esa-sceva/satcom-chunk-collection huggingface dataset:
# Edge and Cloud Computing in Smart Cities
## 2 Architectural Models for Edge and Cloud Computing in Smart Cities
### Clustered Edge-Cloud Architecture with Federated Learning
This FL mechanism ensures privacy preservation, reduces cloud communication costs, and enhances model adaptability to local conditions [53; 54]. The overall system latency, encompassing computational, communication, and learning synchronization delays, is expressed as  
\[T_{\text{sys}}=\sum_{\tau\in\mathcal{T}}\Bigl{(}T_{E_{qi}}+T_{\text{comm}}(E_ {qi},G_{q})+T_{\text{agg}}+T_{\text{C}}\delta_{\tau}\Bigr{)}, \tag{24}\]  
where \(T_{\text{agg}}\) denotes the time required for model aggregation at the cluster level, and \(T_{\text{C}}\) captures additional delays if cloud interaction is required [55]. A key advantage of the clustered architecture lies in its fault tolerance and resilience to node failures. If an edge node becomes unavailable, its computational workload is dynamically reassigned to neighboring nodes within the same cluster. The probability of complete failure is determined by assessing the likelihood that all nodes in a cluster \(q\) fail simultaneously. The following equation aggregates individual node failure probabilities to evaluate system resilience and fault tolerance  
\[P_{f}=1-\prod_{i=1}^{N_{q}}(1-p_{E_{qi}}), \tag{25}\]  
where \(p_{E_{qi}}\) is the failure probability of node \(E_{qi}\). The probability of service continuity increases with the number of edge nodes in the cluster, ensuring robustness in decentralized environments [56]. Energy efficiency in this architecture is enhanced by restricting cloud interactions and optimizing intra-cluster task execution. The total energy consumption across clusters is given by:\[E_{\text{total}}=\sum_{i=1}^{N_{q}}\left(P_{E_{ij}}T_{E_{ji}}+P_{\text{comm}}T_{ \text{comm}}(E_{qi},G_{q})+P_{\text{agg}}T_{\text{agg}}+P_{\text{C}}T_{\text{C}} \delta_{\tau}\right), \tag{26}\]  
where \(P_{\text{agg}}\) represents the power consumption associated with FL model aggregation. Adaptive power management strategies, such as dynamic voltage scaling and sleep scheduling, further improve energy efficiency [57; 58].
# Edge AI for Real-Time Anomaly Detection in Smart Homes
## 4 Experimental Setup
### Data Simulation  
A synthetic IoT dataset was generated to ensure a controlled evaluation environment, simulating sensor readings from a smart home environment. The dataset consists of 100,000 sensor readings with three key parameters:  
* **Temperature (oC)**: Simulated using a normal distribution around typical indoor temperature.
* **Motion detection (binary: 0/1)**: Randomized based on expected human activity patterns.
* **Energy usage (wattage)**: Simulated with baseline values and occasional spikes to mimic high-power consumption events.  
To introduce anomalies, 3% of the samples were deliberately altered to represent unusual events such as sudden temperature spikes, unexpected motion patterns, and excessive energy consumption. These anomalies were injected at random intervals to simulate realistic smart home scenarios.  
The dataset size of 100,000 records was chosen to ensure statistical significance while maintaining computational feasibility for edge-based processing. The 3% anomaly rate aligns with industry reports on rare but impactful deviations in smart home environments.  
Power consumption was measured using the INA219 power monitor sensor, capturing energy usage variations before and after deploying anomaly detection models. The INA219 sensor was configured to sample power usage at a fixed rate of 1 Hz, which provided a practical balance between energy profiling granularity and minimal overhead on system resources. The baseline energy usage of the Raspberry Pi 4 running idle processes was 2.7 W, compared to 4.2 W when executing the LSTM Autoencoder. The baseline power consumption (2.7 W idle, 4.2 W during execution) aligns with previous studies on energy-efficient Edge AI implementations, where models optimized with quantization typically operate within a similar power range [25,26].  
The data pre-processing steps included the following:  
* **Feature scaling**: Min-Max normalization was applied to standardize sensor readings.
* **Noise reduction**: A rolling average filter was used to smooth transient fluctuations in sensor data before feeding it into the anomaly detection models.  
In addition to synthetic data, we evaluated the proposed framework on a subset of the CASAS smart home dataset. This subset includes multivariate sensor streams such as motion and temperature from the 'Kyoto' and 'HH102' environments. The data were preprocessed to match the structure of the synthetic dataset and used to validate model generalizability.
## 2 Structural Reliability
### Mathematical Basis  
Evaluating the structural reliability involves determining the likelihood that a given structure will perform as designed, including the calculation of the failure probabilities that correspond to the limit-state functions associated with structural design. Consider a time-invariant reliability problem characterized by a vector \(\mathbf{X}\) of \(n\) basic random variables, \(\mathbf{X}=(X_{1}\),\(X_{2}\),\(X_{3}\),..., \(X_{n})^{\mathrm{T}}\) Assuming that the random variables have the joint probability density function, \(k(\mathbf{x})\), the probability failure, \(P_{f}\), of the limit-state function, \(g(\mathbf{x})\), can be defined by an \(n\)-fold integral:  
\[P_{f}=P(\mathbf{x}\in\Omega_{F})=\int_{\Omega_{F}}k(\mathbf{x})d\mathbf{x} \tag{1}\]  
where \(\mathbf{x}\) is the realization of the random vector \(\mathbf{X}\), and \(\Omega_{F}\) is the failure domain described in terms of the continuous and differentiable limit-state function, \(g(\mathbf{x})\), that indicates a violation of a design constraint. \(g(\mathbf{x})\) divides the \(n\)-dimensional probability space into the safe domain \(\Omega_{S}=\{\mathbf{x}:g(\mathbf{x})>0\}\) and failure domain \(\Omega_{F}=\{\mathbf{x}:g(\mathbf{x})\leq 0\}\). In general, it is challenging to calculate the \(n\)-fold integral in Equation (1) when the number of random variables, \(n\), is greater than 3 or 4 because no closed-form solution is available. Many methods have been developed to compute the probability integral [27], among which, the FORM and SORM [6; 12] are widely utilized to efficiently evaluate the probability integral defined in Equation (1).
# Swarm Intelligence Algorithms for Feature Selection: A Review
## 5 Swarm Intelligence for Feature Selection  
SI algorithms are a natural choice to be used for optimizing the feature subset selection process within a wrapper model approach. Wrapper models utilize a predefined ML algorithm (i.e., a classifier) to evaluate the quality of features and representational biases of the algorithm are avoided by the FS process. However, they have to run the ML algorithm many times to assess the quality of selected subsets of features, which is very computationally expensive. Thus, the SI algorithms can be used to optimize the process of FS within a wrapper model, their aim being to determine which subset of all possible features provides the best predictive performance when used with a predefined ML algorithm. In this manner, for an FS algorithm, the actual types and values of features are not important; they work well for either homogeneous or heterogeneous data that are continuous, discrete and/or mixed, or even represented as images or videos [28; 29; 30; 31; 32; 33]. As classification is the most common ML task, we use it to demonstrate the FS approach; any other ML technique can be used similarly.  
Given a predefined classification algorithm, a typical wrapper method iterates over the following two steps until the desired goal is achieved (i.e., certain accuracy is reached):  
* Select a subset of features.
* Evaluate the selected subset by performing the selected classification algorithm.  
A general framework for a wrapper model of FS for classification [27] is shown in Figure 5.  
In the next subsections, the SI algorithms are analyzed based on the SI framework from Section 2. Only those derivations of the analyzed algorithms that comprise an FS have been considered.  
Figure 4: Hits for query (“swarm intelligence” AND “feature selection”) in Google Scholar.
## 4 Experiment and Result Analysis
### Result Analysis
#### 4.3.2 Test Results  
In order to verify the performance of the proposed algorithm in practical applications, pictures of three scenarios were extracted from the data test set and the proposed algorithm and the original YOLOv7 algorithm were respectively used for testing, as shown in Figure 9.  
The results of Figure 9 show that in group (a), there are noise interference points in the background of photovoltaic cells; both models show good adaptability and no false detection occurs, but the original YOLOv7 algorithm fails to detect defect points and the proposed algorithm has higher confidence. In the (b) group of pictures, the grayscale between the photovoltaic cell defects and the background is relatively close and the original YOLOv7 algorithm also fails to detect points, which shows that the detection effect of the proposed algorithm on small targets has been improved. The scenario of group (c) is more complex, and small targets overlap. The original YOLOv7 detected one defect, but the algorithm in this paper could identify two defects, indicating that the model has strong recognition ability.  
Figure 10 is a comparison between the middle feature map of the YOLOv7 model and the improved model. It can be seen from the figure that the edge outline of defects in the feature map becomes clearer after the COT self-attention mechanism module is added. The contrast between the defect and the background photovoltaic cell in the feature map is obviously improved.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Accuracy** & **Recall** & **mAP\_0.5\%** & **mAP\_0.5\_0.95\%** & **Training** & **Test Time/s** \\  & **Rate/\%** & **Rate/\%** & **mAP\_0.5\%** & **mAP\_0.5\_0.95\%** & **Time/h** & **Test Time/s** \\ \hline YOLOv7 & 95.54 & 93.21 & 97.82 & 49 & 0.116 & 0.106 \\ YOLOv7-Tiny & 92.02 & 93.21 & 95.5 & 43.14 & 0.079 & 0.056 \\ YOLOv7x & 88.68 & 91.9 & 94.31 & 42.78 & 0.170 & 0.110 \\ Improvement YOLOv7 & 98.08 & 95.06 & 98.83 & 50.34 & 0.117 & 0.107 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison of various models.  
Figure 10: Comparison results of intermediate feature maps. The red boxes in the figure indicate the location of the defect.  
Figure 9: Picture test results. The red boxes in the figure indicate the difference between the detection results of the original YOLOv7 algorithm and the algorithm in this paper for the same defect case.
# Dropped Object Impact Analysis Considering Frequency and Consequence for LNG-FPSO Topside Module
## 2 Literature Review
Similarly, [PERSON] and [PERSON] [20] surveyed the effect of various features of the complete stress-strain curve on the predicted outcome of a collision simulation. Nonlinear finite element analysis simulations of a full-scale impact scenario revealed that the slope of the stress-strain curve determined how strains localized, and thereby when and where fracture propagated. [PERSON] and [PERSON] [21] performed drop tests to evaluate accidental impacts using the analysis system (ANSYS). The comparisons of simulation and experiment demonstrated the strong effect of mesh size, dropped object geometry definition, and material model. [PERSON] [22] analyzed structural crashworthiness under dynamic and impact loading to compute structural deformations or damage and associated reaction forces over time. The energy absorption properties of structures in accidental loads can be calculated by integrating the area below the accidental force-deformation curve. [PERSON] [23] performed a drop object study to identify the effect of dropping heavy items on the floating production storage and offloading (FPSO) hull structure. The drop object impact analyses showed that even though certain structural members were permanently deformed by drop loads, failure did not occur in accordance with the plastic strain criteria outlined in NORSOK standards. They observed that the pillars supporting the main lay-down area had not been subjected to buckling failure through the examined reaction forces. Further, the impact resistance of a deck depended on the plate thickness and the size and spacing of the supporting stringers. For future study, drop experiments on a deck model made of conventional ship-building steels and comparison with numerical findings via nonlinear finite element (FE) analysis are recommended. In addition, the examination of possible statics on the load-response relationship owing to dropped object loads is recommended, primarily to determine the extent of damage caused by the impacted versus impacting structure. Figure 1: Statistics of marine floating structure accidents in the UK sea area from 1980 to 2005. Therefore, this study focused on the impacts of dropped objects by combining frequency and consequence analyses in the design of a modern offshore platform. This paper proposes a modified QRA procedure for assessing production module protection against accidental external loads. Based on the proposed QRA procedure, frequency and consequence analyses were performed to solve the dropped object problem. An exceedance curve was plotted and a high-risk management item was derived through this process. In particular, the difference between the potential energy and impact energy according to the drop orientation was verified by simulation and experimentally. In structural design, the proposed procedure should be used to calculate the maximum impact energy. Numerical simulations were performed to verify the QRA procedure and the results for dropped objects.
## 3 Materials and Methods  
### Landsat-8 Operational Land Imager (OLI)  
Landsat-8 Operational Land Imager (OLI) surface reflectance scenes were obtained from the USGS Landsat Archive [20] distributed on four tiles (195/52, 195/53, 194/52 and 194/53) and covering the period from December 2013 to January 2015 (Figure 2). The datasets contain surface reflectances atmospherically corrected with the L8 SR algorithm [21] and complemented by cloud and cloud shadow  
Figure 1: Overview over the study area in southern Burkina Faso and northern Ghana, delineated by the intersection of four Landsat tiles (194/52, 194/53, 195/52, 195/53) with two MODIS tiles (h17v07, h17v08). Underlying land cover classification has a 250 m spatial resolution for the year 2006 (data source: [PERSON] _et al._[19]).  
masks based on CFmask [21], an open source C code version of the Fmask algorithm [22]. The masks were applied to the reflectance data prior to the calculation of NDVI (Figure 3). After a thorough inspection of the available datasets, scenes with a cloud cover of more than 40% were excluded from the analysis; thus, a total number of 38 Landsat images were used for the fusion. However, the threshold of cloud cover can be defined by the user individually based on expert knowledge of the respective study region.  
Figure 3: Workflow of the automated ESTARFM framework including the pre-processing of input data. The left box shows the pre-processing of MODIS and Landsat input data including the automatic filling of cloud gaps in the Landsat data. The upper right box displays the automatic generation of time series based on the ESTARFM framework. The lower right box summarizes the output layers of the framework. * See also Figure 4.  
Figure 2: Overview of Landsat data used in this study distributed over four tiles for the period December 2013 to January 2015. Upper part shows the weekly precipitation sums over the year 2014 averaged over three rainfall stations in the study area [23; 24].
## 4 Performance Evaluation
### Result Evaluation
#### 4.3.2 Average Throughput vs. Different Number of Nodes  
Figure 7 demonstrates a comparison between the different number nodes and the network throughputs. Notably, when the total amount of nodes rises, the throughputs of the network for the chosen MAC and T-LOHI protocols improve [57]. After a certain level of nodes, the throughputs of both CSMA and SFAMA decline slowly and decrease after a peak [58; 59]. In comparison to existing MAC protocols, the CSMA protocol interprets there being lower network traffic because it does not account for submerged space and time abnormalities. Each phase of the synchronous handshake may transfer additional communicating data packets because of SFAMA's use of several RTS requests to accomplish data packet transmission via separated strings. This is advantageous when there is a lot of traffic in SFAMA. However, the SFAMA protocol's performance falls as the number of nodes rises since more RTS attempts occur as a result of the increasing network density. The T-LOHI protocol successfully solves the significant delays brought on by submerged space and time uncertainty compared to the CSMA procedure, hence lowering collisions and retransmissions.  
Figure 7: Average network throughput vs. no. of nodes.  
Through the use of time synchronization and power management, the suggested MAC protocol prevents communication collisions by using an organizational cluster architecture. As a result, traffic on the network is drastically decreased.
# Preliminary Flutter Stability Assessment of the Double-Deck George Washington Bridge
## 2 Motion Related Wind Load  
The equation describing the motion of the bridge in the smooth flow can be expressed as:  
\[M\ddot{X}+C\dot{X}+KX=F_{ae}. \tag{1}\]  
where, in a Finite Element framework, \(M\), \(K\), and \(C\) represent the global mass, stiffness, and damping matrices, respectively; \(\ddot{X}\), \(\dot{X}\), and \(X\) are the nodal acceleration, velocity, and displacement vectors, respectively; and \(F_{ae}\) denotes the vector of nodal self-excited forces. The three degrees of freedom of the bridge deck cross-section, namely the vertical displacement \(h\), the torsional rotation \(\alpha\), and the horizontal displacement \(p\), to which correspond the aeroelastic forces of lift \(L_{SE}\), moment \(M_{SE}\), and drag \(D_{SE}\), are reported in Figure 2, where \(U\) indicates the undisturbed mean wind velocity. According to Scanlan's formulation [PERSON] (2016), the self-excited aeroelastic forces per unit deck length can be expressed as a linear function of the displacement and velocity parameters of the deck as follows:  
\[L_{SE}=\frac{1}{2}\rho U^{2}B\left[KH_{1}\frac{\dot{h}}{U}+KH_{2}\frac{B\; \dot{\alpha}}{U}+K^{2}H_{3}\alpha+K^{2}H_{4}\frac{h}{B}+KH_{5}\frac{\dot{p}} {U}+K^{2}H_{6}\frac{p}{B}\;\right], \tag{2a}\] \[D_{SE}=\frac{1}{2}\rho U^{2}B\left[KP_{1}\frac{\dot{p}}{U}+KP_{2}\frac{B\; \dot{\alpha}}{U}+K^{2}P_{3}\alpha+K^{2}P_{4}\frac{p}{B}+KP_{5}\frac{\dot{h}}{ U}+K^{2}P_{6}\frac{h}{B}\;\right],\] (2b) \[M_{SE}=\frac{1}{2}\rho U^{2}B^{2}\left[KA_{1}\frac{\dot{h}}{U}+KA_{2}\frac{B \;\dot{\alpha}}{U}+K^{2}A_{3}\alpha+K^{2}A_{4}\frac{h}{B}+KA_{5}\frac{\dot{p}} {U}+K^{2}A_{6}\frac{p}{B}\;\right], \tag{2c}\]  
where \(\rho\) is the air density, \(K=\frac{\omega B}{U}\) is the reduced circular frequency, and \(H_{i}(K),\ P_{i}(K),\ A_{i}(K)\) are the flutter derivatives. In most practical applications, \(p\), \(\dot{p}\), and \(P_{i}\) (\(D_{SE}\)) can be ignored. As mentioned in the Introduction, the most reliable way to calculate flutter derivatives is based on experimental tests made in the wind tunnel. For the bridge chosen as a case study, no data regarding flutter derivatives were found in the literature, therefore a simplified approach was followed for their calculation. It was decided to utilize a  
Figure 2: Three-degrees-of-freedom section model. formulation developed by [PERSON] (1986) for the thin airfoil because it allows one to input the slopes of the steady-state aerodynamic coefficients, and, unlike the quasi-steady approach, it provides a unique formulation for the determination of the derivatives \(H_{2},\ A_{2}\).
###### Abstract
To address the inner parameter perturbations and external disturbances of PEMFC power supply, the backstepping SMC integrated with RBFNN estimation controller of PEMFC power conditioning is proposed. The main works and innovations of this paper are summarized as follows: (1) The proposed PEMFC-fed DC/DC buck converter power supply can be applied to meet low voltage requirements, such as air conditioning and charging ports, and the input voltage of DC/DC buck converter is coupled with load current, so it is not modeled as a state variable. (2) To settle the chattering phenomenon as well as system uncertainties, the RBFNN adaptive law-based backstepping scheme was proposed. (3) RBFNN approximation was developed to estimate system uncertainties as well as external disturbances. Compared with conventional adaptive estimator, it has the characteristic of approximating any continuous function. In addition, the estimated accuracy can be enhanced. The rest of the paper is organized as below. Following this part, PEMFC power supply modeling is presented in Section 2. Current as well as voltage adaptive controls of the power supply are developed in Section 3. Simulations via MATLAB/Simulink and their discussions are described in Section 4. To validate the analytical results, experiments on the prototype of PEMFC-fed DC/DC buck converter through dSPACE (DS1104) controller board were carried out, and the experimental results are given in Section 5. Finally, the conclusion is summarized in Section 6.