esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulations by Finite Element Method  
### Model Definition & Simulation Procedures  
In this study, a beam assembly sample, made of A36 carbon steel and including induced micro-cracks, is used, as shown in Figure 1. In order to create micro-cracks in the sample, a mechanical oscillator is attached to the free end of the sample to apply a cyclic load. As shown in Figure 1, the mechanical oscillator produces cyclic excitation load by a rotation of an un-balanced disk driven by an electrical motor. Such a load is applied until visibly large micro-cracks are created near the welds at T-joints.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input** & **Primary Wave** & **Second Harmonic Wave** \\ \hline \(A\) & \(U_{L}\) & \(U_{NL}\) \\ \hline -\(A\) & -\(U_{L}\) & \(U_{NL}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The sign of the displacement field.  
For numerical studies, this beam assembly is modeled in the two-dimensional finite element method (FEM) simulations, using _COMSOL Multiphysics 5.6._ Figure 2 shows the dimensions of the beam and the transducer locations. For simplicity, the material microstructural defect density is initially assumed to be uniform across a joint as shown in Figure 1, and the corresponding local material nonlinearity is applied over 200 mm\({}^{2}\) across that joint. The material properties of the beam are summarized in Table 2.  
The linear chirp input from 20 kHz to 110 kHz with 0.5 ms duration is injected in the middle (center of mass) of the beam. The initial displacement amplitude, \(A\), varies as 0.15, 0.3, 0.6, and 1.2 \(\upmu\)m, respectively. The maximum mesh size is \(1/10\) of the smallest expected guided wave length (1.76 mm), and 0.2 \(\upmu\)s is chosen as the time step [23]. Three different local material nonlinearities are assigned in the area marked in yellow in Figure 2, using Murnaghan coefficients \(m\) and \(l\). The three different local material nonlinearities are denoted as \(\beta_{0}\), \(\beta_{1}\), and \(\beta_{2}\), respectively, and the corresponding Murnaghan coefficients are listed in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Density (kg/m\({}^{3}\))** & **Young’s Modulus (GPa)** & **Poisson’s Ratio** \\ \hline AISI 4030 & 7850 & 205 & 0.28 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties of the beam used in the simulations.  
Figure 1: A test beam assembly with a mechanical oscillator to create micro-cracks near T-Joints.  
Figure 2: Side view of the beam assembly modeled in the numerical simulations, where locations and dimensions of local changes in the material nonlinearity are given. (Dimensions in meters).
## 5 Results and Discussion
### Model Comparison
Concurrently, the LSTM univariate can identify all the IOC faults it encounters, and the TSF and k-NN single feature models can identify 98.3% and 89.8%, respectively, of all the faults from the set. The multivariate models follow the trend of poor performance it showcased in the OC fault before, with the LSTM model having the ability to recall only 40%, TSF only 35.7%, and k-NN only 28.6% of the relevant instances. The consolidated results for IOC are shown in Table 15.  
The TSF univariate model achieves 100% precision, recall and F1-scores for identifying jamming faults, indicating that the predictions had zero false-positive cases while simultaneously successfully managing to identify all the jamming faults it came across. The LSTM single feature provides the next best performance across all three metrics with precision, recall and F1-score of 77.1%, 90% and 83.0%, respectively. The k-NN univariate and the multivariate models have relatively poor performance, with k-NN univariate the best among the rest with a precision of 61.6%, recall of 42.1%, and F1-score of 49.3%. Out of the multivariate models, the LSTM has the highest recalling ability of 60% against the 51.3% provided by TSF and 12.5% provided by k-NN, while TSF offers better precision (31.9%) when compared to LSTM (20%) and k-NN (20%). The k-NN multivariate model offers the least performance with precision, recall and F1-score of 20%, 12.5% and 15.4%, respectively. Table 16. displays the model's performance for the jamming fault classification.
# RHLP-18T: A Radiation-Hardened 18T SRAM with Enhanced Read Stability and Low Power Consumption
## 3 Simulation Results and Analysis
### Stability Comparison  
The Static Noise Margin (SNM) is a key measure of how stable a memory cell is against DC noise. It tells us the minimum DC noise voltage needed to flip the cell's stored state. The Static Noise Margin (SNM) is evaluated in three operating modes: hold (HSNM), read (RSNM), and write (WSNM).
## 1 Introduction  
The widespread adoption of 5G-enabled IoT devices in smart homes has improved convenience and automation but also raised critical concerns about cybersecurity, privacy, and latency. Traditional centralized security systems, primarily cloud-based intrusion detection systems (IDS), face challenges such as high latency, bandwidth constraints, and potential exposure of sensitive user data [1].  
Federated learning (FL) has emerged as a promising paradigm to address these issues. It allows for the decentralized training of machine learning models directly on distributed edge devices, thereby avoiding raw data transmission and preserving privacy. Recent works such as FedGroup in smart home environments demonstrate the efficacy of group-based FL approaches, achieving comparable performance to centralized models while maintaining data minimization [2].  
At the same time, anomaly detection remains critical for identifying cyber threats in IoT systems. Several surveys report that lightweight and real-time anomaly detection methods--particularly those based on autoencoders, LSTM, or transformer networks--are gaining traction in industrial, smart city, and smart home applications [3]. One recent study showcased high-accuracy LSTM-based federated anomaly detection in smart buildings, emphasizing the reduction of communication latency and improved convergence time [4].  
However, many existing solutions remain unoptimized for resource-constrained edge devices and the unique characteristics of 5G networks. While models like FLiForest demonstrate scalable unsupervised anomaly detection with sub-160 KB memory footprints [5], these are not tailored for heterogeneous smart home deployments with diverse devices and fast 5G uplinks.  
To bridge these gaps, we propose Edge-FLGuard+, a federated learning anomaly detection framework designed specifically for 5G-enabled smart homes. Our contributions are:  
1. A lightweight edge AI model (e.g., autoencoder or GRU-based) optimized for device heterogeneity.
2. A federated learning protocol using FedAvg, enhanced with group-level device clustering to reduce communication.
3. Rigorous benchmarking using public datasets (TON_IoT and CIC-IDS2018), with ablation on latency, detection accuracy, model size, and communication overhead.  
Our experiments reveal that Edge-FLGuard+ achieves \(\geq\) 95% detection performance, with 50-70% less communication overhead compared to centralized FL baselines. This validates its suitability for real-world smart home settings.  
The remainder of the paper is structured as follows: Section 2 reviews related work; Section 3 details our system architecture; Section 4 presents our methodology, dataset processing, and model design; Section 5 reports experimental results and evaluations; and Section 6 concludes with future directions.
## 4 Discussion  
This study focused on the plasma concentration trends of novel cardiovascular biomarkers in patients with severe AS in relation to the presence or absence of PH as determined by echocardiography. In fact, there are only few comparative studies in the literature investigating biomarker trends in severe AS patients with additional PH.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**sPAP \(\geq\) 40 mmHg**} & \multicolumn{2}{c}{**Univariate**} & \multicolumn{2}{c}{**Multivariate**} \\ \hline  & **Hazard Ratio (95\%)** & \(p\)**-Value** & **Hazard Ratio (95\%)** & \(p\)**-Value** \\ \hline Gender (male) & 0.963 (0.400—2.316) & 0.933 & & \\ Weight & 0.980 (0.924—1.039) & 0.490 & & \\ Height & 1.032 (0.940—1.133) & 0.507 & & \\ BMI & 0.862 (0.709—1.048) & 0.135 & & \\ NYHA & 1.756 (0.525—5.876) & 0.361 & & \\ STSScore & 1.498 (1.038—2.163) & 0.031 & 1.357 (0.794—2.317) & 0.264 \\ Diabetes mellitus & 1.282 (0.455—3.616) & 0.639 & & \\ Arterial Hypertension & 1.369 (0.454—4.125) & 0.577 & & \\ Cardiovascular Disease (all) & 1.129 (0.460—2.771) & 0.791 & & \\ CVD—1 vessel & 0.822 (0.237—2.846) & 0.757 & & \\ CVD—2 vessels & 1.719 (0.421—7.015) & 0.451 & & \\ CVD—3 vessels & 1.161 (0.408—3.303) & 0.780 & & \\ Myocardial infarction & 1.860 (0.185—18.684) & 0.598 & & \\ Atrial fibrillation & 5.793 (1.782—18.834) & 0.003 & 9.361 (1.175—74.576) & **0.035** \\ Malignancy & 1.045 (0.363—3.007) & 0.935 & & \\ Stroke & 1.007 (0.224—4.530) & 0.993 & & \\ PAD & 4.717 (0.553—40.265) & 0.156 & & \\ COPD & 0.789 (0.165—3.777) & 0.767 & & \\ LVEF & 0.963 (0.915—1.014) & 0.152 & & \\ LVEDD & 1.085 (0.504—2.335) & 0.835 & & \\ IVSd & 1.074 (0.864—1.335) & 0.518 & & \\ AV Vmax & 1.323 (0.553—3.163) & 0.529 & & \\ AV dPmean & 1.031 (0.989—1.075) & 0.149 & & \\ AV dPmax & 1.013 (0.989—1.037) & 0.299 & & \\ TAPSE & 0.853 (0.744—0.977) & 0.022 & 0.698 (0.529—0.922) & **0.011** \\ AVI \(\geq\) II & 0.696 (0.204—2.372) & 0.562 & & \\ MVI \(\geq\) II & 1.616 (0.645—4.049) & 0.306 & & \\ TVI \(\geq\) II & 4.725 (1.439—15.515) & 0.010 & 3.333 (0.616—18.020) & 0.162 \\ Creatinine (baseline) & 2.548 (1.203—5.395) & 0.015 & 0.855 (0.187—3.902) & 0.839 \\ BNP (baseline) & 8.402 (1.878—37.596) & 0.005 & 1.130 (0.146—8.738) & 0.907 \\ cTnI (baseline) & 3.907 (1.018—14.998) & 0.047 & 0.852 (0.151—4.817) & 0.856 \\ Hkt (baseline) & 1.144 (0.654—2.001) & 0.638 & & \\ Hb (baseline) & 0.602 (0.374—0.969) & 0.037 & 1.833 (0.544—6.174) & 0.328 \\ CK (baseline) & 1.036 (0.671—1.600) & 0.872 & & \\ sST2 (baseline) & 2.080 (1.223—3.537) & 0.007 & 6.021 (1.793—20.219) & **0.004** \\ GDF-15 (baseline) & 1.475 (0.901—2.414) & 0.122 & & \\ H-FABP (baseline) & 2.964 (1.208—7.270) & 0.018 & 0.503 (0.081—3.120) & 0.460 \\ IGF-BP2 (baseline) & 2.273 (0.955—5.413) & 0.064 & 1.799 (0.274—11.812) & 0.541 \\ suPAR (baseline) & 1.518 (0.904—2.548) & 0.114 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._
## 3 Results and Discussion
### Electronic Structure: Magnetic Moments and DOS
Thus, a total spin magnetic moment of 2 \(\mu_{\rm B}\) was observed in the compound TiZrCoIn. The total magnetic moments followed the Slater-Pauling rule: M\({}_{\rm t}\) = \(Z_{\rm t}\) - 18, where M\({}_{\rm t}\) represents the total spin magnetic moments in a unit cell, and Z\({}_{\rm t}\) is the number of the total valence electron. Figure 1: Total density of states (DOS) of the ordered TiZrCoIn quaternary Heusler alloy (shadowed area) and the doped systems (solid line). Note that all DOS are transited to a primitive cell. Notice that x in the figure is the doping concentration. Next, we will discuss the mixed compounds in the series TiZrCoIn\({}_{1-x}\)Ge\({}_{x}\) in detail, to look for the compounds with better half-metallic stability. As shown in Figure 1, all the doped systems showed half-metallicity. The bonding states moved slightly to the low energy area. The higher the doping concentration is, the more pronounced the movement and the wider the band gaps are. The band gaps further widened from 0.914 eV to 1.13 eV at doping concentrations of 3.125%-100%. However, the Fermi levels shifted slightly from the middle of the gap to the edge of the gap. The bonding states in the spin-down channel of doped cases primarily originated from the transition metal Co with high valence, as shown in Figure 2. Meanwhile, the antibonding d states primarily came from the transition metal Ti with low valence. The d orbitals of Ti and Co atoms hybridized initially because of their identical symmetries. Then, the creating bonding d states in turn hybridized with the d orbitals of the Zr atoms. The outlines of the d states near the Fermi levels of Ti, Zr, and Co were nearly identical, especially those of Ti and Co. Therefore, Ti, Co, and Zr had intense interactions. After the addition of the doped element Ge, the number of sp valence electrons of the parent compound increased, and the increase indirectly enhanced d-d hybridization. The d-d hybridization dominated the half-metallicity of the compounds. Nonetheless, increase in total spin moment and energy band gap depend on the sp atoms. This dependence plays a substantial role in the RKKY exchange interaction. Figure 3: Schematic of the d-d hybridization of the ordered TiZrCoIn quaternary Heusler alloy. Figure 2: Partial density of states (PDOS) for the ordered TiZrCoIn quaternary Heusler alloy and the doped systems. The orange line is the atomic PDOS for Ti atom, the blue line is for the Zr atom, the black line is for the Co atom, the magenta line is for the In atom, and the olive line is for the Ge atom. Notice that x in the figure is the doping concentration.
## 3 Methodology
### Reconstructing Graph with Enhanced Affective Knowledge  
To effectively utilize the affective information between the context and aspect, we enhance the representation of the adjacency matrix by leveraging the affective score from SenticNet [31]:  
\[S_{i,j}=SenticNet(w_{i})+SenticNet(w_{j}) \tag{2}\]  
where \(SenticNet(w_{i})\) is the affective score from the SenticNet sentiment lexicon. It is worth noting that if the affective score of \(w_{i}\) is 0, then \(w_{i}\) is neutral or not in the SenticNet sentiment lexicon. We can then extract the affective dependencies from the two dependent nodes, and the sentiment information of the two dependent nodes depends on the sum of their affective scores. Samples of concepts and their corresponding affective scores are listed in Table 1.  
Further, to enhance the dependency relationship between the context and the aspect, consider whether the two nodes where the dependency exists contain aspectual words:  
\[T_{i,j}=\left\{\begin{array}{ll}1,&w_{i}\quad\text{or}\quad w_{j}\quad\text{is} \quad\text{a}\quad\text{aspect}\quad\text{word}\\ 0,&\text{otherwise}\end{array}\right. \tag{3}\]  
Next, we can get the affective-knowledge-enhanced adjacency matrix \(D_{i,j}\) of the sentence:  
\[D_{i,j}=A_{i,j}\times(S_{i,j}+T_{i,j}+1) \tag{4}\]  
Algorithm 1 outlines the procedure for generating the affective adjacency matrix for each sentence.  
```
0: a sentence \(W^{c}=\{w_{1}^{c},w_{2}^{e},\cdots,w_{n}^{c}\}\); a aspect \(W^{a}=\{w_{1}^{a},w_{2}^{a},\cdots,w_{m}^{a}\}\); the dependency tree of the sentence \(dependency(W^{c})\); a collection of sentiment words generated by SenticNet.
1:for\(i=1\to n\)do
2:for\(j=1\to n\)do
3:if\(dependency(w_{i}^{c},w_{j}^{c})\in dependency(dependency(W^{c}))\) or \(i=j\)then
4:\(A_{i,j}\gets 1\)\(\triangleright\) Generated by dependency tree
5:\(S_{i,j}\leftarrow SenticNet(w_{i}^{c})+SenticNet(w_{j}^{c})\)\(\triangleright\) Enhanced by SenticNet
6:if\(w_{i}^{c}\in W^{a}\) or \(w_{j}^{c}\in W^{a}\)then
7:\(T_{i,j}\gets 1\)
8:else
9:\(T_{i,j}\gets 0\)
10:endif
11:\(D_{i,j}\gets A_{i,j}\times(S_{i,j}+T_{i,j}+1)\)
12:else
13:\(D_{i,j}\gets 0\)
14:endif
15:endfor
16:endfor
```  
**Algorithm 1** The generation of the affective adjacency matrix for each sentence.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Concept** & **Intensity** \\ \hline smile & 0.997 \\ wonderful & 0.805 \\ everything & 0.099 \\ hour & 0.093 \\ sad & \(-\)0.910 \\ uncomfortable & \(-\)0.900 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Samples of affective words in SenticNet.
# Low-Resolution Target Detection with High-Frequency Information Preservation
## 2 Related Work
### Small-Object Detection  
Small-object detection is one of the most challenging tasks in object detection [15]. It features the limited coverage area and low spatial resolution of small objects, causing a loss of high-frequency information and undesirable performance. To enhance feature representation for small objects, [PERSON] et al. [18] proposed the feature pyramid network (FPN) to associate features with various scales with different pyramid levels. However, the FPN just aggregates feature maps across different levels of the network without taking into account specific information from the current layer, leading to a disregard for the contextual relationship between anchors. The DSFD model [19] combines the low-level FPN from the PyramidBox model with the receptive field block (RFB) from the RFBNet model, and the feature enhancement module (FEM) was proposed to perform feature enhancement within the same receptive field. Another limitation of the FPN is feature misalignment and information loss in deeper layers, which is particularly problematic for small-object detection. One solution is to integrate multiscale feature fusion or advanced feature alignment mechanisms. [PERSON] et al. [20] proposed IPGNet, which uses image pyramids to extract spatial and textural details from shallow layers. These are then fused with deep-layer features to improve representation, especially for small objects, mitigating feature misalignment and loss.  
Despite the effectiveness of previous approaches in improving detection accuracy, a common challenge is the loss of high-frequency information, especially in detecting small objects. To address this issue, we propose a high-frequency preservation block to counter downsampling effects like pooling and stride. This block minimizes high-frequency loss  
Figure 2: Overview of the proposed high-frequency information preservation (HFIP) block. The HFIP block has three components for processing a given feature map \(f\): (**a**) The dilated frequency-aware channel correlation (DFACC) component encodes multifrequency information and improves high-frequency encoding for successful downsampling with maximal preservation of task-relevant, high-frequency features. (**b**) The decoupled high-frequency learning (DHFL) component addresses high-frequency information loss from interval sampling by decoupling and processing high-frequency and low-frequency information separately. (**c**) The linear fusion (LF) component combines the spatial and frequency domain features to create the downsampled feature maps (i.e., the output) with richer high-frequency details for better low-resolution target detection performance.  
during feature extraction with minimal computational overhead, enhancing the network's ability to detect small and low-resolution objects. Our approach is versatile and applicable to various networks and tasks, promising improved object detection performance.
# Helicopter Takeoff and Landing Point Location in Cities for Emergency Services
## 3 Data Processing Using ArcGIS and Methods
### Data Processing
#### 3.2.4 Feature Layers Filtering  
The first three layers are comprehensively superimposed and erased, and the slope and undulation terrain that meet the requirements are selected. Areas that affect flight safety are filtered to obtain the take-off and landing point location layer. We set constraints (S \(\geq\) 85 m\({}^{2}\)) in the location layer, selected locations that met the conditions and output the geographic coordinates of their centers. There were 56 spots and all met the requirements of terrain undulation, visibility and safe landing dimension. The results can partly be found in Table 4.
# The Ground-Based Absolute Radiometric Calibration of the Landsat 9 Operational Land Imager
## 2 Methodology
### Automated Measurements: The Radiometric Calibration Test Site (RadCaTS)
#### 2.2.7 Determination of TOA Spectral Radiance and TOA Reflectance  
Once the hyperspectral surface BRF has been determined at the RadCaTS for the date and time of interest, the TOA spectral radiance and reflectance are determined using one final run of the radiative transfer code. The resulting output is the hyperspectral TOA spectral radiance (and reflectance, if applicable), which is then band-averaged to the bands of the sensor under testing. In the final step, these values are compared to the sensor imagery, which is downloaded from the USGS Earth Explorer web portal in the case of Landsat. An example of the hyperspectral TOA spectral radiance determined at the RadCaTS for the Landsat 9 overpass on 24 June 2022 is shown in Figure 8, and a summary of the processing scheme is shown in Figure 9.  
Figure 8: TOA spectral radiance at RadCaTS on 24 June 2022 at 18:20 UTC for the Landsat 9 OLI viewing conditions.
# A Review of Machine Learning and IoT in Smart Transportation
## 2 Machine Learning and IoT in Smart Transportation
### Machine Learning Algorithms
#### 2.1.9 Regression Analysis  
Methods in the regression analysis category try to construct mathematical models able to describe, or identify, the relationship between two or more variables. The dependent or response variable is the system's output. By fitting a regression model to the system, the relations between the dependent variable and the independent ones (regressors) can be discovered.  
We have already discussed some of the methods used in [23] to perform traffic congestion predictions. Logistic Regression is compared to a decision tree, an RF, an MLP, and an SVM algorithm. Since the input data are time dependent, the Logistic Regression in this research clearly outperformed the other methods giving 100% Precision, 99.5% recall, and 99.9% Accuracy. Additionally, the small complexity of the Logistic Regression renders it capable to be executed by low end devices according to the authors. Logistic regression is a form of regression analysis, used to describe the relationship of a dependent variable and one or more independent variables. Logistic regression is applicable when the dependent variable has binary values. Since it is a regression analysis, the logistic regression is suitable for prediction tasks in ML.