esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Reference [24] presents an adaptive tracking controller based on output feedback linearization to compensate for dynamical changes in a quadrotor's center of gravity, successfully managing the uncertain gravity parameter and stabilizing the quadrotor in real time, as demonstrated through simulation results. [PERSON] proposes an adaptive feedback linearization (AFBL) control approach, combined with a PID controller, to stabilize a quadrotor's attitude and altitude in the presence of wind disturbances and parameter uncertainties [25]. While simulations demonstrate significant performance improvements over conventional methods, the approach neglects the nonlinear relationship between Euler angle rates and angular velocities in the body frame, making it unsuitable for scenarios involving rapid UAV maneuvers. In another study, [PERSON] designs a feedback linearization model predictive control algorithm with a disturbance observer for quadcopter trajectory tracking, demonstrating effective tracking performance under disturbances through numerical simulations [26]. However, it assumes slight roll and pitch angle rotations and does not address the constraining effect on state variables in the model predictive control design. This paper presents a feedback linearization model based predictive control method (FLMPC) approach for attitude control of quadrotor UAV, targeting precise and rapid tracking performance under system constraints. The core idea is to incorporate the nonlinear mapping between Euler angular velocities and body-frame angular velocities into the feedback linearization process, allowing the inherently nonlinear and coupled dynamics of the quadrotor to be transformed into a fully decoupled linear system. This transformation significantly reduces the computational complexity while enabling the determination of optimal control inputs within predefined constraints, a process in which the constraint on the original system state variables has been modified. The rest of the paper is organized as follows: Section 2 establishes the flight dynamics model of the quadrotor UAV and performs feedback linearization transformation to create a controlled object model, serving as the foundation for controller design and simulation. Section 3 details the design of the quadrotor UAV attitude controller based on FLMPC, including key design principles and constraints. Section 4 conducts simulation experiments, where the proposed method's performance is evaluated through various test scenarios, demonstrating its effectiveness in achieving stable and responsive control. Section 5 provides a comprehensive summary of the findings, discusses the implications of the work, and suggests directions for future research to further refine and expand upon this control strategy. The Appendix A presents the process of designing an attitude controller using MPC.
# A Numerical Approach for the Filtered Generalized Cech Complex
## 5 The Cech.scale Algorithm
### An Example: The Miniball Problem  
The miniball problem or smallest-circle problem in the euclidean space is a classical problem, proposed by [PERSON] in 1857.  
Given a finite point cloud \(N\subset\mathbb{R}^{d}\), the miniball problem consists in finding the center \(c\in\mathbb{R}^{d}\) and minimum radius \(r\in\mathbb{R}^{+}\) of a \(d\)-disk \(D=D(c;r)\subset\mathbb{R}^{d}\) such that \(N\subset D\).  
There exist many different approaches to solve this problem, and a variety of algorithms to reach the miniball data (e.g., [21; 22]). In fact, the Cech scale has a close relation with the miniball problem, as we establish in the next lemma.  
Lemma 7: _Let \(N\) be a finite point cloud in \(\mathbb{R}^{d}\), and let \(N_{1}\) be the associated \(d\)-disk system defined by_  
\[N_{1}:=\{D_{i}(c_{i};1)\subset\mathbb{R}^{d}\ |\ c_{i}\in N\}.\]  
_Then, the Cech scale \(\mu_{N_{1}}\) is the radius of the minimal enclosing ball of \(N\), and the intersection point \(\{c_{N_{1}}\}=\bigcap_{c_{i}\in N}D_{i}(c_{i};\mu_{N_{1}})\) its center._  
Proof: Let \(\mu_{N_{1}}\) be the Cech scale of the disk system \(N_{1}\), and let \(c_{N_{1}}\) be the intersection point of the \(\mu_{M}\)-rescaled disk system. Then, the point \(c_{N_{1}}\) belongs to every disk \(D_{i}(c_{i};\mu_{N_{1}}\cdot 1)\), i.e., \(\|c_{i}-c_{N_{1}}\|\leq\mu_{N_{1}}\) for any \(c_{i}\in N\); thus, \(N\subset D(c_{N_{1}};\mu_{N_{1}})\). On the other hand, by definition of Cech scale, \(\mu_{N_{1}}\) is the minimal radius (scale) with such property. Therefore, by uniqueness, \(D(c_{N_{1}};\mu_{N_{1}})\) must be the minimal ball enclosing the point cloud \(N\).  
In particular, for a point cloud \(N\) in the plane we can apply our algorithm Cech.scale (Algorithm 3) to the 2-disk system \(N_{1}\), and get the minimal enclosing ball of \(N\). However, the Cech scale of an arbitrary 2-disk system \(M=\{D_{1}(c_{1};r_{1}),\ldots,D_{m}(c_{m};r_{m})\}\) cannot be obtained from the minimal enclosing ball data of the point cloud \(M_{0}=\{c_{1},\ldots,c_{m}\}\).  
In Figure 8, we show a point cloud \(N\) (black dots) and the 2-disk system \(N_{1}\) (blue circles). Applying the Cech.scale script to \(N_{1}\) we get the Cech scale \(\mu_{N_{1}}\) (radio of the red circle) and the point \(c_{N_{1}}\) (red point, center of the red circle).  
Figure 7: Average time of the script Cech.scale.  
For the miniball problem there are many efficient algorithms available online, which are easy to find. For example, the C++ script in [23] can compute the miniball for point clouds in any dimension (efficiently up to dimension 10,000). Such algorithms are not comparable with the Cech.scale algorithm because if only disk systems with equal (and unitary) radii were considered, several issues that were addressed in the case of different radii would be avoided.
# Performance Analysis of DF Relay-Assisted D2D Communication in a 5G mmWave Network
## 2 System Model
### Information Model  
To realize the proposed scenario, some assumptions were made. The BS has complete knowledge of the channel state information (CSI). Furthermore, it has prior knowledge of the locations of the D2D users and their respective path loss exponents. It was also assumed that the total number of channels available is equal to the total number of cellular users  
Figure 1: System model representing interference acting on D2D users.  
present in the cell. Moreover, the distribution of the relay nodes follows a PPP distribution in a finite, two-dimensional plane \(\mathbb{R}^{2}\) with density \(\lambda_{RN}\).
## 2 The Distribution Law of Bending Shear Flow in Box Sections
### Basic Coordinate System and Solution Steps  
As shown in Figure 1, a coordinate system is introduced, with the origin \(O\) located at the section center. The \(Z\)-axis aligns with the beam's longitudinal direction, the \(X\)-axis is along the section's width direction, and the \(Y\)-axis is perpendicular to the \(X\)- and \(Z\)-axis. In addition, the cross section is symmetric about the coordinate plane \(Y\)-\(Z\).  
The steps for solving the shear flow distribution of single- and double-cell box sections are organized as follows: first, the shear flow \(q_{0}\) in the open box section is calculated; then, the additional static shear flow \(q_{i}\) is derived using the deformation coordination relationships; finally, the above two parts are added to obtain the total shear flow distribution in the closed box section.  
Notably, in-plane shear stresses are uniformly distributed along the direction of wall thickness, and the magnitude of \(q_{0}\) is mainly determined by the static moment from the free surface to the desired point [27]. The \(q_{0}\) can be expressed as follows:  
\[q_{0}=-Q(z)S_{x}/I_{x} \tag{1}\]  
where \(Q(z)\) represents the cross-sectional shear force; \(S_{x}\) represents the static moment to the \(X\)-axis; and \(I_{x}\) represents the inertial moment to the \(X\)-axis. Consider \(Q(z)/I_{x}\) as constant \(1\) to ease the calculation, and then, the value of \(S_{x}\) represents the magnitude of shear stress.
# Boosting of Denoising Effect with Fusion Strategy
## 3 Combination of the NCSR and the DnCNN
### Pixel Intensity Based Weight Design \((W_{1,n}(i,j))\)  
In this section, we introduce a weight design \(W_{1,n}(i,j)\) that reflects the pixel intensity. A fundamental aspect of the image fusion algorithm is to design \(W_{n}(i,j)\), which reflects the importance of the corresponding pixel; furthermore, it needs to reflect the influence of luminance changes, i.e., to emphasize bright regions and vice versa. [PERSON] et al. [39] presented an image quality measure known as _well-exposedness_ to design a weight in this regard:  
\[W_{n}(i,j)=exp\left(-\frac{(\hat{x}_{n}(i,j)-0.5)^{2}}{2\lambda^{2}}\right), \tag{12}\]  
where \(\lambda\) equals 0.2. Similar to several intuitive weight designs, the measure uses a Gauss curve and provides weights to each pixel intensity \(\hat{x}_{n}(i,j)\) based on the proximity of the intensity value to 0.5. It also can be observed that the \(n\)-th image is the only variable used in this function. Based on this, we present our observations regarding the weight design. First, a weight design that employs Equation (12) cannot assign a large weight to a well-denoised pixel with an intensity value far from 0.5, in bright or dark regions. Therefore, it cannot well emphasize a bright pixel that is well-denoised in an overall dark image or a well-denoised dark pixel in an overall bright image. Hence, we propose a weight design that is relative to the overall image brightness. The proposed weight design assigns a relatively large weight to a dark pixel in a bright image and vice versa. We define \(m_{n}\) as the mean of the pixel intensities of the \(n\)-th initial denoised image, and the weight should emphasize the pixel intensities close to \(1-m_{n}\). In the same form as that of Equation (12), this can be written as \(exp(-(\hat{x}_{n}(i,j)-(1-m_{n}))^{2})\). In addition, we note that several well-denoised pixels should be considered when the brightness of the input initial denoised images \(m_{n}\) and \(m_{n+1}\) has a large difference. Therefore, we assign a large \(\lambda_{n}\) when the brightness of the two images differs substantially. Finally, the first weight \(W_{1,n}(i,j)\) that reflects the relative pixel intensity can be represented as  
\[W_{1,n}(i,j)=exp\left(-\frac{(\hat{x}_{n}(i,j)-(1-m_{n}))^{2}}{2\lambda_{n}^{2 }}\right), \tag{13}\]  
where \(\lambda_{n}\) controls the weight as \(\lambda_{n}=2\alpha(m_{n+1}-m_{n})\) based on the difference between the two input images. From Equation (13), it can be seen that when the input image is bright (\(m_{n}\) is close to 1), dark pixels (\(\hat{x}_{n}(i,j)\) with a relatively low value) will be assigned a larger weight and vice versa. Moreover, a large weight is assigned when there is a large difference in the mean brightness of the two input initial denoised images.
## 3 Calibration Methodologies & Dataset Used
### Transponder Calibration
This event is repeated every 27 days as a result of polar orbits of this satellite. The first transponder  
Figure 8: This is an example of the Sentinel-3A signal responses generated by the transponder at the CDN1 Cal/Val site on 21 December 2017, at the mountains of West Crete. Calibrated Synthetic Aperture Radar (SAR) data, shown as a Fast Fourier Transform, are plotted against zero-padded samples and Ku-pulse index in Cycle No.26 of Sentinel-3A. The Pulse Repetition Internal for Sentinel-3A has been 17,800 Hz within a burst and the burst repetition interval is 80 Hz. Relative scale of signal magnitude is shown in color on the vertical bar (arbitrary units). calibration for Sentinel-3A commenced on 9 April 2016 (Cycle No.3). Since then, roughly 30 transponder calibrations have been carried out, with a success rate of 95% and clear transponder responses on SRAL records (Figure 9). Only two calibrations had to be canceled because of extreme weather conditions (4 January 2017, Cycle No.13) and site maintenance (8 August 2017, Cycle No.21). After each satellite overpass, within 24-h, a movie presentation of the actual and continuous transponder response is created. This movie shows the transponder echo entering the tracking window, reaching its peak (maximum spike at the \"point of closest approach\"), and then leaving the tracking window (Figure 10).
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 3 Real-Time Systems and Real-Time SR
### Discussion and Limitations  
The rapid growth of deep learning-based models for real-time SR has sparked heated debate, notably over the trade-offs between model complexity, computing efficiency, and image quality. The emergence of models such as the RLFN, which relies on residual local features and decreased feature aggregation, has demonstrated a promising balance between performance and processing time, as proven by its first place win in the NTIRE 2022 efficient SR competition. However, this result raises the question as to whether such efficient models may sacrifice generalizability and robustness when dealing with more complicated and diverse image datasets.  
In parallel, RNN techniques like MoG-DUN prioritize explainability and the incorporation of non-local auto-regressive models, which improve accuracy and eliminate artifacts in SISR. Despite their accuracy and capacity to manage various degradations, this raises concerns regarding the feasibility of using such advanced approaches in settings that demand quick processing. GANs provide a new dimension to the discussion, particularly with the introduction of models that include RRDBs and sophisticated attention mechanisms such as the AAM. These models produce excellent image quality by efficiently exploiting a larger number of pixels but at the expense of additional computational overhead. Efforts to ease these demands through GPU parallelism and simpler network designs have made progress, but there are still hurdles in developing models that can retain this high quality while still being practical for real-time application on lower-end devices and in mobile environments.  
The continuing debate in the area centers on how to effectively manage these trade-offs in order to construct SR models that are not only efficient and accurate but that are also effective and responsive to a wide range of real-world scenarios. Achieving this balance is crucial for the widespread use of SR technologies in a variety of industries, including those with limited computing resources.
# Methodology for CubeSat Debris Collision Avoidance Based on Its Active ADCS System
## 5 Conclusions
**Acknowledgments:** The authors thank the University of Vigo for the use of the Educational version of ANSYS STK product. **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature**  
\(a_{drug}\) drag force acceleration  
\(\rho\) atmospheric density  
\(V\) satellite velocity  
\(C_{D}\) drag coefficient  
\(S\) drag area  
\(m\) satellite mass  
\(\varphi\) Yaw angle  
\(\omega\) Angular velocity  
\(Q\) Quaternioun  
\(K_{k}^{i}\) Kalman gain  
**Acronyms/Abbreviations**  
LEO Low Earth Orbit  
ADCS Attitude Determination and Control System  
U Unit  
STK System Tool Kit  
CDM Conjunction Data Message  
ESA European Space Agency  
ESOC European Space Operations Centre  
IADC Inter-Agency Space Debris Coordination Committee  
COPUOS Committee on the Peaceful Uses of Outer Space  
ISO International Organisation for Standardisation  
NASA National Aeronautics and Space Administration  
JSpOC Joint Space Operations Center  
SSN Space Surveillance Network  
ISS International Space Station  
SST Space Surveillance and Tracking  
CDS CubeSat Design Specification  
UAV Unmanned Aerial Vehicle  
SGP4 Standard General Perturbations Satellite Orbit Model 4  
HPOP High-Precision Orbit Propagator  
CA Conjunction Assessment  
TCA Time of Closest Approach  
LVLH Local Vertical, Local Horizontal  
TLE Two-Line Element  
VDD Vertical Distance Difference  
HDD Horizontal Distance Difference  
EOM Engineering Qualification Model  
GISTDA Geo-Informatics and Space Technology Development Agency  
CDS CubeSat Design Specification  
UAV Unmanned Aerial Vehicle  
SSO Sun Synchronous Orbit
## 5 Conclusions  
This study systematically elucidates the response mechanisms of grassland ecosystems to atmospheric drought stress in the arid regions of Xinjiang. Firstly, based on linear regression analysis, GPP exhibited a significant upward trend across the region from 2000 to 2020, with 58.75% of the study area showing significant increases. However, under high VPD conditions, particularly in key atmospheric drought-impacted zones identified by ridge regression contribution analysis, the rate of GPP increase notably slowed down, demonstrating the strong inhibitory effect of atmospheric drought on ecosystems in arid areas. Secondly, through integrated analysis using partial correlation, ridge regression, and SEM, VPD was unambiguously identified as the dominant meteorological factor influencing grassland GPP, exhibiting a significant negative path coefficient (path coefficient \(=-0.58\)). Finally, ridge regression analysis identified a precise VPD threshold of 0.61 kPa for Xinjiang's arid grasslands. Below this threshold, VPD generally promotes GPP; exceeding it suppresses productivity. This threshold is lower than those reported for more humid regions, revealing the heightened sensitivity of plant water regulation under extreme drought conditions. Crucially, the regional average VPD in the main contribution zones already exceeded this critical threshold, having increased significantly from 0.66 kPa in 2000 to 0.74 kPa in 2008 (a 12.1% increase). In summary, this study advances the quantitative understanding of the relationship between atmospheric drought and grassland productivity in arid regions, identifying key thresholds and drivers, and provides a scientific basis for regional ecological conservation and climate adaptation strategies.  
Y.B.: Formal analysis, Conceptualization, Methodology, Writing Original Draft. C.J.: Supervision, Project administration, Reviewing and Editing, Funding acquisition. Y.L.: Formal analysis, Visualization. Y.W.: Writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 42161024), the Central Financial Forestry and Grassland Science and Technology Extension Demonstration Project (2025) (Grant No. Xin [2025] TG 09).  
Not applicable.  
Data Availability Statement: Data will be made available on request.  
The authors thank the editorial team for coordinating the peer-review process.  
The authors declare no conflicts of interest.
## 2 Related Work
### Question Decomposition Approaches  
There has been little research on the use of question decomposition approaches to understand complex questions. Earlier research used a rule-based approach to parallel and nested decomposition of questions using hand-customized rules based on word semantics to achieve question decomposition [21]. Following that, SPLITQA [20] decomposes complex questions into groups of simple questions and reflects complex question semantics by mixing simple questions. A multi-hop question can be broken down into a series of single-hop questions using DECOMPRC [22], which can then combine the single-hop replies to produce the desired result. There is also the application of reinforcement learning for question decomposition, which allows an agent to learn strategies for decomposing complex questions into simple ones while determining the relations between simple questions utilizing three independent relational components [23]. Other approaches use unsupervised seq2 seq learning to build a pseudo-decomposition model that divides a question into several small questions and then builds a recombination model that combines the answers to these simulated questions to achieve the final answer [24]. Unsupervised processes have been utilized to construct decompositions by mapping a complex question to a collection of feasible subproblems in a corpus of simple questions [25]. Decomposition can be used to investigate the model and produce explanations for its logic, according to a recent use case [26].  
However, the analysis above shows the prior models that employed the question decomposition approach to comprehend complex questions using only a small amount of data for training, and it is challenging to obtain good question representations when the traditional models encode only the semantic aspects in the absence of contextual information of the question.
## 1 Introduction
The contribution of the proposed method is its capability of detecting errors by using a gesture multiclass model based on partial least square (PLS) class modelling with the use of an encoding matrix called error correcting output codes (ECOC) while keeping a small size model and low complexity, therefore, maintaining its suitability for real-time classification on a wearable device. To achieve fault tolerance, error detection was implemented. An _error_ is defined as a deviation from the expected or correct behaviour of the system, in this case, the difference between the gesture made and the obtained label. In the context of HMI, an _error_ can lead to a wheelchair driving into an obstacle because of a gesture misclassification or a drone crashing because the GR classifier confused left withright. In the proposed class-modelling approach, if an _error_ is detected, it can take action, such as asking the user to reposition the electrodes or to repeat the gesture, preventing the system from executing any incorrect actions based on the sEMG input signal. Latency and robustness are critical in real-time classification [17]. This work also addresses the unresolved issue of automating the obtention of useful features because there are dozens of them that are usually chosen by hand during the design of the classifier and have a great impact on the outcome of the classifier. This paper is organized as follows: Following this introduction, state-of-the-art approaches are discussed in Section 2, and the elements of the problem are described in Section 3. This includes the notation and experimental procedure to obtain the EMG signals (Section 3.1) and a description of the structural elements of the PLS-ECOC procedure (Sections 3.2-3.8). The results are shown in Section 4, and a discussion of them in Section 5 shows the advantages of modelling the gesture and the accuracy of gesture classification, comparing it with those found in several papers. This paper ends with conclusions and several references.
# Two-Step Classification with SVD Preprocessing of Distributed Massive Datasets in Apache Spark
## 3 Proposed Method
### Classification Algorithms
#### 3.2.2 Random Forest  
Random forest is considered a generalization of the decision tree [43] as it consists of a set of decision trees. In order to classify a new input, we insert it in each tree of the forest, ending in the \"vote\" of an output class. The class that will receive the most votes, is the output class that the random forest will return. To construct each tree, a number of cases from the original data, equal to the number of trees of the forest, are sampled with replacement and in following a subset of the features of that specific selection in order for the size level of tree to be increased, are utilized.
# Fault Diagnosis System of Power Grid Based on Multi-Data Sources
## 2 System Framework and Operation Process
### Brief Introduction of Fault Diagnosis Batch Processing System  
The batch processing system mainly deals with fault recording documents. Due to the large storage space and low real-time requirements of fault recording documents, this subsystem at dispatching center inputs fault recording documents in batches in the form of multiple sets of documents and schedules them in the way of workflow. Then, this subsystem verifies and corrects the diagnosis result of the real-time processing system and finally outputs fault details, including fault locations, phases, types and times.