esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Comparision with the Published Works  
As for the datasets used in the present paper, it has also been used in several published articles, e.g., references [1; 64; 65]. This section aims to compare the superiority of the model proposed in this paper with the existing models in the published papers.  
Reference [64] proposed two models used for the PPV prediction, i.e., the nonlinear multiple regression (NLMR) and the gene expression programming (GEP). Likewise, 102 datasets of blasting operations were randomly divided into 80% for the training set and 20% for the testing set. The input parameters, including BS, HD, ST, PF, MC, and DI, are the same as the input parameters used in the present study. The results of the GEP and NLMP models are presented in Table 7. Reference [1] also used the same datasets to research the PPV prediction. In the research, a special case is that a feature selection technique was used to filter out unimportant input parameters before conducting the modeling. In this way, five variables except for BS, including HD, ST, PF, MC, and DI, were chosen as the input parameters, which is the main difference from the current paper. In their study, five ML models, i.e., CART, CHAID, RF, ANN, and SVM, were developed using the new datasets after feature selection. Furthermore, the datasets used for developing the models were randomly split into two parts, i.e., 70% of datasets for training the models and 30% of datasets for validating the performance of the models. The performance results of these five models are presented in Table 7. Reference [65] also utilized the same datasets to develop two ML models, i.e., RF and Bayesian Network (BN). Similar to Reference [1], feature selection was also used to filter features of low importance before building the ML model. After that, the input parameters that have been identified consist of DI, PF, HD, ST, and MC, in addition to BS.  
Moreover, the datasets were randomly split into two parts, i.e., 70% of datasets for training the models and 30% of datasets for validating the performance of the models. The performance results of these two models are presented in Table 7. Overall, according to the results in Table 7, the improved RF model (i.e., the RF-WOA) proposed in this paper can significantly outperform the models proposed in the published papers, which proves the improved RF model is good enough and robust to predict the PPV caused by mining blasting. Likewise, this shows that it is reasonable and feasible to amend the weights of the decision trees of the RF model as proposed in this paper.
## 4 Experiments
### Qualitative Results  
To further evaluate the performance of our model, we visualized the segmentation results and compared them with the annotations provided by the radiation oncologist. The segmentation results for two different patients across three MRI phases are displayed in Figure 8. The predicted liver contours are in close agreement with the manual annotations, showing minimal discrepancies. For the tumor, the predicted contours exhibit a slight outward expansion relative to the manual annotations, while the overall shape still aligns with the manually drawn contours.  
We present slices from arterial phase MRI images of the same patient, highlighting liver and tumor regions at six distinct locations, along with the segmentation results from various models to visually assess their performance. As illustrated in Figure 9, the prediction outcomes of different models are compared. Notable differences are observed in the segmentation of tumor edges, particularly for smaller tumors. Specifically, 3 DUNet and V-Net do not perform well in tumor segmentation, and 3 DUNet does not even detect small tumors. While the other three baseline models identify the presence of tumors, their  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Volume Similarity (\%) [mean \(\pm\) std]**} \\ \cline{2-3}  & **Liver** & **Tumor** \\ \hline
3 DUNet [60] & \(-10.02\pm 14.96\) & \(-12.34\pm 17.62\) \\ V-Net [61] & \(-4.71\pm 16.93\) & \(-15.89\pm 18.12\) \\ nnU-Net [62] & \(+3.86\pm 11.25\) & \(+8.21\pm 10.56\) \\ UNetR [43] & \(-6.08\pm 18.97\) & \(-13.45\pm 14.22\) \\ SwinUNetR [46] & \(+5.72\pm 12.39\) & \(-7.62\pm 11.73\) \\ Ours & \(+4.32\pm 9.53\) & \(+4.67\pm 8.67\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different methods on Volume Similarity metrics.  
Figure 8: Qualitative comparison of our method in different MRI images.  
results significantly deviate from the ground truth. In contrast, our model demonstrates superior performance in both liver and tumor segmentation.
# Situation Assessment of Air Traffic Based on Complex Network Theory and Ensemble Learning
## 4 Experimental Setup and Details
### Ensemble Learning Model
#### BP neural network model  
We used 10-layer neural network training. After model parameters were optimized by the grid search method, the average classification accuracy score was 0.8702. Each sample's activation function output is shown in Table 7. The _tensorflow()_ was called to build this model. And _tf.nn.softmax()_ was used to calculate corresponding to each sample.
# A Hybrid Architecture for Safe Human-Robot Industrial Tasks
## 2 Safety Standards  
The safety standards represent the preliminary starting point of the proposed methodology. In fact, the main focus of the robotic safety regulations is operators' safety during industrial robotic tasks. The safety standards for these applications are laid out by the International Organization for Standardization (ISO) 10218-1 [2], 10218-2 [3], and by the ISO/TS 15066 [4]. The research work implements a _pre-collision_ scenario called SSM (Clause 5.5.4). As expected by the definition of SSM described in ISO/TS 15066 Clause 5.5.4.1 [4], this collaborative scenario allows the robotic system and the operator to move simultaneously in the collaborative workspace. Risk reduction is achieved by always maintaining at least the protective separation distance, \(S\), between the robot and the operator. During operations, the robotic system never approaches the operator beyond \(S\). When the separation distance between the robot and the operator, \(d\), decreases to less than \(S\), the robot stops. When the operator moves away, the robot automatically resumes the task according to the requirements of this clause while maintaining at least the protective separation distance. When the robot system reduces its speed, \(S\) decreases accordingly.  
The regulation provides additional information and further guidelines to compute \(S\) (Clause 5.5.4.2.3):  
\[S=v_{H}T_{r}+v_{H}T_{s}+v_{R}T_{r}+B+C+Z_{d}+Z_{r}, \tag{1}\]  
where the following definitions apply:  
* \(v_{H}\) represents the maximum speed of the operator and it is assumed as 2000 mm/s with the option to use 1600 mm/s when \(S>500\) mm;
* \(v_{R}\) is the maximum robot speed;
* \(T_{r}\) is the time required by the machine to respond to the operator's presence;
* \(T_{s}\) represents the response time of the machine, which brings the robot to a safe, controlled stop;
* \(B\) is the Euclidean distance traveled by the robot while braking;
* \(C\) is the intrusion distance safety margin, which represents an additional distance, based on the expected intrusion toward the critical zone prior to the actuation of the protective equipment;
* \(Z_{d}\) is the operator position uncertainty (i.e., the sensor uncertainty);
* \(Z_{r}\) is the robot position uncertainty.  
Note that Clause 5.5.4.2.1 specifies that \(S\) must be computed for all human operators within the collaborative workspace. According to Clause 5.5.4.2.2, the maximum allowable speeds, \(v_{H}\) and \(v_{R}\), and the minimum protective separation distances, \(S\), can be constant or variable. For constant values, the maximum allowable speed must be determined through risk assessment during the entire course of the application. For variable values, the speeds must be continuously adjusted based on the relative human-robot speeds and distances.
## 5 Experiments and Results
### BCT Results of INSTEAD in the Official NATO Scenarios  
The BCT consisted of 287 runs for a total of 586 commuters following pre-defined scenarios defined by NATO, which had been kept confidential until the actual performance. Each run involved from 1 to 4 concurrent commuters consisting of volunteers wearing colored jackets, arranged so to have different colors within each group.  
The total accuracy of the INSTEAD pipeline: 2D Video-C&C-3D Video is 97.6%, whereas the relative accuracy of each component is 98.5%, 99.5%, and 99.7% respectively. These results have been discussed in [7], whereas a qualitative evaluation of the data fusion methods follows.  
Figure 12: Examples of four different kinds of tracks captured by the 3D Video in BCT test runs.
# Direct Fabrication of Ultrahydrophobic Laser-Induced Graphene for Strain Sensors
## 3 Results and Discussion
### Piezoresistive Properties  
After the characterization of the material properties of the fabricated LIG, its piezo-resistivity was characterized for strain sensing applications. A standard three-point bending test as shown in Figure 4a was employed to investigate the ability of the LIG fabricated in this work to sense flexural strain along with continuous measurement of resistance change. From the test results, the gauge factor (GF) of the sensor was calculated using the following formula.  
\[\text{GF}=(\Delta\text{R}/\text{R})/\varepsilon \tag{1}\]  
where \(\Delta\text{R}\) is the change in resistance, R is the zero-strain resistance, and \(\varepsilon\) is the applied strain during the bend test. Figure 4b shows the plot of normalized resistance change corresponding to applied strain.  
Figure 3: Raman spectra of generated LIG.  
The obtained data show a linear trend up to the measured strain of 0.16%, and a linear fit provided a GF of 40. It is worth mentioning that the commercial strain sensors typically display a GF < 5, which confirms the good sensitivity of the fabricated LIG sensor.
## 3 Results and Discussion  
### The Predicted Results of the Flow Field in the Simulation Area  
From the commencement of the dewatering test, the water level in the simulation area continuously declined, forming a noticeable drawdown cone around the three horizontal dewatering wells, which spread outward from the center (Figure 11).  
By the end of the first quarter, a drawdown cone had already formed around the three horizontal dewatering wells, with significant water-level differences near the F22 fault, resulting in an irregular, non-circular drawdown cone. By the end of the second quarter,  
Figure 11: The groundwater simulation process: (**a**) the end of the first quarter; (**b**) the end of the second quarter; (**c**) the end of the third quarter; and (**d**) the end of the fourth quarter. Note: the units of the labels in the figure are in meters (m).  
the drawdown cone effect became more pronounced within the mining area, with the overall hydraulic head of the aquifer further decreasing. At the end of the third quarter, the groundwater flow field in the northern and southern parts of the mining area stabilized compared to in the second quarter, while the drawdown cone effect around the three horizontal dewatering wells in the central area became more prominent. By the end of the fourth quarter, a relatively stable groundwater flow field formed, with the drawdown cone around the three horizontal dewatering wells showing a slower decline in the head, creating a larger and more stable drawdown cone with a gentler hydraulic gradient.  
Overall, due to the uplifting effect of the F22 fault in the northern part of the mining area, the terrain was relatively high, resulting in a correspondingly higher water level. Additionally, the impact of the dewatering wells on the water level in the northwestern part of the mining area exhibited significant lag. The water level in Well 2020-Taiguuan2, located in the northern part of the mine, only decreased by 1 m throughout the entire simulation period (Figure 9a).
## 4 Experiments
### Ensemble Model with Hard Voting  
#### 4.3.1 Background  
Generally, machine learning and deep learning algorithms require a large number of samples to train a robust model capable of performing the classification task efficiently. To improve the classification performance, researchers have adopted ensemble-based classification using different voting schemes, such as soft vs. hard voting [12; 54; 55]. In ensemble voting, the decisions of multiple base models are aggregated to produce the final decision that results in the overall classification accuracy surpassing any individual base model [56]. Ensemble techniques have been applied in different domains like document classification [57], image classification [58], and biomedical classification with promising results [59]. However, to the best of the author's knowledge, this study is the first to be used in transportation to address crash severity classification.
# (R-ISSUES) Rural Interoperable System of Systems for Unified Environmental Stewardship
## 1 Introduction
### Building Information Modelling (BIM) and Geographic Information System (GIS)  
Engineering solutions for resilience, like an on-ground fire detection station, a vigilance centre, a water pumping station, or a water pipeline, need digital solutions for defining materials, geometries, constructive elements, and collaboration. This is the aim of Building Information Modelling (BIM). It covers the whole lifecycle of such assets, from the idea to the end of life [20]. BIM is also being requested in the public contracting of civil works. BIM industrialization drives early to prefabricated solutions or shorter delivery times, too, which is interesting for attracting young people to rural areas.  
BIM technology is used throughout the lifecycle of an asset, and different dimensions can be developed depending on the associated information, in addition to 3D, 4D time [21], 5D cost, 6D sustainability [22], or 8D health and safety [23]. This requires collaboration between different professional profiles and the management of increasingly diverse information. On the other hand, it is necessary to standardize collaboration processes according to regulations such as ISO 19650 [24] and the standardization of information exchange under the standard and open Industry Foundation Classes (IFC) data model. An example is the energy behaviour of buildings [25], although this is not covered by data interoperability throughout the entire asset lifecycle and facility management or the facility's industrial processes.  
Integrating richer information is possible by combining IFC with ontology, such as in [26; 27], enabling the connection between the IFC data and other data. [PERSON] et al. [27] pointed out the challenges and opportunities of IFC and ontology integration (IFCOI) by proposing a building lifecycle management model based on IFCOI. Thus, the IfcOWL data model appears to be a scheme between IFC and Web Ontology Language (OWL). Researchers such as [PERSON] et al. [28] proposed the conversion and validation of the WHERE rules used to define data integrity constraints in the IFC scheme.  
The integration of information from BIM models and Geographic Information Systems (GISs) is increasingly common, but this requires the integration of IFC mapping and the city geometry markup language (CityGML) standard, placing particular importance on ontology and semantic mapping [29].
# Mechanical Response Law and Parameter Influence Analysis of Karst Tunnel Dynamic Excavation
## 5 Numerical Results
### Dynamic Excavation Surrounding Rock Vertical Displacement
#### 5.1.1 Comparison of Vertical Displacement between Upper Karst Cave and No Karst Cave  
By monitoring the vertical displacement of the vault and arch bottom at 21 m during the excavation of the tunnel without a karst cave and with an upper karst cave the data shown in Figure 8 below were obtained.  
Figure 8: Displacement curves of monitoring points.  
Figure 6: Vertical displacement of the tunnel with a cavern above: (**a**) Cross-section of the tunnel; (**b**) 21 m section of the tunnel. (Unit: m).  
Figure 7: Vertical displacement of the tunnel with a cavern below: (**a**) Cross-section of the tunnel; (**b**) 21 m section of the tunnel. (Unit: m).  
The curve process of the tunnel excavation process can be divided into four parts: negative space effect section, positive space effect section, damping deformation section and rheological section [30]. The space effect section appears in the initial stage of tunnel excavation. The reason is that after the excavation of the tunnel, the surrounding rock loses the previous deformation constraint and the original equilibrium state changes. The rock makes the movement trend and then deforms and it affects the deformation of the rock mass at a deeper depth. At last, it reaches a certain depth.  
The settlement value of the vault and the uplift of the arch bottom are about 0.1 mm. With the excavation of the tunnel, the settlement value of the vault and the uplift value of the arch bottom increase with the continuous advancement of the tunnel face. When the tunnel face is excavated to 42 m, the settlement value of the tunnel vault reaches 0.47 mm, and the uplift of the tunnel arch bottom is 0.3 mm. The tunnel excavation continues to advance backwards, and its value tends to be stable. At this time, the tunnel surrounding rock has stabilized.  
From Figure 8, it can be seen that when the tunnel is excavated to 18 m, it has had a significant impact on the vertical displacement of the face at 21 m. When there is no karst cave, the vertical displacement of the arch is 0.05 mm at the 18 m top and 0.03 mm at the bottom; When there is a karst cave, the vertical displacement of the arch is 0.02 mm at the 18 m top and 0.02 mm at the bottom. The vertical deformation of the surrounding rock without karst cave development is greater than that with karst cave development, obviously. This is because the karst cave has a stabilizing effect on the surrounding rock of the excavation face.  
When the tunnel is excavated to 25 m, the deformation of the arch bottom without a karst cave increases by 46.7% compared to that with a karst cave. When the tunnel is excavated to 35 m, the deformation of the arch top without a karst cave increases by 26.1% compared to that with a karst cave. Overall, the settlement of the surrounding rock at the bottom of the arch is smaller than that at the top of the arch.
## 3 Materials and Methods
### Method for the Generation of High Temporal Resolution Time Series
#### 3.3.1 Modified Selection of Similar Pixels  
One issue of the original ESTARFM algorithm is how the aforementioned similar pixels are identified. Similar pixels are selected according to Equation (1), where the average number of land cover classes (\(m\)) in the study area must be estimated by the user. In areas of heterogeneous landscapes, a larger number of classes are specified, leading to a stricter condition for the selection of similar pixels [3]. On the one hand, the user estimation introduces an undesirable subjectivity to the process. On the other hand, by using an average number of land cover classes for the entire study area, spatial differences in the heterogeneity of the study area cannot be taken into account. This means that even in a study area containing small-scale mosaics of several land cover types in one part and relatively homogeneous land cover patterns in others, still only one overall number of classes would be considered. This becomes particularly important when working at larger scales.  
In order to address this, we developed a modified approach for the identification of similar pixels in the proposed ESTARFM framework. This approach includes an automated clustering of the surface reflectance values of selected Landsat observations using the ISODATA algorithm [29]. The general possibility of a clustering was already mentioned by [PERSON] _et al._[3], but they neither elaborated on it, nor implemented it in the original ESTARFM. In this study, clustering is performed once per year and on the basis of multi-spectral and multi-temporal Landsat reflectance values. Acquisition dates are selected for clustering which represent the major phenological stages of the vegetation development in the study area. The selection of these dates should be defined according to the seasonal and phenological characteristics of the study area. Then, based on this cluster image, similar pixels are selected as described in Equations (2) and (3). For a pixel to be selected as a similar pixel it has to belong to the same cluster as the central pixel of the moving window (Equation (2)) and the reflectance difference of the pixel to the central pixel must be below one standard deviation of the respective cluster in the moving window (Equation (3)).  
In general, if a 30 m land cover dataset from the same year is available for a study area, it can be used to replace the clustering approach.  
\[C_{s}=C_{c} \tag{2}\]  
\[|R_{s}-R_{c}|\ \leqslant\ \sigma\left(C_{cWin}\right) \tag{3}\]  
where \(C_{s}\) is the cluster of the similar pixel candidate, \(C_{c}\) is the cluster of the central pixel and \(\sigma\left(C_{cWin}\right)\) is the standard deviation of the reflectance values of this cluster in the moving window.
# Production and Processing of a Spherical Polybutylene Terephthalate Powder for Laser Sintering
## 3 Results and Discussion
### Powder Flowability  
In conjunction with the favorable particle shape, the powder flowability shows promising trends as well. Table 5 summarizes the avalanche angle \(\alpha_{\rm a}\), avalanche fractal \(\rm f_{A}\), and Hausner ratio for the PBT powder and the two previously introduced commercially used reference powders.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Powder** & \(\sigma_{\rm E}\) **[-]** & **A [-]** & **S [-]** \\ \hline PBT & 1.06 \(\pm\) 0.04 & 1.22 \(\pm\) 0.24 & 0.93 \(\pm\) 0.02 \\ Duraform(\(\delta\)PA & 1.06 \(\pm\) 0.04 & 1.58 \(\pm\) 0.39 & 0.87 \(\pm\) 0.06 \\ iCoPP & 1.04 \(\pm\) 0.02 & 1.10 \(\pm\) 0.13 & 0.94 \(\pm\) 0.03 \\ \hline \end{tabular}
\end{table}
Table 4: Particle shape descriptors for the PBT powder and commercially used reference powders.  
Figure 7: (**a**) Overview of spherical PBT particles; (**b**) Detail of PBT particles.  
The avalanche angle represents the resistance of the powder to flow, which is mainly due to frictional forces between particles. A lower angle means a powder flows more easily. The standard deviation is a measure for the homogeneity and consistency of flow, where lower values signify a more controlled behavior. The avalanche fractal quantifies the smoothness of the powder surface directly after an avalanche. The Hausner ratio is defined as the ratio between the bulk, free flowing density of the powder and its tapped density. As a general rule, powders with a HR < 1.25 are free flowing, and considered by most authors [13; 29; 32; 33] to be suitable in terms of flowability for laser sintering.  
The PBT powder shows comparable behavior to the commercial powders when avalanche angle and fractal are considered. The most striking difference can be seen in the Hausner ratio. The powder packs very well in its bulk state, which is due in part to the spherical particles, but is also related to the high intrinsic density of the material (1.31 g/cm\({}^{3}\)) relative to the reference powders.