esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Model Validation
### Pumping Test  
One pumping well and two observation wells were used to conduct the single-well pumping test of the partially penetrating well in the phreatic aquifer. The test parameters are shown in Table 2. The test was divided into three stages, and the groundwater drawdowns are 1.21 m, 2.65 m, and 3.93 m, respectively. The results of the pumping test are shown in Table 3 and Figure 5. The groundwater drawdown in the observation wells decreased with the groundwater drawdown in the pumping well. The maximum groundwater drawdown was close to the pumping well, while the minimum value was far away from the pumping well. With the increase of time, the groundwater level first dropped sharply and then stabilized. The permeability coefficient in the phreatic aquifer is calculated based on the test results using the following equation (the Industrial Standard DL/T 5213-2005 of China, 2005) [49].  
\[K=\frac{0.732Q}{(2H-s_{1}-s_{2})(s_{1}-s_{2})}\lg\frac{r_{2}}{r_{1}} \tag{21}\]  
where \(r_{1}\) and \(r_{2}\) are the distances between the pumping well and the observation wells, respectively; \(s_{1}\) and \(s_{2}\) are the groundwater drawdown of the observation wells.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Soil Stratum**} & \multirow{2}{*}{**Soil Number**} & \multirow{2}{*}{\begin{tabular}{c} **Unit Weight** \\ \(\gamma\) **(kN/m3)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Cohesion** \\ **c (kPa)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Internal** \\ **Friction Angle** \\ \(\varphi\) **(\({}^{\circ}\))** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Modulus of** \\ **Deformation** \\ **E\({}_{0}\) **(MPa)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Permeability** \\ **Coefficient** \\ **K (m/d)** \\ \end{tabular} } \\ \hline Fill & \⃝1 & 18.5 & 12 & 10 & 5.7 & 5 \\ Silt clay & \⃝2-2 & 18.0 & 6 & 2 & 7 & 0.003 \\ Silt clay & \⃝1-1 & 18.8 & 33 & 12 & 18 & 0.004 \\ Fine sand & \⃝3-2 & 19.6 & 2 & 28 & 23 & 15 \\ Medium sand & \⃝3-3 & 19.7 & 1 & 31 & 29 & 40 \\ Coarse sand & \⃝1-4 & 19.8 & 1 & 34 & 32 & 80 \\ Gravelly sand & \⃝3-5 & 20.0 & 1 & 36 & 33 & 100 \\ Rounded gravel & \⃝3-6 & 20.5 & 1 & 38 & 35 & 120 \\ Weathered rock & \⃝5-1-3 & 23.9 & 350 & 32 & 70 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and mechanical parameters of soils.  
Figure 4: Section of foundation pit with retaining structure.
## 2 Model Construction and Analysis
### Model Construction
#### 2.1.2 Details of the DCN_Fire Model  
Input layer: input data comprised a segmentation database of significant images with a pixel size of 128 \(\times\) 128 in lightning memory-mapped database format.  
Convolutional layer: eight convolutions (Conv 1-8) were defined in the convolutional layer. The convolutional kernel size was 3 \(\times\) 3, the sliding step was one, and Xavier was used to initially configure the parameter weights. A rectified linear unit (relu) was used on this layer as the activation function and the output was sent to the next volume or pooling layer.  
Pooling layers: these reduced the data dimensions to avoid over-fitting problems. The kernel size of this layer was set to 2 \(\times\) 2 by using the maximum pooling method, and the step size was two. Each fully connected layer (FC1 and FC2) had 256 neurons and Xavier mode was used for weight initialization. In the hidden layer, the exit technique was used to reduce the dependence between neurons, and the activation function in the fully connected layer was a relu.  
Classification layer: the Softmax function is calculated as  
\[Softmax=\frac{exp(a_{i})}{\sum_{j}exp(a_{j})} \tag{1}\]  
Pixels of size 128 \(\times\) 128 were input into the DCNN, after which the error between the actual output value and the required value was calculated. The stochastic gradient descent method was used on the training-depth CNN. After passing the back-propagation error in the previous layer, the method calculated the derivative on the optimization coefficient and weight. The learning process of the model was iteratively optimized until the training error was small enough to be ignored.
###### Abstract  
Deep learning approaches based on autoencoders have been widely used for structural monitoring. Traditional approaches of autoencoders based on reconstruction errors involve limitations, since they do not exploit their hierarchical nature, and only healthy data are used for training. In this work, some health indicators, based on manifold invariance through the encoding procedure, were built for the monitoring of concrete structures strengthened with carbon fiber-reinforced polymers by directly exploring the latent space representation of the input data to a deep autoencoder. Latent representations of experimental observations of different classes were used for the learning of the network, delimiting areas in a low-dimensional space. New synthetic data with their variations, generated with a variational autoencoder, were encompassed to the trained autoencoder. The proposed method was verified on raw electromechanical impedance spectra obtained from lead zirconate titanate sensors bonded on a specimen subjected to different loading stages. The results of this research demonstrate the efficiency of the proposed approach.  
Keywords:fiber-reinforced polymer strengthening structural health monitoring; deep autoencoders; damage indicators; latent space; synthetic data +
Footnote †: journal: Applied sciences
# Workflows for Rapid Functional Annotation of Diverse Arthropod Genomes
## 1 Introduction
### Motivation  
Many high-quality arthropod genomes are being generated, in particular by large-scale genome projects, such as the USDA--Agricultural Research Service's Ag100 Pest Initiative [13; 14], and others under the Earth BioGenome Project umbrella [15]. These new genomes serve as community resources and provide the foundational information required to apply 'omics technologies to a more diverse set of species. Genome assemblies need structural and functional annotations to ensure that these 'omics approaches can be rapidly translated into biological information that provides a better understanding of the system being studied. The Gene Ontology Consortium [16], UniProtKB [17], and KEGG [17; 18] resources generate and maintain functional annotations of many proteomes available in the sequence databases, such as RefSeq and INSDC, and functional annotations produced by these initiatives are widely used and referenced by the scientific community. However, there is a delay before new genomes are processed by these databases, which have been exacerbated by the influx of new genome submissions. In addition, the process of manual curation of published papers is laborious and time-consuming for model species where most publications are focused on gene function [19]. A rapid, first-pass functional annotation workflow quickly provides functional information to support genomic analysesand experimentation and ensures that 'omics approaches can be interpreted to better understand a diverse range of biological systems.  
AgBase [20] and the i5k Workspace@NAL [21] databases serve the arthropod genomics community by providing access and curation tools for arthropod proteomes and genomes, respectively. Here, we report the creation of containerized workflows to fill the need for high-throughput functional annotation of proteins from eukaryotic genome sequencing programs for the scientific communities that we support, as well as the arthropod genomics community at large. We test these workflows using twelve sequenced invertebrate genomes selected to span a broad range of invertebrate classes and to represent genomes with varying assembly quality and sequencing technologies used. The proteins from these sequenced genomes are compared with three reference species, _Drosophila melanogaster_, _Apis mellifera_ (honeybee), and _Tribolium castaneum_ (red flour beetle), have well-characterized GO annotation based on experimental evidence. These workflows are also available on CyVerse to facilitate re-use [22; 23] via a user-friendly web-based interface.
## 1 Introduction
(4) **Adversarial Overfitting Due to Muti-step Attacks**: Research has indicated that multi-step attacks are more likely than single-step attacks to overfit to the parameters of victim model. Due to the discrete nature of textual data, attack algorithms based on keyword replacement typically require multiple iterations, which reduces the effectiveness of transfer-based attacks. To address the above issues, we propose a hard-label attack method named WordBlitz that jointly leverages word importance and adversarial transferability. First, we train a substitute model with limited training data from the victim model and extract the word importance. Then, the Attack Parameter Set (APS), including the candidate pool and word importance, is constructed from the substitute model based on adversarial transferability. Subsequently, adversarial examples are generated and optimized from the APS by a hybrid local search algorithm. Meanwhile, the APS is updated based on the attack result. Furthermore, we utilize the text generated in previous attacks in data augmentation to enhance the generalization of the substitute model. WordBlitz effectively addresses the issues of poor transferability caused by model structural difference, poor classification and overfitting due to multi-step attacks, and the need for improved universality of attack algorithms with respect to victim models. Our contributions are as follows:  
* We propose a transfer-based attack method for the hard-label scenario and change the target of transferability from label to word importance. This approach provides a heuristic method to initialize the attack parameter set from the substitute model. * We introduce an efficient perturbation strategy relying on the attack parameter set. The attack parameter set enables quick searching for optimal results, and is updated with the attack results to leverage historical experience. * We propose a data augmentation method for the substitute model by utilizing the text generated in the perturbation, which improves the adversarial transferability from history. Hence, our approach performs more significantly in broad search spaces, such as Chinese datasets.
# Adaptive Truncation Threshold Determination for Multimode Fiber Single-Pixel Imaging
## 3 Results
### Comparison of Computational Times with Traditional Threshold Methods  
Table 3 presents the average computational time for single recovery using different threshold methods. The L-curve and GCV methods, although capable of obtaining the clairvoyant solution without prior knowledge, have computational complexities proportional to the square of the number of pixels, resulting in average computational times of approximately 76 and 75 s per recovery, respectively. Therefore, they are not suitable for real-time imaging. In contrast, the SURE, ATTD, and fixed truncation threshold methods have significantly lower computational times of 0.04, 0.013, and 0.002 s per recovery, respectively. Notably, ATTD has an advantage over SURE in terms of computational time. While the fixed truncation method is the fastest, it does not achieve the same level of imaging performance as ATTD, which provides a favorable balance between speed and accuracy.
## 2 Study Area and Methods
### Modeling  
The finite element software FLAC3D 6.0 is employed for simulating the actual construction conditions to comprehend the stress-strain shifts in the surrounding rock post-tunnel excavation and to validate and supplement the monitoring outcomes. This simulation illustrates the stress-strain characteristics of the support structure post-tunnel excavation.  
The model should contain a range of at least 3-5 times the tunnel diameter to avoid boundary effects. The excavation diameter of the Xianglushan Tunnel TBM section measures 9.8 m. Based on this principle, the distance from the tunnel axis to the model's bottom boundary is established at 85 m, while the distances from the model's left and right boundaries are determined to be 60 m each. The designated excavation direction measures 100 m, but the actual excavation distance is 60 m. The tunnel axis lies approximately 1050 m from the shallowest point and 1333 m from the deepest point relative to the ground surface. The upper 1140 m of the rock mass is simplified as uniformly distributed loads applied to the model's top surface. The tunnel axis is situated 35 m from the model's top surface. The model's overall dimensions are 120 \(\times\) 120 \(\times\) 100 m (height \(\times\) length \(\times\) width). The Mohr-Coulomb constitutive model is chosen to simulate the surrounding rock formations. The model is divided into two layers: the upper layer represents fractured mudstone, and the lower layer signifies limestone. The tunnel does not penetrate the model's lower soil  
Figure 4: Arrangement of anchor axial force measuring points along the depth (three sensors are inserted into the surrounding rock at depths of 0.5 m, 1.5 m, and 2.5 m).  
Figure 5: Installation diagram of a steel strain gauge welded on the inner side of the arch.  
layer. The anchor load cells in the actual field monitoring process serve only a monitoring function and do not offer support; they are omitted as distinct elements in the simulation. Monitoring occurs exclusively at the sensor locations for simplification.  
A full-face excavation approach is adopted with an excavation length of 1 m in alignment with the actual construction procedure. Following each excavation cycle, the excavated area is supported and a null model is designated to represent the excavation process. This excavation and support sequence is executed 60 times. The section at y = 40 m is chosen as the monitoring section to evaluate the stress-strain behavior post-excavation in the monitoring section and reduce the influence of boundary effects. This study focuses on analyzing the distribution of tunnel stresses and the range of vertical displacements. Boundary constraints are applied to the model's five sides, excluding the top surface. Given that the construction zone is marginally impacted by groundwater, only the surrounding rock's self-weight is considered.
## 3 Results and Discussion
### Performance of the Developed ANN Model
Furthermore, Figure 4 shows the error histogram for the training dataset, which is drawn across the zero error. The error lies only in a narrow range (\(-1.609\) to \(1.784\)), which also indicates the good performance of the proposed ANN model. Figure 4: Error histogram of training data for the (5-15-10-1) network. Figure 3: Optimum mean squared error. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Network topology & 5-15-10-1 \\  & Temperature (K), \\  & PP composition (wt fraction), \\ Input parameters & PS composition (wt fraction), \\  & HDPE composition (wt fraction), and \\  & LDPE composition (wt fraction) \\ Output parameter & Weight left \% \\ Training function & Levenberg–Marquardt \\ Transfer function & Tangent sigmoid (TANSIG) \\ Performance function & Mean squared error (MSE) \\ Error tolerance & 0.001 \\ No. of iterations & 1000 \\ Minimum gradient & \(1\times 10^{-7}\) \\ Validation check & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters of the developed ANN model. To validate the proposed model, it was then checked with 34 new datasets. Table 6 presents the targeted values of the weight left percent of the plastic mixtures during the simulation stage using the new datasets. Figure 5, where the predicted and experimental values are in a full agreement, proves also the high-efficient performance of the ANN model using the new simulated datasets. In addition, as presented in Table 7, the R-value was very high (>0.996) and very low values of MAE, RMSE, and MBE were obtained. Thus, it can be easily concluded that the developed ANN model efficiently predicted the TGA experimental data. An error histogram was created for the simulation data and is shown in Figure 6. As shown in the figure, the error lies also in a small range (\(-4.334\) to \(7.605\)). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**ANN Input Variables**} & \multicolumn{3}{c}{**ANN Output-Targeted Values**} \\ \cline{2-7}
**Test No. ** & \multicolumn{4}{c}{**Weight Fractions**} & **Temp. (K)** & **Mass Left (wt \%)** \\ \cline{2-7}  & **PP** & **PS** & **HDPE** & **LDPE** & **1** & **2** \\ \hline
1 & 0.33 & 0.33 & 0.33 & 0 & 588 & 99.5 \\
2 & 0.33 & 0.33 & 0.33 & 0 & 648 & 95.9 \\
3 & 0.3 & 0.7 & 0 & 0 & 663 & 96.6  
Figure 5: Regression of the simulated datasets for the (5-15-10-1) ANN model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**R** & **RMSE** & **MAE** & **MBE** \\ \hline
0.99678 & 2.73709 & 1.85179 & \(-\)0.27745 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistical parameters of the (5-15-10-1) model for the simulated data. Figure 6: Error histogram of the (5-15-10-1) ANN model for the simulated datasets.
###### Abstract  
With the extensive application of data analysis in various parts of the landscape design process, Building Information Modeling (BIM), City Information Modeling (CIM), and Landscape Information Modeling (LIM) all aim to achieve key data sharing and collaboration in the whole cycle and promote the development of smart cities. Landscape element indicators are complex, diverse, and difficult to quantify, which is one of the reasons for the slow development of LIM. However, with the development requirements of LIM, quantifying landscape elements and transforming landscapes into digital landscape information has become a hot spot in the landscape design industry. Landscape parametric design aims to transform the design elements into quantifiable parameters, obtain the design scheme by changing the value of the parameters, and form the design results based on digital information. It is one of the foundations of LIM. Based on the Rhino + Grasshopper (R + G) platform, this study takes Shuuizhuang Park as an example and establishes the parametric design approach for the park entrance. The approach involves several steps: (1) Confirming the boundary and key points of the park to prepare the basic data for parametric design. (2) Utilizing the Physarealin Algorithm Method (PAM) to simulate crowd paths, the Site Slope Algorithm Model (SSAM) to analyze terrain slope, the Road Intersection Algorithm Model (RIAM) to determine the scope of the park entrance area based on the national and local design codes, and the Crowd Density Algorithm Model (CDAM) and Crowd Convenience Algorithm Model (CCAM) to analyze the density and convenience of the crowd to preliminarily confirm the park entrance. (3) Meeting the basic requirements of the crowd and vehicle gathering and spread by using the Square Area Review Algorithm Model (SARAM) and Parking Lot Review Algorithm Model (PLRAM) to recheck the site area of the park entrance square and park lot to optimize the park entrance. This approach constructs several site analysis models based on the R + G platform and introduces PAM to analyze crowd activity paths, proposing a landscape parametric design method that integrates crowd activity and landscape design requirements. Compared with the classical design, the landscape parametric design derived from the comprehensive data analysis reduces human interference, is more scientific and practical, and better meets the requirements of people entering the park. The approach also provides ideas for other landscape parametric site selections. By adjusting the values of element parameters, the approach can also be applied to the site selection and design of other landscapes.  
lancscape parametric design; park entrance; site selection; crowd simulation; Rhino + Grasshopper +
Footnote †: journal: Applied Sciences
# HSV Color-Space-Based Automated Object Localization for Robot Grasping without Prior Knowledge
## 4 HSV Color-Space-Based Automated Object Feature Extraction
### HSV Color-Space-Based Automated Object Localization Algorithm
#### 4.2.1 Configuration of Reference ROI and HSV Color-Space-Based Auto HSV Color Picking  
The first step of the proposed algorithm is to set the reference region of interest (ROI) and maximum ROI information to automatically recognize the product feature information (color, position) based on the HSV color space in the additive manufacturing facility (3D printer) from the camera device of the robot handler. Each of the maximum ROIs consists of one large bounding box. The bounding box is rectangular, which is determined by the x_start and y_start coordinates of the upper-left corner of the rectangle and the x_end and y_end coordinates of the lower-right corner. Algorithm 1 presents the algorithm for HSV color-space-based auto color picking. ROI_max_region and ROI_ref_region represent the bounding box size of the maximum ROI and the reference ROI, respectively. The reference ROI is set around the center of the bottom of the 3D printer, which is the reference position information where the additive manufacturing to produce the product is first started. The maximum ROI is set slightly higher than the maximum size of the product produced by the additive manufacturing facility. The reference ROI extracts reference color data to automatically identify the color of an object in the input video from the vision camera of the robot handler after product production is completed using a 3D printer.  
```
1:Configuration of ROI_max_region from Robot Handler
2:Set ROI_max_region location info from bounding box (x_start, y_start, x_end, y_end)
3:Input Video from IoT-based Robot Handler
4:Video to image frame conversion and transformation from RGB to HSV through Equations (1)-(3)
5:Set ROI_ref_region location info from bounding box (x_start, y_start, x_end, y_end)
6:Picking automated HSV Color Extraction (HSV values) from ROI_ref_region
7:Registration color threshold of ROI_ref_region
8:Set H, S, V upper and lower bound threshold, ROI_ref_region location
```  
**Algorithm 1** Auto HSV Color Picking  
The reason for setting the reference ROI is to solve the problem of the user's need to set the HSV threshold value every time because the color of the product varies depending on the order.  
Figure 5: Process HSV color-space-based automated object localization algorithm.  
Auto HSV color picking extracts and stores the H, S, and V information through the camera of the robot handler for the color information of the reference ROI after the production is completed by the 3D printer.  
Auto HSV color picking sets the upper and lower thresholds of the stored H, S, and V information. The upper and lower threshold values are set as the maximum and minimum ranges based on the extracted HSV values. It detects changes in product color owing to illumination interference or changes in light conditions.