esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Fatigue Life and Failure Criteria in Ultrasonic Testing Through Thermal Analyses
## 3 Materials and Methods  
### Materials  
The steel used in this study was 34 CrNiMo6, which is used for several mechanical components. The machined specimens were obtained directly from the failed crankshaft under study. The chemical composition and mechanical properties were determined using Optical Emission Spectroscopy and tensile testing, respectively, and are presented in Tables 2 and 3. The steel has a density of 7870 kg/m\({}^{3}\) and a specific heat capacity of 475 J/kg \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Failure Criteria** & **Equations** & **Reference** & **Behavior** \\ \hline [PERSON] et al. & \(N_{f}^{m}=C\;\Delta T\;m\;and\;c\;are\;material\;constants\) & [(22)] & Is inapt for tests \\ \hline [PERSON] et al. & \(\varnothing=\int_{0}^{N_{f}}\Delta T\;dN=constant\) & [(23)] & that do not exhibit \\ \hline \multirow{4}{*}{Meneghetti} & \(1^{\circ}\) Law of & \(N_{f}=C\;Q_{cyc}^{m}\) & \\  & Thermodynamics: & \(N_{f}=C\;Q_{cyc}^{m}\) & \\  & & \(\rho=density\left(\frac{\text{kg}}{\text{m}^{3}}\right)\) & \\  & \(Q_{cyc}=\frac{\rho c_{\text{c}}R_{\theta}}{f}\) & \(c_{p}=specific\;heat\;capacity\left(\frac{\text{I}}{\text{kg}}^{\circ}\text{C}\right)\) & [(15; 17; 18; 19)] & \\  & & \(f=frequency\left(\frac{\text{cycles}}{\text{s}}\right)\) & \\ \hline [PERSON] and & & & & \\ [PERSON] & \(N_{f}=c_{1}R_{\theta}^{c_{2}}\;c_{1}\)_and_\(c_{2}\)_are material constants_ & [(20; 21)] & \\ \hline \end{tabular}
\end{table}
Table 1: Fatigue failure criteria models (adapted by [PERSON] (2018)).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Steel** & **Fe (\%)** & **C (\%)** & **Cr (\%)** & **Mo (\%)** & **Ni (\%)** \\ \hline
34 CrNiMo6 & 95.1 & 0.38 & 1.51 & 0.24 & 1.75 \\ \hline \end{tabular}
\end{table}
Table 2: Chemical composition with main elements.
# Adaptive Backstepping Control of Quadrotor UAVs with Output Constraints and Input Saturation
###### Abstract
In summary, few studies considered input-output constraints for quadrotor UAVs with internal uncertainties and external unknown bounded disturbances. An in-depth study is necessary. In this paper, we study the quadrotor UAV position-attitude trajectory tracking problem with external disturbances, internal uncertainties and input-output constraints. The main contributions of this paper are: (1) An adaptive neural network backstepping controller (ANNBC) in conjunction with DSC and an RBF neural network, and a robust term is proposed to deal with the position-attitude trajectory tracking. (2) A novel auxiliary system is introduced to solve the input saturation problem. This method can avoid the dependence of tracking error on the input amplitude in the method of approximating input saturation by using the smoothing function which is used in most of the literature. (3) A tandem control scheme is used to design a unified position-attitude controller, and BLF is combined to deal with output constraints. The remainder of the article is organized as follows. Section 2 gives the quadrotor UAV dynamic equations and preliminaries. Section 3 proposes an ANNBC combined BLF, adaptive neural network, robust term and anti-saturation auxiliary system, and stability analysis is given. Section 4 verifies the effectiveness of the proposed method through comparative simulation experiments. The conclusions and recommendations for future work are provided in Section 5.
# Polyisocyanurate Foam Pyrolysis and Flame Spread Modeling
## 2 Materials and Methods
### Full-Scale Experiments  
Two 1.46 m tall, 0.60 m wide and 5.08 \(\times\) 10\({}^{-2}\) m thick Tuff-R insulation panels were tested in a vertical corner flame spread setup shown in Figure 1. This setup was developed in a previous study [32] and is very similar to the SBI standard [31] with a notable distinguishing feature of being symmetric with respect to the plane bisecting the corner. This symmetry was imposed in the current setup to help simplify diagnostics and analysis.  
A triangular sandbox burner, built in accordance with the standard specification [31] and fueled with a constant flow of propane corresponding to the HRR of 30 kW, was used as an ignition source for the panels. This burner was ignited at the beginning of each test and was turned off at the end. The corner wall and burner assembly were placed into a well-ventilated enclosure equipped with a fast (13 s) response oxygen consumption calorimetry, which incorporated corrections for scrubber-induced changes in the oxygen concentration in the sensor flow [32].  
In addition to the measurement of HRR, flame heat flux to the panels, \(\dot{q}^{\prime\prime}\), was measured with seven 9.5 mm diameter water-cooled Schmidt-Boelter heat flux gauges. These gauges were installed flush with the panel surface facing the burner in specific positions defined by coordinates (\(x\), \(y\)) expressed in meters. The \(x\) coordinate was the horizontal distance from the corner; the \(y\) coordinate was the vertical distance from the top of the burner. The locations of the heat flux gauges, (0.10, 0.10), (0.10, 0.30), (0.10, 0.50), (0.05, 0.70), (0.22, 0.70), (0.10, 0.90), and (0.10, 1.10), are shown in Figure 1. These locations were selected to capture flame growth on the panels based on earlier, more spatially resolved measurements performed on PMMA [32]. Only one panel was equipped with the heat flux gauges because the readings in the same positions on both panels were previously shown to be effectively identical [32].  
Two tests were performed in this setup on the foam panels without facers and one test was conducted on the panels with the facers to quantify the impact of their presence on the dynamics of fire growth. The duration of each test was 600 s.
## 3 Materials and Methods  
This study focuses on extracting roads from high-resolution satellite imagery using an advanced CNN-based model. To achieve this goal, we first revisit the foundational concepts of atrous convolution, depthwise dilated separable convolution, and ASPP. We then detail the proposal and integration of DenseDDSSPP into the DeepLabV3+ architecture. This integration aims to enhance road extraction performance beyond current state-of-the-art models by leveraging the efficiency of dense feature extraction at multiple scales.
# The Impacts of Climate and Human Activities on Grassland Productivity Variation in China
## 3 Results
### The Contribution of Climate Factors and Human Activities to GPP  
The contribution of climate and human activities to controlling changes in grassland GPP in each province (autonomous region) was calculated based on LMG methods (Figure 8). The variation in the GPP of grassland in China was dominated by human activities; seven provinces (autonomous regions) had a human activity contribution greater than 50%, especially on the Loess Plateau, including GS, SX, and SAX. However, the provinces (autonomous region) of SC (77.60%), QH (59.58%), and IMG (53.83%) were dominated by climate factors.  
In terms of the overall impact of the three climate factors and six human activities, the effects of climate and human activities on grassland variation in different provinces (autonomous regions) were significantly different, as shown in the radar chart (Figure 9). Generally, sheep, PRE, and TEM were the primary factors affecting grassland dynamics, and they had the highest contribution to grassland dynamics in most provinces. Therefore, the sheep factor, which played a leading role in SAX, GS, NX, and SX provinces (autonomous regions), was most important on the Loess Plateau, and the contribution was greater than 50%. PRE and TEM were the dominant climate factors that played a dominant role in arid and semi-arid regions (including XJ, IMG, and HB provinces (autonomous region) and so on) and the Qinghai Tibet Plateau region (including TB, QH, and SC provinces (autonomous regions)), respectively. However, compared to sheep, PRE, TEM, RAD, large livestock, population, primary industry, forbidden grazing, and rodent and pest control contributed less. Moreover, the RAD, population, primary industry, forbidden grazing, and rodent and pest control factors contributed as the secondary contributing factors distributed in SC, SX, and SAX, TB and GX, XJ, and QH provinces (autonomous regions), respectively.  
Figure 8: Contribution rate of climate factors (**a**) and human activity (**b**) to GPP during 2000–2018 in ten provinces (autonomous regions).
## 3 Results
### Feature Selection and Performance of Machine Learning Models in Evaporation Prediction
Whereas the ANN model, owing to its complex training requirements and high data demands, typically exhibits poorer performance. This finding is further confirmed by the scatter plots between the measured and predicted E values in Figure 3. All six models show consistent distributions of the predicted E values, with satisfied accuracy in the range of 0-100 W/m\({}^{2}\). However, they significantly underestimate E when its values exceed 100 W/m\({}^{2}\). Compared with other models, XGBoost predicts E values that are more concentrated around the 1:1 line with fewer outliers, which leads to the highest R and NSE and the lowest RMSE and MAE. We further compare the performances of XGBoost and RF models at the 16 cropland sites (Supplementary Figure S1) and find that XGBoost has the highest performance in 13 cropland sites, which prompts us to choose the XGBoost model at the best model for further analysis. We further evaluate the performance of the XGBoost model with feature combinations from A8 experiment at each cropland site (Figure 4). Overall, the XGBoost model performs well at most site, with correlation coefficients (R) greater than 0.8 at 12 out of 16 sites, NSE values above 0.6 at 13 sites, RMSE values below 10 W/m\({}^{2}\) at 14 sites, and MAE values below 6 W/m\({}^{2}\) at all sites. On the whole, the evaluation results indicate that the accuracy of the XGBoost model in the A8 experiment is reliable in E prediction and T/ET estimation. Nevertheless, some divergences in the performance of the XGBoost model are still observed due to the differences in their geographic location, background climatic conditions, and crop rotations and varieties. The highest accuracy is found in US-Twt (R = 0.96, RMSE = 8.17 W/m\({}^{2}\), NSE = 0.93, and MAE = 5.03 W/m\({}^{2}\)), and the lowest accuracy is noted for DE-Geb (R = 0.65, RMSE = 3.79 W/m\({}^{2}\), NSE = 0.43, and MAE = 2.03 W/m\({}^{2}\)). According to the Koppen-Geiger climate classification (Figure 1), US-Twt is a hot summer Mediterranean climate (Csa), and DE-Geb is a temperate oceanic climate (Cfb). Through the comparison of different cropland sites, it is observed that the impact of climate classification on model accuracy is relatively minor. Besides the US-Twt site, the predictive capabilities of other hot summer Mediterranean climate sites (IT-BCi, IT-CA2) are not notably high. Further analysis reveals that the cropping system is a critical factor influencing the predictive accuracy of the model between sites (cultivation information of eight typical crops in
## 4 An Indoor Positioning Algorithm Combining IMU and LBP-FFT-CNNs
### Inertial-Based Positioning Algorithm Using IMU
#### 4.1.2 Orientation Estimation  
Orientation \(\overset{\rightarrow}{\theta}(t)\) is computed using an extended Kalman filter (EKF) [22] fusion algorithm by integrating angular velocity:  
\[\overset{\rightarrow}{\theta}(t)=\int\overset{\rightarrow}{\omega}(t)dt \tag{19}\]  
where \(\overset{\rightarrow}{\omega}(t)\) is angular velocity vector over time. This determines the orientation of sensor frames relative to a global frame.
###### Abstract  
Analyzing user behavior in online spaces is an important task. This paper is dedicated to analyzing the online community in terms of topics. We present a user-topic model based on the latent Dirichlet allocation (LDA), as an application of topic modeling in a domain other than textual data. This model substitutes the concept of word occurrence in the original LDA method with user participation. The proposed method deals with many problems regarding topic modeling and user analysis, which include: inclusion of dynamic topics, visualization of user interaction networks, and event detection. We collected datasets from four online communities with different characteristics, and conducted experiments to demonstrate the effectiveness of our method by revealing interesting findings covering numerous aspects.  
online community; user behavior; topic modeling; visualization +
Footnote †: journal:  
for example, a model using rating scores cannot work for web communities that do not provide a user-rating system.  
Instead of developing a complex probabilistic model for analyzing user behavior, we adopted the simple form of the topic modeling method to ensure flexibility, but we make an important and effective substitution. Specifically, we use the LDA technique, substituting the concept of \"words\" in the textual data with \"users\" in the online community. We also present a preprocessing method that should be considered when taking into account the exploitation of online community datasets, which enables the proposed method to capture both the temporal and thematic features of user behavior effectively. Although the concept itself has been introduced in our previous work [18], we provide extensive experimental results in this work especially focusing on demonstrating the capability of analyzing user behavior in web communities from many perspectives, which the previous work did not cover.  
The contributions of this paper are as follows:  
* We present an analysis method, using topic modeling, for user behavior in online communities, with applications.
* The proposed method is simple because it uses standard LDA with the small substitution of user participation for word occurrence. It is also flexible, because it does not use too many features, yet ensures sufficient functionality in many applications of user behavior analysis.
* We also demonstrate the effectiveness of our method by conducting experiments on various datasets and revealing interesting findings.  
The remainder of the paper is organized as follows. After briefly reviewing related work in Section 2, we introduce the main concept of the proposed method, including the analogy between standard topic modeling and our work, with procedural details, in Section 3. In Section 4, we describe the datasets which will be used for the case studies, showing the application of the proposed method in Section 5. We present our conclusions in Section 6.
# Multi-Level Credit Assignment for Cooperative Multi-Agent Reinforcement Learning
## 2 Background
### [PERSON] proposes a classical policy-based MARL algorithm, COMA, to address the credit assignment issue [11]. COMA follows the actor-critic approach and actors, i.e., policies, are independent and trained by backpropagating the gradient estimated by a critic. One of COMA's contributions is that the critics are not independent, but centralized, which means all agents share the same critic. The critic only appears in the training phase,which satisfies the CTDE digram and the critic can use all available information in the training phase and each agent's policy stays independent in the execution phase.  
Another contribution is the introduction of the counterfactual baseline that is inspired by difference rewards [11; 13; 14]. Difference rewards are a powerful way to perform multi-agent credit assignments but it requires a default action to compare with the reward obtained by the current action, which also requires access to the simulator. COMA avoids this requirement by introducing an advantage function  
\[A^{a}(s,u)=Q(s,u)-\sum_{u^{a}}\pi^{a}(u^{\prime a}|\tau^{a})Q(s,(u^{-a},u^{ \prime a})), \tag{2}\]  
where \(Q(s,u)\) is the centralized critic, \(u^{-a}\) denotes other agents' actions, \(\pi^{a}(u^{\prime a}|\tau^{a})\) denotes the probability of chosing action \(u^{\prime a}\) condition on the history of observations \(\tau^{a}\). Hence, COMA uses this advantage function as the object to optimize and learn directly from agents' experiences instead of relying on extra simulations, a reward model, or a user-designed default action.
# Soft-Output Detector Using Multi-Layer Perceptron for Bit-Patterned Media Recording
## 5 Conclusions  
Herein, we introduce a soft-output detector using multi-layer perceptron for a BPMR system to predict reliable information. A multi-layer perceptron (MLP) can be commonly used for regression or classification tasks. Regression MLP output is the predicted value and classification MLP output can be interpreted as the estimated probability. However, the output of the regression MLP converges toward the target value, and the range of the output of the classification MLP is restricted between 0 and 1. Thus, it is difficult to achieve some decoding gain when conducting error correction coding and modulation coding of the data. To provide reliable information similar to the soft decision value, we design the MLP detector that uses the identity function as an activation function in the output layer and binary cross-entropy as the loss function. Additionally, techniques such as BN and ensemble are applied to the MLPs to increase the accuracy. An ensemble of MLP detectors reduces the overlapping area and provides a smaller variance than the PRML detector and MLP detectors alone. It is clear that the ensemble of the MLP decoders estimates reliable information. As the MLP detector provides reliable information similar to the soft decision  
Figure 8: BER performance of detectors and demodulators according to MLP detectors.  
Figure 7: BER performance comparison of the models using the 4/6 modulation coding scheme at TMR of 0 and 30%.  
value, the ensemble of the MLP detectors with and without the demodulator improves the BER performance and is more robust to TMR compared to PRML.  
[PERSON] contributed to this work in experiment planning, experiment measurements, data analysis and manuscript preparation; [PERSON] contributed to experiment planning, data analysis, and manuscript preparation. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government. (MSIT) (2021R1A2C1011154).  
Not applicable.  
The authors declare no conflict of interest.
# Study of Information Dissemination in Hypernetworks with Adjustable Clustering Coefficient
## 4 Simulation Results and Analysis
### The Results of Information Communication Theory Analysis
#### 4.2.7 Compare with BA Hypernetwork  
To analyze the process of information propagation under diverse hypernetwork structures, two hypernetworks were generated with \(N=1000\): the aggregation-phenomenon hypernetwork and the BA scale-free hypernetwork. The network structure parameters were set as \(m_{0}=6\), \(m_{1}=3\), \(m_{2}=3\), \(m=3\), \(\beta=0.1\), and \(\gamma=0.1\), and the clustering coefficient of the aggregation-phenomenon hypernetwork was \(p=0\). Figure 15 illustrates the comparison between the aggregation-phenomenon hypernetwork and the conventional hypernetwork under the same parameters, where the curves of the propagation rate and steady-state values are almost identical. In fact, when the clustering coefficient \(p=0\), the model becomes the BA hypernetwork model, which the simulation results corroborate. Thus, the model introduced in this paper is a generalization of the BA hypernetwork model. Moreover, in social hypernetworks, friends of friends are more likely to build friendships. Consequently, the hypernetwork structure of aggregation phenomena better reflects reality, providing a distinctive advantage that cannot be achieved by BA hypernetworks.  
Figure 14: Information propagation curves of different \(m\)’s.  
Figure 15: Comparison of information propagation curves for BA hypernetwork and aggregation-phenomenon hypernetwork.
## 3 Methodology  
This section describes the proposed federated unlearning framework, which comprises two complementary methods: Efficient Gradient-Based Federated Unlearning (EG-FedUnlearn) and Ontology-Facilitated Unlearning (OFU-Ontology). Both methods aim to remove a specific client's data influence from the global model while preserving overall model utility. EG-FedUnlearn provides an efficient, retraining-free mechanism based on gradient elimination, while OFU-Ontology introduces domain knowledge to guide relevance-based selective unlearning.  
We consider a standard federated learning (FL) setup consisting of a central server and \(N\) participating clients. Each client \(i\) holds local data \(D_{i}\) and collaboratively trains a global model by exchanging parameter updates with the server. The server aggregates these updates, e.g.,  
\[\theta^{(t+1)}=\theta^{(t)}-\eta\sum_{i=1}^{N}\frac{n_{i}}{n}\
abla L_{i}\Big{(} \theta^{(t)}\Big{)} \tag{1}\]  
where \(\theta(t)\) are model parameters at round \(t\), \(\eta\) is the learning rate, \(n_{i}\) is client i's data size, and \(n=\sum n_{i}\). Federated unlearning (FU) requires that after training, the server can remove a target client's influence. The challenge is to achieve privacy compliance (GDPR \"right to be forgotten\"), minimal accuracy loss, and low communication and computation costs.
## 4 Results
### AI in Forecasting TCO and Saving Costs
#### 4.3.1 AI Algorithms in TCO Prediction
Another observation is that most studies focused on time series forecasting, as most data related to CWPs are fundamentally time series data [3,6,20,48,86,118,119,120,117]. This includes hourly [52,118], daily [48], weekly [117], monthly [3,117], and yearly [6,52] readings of factors such as cooling load, energy consumption, and power consumption, as well as external variables like weather conditions and electricity prices. However, although AI-based methods offer temporary relief, [PERSON] et al. [122] highlighted the issue of data scarcity and the lack of publicly available large-scale datasets, which remains a limitation in this field. Different types of simulation and implementation platforms were utilized to build and validate AI models in the reviewed studies. Several studies adopted programming languages or tools such as Python [110, 112, 115, 126] and MATLAB [6, 7, 121] due to their compatibility with open-source libraries and frameworks. Python emerged as a dominant programming language, employed by researchers such as [PERSON] et al. [112], [PERSON] et al. [110], [PERSON] et al. [126], and [PERSON] et al. [115] for developing machine learning models and simulation platforms, often leveraging frameworks like TensorFlow, Keras, and PyTorch. [PERSON] and [PERSON] [7], [PERSON] and [PERSON] [121], and [PERSON] et al. [6] relied on MATLAB to implement optimization algorithms and surrogate models. Additionally, common simulation tools included EnergyPlus, HOMER, and TRNSYS [11, 20, 47, 48, 49, 50, 51], which are based on comprehensive physics-based equations. For instance, [PERSON] et al. [114] implemented control agents in Python and MATLAB, leveraging the Building Control Virtual Test Bed (BCVTB) for integration. [PERSON] et al. [30] and [PERSON] and [PERSON] [121] linked TRNSYS or EES (Engineering Equation Solver) simulations to MATLAB for optimization, highlighting the role of intermediary systems such as ANNs in reducing computation time. Similarly, [PERSON] et al. [116] combined EnergyPlus with MATLAB to develop parametric energy models.