esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Overall System
### Visual Recognition System
#### 3.1.3 Intersection Point Recognition System
The intersection point is then checked to ensure that it lies within the image boundaries and the actual range of the segments, excluding any points outside the segment extensions. The confidence of the intersection is calculated based on factors such as the distance to the segment midpoints and segment lengths. Higher confidence is given to intersections closer to the midpoints and with longer segments, allowing for the retention of reliable intersections while filtering out ambiguous ones. The formula for calculating the confidence is shown in Equation (7). \[\beta=\frac{|sin\theta|}{1+(d_{\text{AB}}^{2}+d_{\text{CD}}^{2})}\times 100\% \tag{7}\]  
Figure 5:0 Process of merging line segments through bidirectional projection and endpoint combination. In this equation, \(\theta\) represents the angle between the two lines, and \(d_{AB}^{2}\) and \(d_{CD}^{2}\) are the squared distances from the detected intersection point to the midpoints of the two line segments. The formula uses a nonlinear function to increase the weight of the distance, ensuring that the confidence of the intersection point decreases rapidly as it moves further from the segment midpoints. Additionally, the angle between the two line segments is considered. If the segments are nearly parallel, the confidence of the intersection should be lower. Therefore, an angular factor is included to optimize the confidence calculation. After the intersection points are detected, the path planning system is used to assign labels to the detected points, providing the robot arm with the binding sequence. The coordinate transformation system is then used to convert the intersection points into 3D coordinates in the robot arm's coordinate system.
## 3 Framework
### Requirements and Assumptions  
In this paper, we focus on two types of applications that must be supported by the combination of TDMA/CA and SERENA. With regard to unicast communications, we distinguish:  
* _general applications_ where any node can communicate with any other node in unicast mode. We also call them general communications. This type is the default one. It is used in the absence of knowledge of the application, because it introduces no restriction.
* _data gathering applications_ where all sensor nodes send their data to a sink in unicast mode. Consequently, the only unicast communications that are used are either between a node and its parent, or between a node and its children. We also call them tree communications.  
Notice that these two types of application can support broadcast in addition.
# Wavelet-Based Estimation of Damping from Multi-Sensor, Multi-Impact Data
## 3 Damping Extraction Methods  
Damping extraction methods are crucial in determining the energy dissipation characteristics of structures. These methods help in understanding how structures respond to various dynamic loads by quantifying the rate at which they dissipate energy. The extraction of damping characteristics can be achieved through various techniques, including time-domain, frequency-domain, and time-frequency (wavelet-based) methods, each offering unique advantages and levels of accuracy. This section provides an overview of some of the most popular and well-known damping extraction methods, focusing on frequency-domain and wavelet-based approaches.
## 3 Methodology
### Data Preprocessing
#### 3.2.1 Data Cleaning and Preparation
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Factor** & **Description** \\ \hline \multirow{4}{*}{Physical} & Age/year-built & Year of construction \\  & Structure length & Length of the structure \\  & Width & Width of deck \\  & Number of spans & Number of spans in main unit \\  & Deck & Condition ratings of the bridge component \\ \hline \multirow{2}{*}{Region} & Longitude & Longitude \\  & Latitude & Latitude \\ \hline \multirow{4}{*}{Structural Type} & Material type & Kind of material used such as Concrete, Steel, Wood \\  & & or Timber \\  & Deck protection & Protective system on bridge deck \\  & Membrane type & Membrane used as a part of protective system on bridge deck \\ \hline \multirow{4}{*}{Environmental} & Precipitation & Annual mean precipitation in inches or millimeters \\  & Snowfall & Annual mean snowfall \\  & Freeze-thaw & Annual mean freeze-thaw \\ \hline \multirow{2}{*}{Service} & Owner & Maintenance responsibility of bridges \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Categorization and description of variables. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Factor** & **Mean** & **Median** & **Std. Dev** & **Min** & **Q1** & **Q3** & **Max** \\ \hline Traffic & 1820.28 & 50 & 7873.3 & 2 & 25.50 & 235 & 165,270 \\ Deck-age & 40.52 & 34 & 25.39 & 1 & 19 & 69 & 122 \\ Area & 234.65 & 100.5 & 600.18 & 28.81 & 61.25 & 229.99 & 20,659 \\ Snowfall & 57.41 & 58 & 7.39 & 41.97 & 51.05 & 62.92 & 73.315 \\ Freezethaw & 105.79 & 103.81 & 6.65 & 98.44 & 101.97 & 106.76 & 133 \\ Precipitation & 2.03 & 2.08 & 0.265 & 1.05 & 1.94 & 2.18 & 2.5 \\ Longitude & \(-94.40\) & \(-97.35\) & \(18.241\) & \(-104.95\) & \(-98.58\) & \(-96.56\) & 0.0 \\ Latitude & 39.74 & 41.125 & 7.68 & 40.0 & 40.53 & 41.75 & 43.16 \\ Deterioration & 0.0971 & 0.045 & 0.145 & 0.173 & 0.0002 & 0.103 & 1.5 \\ Length & 29.65 & 15.8 & 54.06 & 6.1 & 9.8 & 30.8 & 1644.7 \\ Spans & 1.81 & 1.0 & 1.57 & 1 & 1 & 3 & 52 \\ Performance & 0.748 & 0.67 & 0.51 & 2.86 & 0.34 & 1.06 & 3.18 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Descriptive summary of numerical variables.
## 3 Results  
Figure 1 shows the solar wind parameters and geomagnetic activity indices for 4 August 2020. Several studies have analysed the solar wind conditions associated with the Beirut port explosion, all of which confirmed the presence of quiet conditions during the event [25]. The vertical dashed red line indicates the Beirut explosion at 15:08 UT. In this study, we further confirmed these conditions by including the Polar Cap Index, which may serve as a convenient proxy of the solar wind energy inflow into the magnetosphere and farther down to the ionosphere/atmosphere. The absolute value of the polar cap north index (PCN) during the Beirut port explosion was less than 1 (mV/m); therefore, the probability of magnetospheric energy loading was low according to a comprehensive analysis of the PC limits [44; 45; 46; 47]. Also, the real time auroral electrojet demonstrated a low AE value ([[https://wdc.kugi.kyoto-u.ac.jp/ae_realtime/202008/index_20200804.html](https://wdc.kugi.kyoto-u.ac.jp/ae_realtime/202008/index_20200804.html)]([https://wdc.kugi.kyoto-u.ac.jp/ae_realtime/202008/index_20200804.html](https://wdc.kugi.kyoto-u.ac.jp/ae_realtime/202008/index_20200804.html)), accessed on 11 May 2025). Therefore, the probability of ionospheric disturbance from auroral regions at the time of the Beirut port explosion is extremely low. Solar wind parameters: the interplanetary magnetic field (IMF-Bz) did not decrease less than \(-2\) nT and no sudden increase in the solar wind speed or density occurred before or during the explosion time. The symmetric (SYM-H) index, which is a high-resolution proxy for the Dst index, was close to zero. It is worth noting that the Dst index represents variations in the Earth's magnetic field due to the magnetospheric ring current; therefore, the magnetosphere is very quiet. Generally, the quiet-time Dst index is defined to be within \(\pm 20\) nT [36; 44; 48]. Subsequently, any variation in the magnetic field or ionospheric dynamics in the explosion region reflects the impact of the explosion.
## 5 Experimental Results
### Digital Images of the Discharges

Table 1: Main parameters of the linear regression between \(\mathit{INT}_{\mathit{normalized}}\) and \(\mathit{E}_{\mathit{displayes}}\)[0.5] (\(\mathit{INT}_{\mathit{normalized}}=\mathit{INT}_{o}+k\cdot\mathit{E}_{\mathit{displayes }}\)[0.5]). Figure 5: Long exposure photographs of the discharges under the experimental conditions described in the images (voltages expressed in RMS value). The images indicate the operating pressure, the applied voltage, and the value of the discharge severity indicator \(\mathit{INT}_{\mathit{normalized}}\). Results summarized in Figure 6 and Table 1 clearly show the strong linearity between the normalized intensity of the discharge images \(INT_{normalized}\) and the square root of the electric energy dissipated by the discharges \(E_{ discharges}\)[0.5]. These results confirm that \(INT_{normalized}\) can be used as an indicator of the discharge severity.
# Experimental Study on the Permeability of Ecological Slopes under Rainfall Infiltration Conditions
## 3 Test Results and Analysis
### Calculation of Unsaturated Permeability Coefficient
#### 3.3.3 Calculation of Water Head  
The water head of a point in the soil is composed of the position head, pressure head, and velocity head. However, the water transport in the soil is slower compared to the position head, leading to that the velocity head can be ignored. Then, the water head of a point in the soil can be calculated as:  
\[H_{w}=Y+\frac{\mu_{w}}{\rho_{w}g} \tag{5}\]  
where \(H_{w}\) represents a point of the total head (cm); \(Y\) denotes a point in the height above the reference plane (cm); and the reference plane in this paper is a distance of 20 cm from the slope surface then \(Y=-20\) cm; \(\mu_{w}\) is the pore water pressure (kPa), and according to the shear strength formula of unsaturated soil, the pore water pressure at a certain point in the soil is opposite to the matrix suction; \(\rho_{w}\) depicts the density of water with a value of 1.0 g/cm\({}^{3};g\) is the acceleration of gravity with a value of 9.8 N/kg.
## 3 Materials and Methods
### Feature Selection
#### 3.3.2 Fisher Score (FS)  
FS is one of the most commonly utilized techniques for identifying highly discriminative feature subsets. One of the most descriptive features is selected after the ranking of each feature has been calculated [42,43], as follows:  
\[F(j)=\frac{\sum_{k=1}^{c}n_{k}\left(\mu_{k}^{j}-\mu_{j}\right)^{2}}{\left( \sigma^{j}\right)^{2}},\quad j=1,2,\ldots,\ p \tag{6}\]  
\[\sigma^{j}=\sum_{k=1}^{c}n_{k}\left(\sigma_{k}^{j}\right)^{2} \tag{7}\]  
In Equations (6) and (7), \(n_{k}\) is the number of classes \(k\), \(\mu_{j}\) and \(\sigma^{j}\) are the Mean and Std relating to feature \(j\), and \(\mu_{k}^{j}\) and \(\sigma_{k}^{j}\) are the Mean and Std of class \(k\) relating to feature \(j\), respectively. The most discriminative features are chosen from the top m-ranked features with the highest number of scores.  
Wrapper methods are advanced Feature Selection Techniques that assess the performance of a Machine Learning (ML) model by systematically evaluating different feature subsets to determine the optimal combination of features [37]. These methods rely on the validation error of the trained model to determine which features to select, with features being iteratively added or removed based on their impact on model accuracy. This method involves evaluating all potential feature subsets to identify the most optimal combination of features. By adding and removing features from the subset, the best set is determined. For large data, this method is not cost-effective in terms of time or computational complexity. Wrapper methods often lead to overfitting. In this study, two wrapper methods are employed: SFS and RFE.
# Exploiting Lunar Navigation Constellation for GNC Enhancement in Landing Missions
## 4 Results
### LNS Constellation Architecture  
The LNS constellation considered for this assessment has been obtained in our previous paper through a multi-objective optimisation, based on maximising the Dilution Of Precision (DOP) performance for a set of polar LLO users. The details of how we set up the objectives and the genotype can be found in [14], together with the related navigation performance for a naturally evolving LLO user. The constellation is composed of six servicers posed in Elliptic Lunar Frozen Orbits (ELFO, details provided in [36]), considered as the best candidates for an LNS constellation due to their relatively high stability in the long-term, thus substantially reducing the associated station-keeping \(\Delta v\). The selected orbital architecture (tagged llo_sgl 6A in the original work) is presented in Figure 4.  
The six orbits share the same value of semi-major axis, eccentricity, inclination, and argument of pericentre. The former is imposed in order to have a period of 24 h, which is a prominent feature for operative reasons for such an asset. In addition, the argument of pericentre is imposed a priori, with a value of \(90^{\circ}\) in order to have the aposelene above the South Pole and prioritise the visibility of such a scientifically relevant location. The eccentricity of 0.489 is instead an output of the optimisation procedure. With the previous parameters fixed, the ELFO constraint leads to an inclination of \(47.5^{\circ}\). The right ascension of the ascending nodes and the true anomalies are instead the other optimised parameters, different per each element of the constellation. As visible in Figure 4, this architecture presents a clear clustering of the servicers, with only two planes considered, with right ascension values of approximately \(\pm 90^{\circ}\). The true anomalies instead are almost equally distributed.  
As thoroughly described in [14], this constellation is able to provide around-metre navigation accuracy when more than three servicers are visible. Such a feature is fundamental for a landing scenario, in which satisfying the accuracy requirements is often extremely challenging. Indeed, in this case, a complete closed-loop GNC strategy is considered, adding propulsion to the orbital user, which introduces more complex navigation needs.
## 3 Material and Methods
### Machine Learning Model Descriptions
#### 3.3.6 CatBoost  
The CatBoost algorithm integrates gradient boosting decision tree (GBDT) technology with advances in the effective use of categorical features. This approach improves the model's ability to efficiently process and learn categorical data without the need for extensive preprocessing. CatBoost uses unregistered trees, which contributes to its high performance and accuracy by simplifying the handling of categorical variables. This methodology significantly helps in minimizing overfitting and thus improves the generalization capabilities of the model. CatBoost can be applied to both regression and classification problems and shows particularly high performance on large datasets. One of the main advantages of the algorithm is that it can use categorical variables directly without requiring preprocessing, which simplifies and speeds up the modeling process [25].  
CatBoost works by creating a set of decision trees that are weighted to reduce the prediction error of the target variable at each step. Before adding each tree, the model randomly selects a subset of the dataset and performs gradient calculations for each element in that subset. These gradients determine how new trees are created, helping the model reduce its error rate. Additionally, CatBoost has the ability to calculate the importance of each feature, which allows the model to make more accurate predictions.
## 1 Introduction  
Porosity and permeability are the greatest evaluation parameters for assessing reservoir quality during oil exploration [1]. However, the prediction of porosity and permeability in the deeply buried strata, especially sandy conglomerates, is more complicated due to the strong heterogeneity [2; 3; 4; 5]. Consequently, a clear understanding of their formation and repartition is cardinal for exploring and exploiting deeply buried coarse-grained deposits. The depositional porosity and permeability are chiefly determined by the depositional environment and hydrodynamic conditions, including grain shape, size, mineralogy, and sorting [6; 7; 8]. Generally, the porosity and intergranular volume decrease with the augmentation of the burial depth [9]. During the burial, the digenetic mechanism, including compaction, cementation, and dissolution, can reduce or augment the porosity [10; 11; 12].  
The present focuses on the pore type related to the high-quality formation in Es4s and Es4x and compares the progradation and retrogradation sequences in two areas belonging to the north of the Dongying Subsag. Recent work in the study area has demonstrated an abnormally high porosity in the deep burial coarse-grained strata [13]. Furthermore, the recent work of [PERSON] et al. [4] has revealed that the secondary dissolution pore is a significant controlling factor of good reservoir formation. However, the type of pores and their formation mechanisms are still unclear. Furthermore, the authors demonstrated that the reservoir quality in the progradation is moderately better than the retrogradation. However, no comparison has been made within the whole steep slope belt in the study area. Thus, determining the elaborated porosity is crucial to understand the genesis of good reservoirs in deeply buried coarse-grained sediments. Thus, this paper discusses the fundamental question: what are the elaborated pores related to high-quality reservoir establishment? We address the criteria of pore type in the conglomerate reservoir in the study area. We compare the different fans based on the petrophysical properties and diagenesis characteristics to understand a better place for a high-quality reservoir distribution.
## 2 Problem Formulation  
### Structure and Agent Definitions  
A typical architecture of CHP system is shown in Figure 1. Fed by multiple-energy-carriers, the energy resources of the CHP system consist of available distributed generators (DGs) that contain renewable generators (RGs) and fuel generators (FGs), distributed heating devices (DHDs) that contain renewable heating devices (RHDs) and conventional fuel heating devices (FHDs), distributed energy storage devices (DESDs) that contain electrical energy storage devices (EESDs) and heat energy storage devices (HESDs), CHPs that contain renewable combined heat and power (RCHP) and conventional fuel combined heat and power (FCHP), and loads that contain electrical loads and heat loads.  
It is worth noting that the energy resources discussed above may generate different types of energy (electric and/or heat) power. To facilitate the system modeling, three types of agents are defined in this paper according to different energy power generation types. The three different agents include electricity-only agents (EOAs), co-generation agents (CGAs) and heat-only agents (HOAs). Therein, the EOAs, which can generate electrical power only, consist of various kinds of DGs and EESDs. The CGAs, which can generate not only electrical power but also heat power, consist of various kinds of CHPs. The HOAs, which can generate heat power only, consist of various kinds of DHDs and HESDs. Then, each type of agent will be formulated to build the system model in the next section.
# An Extended Functional Design Approach Based on Working Principle and Property and Its Application
## 1 Introduction  
Conceptual design is a process to generate a conceptual design solution to meet customer needs or realize desired functions. It is an important vehicle for meeting the increasingly specialized demands of customers, and it also has a powerful impact on manufacturing productivity and product quality ([PERSON] et al., 2002) [1]. The concept generated at this stage affects the basic shape generation and material selection of the product concerned [2]. In the subsequent detailed design phase, it is extremely difficult or even impossible to overcome the shortcomings of a poor design concept that is formulated in the conceptual design phase [3].  
Functional design is a significant activity during conceptual design, and it is primarily responsible for analyzing customer needs and developing possible solution concepts for a desired product or system. Since functional design can provide a systematic approach for transforming functions into structural solutions, many researchers in the engineering design community have conducted considerable functional design research, yieldingthe development of many valuable approaches (e.g., [PERSON], 1990 [4]; [PERSON], 1998 [5]; [PERSON], 2001 [6]; [PERSON] and [PERSON], 2004 [7]; [PERSON] and [PERSON], 2007 [3]; [PERSON] et al., 2008 [8]; [PERSON] et al., 2013 [9]; [PERSON] et al., 2015 [10]).  
However, most of the existing functional design approaches mainly focus on how to assist designers in searching for design solutions for desired functions, and rely too much on established design knowledge. This type of approach is usually characterized by design analogy, especially case analogy, or structure analogy. Such an approach cannot generate new design solutions if there is little established design knowledge in the field. Therefore, it is increasingly necessary to develop a functional design approach that can assist designers to generate new design structure solutions without relying too much on established design knowledge. This paper chooses working principle and property as intermediate transformation variables, integrates them into the function-to-structure mapping process, and develops an extended functional design approach for generating new design structure solutions. The rest of this paper is organized as follows. Section 2 reviews the related work. In Section 3, the reason for choosing the working principle and relevant property is elaborated, and their contents are analyzed explicitly. Then, an extended functional design approach (function-working principle-property-structure) is proposed in Section 4. Section 5 illustrates the conceptual design of a self-adaptive luggage transfer device. Finally, Section 6 concludes this paper.
## 4 Model Validation
### History Matching for Marcellus Shale  
In order to further verify the reliability of the fully coupled model, a history matching of field data from Marcellus Shale was applied. In general, flowing bottom hole pressure and gas production data from shale wells are used to perform history matching to validate reservoir models. The reservoir parameters and flowing bottom hole pressure curve for Marcellus Shale were obtained from the published literature [6; 28; 35], as show in Table 1. The parameters for the shale matrix and hydraulic fractures were obtained by matching the production data as listed in Table 1.  
The results derived from the fully coupled model were highly correlated with the field data (Figure 5), with a high positive correlation (\(R^{2}\) = 0.88) and small error percentages (avg. 7.29%), indicating that the fully coupled model can be applied for the analysis of all the flow regimes in pores and fractures at differing scales. In addition, the initial gas production rate simulated by the fully coupled model was higher than the actual gas production rate, and the error percentage between them even can reach 35.6%. This is due to the fact that water flow back will reduce the initial gas production rate, but the gas will dominate the production over the later period of the depletion [6].  
Figure 4: Comparison of the results of the present proposed model and the traditional models.