esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Strong Tracking CKF Based on Multiple Fading Factors  
### CKF Algorithm  
Taking the hypersonic target motion model as the state equation and the single-station radar observation model as the measurement equation, the following nonlinear system model is formed:  
\[\begin{cases}\mathbf{x}_{k}=\mathbf{f}(\mathbf{x}_{k-1})+\mathbf{w}_{k-1}\\ \mathbf{z}_{k}=\mathbf{h}(\mathbf{x}_{k})+\mathbf{v}_{k}\\ \end{cases} \tag{11}\]  
where:  
\[\mathbf{x}_{k}=\begin{bmatrix}x&\dot{x}&\ddot{x}&\dddot{x}&y&\dot{y}&\ddot{y}& \ddot{y}&z&\dot{z}&\ddot{z}&\dddot{z}\end{bmatrix}^{T}\in R^{n} \tag{12}\]  
It refers to the flight state of the hypersonic target at time \(k\), including the position, speed, acceleration, and other \(n\) dimensional state information of the target. \(\mathbf{z}_{k}=\left[a\;e\;r\right]^{T}\) is  
Figure 3: The geocentric coordinate system and reference coordinate system of the radar station. The radar position \(R\) is the origin of the coordinate system, and the \(RE\), \(RN\), and \(RU\) axes point to the east, north, and sky directions perpendicular to each other.  
the observation vector of the radar system at time \(k\), representing the measurement element of the radar. Additionally, \(\mathbf{f}(\cdot)\) is the process function and \(\mathbf{h}(\cdot)\) is the measurement function of the nonlinear tracking system. \(\mathbf{w}_{k}\) and \(\mathbf{v}_{k}\) are uncorrelated zero-mean Gaussian white noise, and the corresponding covariance matrices are \(\mathbf{Q}_{k}\) and \(\mathbf{R}_{k}\).  
CKF is a nonlinear Gaussian filtering algorithm, whose core is to select the sampling points through the spherical radial cubature criterion. The traditional calculation process is as follows [17].
## 4 Results and Discussion
### Mechanical Performance
#### 4.4.2 Effect of Infill Density on FDM Mechanical Properties  
The tensile properties of FDM prints depend significantly on infill density, which was set to 20% in this study as a trade-off between strength, material efficiency, and print speed. Higher infill densities--such as 50% or 100%--would result in improved tensile strength and stiffness, but at the cost of increased print time, material usage, and weight. Previous research suggests that increasing infill to 100% can enhance tensile strength by up to 30-50%, depending on layer adhesion and print quality. However, for many practical applications, such as prototyping and lightweight structural components, lower infill percentages are preferred to maintain reasonable production efficiency.  
Additionally, unlike SLA prints, which are inherently solid, FDM prints rely on internal infill structures to balance strength and efficiency. The honeycomb or grid-like structures used in FDM prints influence not only the tensile properties but also the impact resistance and failure mode of the material. A higher infill setting would reduce deformation under load and improve failure tolerance, making FDM a more competitive choice for functional parts requiring mechanical durability. Future studies could explore the optimal infill-density-to-strength ratio in more complex geometries and real-world load conditions.
# Spatiotemporal Dynamics of Net Primary Productivity in China's Urban Lands during 1982-2015
## 2 Materials and Methods
### Piecewise Regression Analysis  
Normally, linear regression is used to analyze the changing trend of a time series. However, previous studies have indicated that ordinary linear regression models cannot describe long-term vegetation changes due to climate change and human activities [30; 31; 32]. In this study, some linear regression models were transformed into piecewise regression models according to the Akaike information criterion (AIC). The AIC is a standard to measure the goodness of fit of statistical models. The criterion is calculated by the number of parameters (\(k\)), residual sum of squares (RSS), and sequence length (\(n\)), as shown in Equation (1):  
\[\text{AIC}=n\times\ln(\text{RSS}/n)+2\times k+2\times k\times(k+1)/(n-k-1) \tag{1}\]  
For ordinary linear regression equations, k = 2, and k for the piecewise models is 5. [PERSON] and [PERSON] [33] showed that a threshold of \(-2\) for the difference in the AIC between two models could be used to select a better model. In this study, the difference between the AIC of the piecewise and ordinary linear regression models (\(\delta\text{AIC}=\text{AIC}_{\text{piecewise}}-\text{AIC}_{\text{linear regression}}\)) is less than \(-2\), which means that the piecewise model can better represent the NPP trends and the turning point at which the NPP trend changes are extracted. Conversely, there was no significant difference in NPP changes before and after the turning point.
## 2 Setup and Modeling
### Modeling  
#### 2.2.1 RSOA Input  
The RSOA receives from its optical input port a CW light beam of constant power \(P_{CW}\), while its total driving current, which results from the combination of the dc current and the current induced by a NRZ pseudorandom binary sequence (PRBS) of length \(N\) and repetition period \(T_{rep}=1/B_{rep}\) applied to the RSOA electrical interface, is expressed as  
\[I(t)=(I_{dc}-I_{m})+2I_{m}\Bigg{\{}H(t)\bigg{[}1-\mathrm{e}^{-t^{2}/t_{t}^{2} }\bigg{]}-H(t-T)\bigg{[}1-\mathrm{e}^{-(t-T)^{2}/t_{t}^{2}}\bigg{]}\Bigg{\}} \tag{1}\]  
where \(H(t)\) is Heaviside step function, \(I_{dc}\), \(I_{m}\) and the RSOA current at transparency, \(I_{0}\), define the modulation depth, \(I_{m}/(I_{dc}-I_{0})\), which is smaller than unity [27], and \(t_{r}\) is the electrical pulse rise time, which is a small fraction of \(T_{per}\).
# An Improved Method Based on EEMD-LSTM to Predict Missing Measured Data of Structural Sensors
## 4 Comparative Analysis of the Results
### Performance Evaluation of the Multi-Steps
At the same time, the EEMD-LSTM fit better with the original measured data, indicating that EEMD-LSTM had better performance for missing data and could better capture the characteristics of the original acceleration data. Figure 12: Comparison of the imputations with the four EEMD-based models. Figure 13: Comparison of the four evaluation metrics of the different EEMD-based models. The prediction results for the El-Centro and RG datasets can be seen in the Appendix A Figures A1 and A3. The variation in the evaluation metrics of the EEMD-based models with increasing step size can be seen in Figure 15. To further analyze the error accumulation pattern, the  
Figure 14: Comparisons results for the white dataset using the EEMD-based algorithms (from top to bottom, the order was 1-step forecasting, 5-step forecasting, 10-step forecasting, and 15-step forecasting). reduction rates of \(R^{2}\) from step 1 to step 15 were calculated for EEMD-BiGRU, EEMD-GRU, EEMD-LSTM, and EEMD-DNN, where the reduction rates of \(R^{2}\) for EEMD-BiGRU, EEMD-GRU, EEMD-LSTM, and EEMD-DNN were 41.36%, 39.9%, 38.90%, and 49.31%, respectively. EEMD-LSTM had the lowest error accumulation rate, which indicates that the EEMD-LSTM model had a slower error accumulation rate than the other three methods. LSTM has the advantage of mapping all historical data to each forecast and, thus, memorizing the long-term temporal correlation of the time series. At the same time, EEMD decomposes the data to obtain a linear combination of a finite number of intrinsic mode functions (IMFs) with high to low frequencies, which increases the amount of input data for the entire EEMD-based model. This result is consistent with the results in the literature [27; 28].
# LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review
## 2 LoRaWAN
### Device Activation
#### 2.6.1 LoRaWAN v1.0  
Prior to the OTAA procedure in LoRaWAN v1.0, the application identifier **AppEUI**, the device identifier **DevEUI** as well as the secret key **AppKey** are stored in an end device and also provisioned to the network in which the device is joining. As the first step, the device composes a join request message with **AppEUI**, **DevEUI**, a nonce value (**DevNonce**) and a message integrity code (MIC), which is calculated with the join-request-message fields and **AppKey**. Once the join request arrives at the network server and the request is permitted, the network server replies with the join-accept message. This message contains a random nonce (**AppNonce**), a network ID (**NetID**), a device address (**DevAddr**), and parameters for physical-layer signaling, followed by a MIC. The join-accept message is encrypted using **AppKey**. Once the join-accept message arrives at the end device, the application session key (**AppSKey**) and the network session key (**NwkSKey**) are derived using **AppNonce** and **Appkey**. Finally, to establish end-to-end encryption, the network server delivers **AppSKey** to the application server.  
In contrast to OTAA, in ABP activation, **DevAddr**, **AppSKey** and **NwkSKey** are a priori programmed on the end device, the network server and the application server.
# Research on TBM Cutterhead Crack Damage and Fatigue Reliability
## 2 Analysis of TBM Cutterhead Crack Damage Characteristics
### Calculation of the Stress Intensity Factor and an Analysis of the Influencing Factors
When the crack position angle was closer to 0\({}^{\circ}\), the equivalent SIF in the fore-end of crack was larger, which indicated that the crack growth trend was relatively strong, and showed that the damage of the cutterhead caused by such cracks was more serious. To study the influence of the crack shape ratio on the SIF of the cutterhead crack, this study fixed \(c_{1}\) = 12 mm and calculated the SIF under different crack shape ratios by changing the size of \(a_{1}\). The calculation results of \(K_{\rm I}\), \(K_{\rm II}\), and \(K_{\rm III}\) are shown in Figure 6.  
It can be seen from Figure 6 that with the decrease in the shape ratio, the value of \(K_{\rm III}\) was asymmetric, where the absolute value on the left side was larger than that on the right side, which may have been caused by the complex stress state of the cutterhead. In addition, the smaller the shape ratio, the clearer was the difference in \(K_{\rm I}\), which indicated that the trend of the crack growth was stronger, which will affect the reliability of the cutterhead. In order to further study the fatigue reliability of the cutterhead crack, a reliability evaluation model was established (discussed in the next section), where the fatigue reliability of the cutterhead crack under different crack parameters is discussed. Figure 4: Distribution of the stress intensity factor (SIF) at different crack position angles: (**a**) \(K_{\rm I}\), (**b**) \(K_{\rm II}\), and (**c**) \(K_{\rm III}\).
## 2 Literature Review
The order planning problem is actually the flow shop (FS) problem, which is an NP-hard problem ([PERSON] et al., 2018). Most exact methods cannot solve large-scale problems within an acceptable amount of time. Therefore, heuristics ([PERSON] et al., 2018; [PERSON] et al., 2018) have widely been applied currently, which produce good feasible solutions timely and effectively. In this paper, to optimize an order planning problem, namely to minimize total relevant costs and maximize customer satisfaction level, RSM was used to determine the best combination of the system parameters. Independent variables, which are the review period (T), order quantity (Q), and safety stock (SS), were treated as factors for response variables, which are the total cost (TC) and customer service level (CSL). Analytical approaches have many limitations to simultaneously solve two or three factors ([PERSON] et al., 2018). It is difficult to observe the factor interactions and predict their effects on the overall objective when multiple factors are considered at a time. By using RSM, our research aims to determine the best parameter settings of considered factors to find the possible optimum values of the responses. Findings are expected to offer purchasing managers or decision-makers to obtain the optimal combination of the levels of independent factors, which strongly affect the key system performance measures, i.e., total relevant costs and customer satisfaction level. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Independent Variables** & **Response Variables** & **Methods/Techniques** \\ \hline \multirow{3}{*}{[PERSON] et al. (2018), 2015} & Number of orders & Penalty for delivery & \\  & Planning horizon & Penalty for order & NLP \\  & Production capacity & Inventory cost & PSO algorithm \\  & & Production cost & \\ \hline \multirow{3}{*}{[PERSON] et al. (2018), 2015} & Due date of order & Completion time & \\  & Production workload & Production capacity & MOLP \\  & Capacity efficiency & Processing time & \\ \hline \multirow{3}{*}{[PERSON] et al. (2018), 2017} & Demand variability & Holding cost & \\  & Lead time & Supply cost & \\ \cline{1-1} \cline{3-4}  & & Safety stock & MOLP \\ \hline \multirow{3}{*}{[PERSON] et al. (2018), 2017} & Reorder point & Ordering cost & \\  & Order quantity & Shortage cost & \\ \cline{1-1}  & Inventory policies & Holding cost & \\ \cline{1-1} \cline{3-4}  & & Over-ordering cost & \\ \hline \multirow{3}{*}{[PERSON] et al. (2018), 2019} & Planned lead time & Holding cost & \\ \cline{1-1}  & Production plan & Penalty cost & \\ \hline \multirow{3}{*}{[PERSON] et al. (2019), 2019} & Time-varying & Backorder & EOQ \\ \cline{1-1}  & Stock demand & Lost sales & EPQ \\ \cline{1-1} \cline{3-4}  & Lot size & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of common problem characteristics for several previous studies.
## 4 Landslide Volume Prediction Model
### Comparison of the Improved S-Curve-Based Model with the Time Series Analysis Moving-Average Model  
A simple time series analysis moving-average can be used to predict landslide volume [35; 36]. Equation (9) uses the average of the landslide volume at three consecutive moments as the predicted value for the fourth moment.  
\[\mathrm{V_{t}^{\prime}=\frac{V_{t-1}+V_{t-2}+V_{t-3}}{3},\ t\geq 4} \tag{9}\]  
where \(\mathrm{V_{t}^{\prime}}\) is the output object of the prediction model at time t. \(\mathrm{V_{t-1}}\), \(\mathrm{V_{t-2}}\), and \(\mathrm{V_{t-3}}\) are the observed data at times t - 1, t - 2, and t - 3, respectively. The volumes of the landslide deformation areas of experiments S1-S16 are predicted using Equations (8) and (9), respectively, and the predicted results are shown in Figure 8 (S11-S16) and Table 4 (S1-S16).  
First of all, it can be seen from Table 4 that the prediction result of the improved S-growth curve model is better than that of the moving average model. The S-curve prediction model contains information on the S-curve trend, so it can describe the whole process, especially the phase change. Secondly, the prediction of the moving average model  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Experiment**} & \multicolumn{1}{c}{\begin{tabular}{c} **S-Growth Curve** \\ **Model Prediction** \\ **Error** \\ \end{tabular} } & \multicolumn{1}{c}{\begin{tabular}{c} **Moving Average** \\ **Model Prediction** \\ **Error** \\ \end{tabular} } & \multicolumn{1}{c}{\
\begin{tabular}{c} **S-Growth Curve** \\ **Model Prediction** \\ **Error** \\ \end{tabular} } & \multicolumn{1}{c}{
\begin{tabular}{c} **Moving Average** \\ **Model Prediction** \\ **Error** \\ \end{tabular} } \\ \hline S1 & 16.77\% & 68.89\% & S9 & 5.57\% & 24.65\% \\ S2 & 12.70\% & 30.73\% & S10 & 4.34\% & 23.55\% \\ S3 & 6.05\% & 6.39\% & S11 & 12.21\% & 29.33\% \\ S4 & 10.59\% & 37.94\% & S12 & 16.45\% & 32.02\% \\ S5 & 6.25\% & 30.62\% & S13 & 13.57\% & 34.94\% \\ S6 & 6.52\% & 35.00\% & S14 & 6.01\% & 32.63\% \\ S7 & 10.78\% & 31.30\% & S15 & 10.98\% & 30.47\% \\ S8 & 4.36\% & 24.00\% & S16 & 10.83\% & 26.13\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Landslide volume error (MAPE) predicted by S-growth curve model and moving average model.  
Figure 8: Moving average model prediction results (S11–S16).  
depends on the previous data, but it cannot simulate the sudden change from the presliding stage to the sliding failure stage. Therefore, the error always exists in the sliding failure stage. It is obvious from Figure 8 that all prediction results of the moving average model lag behind the observed values to a certain extent. Finally, the moving average model is similar to a \"black box\". Its use only involves input and output without any clear physical meaning or process. In our research, the S-shaped growth curve prediction model needs external influencing factors and a continuous landslide volume as parameters, and it can also describe the three stages of deformation zone growth from a physical point of view.
## 4 Conclusions
6. The image-gridding method was employed to deeply analyze the relationship between crack density and AE event characteristics. In areas with high relative crack density, the amplitude and peak frequency parameters of AE events have bigger values. In the adjacent with pre-existing cracks areas, more intensive AE signals are detected, and the crack development rate is higher regarding the potential crack propagation destinations. Moreover, the peak frequency is higher in samples with a considerable number of vertical cracks compared to samples with horizontal cracks. 7. The time-series analysis of AE and DICM showed the correlation between strain concentration and AE energy release. The strain allocation evidences the significant release of AE energy relating to fracture development and contributes to the main deformation processes. Increasing the deformation rate can be noticed after a significant AE energy release event. It can be concluded that pre-existing defects resulting from the severe environmental condition and operation period affect the fracture behavior in the concrete under compression. The more crack density within the concrete matrix, the higher intensity of the AE events and strain concentration in the weak regions which can lead to future material deterioration and failure. Using a combination of AE and DICM techniques can give a comprehensive damage evaluation of the structure. These findings can be useful in monitoring the concrete structure condition and in identifying potential areas of defects that require attention to prevent failure or damage. Conceptualization, N.M.; methodology, N.M. and K.S. ; validation, Y.S. and T.S. ; data curation, K.S. ; writing--original draft preparation, N.M.; writing--review and editing, Y.S. and T.S. ; supervision, T.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The datasets collected and/or analyzed during the current study are available from the corresponding author on reasonable request. The authors declare no conflict of interest.
## 5 Attributes and Characteristics of FL
### Non-IID Data in FL
#### 5.8.3 Non-IID: Potential Strategies  
Addressing non-IID challenges also requires two approaches.
* Data Preprocessing: A foundational step towards countering data imbalances is the use of preprocessing measures. Implementing techniques like data augmentation, stratified sampling, and synthetic data generation can equalize the data distribution across nodes, ensuring a more balanced training environment.
* Model Aggregation Approaches: Aggregation techniques bear significance in a non-IID environment. Recent methodologies, such as those echoing the principles of FedAvg and FedProx, show promise. These methods aim to address the challenges posed by skewed data, enhancing both model performance and convergence.
## 3 Results and Discussion
### Torque and Power Coefficients  
Torque coefficients were obtained using the XFlow software by time averaging the instantaneous torque predictions. Results obtained with 2,500,000 lattices are displayed and compared with experimental data in Figure 5.  
Figure 5 shows that the results do not all agree with the referenced experimental results. Here solutions with the Spalart-Allmaras model showed the worst agreement,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Overall CPU Time (s)**} \\ \cline{2-4}
**Number of Lattices** & **RANS** & **LES** & **LES** \\  & **Spalart Allmaras** & **WALE** & **Smagorinsky** \\ \hline
2,500,000 & \(1.13\times 10^{4}\) & \(1.16\times 10^{4}\) & \(1.18\times 10^{4}\) \\ \hline
5,000,000 & \(1.64\times 10^{4}\) & \(1.77\times 10^{4}\) & \(1.87\times 10^{5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overall CPU time (s) concerning several lattices in different turbulence models.  
Figure 5: Variations in torque coefficients according to turbulence models and experimental results [15] for 2,500,000 lattices.  
while the other predictions remained within the error bars of the measurements for TSRs of 1 and 1.2. Among the solutions, LES predictions with the Smagorinsky model yielded the best agreement.  
To see the effect of several lattices on predictions, numerical solutions were repeated for a lattice size of 5,000,000. The corresponding torque coefficient predictions are displayed in Figure 6, along with the experimental data. The improvement in the predictions is evident from this figure, where all of them appeared inside the error bars of the measurement. Solutions obtained with the WALE method showed the best agreement with the experiment for this configuration.  
Overall, the agreement between the numerical predictions and experimental measurements was satisfactory for 5,000,000 lattices. Therefore, this lattice size was used in the rest of the study.  
Figure 7 demonstrates the agreement between the WALE turbulence model and experimental results. Additionally, it can be observed that the power coefficient reaches a maximum at TSR 1.2 for 5,000,000 lattices.  
Figure 6: Variations in torque coefficients according to turbulence models and experimental results [15] for 5,000,000 lattices.  
Figure 7: Variations in power coefficients according to turbulence models and experimental results [15] for 5,000,000 lattices.
# A Review on the Prospects of Mobile Manipulators for Smart Maintenance of Railway Track
###### Abstract  
Inspection and repair interventions play vital roles in the asset management of railways. Autonomous mobile manipulators possess considerable potential to replace humans in many hazardous railway track maintenance tasks with high efficiency. This paper investigates the prospects of the use of mobile manipulators in track maintenance tasks. The current state of railway track inspection and repair technologies is initially reviewed, revealing that very few mobile manipulators are in the railways. Of note, the technologies are analytically scrutinized to ascertain advantages, unique capabilities, and potential use in the deployment of mobile manipulators for inspection and repair tasks across various industries. Most mobile manipulators in maintenance use ground robots, while other applications use aerial, underwater, or space robots. Power transmission lines, the nuclear industry, and space are the most extensive application areas. Clearly, the railways infrastructure managers can benefit from the adaptation of best practices from these diversified designs and their broad deployment, leading to enhanced human safety and optimized asset digitalization. A case study is presented to show the potential use of mobile manipulators in railway track maintenance tasks. Moreover, the benefits of the mobile manipulator are discussed based on previous research. Finally, challenges and requirements are reviewed to provide insights into future research.  
mobile manipulator; railway track maintenance; robotic maintenance technology; autonomous systems +
Footnote †: journal: _Applied_  
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