
## 3 Adaptive Bat Algorithm Optimizes Observation Matrix
### The Search Methods of Adaptive Bat Algorithm  
To improve the global search ability, an adaptive bat algorithm is designed in this paper. The main search method for ABA is that firstly, the optimization center and a preferred radius for the global optimal position are determined; then, update the velocity and position for individuals within the search radius, inherit the information of the previous generation and the optimal position, and accelerate the search of the global optimal solution by the algorithm. For an individual outside the optimization radius, since the individual is not closest to the optimal solution, the individual is randomly searched within the defined domain. This strategy avoids the individual falling into local extremum and increases the global search ability of the algorithm. However, when the position of the optimal global solution is unknown, the optimal position of each generation of bat individuals is taken as the optimization center, and the optimization radius of the optimization center is calculated. Finally, when the individual does not have a search center, a random search is used to increase the global search ability of the population. Next, the search methods of ABA are introduced.  
For the individuals within the optimization center, velocity and position updates are as follows:  
\[v_{i}^{t}=v_{i}^{t-1}+(x_{i}^{t-1}-x_{gbest})\times f_{i} \tag{4}\]  
\[x_{i}^{t}=x_{i}^{t-1}+v_{i}^{t} \tag{5}\]  
where \(x_{i}^{t}\) represents the position of bat \(i\) at time \(t\), \(v_{i}^{t}\) represents the velocity of bat \(i\) at time \(t\), \(x_{gbest}\) represents the global optimal solution, \(f_{i}\) represents the pulse frequency.  
The optimization process of the observation matrix is based on the transformation base \(\Psi\) and OMP reconstruction algorithm. Equation (6) is considered the objective function (fitness).  
\[f(x)=\lVert A_{CS}\alpha-Y\rVert_{2}^{2} \tag{6}\]where \(f(x)\) represents the fitness value function, \(Y\) represents the observed signal, \(\alpha\) represents the sparse representation of the original signal on the transformation base. And the individuals with the minimum fitness value are selected as the optimal solution.  
Additionally, the dynamic adjustment of the radius is achieved by the following:  
\[radius=\frac{f_{x_{gbst}^{t}}}{\sum\limits_{i=1}^{p}f_{x_{i}}^{t}} \tag{7}\]  
where \(x_{gbst}^{t}\) represents the optimal position at time \(t\), \(P\) represents individual number, \(radius\) represents the search radius. Apparently, the radius decreases as the objective function decreases.  
To reduce the reconstruction failures probability, the flow of the proposed adaptive bat algorithm search is shown in Figure 1.  
In this process, a new optimization center and radius are added in each generation of bat algorithm, and the local and global search ability is enhanced.
# Geotechnical Property Modeling and Construction Safety Zoning Based on GIS and BIM Integration
###### Abstract  
The increase in population and urbanization needs attention towards intense construction activities to meet the social and economic needs. Soil excavation is a primary step in every construction project that needs proper surface and subsurface information modeling since it is vulnerable to construction hazards. Geographic information system (GIS) provides significant information about the existing contextual surface information while building information modeling (BIM) gives information about the asset in a great detail that has been integrated into the construction industry for many applications. However, the integration of BIM and GIS for the subsurface geotechnical property modeling and classification into zones has been rarely explored. This paper presents the integration of BIM and GIS for modeling geotechnical properties and safe construction zones based on soil type. The use of open standard IFC classes such as IfcBorehole, IfcGeoslice, and IfcGeomodel enhances the collaboration and allows the exchange of geotechnical information among different stakeholders. The method has been applied to the in-situ and laboratory test dataset of the Peshawar, region, to validate the proof of concept. The results demonstrate that the proposed method successfully integrates BIM and GIS providing a three-dimensional surface and subsurface model. The 3D digital geotechnical model has excellent potential to provide information about soil type, properties, depth, and volume of each available soil layer that can be used by construction planners and managers to identify best construction practices and plan for safe construction.  
GIS; BIM; geotechnical modeling; construction safety; borehole information Article
# A Knowledge and Semantic Fusion Method for Automatic Geometry Problem Understanding
## 6 Experiment and Application
### Experiment and Analysis
#### 6.1.2 Application Verification Test and Error Analysis
Consequently, based on an atomic-level design for relationship extraction, the error propagation scope shifts from the sentence level (where an error affects the entire clause) to the atomic unit level (where an error only affects a single relationship mention). Consequently, errors change from being positively correlated with the complexity of the problem's logic (e.g., the number of nested relationships) to being positively correlated with the complexity of the atomic relationship expressions (e.g., whether they span across clauses). Above all, based on the statistics and analysis of the processing results, the following conclusions can be drawn: First, for the processed relationship types and entity types, the model consistently achieves high accuracy and efficiency in entity relationship extraction. This validates the effectiveness of the dynamic componentized template and the entity relationship extraction process. Second, the impact of errors caused by unprocessed entity/relationship types is limited to atomic relationship units rather than affecting entire clauses or sentences as before, demonstrating that the template design approach based on atomic relationships is both reasonable and effective. Third, the templates based on atomic relationship units are further modularized; the matching units are decoupled into the smallest granular arguments, and, guided by knowledge, more flexible templates are realized. This approach effectively addresses relationship mentions in sentences with complex structures, solving issues that traditional rules/templates have struggled with. Fourth, the model exhibits robustness, fault tolerance, and scalability. In the experiments, even when some descriptions contained spelling errors (unrelated to the extraction elements), the model was still able to successfully extract the relationships, demonstrating its robustness; errors in entity relationship extraction were confined to the atomic relationship unit and do not affect correctly processed relationships, ensuring fault tolerance; and the plug-and-play, zero-cost expansion of target elements based on tree-structured knowledge subtrees greatly facilitated model updates.
## 3 Results and Discussion
### Use of Organic Compounds  
The addition of the external carbon source was dispensed gradually, in order to ensure sufficient amounts of carbon for biofilm development, because hydroponic effluent has a low C/N ratio. An external carbon source added in incorrect quantities can become a secondary contaminant [27]. The organic load, supplied to the reactor with the effluent, was 1184.4 \(\pm\) 9.6 mg C/d. The value of organic load removed decreased with growing electric current density (Figure 5). The TOC removal efficiency ranged from 67.7% (R4) to 95.9% (R1). Organic compounds in this study were used as a carbon source by the microorganisms, effecting heterotrophic denitrification. Moreover, part of the organic compounds in electrochemical systems can be removed by adsorption of dissolved organic compounds on the surface of hydroxy floccules, and by direct oxidation of organic matter on the anode surface [28; 29; 30].  
Figure 4: Ca\({}^{2+}\) and Mg\({}^{2+}\) concentration in hydroponic effluent and in treated wastewater.  
[PERSON] et al. [10] and [PERSON] et al. [19] showed that the growth of organic compound concentration with increasing current density in the bioreactor is a consequence of the decreasing activity of heterotrophic bacteria. [PERSON] et al. [21] treated effluent from hydroponic cultivation in an SBR reactor with methanol as an external carbon source, and showed that phosphorus, nitrogen and COD removal efficiency of 99.8%, 89.5% and 100%, respectively, can be achieved with a carbon dose of 2.86 g O\({}_{2}\) COD/g NO\({}_{3}\)\({}^{-}\).