esa-sceva/satcom-chunk-collection huggingface dataset:
# A BIM Based Framework for Damage Segmentation, Modeling, and Visualization Using IFC
## 3 Methodology
### Use Cases  
Subsequent use cases may be categorized as manual, for example, planning an inspection, or automatic, for instance, condition rating. A few use cases in the operations phase have been automatized. Some examples are the automated generation of FEA models [52], automated assessment [53], or automated deterioration prediction [54]. Generating FEA models requires material data for parameters, such as tensile strength, and damage geometry data for cross sections. Material data is included in up-to-date BIM models; damage geometry data is included in DIM models. These data are processed to generate structural models with either slabs, shells, and bars, or volumetric components. Performing a volumetric FEA, the 3D geometry has to be meshed. All necessary data is included in the provided data model.  
Automated assessment could be based on semantic data, like measurements or classifications, on image data, geometric, or a combination. Numerous images may be collected during the inspection and stored in the model. These data may be processed by additional image processing algorithms, neural networks, or other data processing techniques to identify further damage parameters or directly rate defects and components. Further damage parameters and also the final assessment may be stored in the model provided in this paper.  
Similarly, predictions for damage extent and severity are possible. Stochastic models, like Markov chains [54], predict damage ratings based on past ratings. Other models focus on the prediction of damage parameters, for example, the width or length of a crack. These approaches use finite element analysis, boundary element analysis, or similar techniques for their predictions [55]. Subsequent ratings are based on the calculated parameters. Parameters required by these methods, may be also extracted from the given data model.  
However, numerous tasks in the operations phase are performed manually and require an appropriate visualization still. Although, the model proposed in this paper provides the required data for multiple use cases, only visualization is explained to limit the scope.
###### Abstract  
Magnetic polymer microspheres (MPMs) using glycidylmethacrylate (GMA) as a functional monomer were synthesized in the presence of Fe\({}_{3}\)O\({}_{4}\) nanoparticles via dispersion polymerization. After polymerization, the magnetic polymer microbeads were modified with ethylenediamine (EDA). The obtained ethylenediamine-functionalized magnetic microspheres (EDA-MPMs) were characterized by scanning electron microscope (SEM), X-ray diffraction (XRD), vibrating-sample magnetometer (VSM) and Fourier transform infrared (FT-IR) spectroscopy. Then the EDA-MPMs were applied as adsorbents for the removal of Cr(VI) from aqueous solution. Langmuir equation was appropriate to describe the experimental data. The maximum adsorption capacities obtained from the Langmuir model were 236.9, 242.1 and 253.2 mg/g at 298, 308 and 318 K, respectively. The Cr(VI) adsorption equilibrium was established within 120 min and the adsorption kinetics was compatibly described by the pseudo-second order equation. The thermodynamic parameters (\(\Delta\)G\({}^{\circ}\), \(\Delta\)H\({}^{\circ}\), \(\Delta\)S\({}^{\circ}\)) of the sorption process revealed that the adsorption was spontaneous and was an endothermic process. The regeneration study demonstrated that the EDA-MPMs could be repeatedly utilized with no significant loss of adsorption efficiency.  
magnetic polymer microspheres; chromium(VI); adsorption; kinetic; isotherms Article
## 2 The Principle of Inverse Identification  
The inverse method contains three basic contents: a vibration test, theoretical modeling, and a matching calculation. In view of the fact that the viscoelastic core layer is mainly subjected to shear stress, the fractional derivative model to be identified can be described as [15]:  
\[G_{\
u}^{*}(\omega)=\frac{G_{0}+G_{\infty}\left(\mathrm{i}\omega\tau\right)^{ \alpha}}{1+\left(\mathrm{i}\omega\tau\right)^{\alpha}} \tag{1}\]  
where, \(G_{\
u}^{*}(\omega)\) is the complex shear modulus of the viscoelastic material (* is complex number). The matrix or vector containing \((\omega)\) means that it is influenced by the mechanical parameters of viscoelastic materials with frequency-dependent characteristics and is the function of the excitation frequency \(\omega\). The model consists of four parameters, and \(G_{0}\) and \(G_{\infty}\) represent the shear modulus of the viscoelastic materials corresponding to the frequency of zero and an infinite figure, respectively. Symbol \(\tau\) is the relaxation time and \(\alpha\) is the fractional order parameter. The principle of inverse identification of the fractional derivative model of viscoelastic material is shown in Figure 1.  
The vibration test involved in the identification principle consists of two parts. One is used to obtain the modal damping ratio of the cantilever plate without damping treatment, and the other is employed to obtain the frequency response of the CLD plate under base excitation. The identified modal damping ratios will be inputted into the analysis model of the viscoelastic composite plate as the remaining equivalent viscous damping, while the measured frequency response is mainly used to identify the viscoelastic material parameters. The theoretical analysis is mainly used to solve the vibration response of the CLD plate. In the established model, both the viscoelastic material damping and the remaining equivalent viscous damping were considered, which is described in detail in Section 3.  
The matching calculation is applied to minimize the difference between the theoretical and experimental values of the vibration response, thus identifying the fractional derivative model parameters. Taking the vibration response as the objective function, the method of the matching calculation based on sensitivity is proposed and described in detail in Section 4. It should be noted here that the matching calculation can be used to identify the material parameters from the resonance and non-resonance frequency points, and the resonance point should be preferred for its relatively high signal-to-noise ratio. In addition, the total number of the selected resonance and non-resonance  
Figure 1: Principle of inverse identification of the parameters of the fractional derivative model.  
points should be larger than that of the parameters to be identified. Therefore, the number of frequency points selected for this case should be greater than four.
## 3 Results and Discussion
### Compactness of Microcapsules  
Figure 3 describes the weight loss rate of microcapsules. The weight loss rates of WM1 at 1, 2, 4, 7, 14, 30, 45 and 60 days were 5%, 8.6%, 10.6%, 11.5%, 12.1%, 12.3%, 12.4% and 12.4%, respectively. As seen in Figure 3, the weight loss rate of WM1 rose rapidly from 1 to 14 days and then leveled off. However, the weight loss rates for WM2 were 0.7%, 1.1%, 1.3%, 1.4%, 1.5%, 1.6%, 1.6% and 1.6% at 1, 2, 4, 7, 14, 30, 45 and 60 days, respectively. For WM2, there was a small increase in the weight loss rate over a period of 4 days. After 60 days, the weight loss rate of WM2 was only 1.6%, which was much less than that of WM1 (12.4%), and the results indicated that WM2 was better compacted than WM1. This is due to the fact that after adding nano-CaCO\({}_{3}\) to cceresine wax, the shell of microcapsules (nano-CaCO\({}_{3}\)/ceresine wax) is denser, and the microdefects on the surface are filled, which improves the encapsulation ability of E-44 epoxy resin, thus its weight loss rate decreases.  
Figure 3: Weight loss rate of microcapsules.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Microcapsule** & **Core Content** \\ \hline WM1 & 75.4\% \\ WM2 & 80.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Core content of microcapsules.
## 3 Role of Biofilms in the Biological Wastewater Treatment System
Among the toxic heavy metals, arsenic, cadmium, mercury, and lead have been reported to be in the top ten major toxic heavy metals that affect the biological wastewater treatment. Additionally, these heavy metals have been shown to inhibit some microbial processes that are important to different biogeochemical cycles. The increased heavy metal concentration causes a decrease in microbial respiration, leading to the inhibition of biological processes. Nitrification and the biological phosphorus removal process are among the biological processes that are heavily inhibited by high heavy metal concentrations [33]. Several studies have investigated the nitrification effects of different heavy metals, with some studies reporting inhibition thresholds of 25.0 mg/L for cadmium [43], 0.5 mg/l for chromium [44], 0.5 mg/L for copper [45], 1000 mg/L for lead [46], 0.2-4.0 mg/L for nickel [45], and 0.1-10 mg/L for zinc [46]. PAOs have also been reported to be inhibited by organic substrates, nitrite or free nitrous acid (FNA), nitrate, ammonium or free ammonia (FA), and other heavy metals. Biofilms are essential in WWTPs, since the wastewater may contain a hazardous compound that negatively affects the biological wastewater treatment systems. The impact of external contaminants on biofilm-mediated biological wastewater treatment systems is not as severe as on non-biofilm-mediated systems. The microorganisms that aggregate in the biofilm structure are protected from chemical stresses; thus, biofilms are said to have resistance to external stress. This high-stress resistance makes these biofilm structures especially important in sensitive biological processes such as SND [6; 47]. It has been noted that biofilm cells are 10- to 1000-fold less prone to being killed as compared to planktonic bacteria. This is due to several factors that are associated with the defense mechanisms of biofilm possesses. Among the defense mechanisms, EPSs represent the main defense mechanism against eternal stresses for biofilms.
## 5 Use-Case: Energy Imbalance Billing at the Primary Substation  
The use-case presented herewith aims at illustrating how the ENERGOS architecture allows intelligence, responsibility, and action distribution. Current power networks present an automatic mechanism to couple with (non-severe) imbalances: in these cases (_i.e._, supply and the demand do not match in a certain area), power flows into or out of the distribution system, and the alteration is absorbed by reserve power plants appointed to that end and placed at the transmission system. Moreover, in order to understand the benefits of the ENERGOS architecture, we need to consider the effect of the system's performance on the performance of the ENERGOS architecture.  
Figure 5: UML diagram of the CIM ontology part dealing with the connection of power system resources.  
to be able to locate, invoice and charge occurred deviations, the Transmission System Operator (TSO) performs _ex-post_ the so-called _Energy Imbalance Billing_ (EIB) process. More accurately, EIB aims at calculating the imbalances occurred during a certain time period (usually a day) operation between the contracted consumption and the actual generation. The objective of EIB is detecting and charging accordingly deviations in one sense or another made by generators or customers.  
Carried on from the PriST PGDIN perspective, load balancing consists in comparing the current value of its own meter with the aggregated consumptions registered by the secondary substation meters connected to it (and, eventually, SDCs or HV-meters directly connected to the PriST PGDIN). If the amounts do not match, the PriST PGDIN triggers an alarm and the corresponding PGDIN at the utility starts a process to clear the source of the anomaly (_i.e._, determine whether there is a failure in a meter, a potential fraud, _etc._) It causes subsequent reply processes in the SecSTs since they start a mirror validation to check that their own meter does equal the aggregated consumption of the SDCs and meters attached. Therefore, assuming the power network presents a family-tree architecture (Figure 3 illustrates the kinship in the use-case), this process involves coordinating an ancestor root-node (PriST) and all the descendants until the youngest members or leaves (the smart-meters).
## Problem Statement
### System Dynamics
: _In Equation (9), considering problems such as passenger fluctuation and parameter perturbation, the actual dynamic equation of the magnetic-suspension system can be rewritten as follows:_  
\[(m+\Delta m)\ddot{x}_{m}(t)=-\frac{\mu_{0}A_{m}N_{m}^{2}}{4}u+(m+\Delta m)g+f _{d} \tag{13}\]  
Lumped uncertainty term is defined as \(n_{u}=\Delta mg-\Delta m\ddot{x}_{m}(t)+f_{d}\), so Equation (13) can be rewritten as follows:  
\[m\ddot{x}_{m}(t)=-\frac{\mu_{0}A_{m}N_{m}^{2}}{4}u+mg+n_{u}. \tag{14}\]  
Then, Equation (11) can be rewritten as:  
\[\left[ \begin{array}{c}\dot{x}_{1}\\ \dot{x}_{2}\end{array} \right]=\left[ \begin{array}{cc}0&1\\ 0&0\end{array} \right]\left[ \begin{array}{c}x_{1}\\ x_{2}\end{array} \right]+\left[ \begin{array}{c}0\\ \frac{1}{m}\end{array} \right](-\frac{\mu_{0}A_{m}N_{m}^{2}}{4}u+mg+n_{u}) \tag{15}\]  
or  
\[\dot{X}=A_{1}X+B(-\frac{\mu_{0}A_{m}N_{m}^{2}}{4}u+mg+n_{u}), \tag{16}\]  
where \(A_{1}=\left[ \begin{array}{cc}0&1\\ 0&0\end{array} \right]B=\left[ \begin{array}{c}1\\ \frac{1}{m}\end{array} \right]\) and \(X=\left[ \begin{array}{c}x_{1}\\ x_{2}\end{array} \right]\). **Remark 4**. : _In the actual system, signal transmission in the maglev control system would be delayed, which may cause serious safety accidents [12; 13; 14]. In this paper, time delay was introduced into the dynamic modeling. Time delay in states was only considered to facilitate controller design, and time delay in the control signal caused by inductance was not considered. Thus, the magnetic-levitation-system dynamics model shown in Equation (16) can be written as follows:_  
\[\dot{X}=A_{1}X+A_{2}X(t-\tau)+B(-\frac{\mu_{0}A_{m}N_{m}^{2}}{4}u+mg+n_{u}), \tag{17}\]  
_where \(\tau\in R^{+}\) represents time delay in this closed-loop system. \(A_{2}\) is a known matrix, and \(A_{2}\in\mathbb{R}^{2\times 2}\)._
## 6 Conclusions  
This paper presents an innovative approach to satellite handover strategies within LEO satellite constellations, leveraging the synergistic potential of GNN and DRL. Our proposed MPNN-DQN algorithm addresses the dynamic and complex nature of satellite networks, offering a robust solution to ensure high-quality, continuous communication services. By comparing simulation with traditional handover strategies, our method reduces handover delay and frequency while ensuring user data rate as much as possible. This plays a important role in emergency communication scenarios for power systems.  
Conceptualization, H.Y. and K.Z.; methodology, H.Y. and K.Z.; investigation, H.Y. and W.G.; writing--original draft preparation, H.Y.; writing--review and editing, W.G., H.Y. and K.Z.; project administration, K.Z. and W.G. All authors have read and agreed to the published version of the manuscript.  
The research is supported by Beijing Natural Science Foundation-Changing Innovation Joint Fund Project (Grant No. L234025).  
Data Availability Statement: Data will be made available on request.  
The authors declare no conflicts of interest.
# WFO: Cloud-Edge Cooperative Data Offloading Strategy Akin to Water Flow
## 3 System Model and Problem Statement  
As illustrated in Figure 1, we consider static system integrated edge computing and cloud computing nodes. Each of the edge computing nodes is equipped with a data server to provide data services to a community. Take a large video conference as an example. At one point, an online video conference was organized on the cloud node, and participants accessed the conference through their own edge computing nodes. The minutes of the video conference and the dynamics of all participants during the conference are stored on the cloud in the form of video files. A large amount of video data will be generated. After the conference, the conference video needs to be copied to edge computing nodes for backup, in order to facilitate review and let non-participants watch. **Therefore, the problem of this paper is how to quickly realize the backup of cloud data on many edge nodes through cloud-edge collaboration.** Then, we elaborate the related parameters, delay model, and overview of the framework.
# Time Domain Damage Identification Approach of the Newly Hollow Floor Structure with TRMD
## 2 Theoretical Formulation
### Motion Equation for an MDOF Structure with TRMD  
The motion equation for an MDOF structure with TRMD can be written in matrix form as follows:  
\[\mathbf{M}\ddot{\mathbf{x}}(t)+\mathbf{C}\dot{\mathbf{x}}(t)+\mathbf{K} \mathbf{x}(t)=\mathbf{F}_{\mathbf{load}}(t)+\mathbf{H}f(t) \tag{8}\]  
where \(\mathbf{M},\mathbf{C}\), and \(\mathbf{K}\) represent the \((n+1)\times(n+1)\) mass, damping, and stiffness matrices of the MDOF controlled structure with TRMD, respectively. \(\ddot{\mathbf{x}}(t),\dot{\mathbf{x}}(t)\), and \(\mathbf{x}(t)\) are the acceleration, velocity, and displacement vectors, respectively. \(\mathbf{x}(t)=\left[x_{1},x_{2},\cdots x_{n},\theta\right]^{\mathrm{T}}\) includes \(n\) degrees of freedom in the main structure and one degree of freedom for the tuned oscillator. \(\mathbf{F}_{\mathbf{load}}(t)\) is the vector of the loading force. \(f(t)=-\frac{5\delta}{\gamma\rho}\theta\) donates the interaction force between the main structure and the oscillator. \(\mathbf{H}\) indicates the location of \(f(t)\). In Equation (8), \(\mathbf{M},\mathbf{C}\), and \(\mathbf{K}\) can be expressed as:  
\[\mathbf{M}=\begin{bmatrix}\mathbf{M}_{\mathbf{s}}+\mathbf{\Gamma}\mathbf{ \Gamma}^{T}m&\mathbf{\Gamma}m\rho\\ \mathbf{\Gamma}^{T}\frac{5}{\gamma\rho}&1\end{bmatrix},\quad\mathbf{C}= \begin{bmatrix}\mathbf{C}_{\mathbf{s}}&0\\ 0&0\end{bmatrix},\quad\mathbf{K}=\begin{bmatrix}\mathbf{K}_{\mathbf{s}}&0\\ 0&0\end{bmatrix} \tag{9}\]  
In Equation (9), \(\mathbf{M}_{\mathbf{s}},\mathbf{C}_{\mathbf{s}}\), and \(\mathbf{K}_{\mathbf{s}}\) are the \(n\times n\) mass, damping, and stiffness matrices of the main structure, respectively. Considering the TRMD is installed at the top of the structure, \(\mathbf{\Gamma}=\begin{bmatrix}0,0\cdots 0,\\ (n-1)\end{bmatrix}^{\mathrm{T}}\) thus represents the location of the TRMD. The acceleration,  
Figure 3: The motion model of TRMD oscillator.  
Figure 2: The structure model with TRMD.  
velocity, and displacement responses of the MDOF structure with TRMD can be solved by the Newmark method from Equation (8).
# Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5
## 4 Results
### Experimental Setup and Results Analysis
#### 4.1.3 Model Evaluation  
In this experiment, Indexes such as Precision, Recall, F1, AP, mAP_0.5 and mAP_0.50.95 were selected to evaluate the performance of the grassland animal object detection model after training.  
Precision reflects the ability of the model or classifier to correctly predict the accuracy of positive samples. The larger the value, the better the performance. Recall is the proportion of positive samples predicted to be positive samples to the total positive samples, and its performance is the same as Precision. Precision and Recall influence each other. Generally, if the accuracy rate is high, the recall rate will be low, and if the accuracy rate is low, the recall rate will be high. The \(F1\) value is the weighted harmonic average of precision and recall. Taking an elephant to be detected in the picture as an example, TP means that the target in the picture was correctly recognized as an elephant, FP means that another target was detected was incorrectly recognized as an elephant, and FN means that the target in the picture was wrongly identified as belonging to another category.  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{5}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{6}\]  
\[F1=\frac{2\times(\text{Precision}\times\text{Recall})}{\text{Precision}+ \text{Recall}} \tag{7}\]  
AP represents the area under the Precision-Recall curve, while mAP denotes mean average precision, which is the average value of each category of AP. mAP_0.5 refers to the average value of all APs when the IOU threshold is set to 0.5. mAP_0.50.95 represents the average mAP for different IOU thresholds (0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95). C represents the number of target types, N represents the number of IOU thresholds, _K_represents the current IOU threshold, \(P(K)\) and \(R(K)\) represent precision and recall.  
\[\text{AP} =\sum_{k=1}^{N}P(K)\Delta R(K) \tag{8}\] \[\text{mAP} =\frac{1}{C}\sum_{k=1}^{N}P(K)\Delta R(K)\] (9) \[\Delta R(K) =R(K)-R(K-1) \tag{10}\]
###### Abstract  
The accurate estimation of gross primary productivity (GPP) plays an important role in accurately projecting the terrestrial carbon cycle and climate change. Satellite-driven near-infrared reflectance (NIRv) can be used to estimate GPP based on their nearly linear relationship. Notably, previous studies have reported that the relationship between NIRv and GPP seems to be biome-specific (or land cover) at the ecosystem scale due to both biotic and abiotic effects. Hence, the NIRv-based estimation of GPP may be influenced by land cover changes (LCC) and the discrepancies in multisource products (DMP). However, these issues have not been well understood until now. Therefore, this study took the Yellow River basin (YRB) as the study area. This area has experienced remarkable land cover changes in recent decades. We used Moderate-Resolution Imaging Spectroradiometer (MODIS) and European Space Agency (ESA) Climate Change Initiative (CCI) land cover products (termed MCD12C1 and ESACCI, respectively) during 2001-2018 to explore the impact of land cover on NIRv-estimated GPP. Paired comparisons between the static and dynamic schemes of land cover using the two products were carried out to investigate the influences of LCC and DMP on GPP estimation by NIRv. Our results showed that the dominant land cover types in the YRB were grassland, followed by cropland and forest. Meanwhile, the main transfer was characterized by the conversion from other land cover types (e.g., barren) to grassland in the northwest of the YRB and from grassland and shrubland to cropland in the southeast of the YRB during the study period. Moreover, the temporal and spatial pattern of GPP was highly consistent with that of NIRv, and the average increase in GPP was 2.14 gcm\({}^{-2}\)yr\({}^{-1}\) across the YRB. Nevertheless, it is shown that both LCC and DMP had significant influences on the estimation of GPP by NIRv. That is, the areas with obvious differences in NIRv-based GPP closely correspond to the areas where land cover types dramatically changed. The achievements of this study indicate that considering the land cover change and discrepancies in multisource products would help to improve the accuracy of NIRv-based estimated GPP.  
NIRv; GPP; land cover; MCD12C1; ESA CCI LC maps; Yellow River basin +
Footnote †: journal: Remote Sens. 2023, 15, 4693. [[https://doi.org/10.3390/rs15194693](https://doi.org/10.3390/rs15194693)]([https://doi.org/10.3390/rs15194693](https://doi.org/10.3390/rs15194693))  
[[https://www.mdpi.com/journal/remotesensing](https://www.mdpi.com/journal/remotesensing)]([https://www.mdpi.com/journal/remotesensing](https://www.mdpi.com/journal/remotesensing))