esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment and Result Analysis
### Unsupervised Feature Selection
#### 4.1.4 Experimental Results
\begin{table}
\begin{tabular}{r r r r r r r r r r r r r r r r r r r r r r r r r} \hline
**Dataset** & **Ratio** & **Baseline** & **Baseline** & **ExpScore** & **GSR** & **RFS** & **GSER** & **mean** & **GSER** & **mean** & **GSER** & **biasForest** & **GSER** & **DGM** & **GSER** & **MIR** & **AGE** & **HQ-US** & **UPS-ID** \\ \hline  & & **ACC** & **NMI** & **ACC** & **NMI** & **ACC** & **NMI** & **ACC** & **NMI** & **ACC** & **NMI** & **ACC** & **NMI** & **ACC** & **NMI** & **ACC** & **NMI** & **ACC** & **NMI** & **ACC** & **NMI** & **ACC** & **NMI** & **ACC** & **NMI** & **ACC** & **NMI** & **ACC** & **NMI** & **ACC** & **NMI** \\ \hline  & & 0 & 48.1 & -41.5 & -49.9 & -41.9 & -95.7 & -47.2 & -41.4 & -56.4 & -46.6 & -46.4 & -0.55 & -49.3 & -41.7 & -51.2 & -43.5 & -52.6 & -44.1 & -54.7 & -48.7 & -85.7 & -58.7 & -48.9 & **58.5** & **54.9** \\  & & 0 & 42.5 & 42.5 & 42.5 & 42.2 & -43.0 & -42.4 & -2.1 & -41.7 & -22.6 & 42.2 & -42.2 & -42.0 & -41.9 & -42.2 & -41.7 & -43.0 & -3.32 & -3.31 & -42.9 & -2.9 & -2.9 & -33.0 \\  & & 0 & 46.6 & -37.7 & -49.1 & -40.5 & -50.3 & -43.7 & -55.7 & -47.4 & -49.4 & -44.4 & -50.7 & -49.9 & -57.2 & -47.2 & -57.5 & -47.1 & -58.7 & -48.7 & -48.7 & -58.7 & -48.3 & -53.8 & **55.0** \\  & & 0 & 43.0 & -42.3 & -43.0 & -42.9 & -42.2 & -42.1 & -42.0 & -42.8 & -41.5 & -42.1 & -42.1 & -42.5 & -41.8 & -41.9 & -42.4 & -42.0 & -42.6 & -42.3 & -42.6 & -42.3 & -43.6 \\  & & 0 & 45.4 & -37.2 & -45.9 & -39.7 & -52.2 & -54.7 & -60.1 & -52.9 & -48.8 & -40.7 & -53.1 & -44.3 &  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline
**Dataset** & **Ratio** & **Baseline** & \multicolumn{2}{c}{**Image**} & \multicolumn{2}{c}{**CSR**} & \multicolumn{2}{c}{**RFS**} & \multicolumn{2}{c}{**CSR**} & \multicolumn{2}{c}{**CSR**} & \multicolumn{2}{c}{**RFS**} & \multicolumn{2}{c}{**CSR**} & \multicolumn{2}{c}{**RFS**} & \multicolumn{2}{c}{**RFS**} & \multicolumn{2}{c}{**RFS**} & \multicolumn{2}{c}{**RFS**} & \multicolumn{2}{c}{**RFS**} & \multicolumn{2}{c}{**RFS**} & \multicolumn{2}{c}{**RFS**} & \multicolumn{2}{c}{**RFS**} \\ \cline{2-19}  &  
Finally, in Section 3.2, the convergence of the proposed algorithm in solving the objective function, namely, (6), is theoretically proven. Figure 3 shows the variation of the objective function value on the Iris data set with the number of iterations; it displays the convergence curve of the proposed UFS-ID approach, which showed a fast convergence.
# Active Tendon Control of Stay Cable by a Giant Magnetotrstive Actuator Considering Time-Delay
## 6 Simulation for the Active Control of Cable with the Time-Delay  
### Free Vibration  
The cable model in simulation is the same as shown in Table 1. The initial conditions of free vibration is \(\mathbf{Z_{0}}=[0.005,\,0.005,\,0.005,\,0,\,0,\,0]^{\text{T}}\). Four conditions, including without control, control without time-delay, non-compensation control with time delay and time-delay compensation control, are simulated. The weighting matrix \(\text{Q}=\mathbf{I_{6\times 6}}\) and weighting factor \(R=0.001\). When the time-delay is set to 0.11 s, the displacement response of the cable at mid-span is illustrated in Figure 6. From Figure 6, the RMS of displacements at mid-span are 0.00307 m, 0.00132 m, 0.00276 m and 0.00143 m in the four cases, respectively. For the three control strategies, the mitigating ratios of the cables at mid-span are 57.13%, 9.87%, and 53.42%, respectively. The results show the control effect of time-delay compensation control is close to the control state when time-delay is not considered. Moreover, it is implied that non-compensation control with time delay cannot effectively suppress the vibration of the cable when the time-delay reaches 0.11 s.  
Figure 5: Giant magnetostrictive actuator. (**a**) Magnetostrictive rod. (**b**) The internal structure of GMA; 1- output rod; 2- nonmagnetic cover; 3- preprocessing spring; 4- permeability cover; 5- magnetized; 6-bias coil skeleton; 7- excitation coil skeleton; 8- Giant magnetostrictive material rod; 9- permeability wall; 10- non-magnetic outer wall; 11-permeability lower cover; 12- non-magnetic lower cover. (**c**) Manufactured GMA.  
The results of time-delay compensation control with different time-delay amounts under the free vibration are summarized in Table 2. It can be seen that the control effect of the cable vibration gradually decreases as the time-delay increases under non-compensation control with time delay. In particular, when the time-delay increases to a certain value, the response of the cable is greater than that without control state, meaning the control is woefully inefficient. On the contrary, when the phase-shift method for time-delay compensation is applied, satisfactory results demonstrate the cable vibration is significantly suppressed.
## 4 Current Status and Identified Needs
### Level 1 TOA Radiometry and Geometry
#### 4.1.5 Viciarious Calibration with the Moon  
Another category of invariant vicarious targets are the celestial bodies, such as the Moon [87] or the stars [88], many of which are truly invariant for most of the practical purposes. They offer radiometric references that are independent of the Earth's surface reflectance and atmospheric variations, therefore reducing the intrinsic uncertainties associated with the Earth's vicarious targets. Among the various celestial bodies, the Moon is an ideal target for stability monitoring since its variations in brightness due to phase angle, non-uniform albedo, distance and the lunar librations can be accurately characterised with models, such as the widely used Robotic Lunar Observatory (ROLO) model, developed by USGS [87].  
The Moon has been and still is routinely used for monitoring the on-orbit calibration stability of various optical sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) [89], the Landsat-8 Operational Land Imager (OLI) [90] and Proba-V [72]. For these sensors, the use of the Moon has proven extremely successful, providing trending accuracy better than PICS. For instance, the MODIS Terra Collection 6 recalibration was largely based on the Moon for long-term trending analysis in the solar bands [89]. Similarly, the moon observations will be exploited in the future S2 and S3 satellites. Regular acquisitions of the Moon are now becoming standard also in the operational planning of commercial Smallsat constellations, such as for the Planet Dove satellites [91].  
Despite the interest in using the Moon as a vicarious target, the currently available ROLO model's accuracy (estimated uncertainty is 5-10%) does not meet the requirement for an absolute radiometric assessment [92; 93; 94], hence, this method is still predominantly used for stability monitoring and intercalibration. In order to overcome this limitation, EU-METSAT, in collaboration with USGS, developed an improved version of the ROLO model. The new lunar model, named GIRO (GSICS Implementation of the ROLO model) [95; 96], has been endorsed as the established community-available reference for lunar calibration. Future updates of the GIRO model are currently being considered with the target to attain the required radiometric accuracy (better than 1%) for using the Moon as an absolute calibration target. In parallel, ESA has run a project dedicated to improving current lunar observations and modelling [97]. The resulting Lunar Irradiance Model ESA (LIME), has a radiometric uncertainty at 2-sigma level of about 2%, as estimated with a Monte-Carlo method [98]. The LIME model was recently used for calibrating S3 OLCI data [99], and its accuracy is being evaluated in the frame of a current GSICS inter-comparison exercise with ROLO, GIRO and other Space Agencies' lunar models [100].
# Fractional-Order Closed-Loop Model Reference Adaptive Control for Anesthesia
## 5 Fractional-Order Parameter Identifier
Consider the following Lyapunov candidate function  
\[V(\tilde{\theta},e)=\frac{1}{2}e^{T}P_{c}e+\frac{1}{2}\delta^{T}\Gamma^{-1} \tilde{\theta}, \tag{43}\]  
where \(\Gamma=\Gamma^{T}>0\) is a constant matrix, and \(P_{c}=P_{c}^{T}>0\) satisfies the algebraic equation  
\[ \begin{split} P_{c}A_{c}+A_{c}^{T}P_{c}=-Q,\\ P_{c}B_{c}=C_{c}.\end{split}  \tag{44}\]  
By applying the Caputo derivative to (43), we get  
\[ \begin{split}{}_{0}D_{t}^{\alpha}V\leq& e^{T}P_{c0}D_{t}^{\alpha}e+\tilde{\theta}^{T}\Gamma^{-1}{}_{0}D_{t}^{ \alpha}\tilde{\theta}\\ \leq& e^{T}[P_{c}A_{c}+A_{c}^{T}P_{c}]e-e^{T}P_{c}B_ {c}\tilde{\theta}\phi+\tilde{\theta}^{T}\Gamma^{-1}{}_{0}D_{t}^{\alpha}\tilde{ \theta}\\ \leq&-e^{T}Qe-e^{T}P_{c}B_{c}\tilde{\theta}\phi+ \tilde{\theta}^{T}\Gamma^{-1}{}_{0}D_{t}^{\alpha}\tilde{\theta}.\end{split}  \tag{45}\]  
From (45), we need  
\[-e^{T}P_{c}B_{c}\tilde{\theta}\phi+\tilde{\theta}^{T}\Gamma^{-1}{}_{0}D_{t}^{ \alpha}\tilde{\theta}=0. \tag{46}\]  
From (44), we obtain \(P_{c}B_{c}=C_{c}\), which implies that \(e^{T}P_{c}B_{c}=\epsilon\). Then, (46) can be written as  
\[-\tilde{\theta}^{T}\phi\epsilon+\tilde{\theta}^{T}\Gamma^{-1}{}_{0}D_{t}^{ \alpha}\tilde{\theta}=0 \tag{47}\]  
which leads to (41). Let the adaptation law be given in (41), and then  
\[{}_{0}D_{t}^{\alpha}V\leq-e^{T}Qe. \tag{48}\]  
Since (48) is negative semidefinite from Theorem 2, the stability of the closed-loop system can be concluded. By applying the Riemann-Liouville integral in both sides of the inequality (48) and property 1, we have  
\[I^{\alpha}e^{T}Qe\leq V(0). \tag{49}\]  
Then, the integral (49) exists and in accordance with Lemma 3, it is concluded that the identification error \(e\to 0\) when \(t\to\infty\).
# Internet of Nano-Things, Things and Everything: Future Growth Trends
## 6 Challenges and Impediments to IoT
### Evolution of Communication from H2H to M2M  
Communication has evolved from human-to-human (H2H) interaction to that between machine-to-machine (M2M), especially in the domain of the IoT universe. In fact, H2H, particularly voice communication, was the focus of early communication technologies. Thus, the existing network infrastructure, architecture and protocols are optimized mainly for human-generated data/traffic. This poses a challenge to the successful implementation of IoT, which can be overcome by the adoption of a new set of protocols to specifically support M2M communication as an alternative to those used just for digitally conveying the human voice.  
Effective global H2H communication can coexist with M2M communication throughout the intermediary link. With IoT devices now outnumbering human operated devices, M2M communicationsnaturally is the dominant and fastest growing technology because it also enables human contact as the ultimate end users. Because of the requirements of network resilience, fault tolerance and redundancy, an even greater number of M2M devices are being deployed, leading to its exponential growth. Device-to-device (D2D) forms the backbone of M2M communication [94]: this uses all available means of the global communications network to carry out its tasks.
## 3 The Proposed Method
### Ensemble Methods  
As explained in Figure 1b, the DPGA can produce many prediction models, which can constitute an ensemble of solutions. Herein, we employed the voting ensemble and the Kalman filter ensemble [53] for the fault-type classification and the RUL prediction, respectively. For the former case, we applied a soft voting rule to achieve the combined results of multiple optimal classifiers. The voting ensemble is based on the sums of the predicted probabilities from well-calibrated classifiers. The Kalman filter ensemble can provide a mechanism for fusing multiple model predictions over time for a stable and high prediction performance.  
Figure 3: An example of the crossover used in DPGA.
# Lightweight Anomaly Detection in Digit Recognition Using Federated Learning
## 1 Introduction
Our goal is to implement and evaluate an autoencoder-based AD system on an MCU, including FL scenarios with multiple real-world devices. Our main contributions are as follows:  
* We design, implement, and evaluate several fully connected autoencoder models for digit-based anomaly detection, deployed fully on the ESP32-CAM microcontroller. Our approach enables on-device training and inference using standard 32-bit floating-point precision and incorporates an innovative dual-phase early stopping mechanism to optimize training efficiency. The best-performing models achieve an F1 score of up to 0.87 when optimized for MNIST and EMNIST anomaly detection, with on-device training times as short as 12 min and inference latency as low as 9 ms.
* We implement a real-world federated learning testbed across 10 ESP32-CAM devices and test various IID and non-IID data distributions, including corrupted datasets, to reflect realistic edge scenarios. Our extensive experimental results demonstrate strong and consistent F1 scores, achieving up to 0.87 in the standard IID setting and 0.86 in the extreme non-IID setting. * We analyze the robustness and resilience of FL in the presence of heterogeneous and corrupted data sources without relying on explicit malicious device detection. We evaluate scenarios where one to five out of the ten devices have corrupted datasets. The results show that federated aggregation effectively mitigates the impact of anomalous datasets, rendering their influence negligible for up to two corrupted devices and causing only minor performance degradation when more devices are affected. * We characterize the ESP32-CAM's memory footprint by analyzing program size, dataset allocation, and memory consumption during training and inference to establish practical limits for embedded model deployment. The results demonstrate that more than half of the evaluated models fit entirely within the internal RAM, enabling faster training speeds while maintaining satisfactory performance. The paper is organized as follows. Section 2 reviews the related work in the fields of anomaly detection, edge ML models, and federated learning. Section 3 presents the system architecture used throughout the experiments. Section 4 details the lightweight autoencoder deployment on the ESP32-CAM, including dataset preparation, model and training strategy, and embedded implementation. Section 5 introduces the federated training approach and evaluates performance under non-IID and IID data, including experiments on robustness to anomalous training data. Section 6 analyzes resource usage, focusing on memory footprint, dataset allocation, and training and model memory usage. Section 7 presents the experimental results on anomaly detection performance and on-device training feasibility, along with a comparison to the related work. Finally, Section 8 concludes the paper and outlines directions for future research.
# Performance Evaluation of Steel Slag Asphal Mixtures for Sustainable Road Placement Rehabilitation
## 1 Introduction
Studies of the literature showed the benefits of using SSA considering various factors, such as the type of asphalt mixture, origin of natural or recycled aggregate, grading, and function of SSA [15; 16; 17; 18; 19; 20; 21; 22]. Several mechanical and functional properties were analyzed (e.g., cracking, moisture damage, rutting, skid resistance) that confirmed the acceptable performance of the asphalt mixtures with SSA [23; 24; 25; 26; 27; 28; 29]. Regarding the use of SSA in asphalt mixtures, most studies in the literature have considered a selected grading (crushed steel slag fine or coarse aggregates). Such an approach can be laborious or challenging to implement in the case of actual conditions of production and pavement application. In Europe, SSA is produced under Factory Production Control, and it should be in conformity with the marking requirements of the Construction Products Regulation. In Portugal, SSA is available through different aggregate dimensions, and the most common is 0/40 all-in aggregate. The innovation of this work is related to the SSA incorporation method: two fractions of aggregate dimension 0/20 of natural aggregates and SSA were simply mixed in different percentages: 0%, 20%, and 35% of SSA. In this process, there was no need to make a previous and complex grading selection of the aggregates, therefore easing its implementation in the production of asphalt mixtures. The percentages of SSA incorporation were selected by considering the existing literature and earlier work from the authors [25]. The use of high ratios of SSA incorporation is not recommended. Due to the high density of the SSA, approximately 1 Mg/m\({}^{3}\) higher than that of a natural aggregate [30; 31], high incorporation rates may limit the volume carried by trucks increasing the transportation costs. This paper analyzes and discusses the mechanical performance of the asphalt mixtures containing SSA (0%, 20% and 35%) evaluated through the following laboratory tests: affinity between binder and aggregate, Marshall and volumetric properties, stiffness, resistance to permanent deformation, fatigue resistance, and water sensitivity. The results of this work are intended to be a valuable contribution towards achieving the Sustainable Development Goals adopted by the United Nations on transport infrastructure [1].
# Algebraic Point Projection for Immersed Boundary Analysis on Low Degree NURBS Curves and Surfaces
## 3 Background on Algebraic Level Sets  
In addition to point projection, blending behaviors on immersed boundaries with domain also requires distance estimates from the boundary to a point in the domain. This is because the behavioral influence of the immersed boundaries decays monotonically with distance. While traditionally distances are estimated using Newton-Raphson iterations, recently, [PERSON] et al. [23; 24] developed algebraic procedures for efficient computation of distance estimates from curves and surfaces. The method developed by [PERSON] et al. relies on converting the parametric NURBS geometry to its implicit form using the Dixon resultant, and constructing level sets on the implicit form of the geometry. [PERSON] et al. termed these level sets as algebraic level sets. The construction of the algebraic level sets requires one to decompose the NURBS entity into constituent Bezier patches and later to blend the level sets constructed on Bezier patches using R-functions. The algebraic level sets provide monotonic measures of distance that are accurate to exact distance near the boundary. While the algebraic level sets are approximate measures of distance, they are sufficient to capture the interaction of domain approximations with those on the immersed boundary during analysis. The algebraic level sets have the following properties:  
1. Accurate measure of distance locally near the curve/surface
2. Monotonic function of Euclidean (exact) distance
3. Sufficiently smooth for engineering applications
4. Efficiently obtained without numerical iterations for points close to the curve/surface  
For the sake of completeness, we briefly review the computation of algebraic level sets and illustrate the procedure through simple examples.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Description** & **Reference** & **Algebraic** &
\begin{tabular}{c} **Initial Guess** \\ **Dependent?** \\ \end{tabular} & **Efficiency** & **Accuracy** & **Smoothness** \\ \hline Subdivision method & [22] & Yes & No & High & Medium & No \\ \hline Subdivision method + & [12; 14; 15] & No & Yes & High & High & No \\ Newton–Raphson method & [16; 17; 18; 19; 20; 21] & No & Yes & High & High & No \\ \hline Proposed method & & Yes & No & High & Medium & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of methods used for point projection in literature
###### Abstract  
In heterogeneous networks (HetNets), non-orthogonal multiple access (NOMA) has recently been proposed for hybrid-access small-cells, promising a manifold network capacity compared to OMA. One of the major issues with the installation of a hybrid-access mechanism in small-cells is the cross-tier interference (ICTI)) caused by the macrocell users (MUs) that are unable to establish a connection to the small-cell base station (SBS). In this paper, a joint strategy is proposed for hybrid-access small-cells using the Walsh-Hadamard transform (WHT) with NOMA and interference rejection combining (IRC) to achieve high performance gains and mitigate intercell interference (ICI), respectively. WHT is applied mathematically as an orthogonal variable spreading factor (OVSF) to achieve diversity in communication systems. When applied jointly with NOMA, it ensures better performance gains than the conventional NOMA. It reduces the bit error rate (BER) and enhances subsequent throughput performance of the system. IRC is used at the receiver side for managing the cross-tier interference caused by MUs that are unable to connect to the small-cell base station (SBS) for hybrid-access. The work considers both ideal and nonideal successive interference cancellation (SIC) conditions for NOMA. Mathematical modeling is provided for the proposed joint strategy for HetNets and the results validate it in terms of BER and subsequent user throughput performance, compared to the conventional NOMA approach.  
heterogeneous networks (HetNets); non-orthogonal multiple access (NOMA); successive interference cancellation (SIC); interference rejection combining (IRC); Walsh-Hadamard transform 40/1.