esa-sceva/satcom-chunk-collection huggingface dataset:
# Model-Free Identification of Nonlinear Restoring Force with Modified Observation Equation
## 1 Introduction
### Scope  
Most of the aforementioned nonparametric identification methods are based on the usage of some specific nonparametric expressions to describe or approximate the nonlinear properties. The key of these methods is to properly determine the values of the coefficients in their nonparametric expressions basing on the input and output data. Such approximations may, however, result in undesirable oscillations or inaccurate results around unsmooth points. In this paper, an EKF-based model-free nonlinear identification approach is proposed for the identification of NRFs and other structural parameters. By using a projection matrix, a modified version of observation equation is obtained. The NRF is treated as 'unknown fictitious input' and identified by means of LSE. Based on the principle of EKF, the recursive solution of the proposed approach is analytically derived. The proposed approach requires input and output data only, without knowledge or intuitive assumptions of any nonlinear model class in advance.  
The present research work is organized as follows: Section 2: formulation of the proposed model-free NRF identification procedure; Section 3: validation of the proposed approach via a building structure equipped with MR damper and SMA damper; Section 4: conclusions and discussions of the proposed approach.
## Photo-Detectors Integrated with Resonant Tunneling Diodes  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 2, [PERSON] 3 and [PERSON] 1**  
1 Centro de Electr enica, Optoelectr onica e Telecomunica ces (CEOT), Departamento de F sica, Universidade do Algarve, Campus de Gambelas, 8005-139 Faro, Portugal;  
E-Mail: [EMAIL_ADDRESS]
2 INESC TEC, Faculdade de Engenharia, Universidade do Porto, 4200-465 Porto, Portugal;  
E-Mails: [EMAIL_ADDRESS] (L.P.); [EMAIL_ADDRESS] (H.S.)
3 School of Engineering, University of Glasgow, Glasgow G12 8 LT, UK;  
E-Mail: [PERSON]
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +351-289-800-905; Fax: +351-289-800-066.  
_Received: 14 June 2013; in revised form: 9 July 2013 / Accepted: 16 July 2013 / Published: 22 July 2013_  
**Abstract:** We report on photo-detectors consisting of an optical waveguide that incorporates a resonant tunneling diode (RTD). Operating at wavelengths around 1.55 \(\mu\)m in the optical communications C band we achieve maximum sensitivities of around 0.29 A/W which is dependent on the bias voltage. This is due to the nature of RTD nonlinear current-voltage characteristic that has a negative differential resistance (NDR) region. The resonant tunneling diode photo-detector (RTD-PD) can be operated in either non-oscillating or oscillating regimes depending on the bias voltage quiescent point. The oscillating regime is apparent when the RTD-PD is biased in the NDR region giving rise to electrical gain and microwave self-sustained oscillations Taking advantage of the RTD's NDR distinctive characteristics, we demonstrate efficient detection of gigahertz (GHz) modulated optical carriers and optical control of a RTD GHz oscillator. RTD-PD based devices can have applications in generation and optical control of GHz low-phase noise oscillators, clock recovery systems, and fiber optic enabled radio frequency communication systems.  
**Keywords:** double barrier quantum well structures; clock recovery; microwave-photonics; optical injection locking; optoelectronic circuits; oscillators; photo-detectors; radio-over-fiber; resonant tunneling diodes
## 3 Method
### Internal Calculations
#### 3.3.4 Deviation of the CP from the Set Point \(\left(\sigma_{CP}\right)\)  
Like the \(WHP\) deviation (Equation (1)), this parameter value is defined as the measured pressure value subtracted from the set point (or the expected pressure value). Since no pumping pressure model is used to calculate the pressure, the average pressure while pumping without milling or \(WOB\) was used as the set point. However, due to the CP being measure upstream from the \(WHP\), a correction is necessary.  
All pumped fluid usually performs a loop where it enters the CT string, flows to the bottom of the well where actuates the PDM, flow back through the annular space CT/completion tubing and then flow through the choke. Due to the proximity of the pump and CT reel manifold, the pressure drop between them is neglected. The pressure measured at the pump or CT reel gauge (\(CP_{sp}\)) is the sum of all those pressure losses, as shown in Equation (4), recognizing that the pressure at the choke and the \(WHP\) are the same.  
\[CP_{sp}=\Delta P_{string}+\Delta P_{PDM}+\Delta P_{an}+WHP \tag{5}\]  
where: \(\Delta P_{reel}\) is the pressure loss due to friction inside the string; \(\Delta P_{PDM}\) is the pressure loss inside the PDM and \(\Delta P_{an}\) is the pressure loss inside the annular space coiled tubing string and well bore tubulars.  
The pressure losses \(\Delta P_{string}\) and \(\Delta P_{an}\) are always constant at a given pump rate and using a given fluid, and the fact that the PDM is milling or not should not change those parameters. The \(WHP\) is measured by the coiled tubing unit and acquired in real-timeby the system, so the effect of any fluctuation of the _WHP_ on the CP can be corrected to Equation (6).  
\[\sigma_{CP}=\Delta P_{PDM}-\sigma_{WHP} \tag{6}\]  
This correction avoids misinterpretation about events in which changes in the _WHP_ reflects on the CP. For instance, if the choke operator closes in the choke during a milling operation and increases the _WHP_ by 500 psi, the CP will increase by the same amount and could be falsely interpreted as a motor stall. However, if the CP increases and the _WHP_ and pump rate are constants, the change in CP is due to a change in the pressure drop at the PDM stator, which can indicate it is milling solids downhole or going into a stall.
systematically analyzed the impact of incoherent integration and post-integration waveforms on the accuracy of interferometric altimetry [59]. Furthermore, studies indicate that the minimum spatial resolution of spaceborne GNSS-R altimetry footprints is approximately 10 km [55]. In 2024, our previous work demonstrated through simulations that PARIS can achieve high-precision inversion of marine vertical deflection (referred to as the deflection of vertical, DOV, another parameter for describing the gravity field) with a 10 km along-track resolution. Moreover, it is further speculated that its global coverage and revisit capabilities may contribute to the inversion of high-spatial resolution marine time-variable gravity fields [60]. Based on our previous work [60], this paper aims to assess the potential contribution of spaceborne GNSS-R interferometric altimetry technology in improving the current spatial resolution and accuracy of marine time-variable gravity fields. Specifically, it evaluates the precision of global monthly marine gravity anomaly inversion using one month of simulated data at various resolutions. Additionally, the impact of dual-satellite constellation observations and different instrument configurations on the accuracy of monthly marine gravity anomaly inversion will be analyzed.
## 2 Related Theoretical Foundation and Modeling Framework
### Uncertainty Theory  
**Definition 1**.: _Uncertainty measure: Let \(\Gamma\) be a nonempty set, \(L\) be a \(\sigma\)-algebra over \(\Gamma\), and the element \(\Lambda\) in \(L\) be called an event. Uncertainty measure \(M\) is a set function from \(L\) to \([0,1]\) satisfying the following axioms._  
**Axiom 1**.: _(Normality axiom): for the universal set \(\Gamma\), \(M\{\Gamma\}=1\)._  
**Axiom 2**.: _(Duality axiom): \(M\{\Lambda\}+M\{\Lambda^{\text{c}}\}=1\), for any event \(\Lambda\), where \(\Lambda^{\text{c}}\) is the complementary set of \(\Lambda\)._  
**Axiom 3**.: _(Subadditivity axiom): For a countable sequence of events \(\Lambda_{1},\Lambda_{2},\cdots\)_  
\[M\{\overset{\infty}{i=1}\Lambda_{i}\}\leq\overset{\infty}{i=1}M\{\Lambda_{i}\} \tag{1}\]  
**Axiom 4**.: _(Product axiom): For a column of uncertain space \(\{I_{k},L_{k},M_{k}\}\), \(k=1,2,\cdots\)_  
\[M\{\overset{\infty}{i=1}\Lambda_{k}\}=\text{min}_{k=1}^{\infty}M_{k}\{\Lambda _{k}\} \tag{2}\]_where \(\Lambda_{k}\) is an arbitrary event chosen from \(L_{k}\), \(k=1,2,\cdots\)._  
**Definition 2**.: _Uncertainty variable: let \(\xi\) be a function from an indeterminate space \(\{\Gamma,L,M\}\) to a set \(R\) of real numbers if for any Borel set \(B\), the set \(\{\xi\in B\}=\{\Gamma\in\Gamma|\xi(\Gamma)\in B\}\) is an event, so \(\xi\) is said uncertain variable._  
**Definition 3**.: _Uncertainty distribution: let \(\xi\) be an uncertain variable, then the function \(\Phi(x)=M\{\xi\leq x\}\) is called uncertain distribution._  
**Definition 4**.: _Inverse Uncertainty distribution: let \(\xi\) denote an uncertain variable with a regular uncertainty distribution \(\Phi(x)\), then the inverse function \(\Phi^{-1}(\alpha)\) of \(\Phi(x)\) will ride the inverse uncertainty distribution of \(\xi\)._  
The uncertainty algorithm is as follows: let \(\xi_{1},\xi_{2},\cdots,\xi_{n}\) be a column of independent canonical uncertain variables, and its uncertainty distribution is \(\Phi_{1},\Phi_{2},\cdots,\Phi_{n}\). If the function \(f(x_{1},x_{2},\cdots,x_{n})\) is monotonically increasing with respect to \(x_{1},x_{2},\cdots,x_{n}\), and strictly monotonically decreasing with respect to \(x_{m+1},x_{m+2},\cdots,x_{n}\), then the uncertainty distribution of the uncertain variable \(f(\xi_{1},\xi_{2},\cdots,\xi_{n})\) is the following equation:  
\[\begin{array}{c}\Phi^{-1}(\alpha)=f^{-1}(\Phi_{1}^{-1}(\alpha),\Phi_{2}^{-1} (\alpha),\cdots,\\ \Phi_{m}^{-1}(\alpha),\Phi_{m+1}^{-1}(1-\alpha),\cdots,\Phi_{n}^{-1}(1-\alpha) )\end{array} \tag{3}\]
# Energy-Efficient User Pairing for Downlink NOMA in Massive MIMO Networks
## 8 Conclusions  
In this paper, the EE optimization problem for the downlink NOMA-based massive MIMO system is investigated with the constraints of BS power transmission and minimum user rate. The problem is decoupled into the user pairing subproblem and fair power allocation subproblem to reduce complexity. For the user pairing subproblem, three different pairing methods were employed for pairing cell-center users with cell-edge users to  
Figure 8: Complexity versus User number, with \(A=64\) BS antennas.  
Figure 7: Complexity versus number of BS antennas, with \(U=20\) users.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Pairing Approach** & **Complexity** \\ \hline GS algorithm & 100 \\ \hline Hungarian algorithm & 1000 \\ \hline Correlation & 14,400 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Complexity calculations for different pairing approaches.  
guarantee maximum EE for NOMA groups. For the power allocation subproblem, we mathematically formulated the max-min EE into a non-convex fractional programming problem, which is transformed into a sequence of subtractive form, followed by the SCP approach to obtain the optimal solution. A practical detailed power consumption model is introduced for the downlink NOMA-based scheme. Simulation results show that the NOMA-based scheme outperforms the mMIMO-based scheme for the large number of users. Moreover, the increase in the number of BS antennas reduces the EE of NOMA-based scheme due to increased power consumption. Additionally, the correlation pairing method has the highest computational complexity compared to the other two pairing methods. The correlation-SCP and Hungarian algorithm-SCP achieve identical EE results and outperform the GS algorithm-SCP. The Hungarian algorithm-SCP gives the same superior performance of the joint user-RB PA method but with lower computational complexity and outperforms the NOMA-SCP-GA method. In the future, our work will investigate the design of a beamforming technique for the NOMA-based scheme that improves the performance of NOMA groups and makes the results of different pairing algorithms more distinctive.  
Conceptualization, M.A.E.-g.; Investigation, M.A.E.-g.; Methodology, F.N.; Software, M.A.E.-g.; Supervision, F.N.; Validation, M.R.E.-m.; Visualization, M.M.I.; Writing--original draft, M.A.E.-g.; Writing--review & editing, M.R.E.-m. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
To accurately monitor the operational state of Internet Data Centers (IDCs) and fulfill integrated management objectives, this paper introduces a bidirectional cross-attention LSTM-Informer with uncertainty-aware multi-task learning framework (BiCA-LI) for time series analysis. The architecture employs dual-branch temporal encoders--long short-term memory (LSTM) and Informer--to extract local transient dynamics and global long-term dependencies, respectively. A bidirectional cross-attention module is subsequently designed to synergistically fuse multi-scale temporal representations. Finally, task-specific regression and classification heads generate predictive outputs and anomaly detection results, while an uncertainty-aware dynamic loss weighting strategy adaptively balances task-specific gradients during training. Experimental results validate BiCA-LI's superior performance across dual objectives. In regression tasks, it achieves an MAE of 0.086, MSE of 0.014, and RMSE of 0.117. For classification, the model attains 99.5% accuracy, 100% precision, and an AUC score of 0.950, demonstrating substantial improvements over standalone LSTM and Informer baselines. The dual-encoder design, coupled with cross-modal attention fusion and gradient-aware loss optimization, enables robust joint modeling of heterogeneous temporal patterns. This methodology establishes a scalable paradigm for intelligent IDC operations, enabling real-time anomaly mitigation and resource orchestration in energy-intensive infrastructures.  
IDC equipment; multi-task model; bidirectional cross-attention; dynamic bayesian loss 2020 IEEE/RSJ International Conference on Intelligent Systems (IEEE), pp. 1-10.
# Energy Demand Forecasting Scenarios for Buildings Using Six AI Models
## 3 Results and Discussions
### Practical Application  
The potential implications of the energy demand forecasting models are highly applicable in real-world energy management decisions. As the Autoencoder model has consistently demonstrated the best performance by a considerable margin in regression metrics such as Mean Absolute Percentage Error (MAPE) and Kling-Gupta Efficiency (KGE), stakeholders across any number of building types can have confidence using its predicted energy usage to optimize energy consumption practices. For example, with an MAPE of only 0.1% at the FUHEM building, energy managers can estimate heating needs with unparalleled accuracy to inform the scheduling of energy resources to maximize operational efficiency. Such accuracy not only aids in current energy management practices but also contributes to future energy management and infrastructure planning, which ultimately can increase sustainability in urban settings.  
Moreover, understanding the training times of different models provides essential insights into their deployment in real-world applications. The variability in training times emphasizes the importance of balancing computational efficiency with predictive performance. For example, while the Random Forest and Decision Tree models offer faster training times, the superior accuracy of the Autoencoder may justify the additional computational time for critical applications where precision is paramount. This knowledge enables energy managers to make informed decisions about which models to implement based on  
Figure 8: The energy demand after 3 years using linear regression: (**a**) LUCIA building HVAC (nZEB), (**b**) FUHEM building (heating), (**c**) EII building (cooling) and (**d**) EII building (heating).  
specific operational needs, ensuring that energy consumption is managed effectively while also preparing for future challenges associated with climate change and urban growth.
# Mitigating DDoS Attacks in LEO Satellite Networks Through Bottleneck Minimize Routing
###### Abstract  
In this paper, we focus on defending against distributed denial-of-service (DDoS) attacks in a low-earth-orbit (LEO) satellite network (LSN). To enhance the security of LSN, we propose the K-Bottleneck Minimize routing method. The algorithm ensures path diversity while avoiding vulnerable bottleneck paths, which significantly increases the cost for attackers. Additionally, the attacker's detectability is reduced. The results show that the algorithm avoids the bottleneck paths that are vulnerable to attacks, improves the attacker's cost by about 13.1% and 16.6% on average and median, and improves the detectability of attackers by 48.5% and 45.4% on average and median. The algorithm generates multiple non-overlapping inter-satellite paths, preventing the exploitation of bottleneck paths and ensuring better robustness and attack resistance.  
LEO satellite network; routing algorithm; bottleneck; DDoS +
Footnote †: journal: Accepted: 4 June 2025  
1
Footnote 1: institutetext: Correspondence: [EMAIL_ADDRESS] (X.Y.); [EMAIL_ADDRESS] (Y.Z.)
# Trajectory Tracking Control Method Based on Adaptive Higher Order Sliding Mode
## 1 Introduction
To solve the abovementioned problems, this paper first establishes an optimal trajectory that comprehensively considers the minimum time and the square of the control variable overload change rate as the performance index. Second, traditional sliding mode is characterized by anti-interference and strong robustness, so it is suitable for trajectory tracking control. Combined with the adaptive algorithm, a trajectory tracking control method based on high order sliding mode is proposed to solve the chattering problem of control quantity. The rest of this paper is established as follows. In Section 2, the proposed AHSTC method is explained in detail, and the compared method is explained in brief. In Section 3, the model verification and method comparison are introduced in details. Finally, some conclusions are discussed in Section 4.
## 4 Conclusions  
Biochar ceramsite was prepared from the residual sludge of Chaozhou No. 1 Sewage Treatment Plant at different temperatures.  
1. BET, compressive strength, and toxic leaching tests show that the prepared ceramsite has larger specific surface area and better compressive strength without additional auxiliary materials. The leaching amount of toxic heavy metals is low when the temperature is higher than 650 \({}^{\circ}\)C.
2. According to SEM, with the increase of calining temperature, the volatile substances in the sludge begin to volatilize, the sludge starts to expand, and the pores begin to increase. When the temperature rises to 650 \({}^{\circ}\)C, the volatile components are completely consumed, and the specific surface area should be the maximum. When the temperature rises to 950 \({}^{\circ}\)C, part of the material begins to melt. Although the porosity is high, the micropores have been blocked, so the specific surface area decreases.
3. According to XRD, the main component of sludge is quartz, with a small amount of kaolin and mica, which can prepare high-strength ceramsite. Calcination converts the weak crystalline iron minerals to hematite.
4. The large amount of iron and aluminum in the sludge is the main reason for its good adsorption effect. According to the adsorption performance test of Cr(VI), the adsorption effect of ceramsite on Cr(VI) is well at low pH, and the adsorption isotherm fits well with Langmuir and Freundlich types. The adsorption process has both monolayer adsorption and multilayer adsorption, and the adsorption process conforms to the pseudo-second-order kinetics.  
In general, the biochar ceramsite prepared by the residual sludge meets the basic requirements of wetland padding. The following experiment is to simulate the constructed wetland with ceramsite as padding to test the sewage purification effect.  
Conceptualization and methodology, S.L.; data curation and writing--original draft preparation, C.L.; resources, G.L.; writing--review and editing, S.L. All authors have read and agreed to the published version of the manuscript. This research was funded by Education Department of Guangdong Province and Science and Technology Bureau of Chaozhou, grant number 2017 KQNCX128, 2017 GY42. The study did not involve humans or animals. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Not applicable.  
Figure 5: Pseudo-second-order adsorption kinetics fit.  
**Acknowledgments:** We are thankful to our colleagues who provided test data that greatly assisted the research. Thanks for the raw material support provided by Chaozhou No.1 Sewage Treatment Plant and the testing provided by South China Normal University.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Related Works
### Sparse Representation Classifier  
Sparse representation has been widely used in the field of signal processing. The purpose of sparse representation is to find the sparsest representation of test samples using a linear combination of these training samples [43].  
Consider a training sample set \(Y=[Y_{1},Y_{2},\ldots,Y_{K}]\in R^{m\times n}\) with \(K\) classes. Here, \(m\) represents the length of the sample, while \(n\) denotes the number of samples. \(Y_{i}=[y_{i}^{1},\ y_{i}^{2},\ \ldots\,\ \ y_{i}^{n_{i}}]\in R^{m\times n_{i}}\) represents the training samples of class \(i\), and \(y_{i}^{j}\) is the \(j-\) th of \(Y_{i}\). \(y\) is a column vector representing the test sample. The sparse representation problem can be denoted by the following equation:  
\[\hat{\alpha}=\underset{\alpha}{\text{argmin}}\|y-Y\alpha\|_{2}^{2}\ s.t.\ \ \ \|\alpha\|_{0}\leq T \tag{1}\]  
where \(\alpha\) is the sparse representation coefficient vector; \(T\) is the sparse threshold; \(\|\cdot\|_{0}\) means \(L_{0}\) norm; and \(\ \|\alpha\|_{0}\) reflects the number of non-zero elements in \(\alpha\). The optimization of the \(L_{0}\) norm is an NP-hard problem in Equation (1); therefore, in practical applications, we usually use the \(L_{1}\) norm instead of the \(L_{0}\) norm. In this case, Equation (1) can be expressed as  
\[\hat{\alpha}=\underset{\alpha}{\text{argmin}}\Big{\{}\|y-Y\alpha\|_{2}^{2}\ +\ \ \gamma\cdot\|\alpha\|_{1}\Big{\}} \tag{2}\]  
where the parameter \(\gamma\) is a factor used to balance reconstruction error and sparsity.  
As for the sparse representation classifier, it makes use of the reconstruction errors of various classes to predict the class of the test sample \(y\). The detailed process is presented in Equation (3).  
\[identity(y)=\underset{i}{\text{argmin}}\{error_{i}\}\ \ =\underset{i}{\text{ argmin}}\{\|y-Y_{i}\hat{\alpha}_{i}\|_{2}\},\ \ \ i=1,2,\ldots,K \tag{3}\]  
where \(\hat{\alpha}_{i}\) is the sparse coefficient vector corresponding to the \(i-th\) class. Additionally, \(identity(y)\) denotes the predicted class of the test sample \(y\), and \(error_{i}=\|y-Y_{i}\hat{\alpha}_{i}\|_{2}\) represents the reconstruction error if the test sample \(y\) corresponds to \(Y_{i}\). Finally, the test sample \(y\) is divided into the class with the minimum reconstruction error.