esa-sceva/satcom-chunk-collection huggingface dataset:
# Generative AI-Enabled Energy-Efficient Mobile Augmented Reality in Multi-Access Edge Computing
## 3 System Model
### Analytical Models
#### 3.2.1 SR Latency Analytics  
As observed, SR is a type of GAI technology that uses neural networks to upscale low-resolution images to high-resolution images. While SR enhances the accuracy of AR services by providing higher-resolution images, it also imposes a significant computational burden, which can increase the overall latency in MAR. Understanding the relationship between SR latency and other parameters is crucial for optimizing the efficiency of MAR services.  
SR latency is significantly affected by the SR model used, the size of the input image (or its source resolution), and the available computing resources. Given that computing resources are limited and a more advanced SR model demands additional resources, the source resolution becomes the key parameter for influencing the SR latency. To optimize the SR computation latency, it is essential to understand how the resolution of the source image impacts this latency. Figure 3 illustrates the empirical results on how input image resolution influences SR computation latency. In this study, we employed two SR models: EDSR, based on the ResNet architecture, and SRGAN, based on the GAN framework. The \(Urban100\) dataset from [26] was used for experimentation. As shown in Figure 3, increasing the resolution of the source image leads to a higher SR computation latency, following a consistent pattern across both SR models for the same upscaling factor. This allows us to define the relationship between the input image resolution \(R\), denoted as the number of pixels per height, and the SR computation latency \(l_{SR}\) (ms). Here, \(l_{SR}\) increases as a function of \(R\) because the computational workload grows with \(R\). However, due to the complexity of analytically determining this relationship, a data-driven approach is more practical. This involves using offline training with empirical data to model the relationship. We employed regression-based modeling, a common technique seen in areas like mobile CPU property modeling, including CPU power and temperature variations [21]. Thus, we conclude that \(l_{SR}\) can be modeled as  
\[l_{SR}=\alpha R^{2}+\beta R+\gamma, \tag{1}\]  
where \(\alpha,\beta,\gamma\) are hyperparameters decided by the SR model. This equation (Equation (1)) makes quantifying the latency of SR computation possible via input image resolution.
## 5 Methodology
### Features Selection
#### 5.4.2 Attribute Evaluators  
Of the available feature selection methods in WEKA, the two most frequently employed will be chosen:  
* Wrapper methods: employing the ClassifierAttributeEval routine within WEKA will permit evaluation or certain approaches. The predictors below will be executed.
* Linear Regression: This allows for swift computation, with coefficients being fixed for all features.
* Random Forest: As previously stated, this is a tree-based algorithm frequently employed for classification.
* Multilayer Perceptron (MLP): This algorithm makes an estimation of the relative contributions of input units (which represent the attributes) and the output neurons (those which correspond with the problem classes) and uses the information to identify a subset of pertinent usable attributes to be employed in supervised pattern classification [63].  
Instance-Based k-nearest neighbor algorithm (IBk) [64]: this is a K-nearest neighbor classifier that selects a suitable value for K on the basis of CV; it can also perform distance weighting.  
Filter Methods. For univariate methods, we will employ the predictors listed below.  
Relief Attribute (Rlf) [65]: Relief feature selection works on the basis of creating a score by identifying feature value differences for nearest neighbor instance pairs.  
Principal Component Analysis (PCA) [66]: With this method, we introduce a novel set of orthogonal coordinate axes, simultaneously maximizing sample data variants. This makes other directions with more minor variants have less significance and so they can be cleaned from the dataset. PCA is extremely effective in transforming data at lower dimensions and can also show us simplified underlying data patterns.
## 2 Materials and Methods
### Data Analysis  
For demographic data, between-group differences (PD with (PDCI) and without CI (PD non-CI), and controls) were examined using analysis of variance (ANOVA) and post hoc tests (Tukey's HSD). For clinical data, between-group differences (PDCI and PD non-CI), time living with PD, levodopa equivalent (medication), and MDS-UPDRS were examined using t-tests, assuming equal variances and independent observations. These assumptions were examined using QQ plots, box plots, and histograms.  
For CPET data, differences between groups were evaluated using linear regression models while adjusting for gender, age, height, time living with PD, levodopa equivalent, and body mass index (BMI). HR and metabolic data from boxing sessions were fitted for both completed and non-completed boxing rounds within the same linear mixed-effects model to account for repeated measures. However, model fit statistics were poor due to zero inflation which resulted in a non-normal distribution. To overcome this, we fitted models to the completed rounds only. Fixed effects included group, session, and rounds. Adjustments were made for the same covariates as the CPET data model, with the inclusion of VO2 peak (from CPET) as an additional covariate. The random effects (session and group) accounted for individual variability in the intercept across different sessions for each participant. Data were plotted to assess normality assumptions of residuals.  
Figure 1: Each boxing round had a total duration of 4 min and 30 s, divided into three bouts of 80 s of boxing and 15 s of passive recovery (standing). During each round, participants were reminded of the following combinations. Combination A: jab, cross, jab, rear hook, 2x skip; Combination B: jab, jab, cross, lead hook, 4x skip; Combination C: jab, rear hook, jab, cross, 2x duck; Combination D: 4x jab, cross, 2x skip, 4x hook, 2x skip; Combination E: 4x jab, cross, 4x skip, 4x hook, 4x skip; Combination F: 4x jab, cross, 2x duck, 4x hook, 2x duck; The 2 min interval represents passive recovery (sitting).  
ANOVA was conducted to assess the significance of independent variables in regression models. Pairwise comparisons between different levels of the 'Group' factor were also conducted. For boxing data, comparisons were performed within each combination of 'round' and'session'. Estimated means are reported with standard errors (SE) and 95% confidence intervals. The level of significance was set at \(p\leq 0.05\). Data analysis was performed using R software (version 2024.12.0+467) for statistical computing [53].  
Although this was an exploratory study, we estimated sample size prior to data collection with HRmax as the primary outcome. Using HRmax (PD 132 \(\pm\) 20, control 158 \(\pm\) 13) reported by [PERSON] et al. [54] and CI prevalence data of around 40-50% [31,34], and taking \(\alpha\) < 0.05 with 80% power, a sample of 16 PD participants was required.
# Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models
## 3 Queueing Models
### M/M/1 Model  
This queue is used to model uninterrupted service of messages (transmission, reception or processing) as part of an (end-to-end) IoT interaction. It corresponds to the classical M/M/1 queue (Figure 1a) where Poisson arrivals are serviced by a single server for an exponentially distributed service time.  
An M/M/1 queue (\(q_{\text{m/m/1}}\)) can be defined via the tuple:  
\[q_{\text{m/m/1}}=(\lambda,\mu). \tag{1}\]where \(\lambda\) is the message arrival rate to the queue and \(\mu\) is the service rate of messages. Let \(D=1/\mu\) be the service demand for the processing delay (service time) of a message. On the basis of [14], the time that a message remains in a M/M/1 system (corresponding to queueing time + service time; we also call it mean response time) is determined via  
\[\Delta^{\text{m/m/1}}\;=\;\frac{D}{1\;-\;\lambda D}. \tag{2}\]
## 3 Experimental System  
### The MWe Scale sCO\({}_{2}\) Compressor Test Loop  
A general experimental platform for the MWe sCO\({}_{2}\) compressor was built by CAS (Chinese Academy of Sciences). The main performance indexes of the experimental platform  
Figure 4: Schematic layout of the single-stage compressor.  
Figure 5: Schematic figure of the rotor (**left**) and impeller (**right**).  
are shown in Table 4. Its power system has a capacity of 40,000 RPM and 800 kW maximum power. Its test system allows a maximum of 25 kg/s flow, and the inlet experimental condition in the range of 0-50 \({}^{\circ}\)C and 0-10 MPa covers the test environments of different inlet states, such as the supercritical phase, critical phase, gas phase and liquid phase.  
The general experimental platform for the MWe sCO\({}_{2}\) compressor, as shown in Figure 6, includes the compressor test unit, sCO\({}_{2}\) filling system, measurement and lubrication system and cooling system.  
The compressor test unit is the core of the general experimental platform, including compressor and gearbox, rotor, pipe heater and buffer tank. The sCO\({}_{2}\) filling system prepares, stores and replenishes sCO\({}_{2}\) for compressor experiments. The lubricating system provides suitable lubrication oil for high-speed equipment (compressor and high-speed gear box). The cooling unit provides cooling water for the equipment in the experimental platform.  
Figure 7 shows the 3-D graphic model of the experimental platform. The outdoor area of the experimental platform is the auxiliary system which mainly includes the sCO\({}_{2}\) filling system and part of the cooling system. The indoor area is the testing section which is mainly used for placing the compressor test system and lubricating oil system.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance Index** & \multicolumn{2}{c}{**Test Capability**} \\ \hline Experimental speed & 0–40,000 & RPM \\ Experimental mass flow & 0–25 & kg/s \\ Experimental power & 0–800 & kW \\ Compressor inlet pressure & 0–10 & MPa \\ Compressor outlet pressure & 0–22 & MPa \\ Compressor inlet temperature & 0–50 & \({}^{\circ}\)C \\ Compressor outlet temperature & 0–100 & \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 4: Main performance of experimental platform for MWe sCO\({}_{2}\) compressor.  
Figure 6: General experimental platform for the MWe sCO\({}_{2}\) compressor.  
As shown in Figure 8, the compressor test system is sketched, including the compressor, sCO\({}_{2}\) tank, electric heater, buffer tank, water cooler, surge tank, inlet pressure relief valve (EV007), outlet regulating valve (EV015), system vent valve (EV017), compressor inlet vent valve (EV010) and compressor outlet surge prevention valve (EV014).
# Enhancement of Yaw Moment Control for Drivers with Excessive Steering in Emergency Lane Changes
## 3 Control System Configuration  
Configuration of the control system in this study is depicted in Figure 3. It consists of three modules: the Reference Model Module, Control Law Module, and 4-Wheel Torque Distribution Module. The Reference Model Module provides the reference state \((\beta_{ref},\gamma_{ref})\) and reference steering angle \(\delta_{def}\), which are used by the Control Law Module to implement the schemes of saturation angle \(\delta_{sat}\) and enhanced yaw moment \(M_{z}\). These schemes work together to ensure the front-wheel steering angle \(\delta_{f}\) and vehicle state \((\beta,\gamma)\) closely follow their references. Additionally, this collaborative effort maintains tire/road friction within the linear operation range and allows the DYM to focus on other critical stabilization tasks, especially when drivers overreact with excessive steering. Finally, the Torque Distribution Module generates 4-wheel torques \(T_{j}\), \(j=1,2,\ldots,4\), to implement the enhanced yaw moment. More details are provided in the subsequent sections.
# Spatiotemporal Prediction and Proactive Control Method for Excavation-Induced Wall Deflection
## 4 Results  
This section first shows the predictive performance of the proposed ST-GAT neural network for wall deflections induced by excavation, highlighting how the model uses spatial relationships between adjacent monitoring points. It then explores the effect of adjacent wall deflection and axial force on the model performance, demonstrating its potential to capture the interdependence of different monitoring points. A comparative analysis of alternative models follows, emphasizing the advantages of the ST-GAT. The section concludes with an interpretable analysis that further validates the effectiveness of the proposed model along with the introduction of an intelligent control strategy for the proactive control of wall deflection during excavation.
# Provably Secure ECC-Based Anonymous Authentication and Key Agreement for IoT
## 4 The Proposed Protocol
### Initialization Phase  
TA generates and releases parameters for the system as follows:  
\begin{table}
\begin{tabular}{l l} \hline
**Notation** & **Description** \\ \hline \(SM_{i}\), \(ID_{SM_{i}}\) & \(i^{th}\) smart meter and its identity \\ \(E_{q}(a,b)\) & A non-singular elliptic curve \\ \(P\) & A base point of \(E_{q}(a,b)\) \\ \(t\), \(T_{pub}\) & Private-public key pair of \(TA\)[24] \\ \(SK_{ij},SSK_{i}\) & Session key \\ \(\oplus\), \(\parallel\) & Bitwise XOR and concatenation operations \\ \(TS\) & Timestamps \\ \(\Delta T\) & Maximum transmission delay \\ \(h(\cdot)\) & One-way hash functions \\ \(S\),\(SP\) & End device, Server \\ \(k/K\) & Private/public key of a entity \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._TA1: TA selects an elliptic curve \(E(a,b)\) over finite field \(F_{q}\) with a base point \(P\); TA2: Then, TA picks \(h(\cdot)\) as the collision-resistant one-way hash function; TA3: TA issues \(\{(E(a,b),p,q,P,h(\cdot)\}\) publicly.
# Optimal Partitioning of Unbalanced Datasets for BGP Anomaly Detection
## 5 Results Analysis
### Comparison with Other Recent Approaches  
Table 11 illustrates that the proposed OP-ELM model is markedly better than the known state-of-the-art methods in terms of accuracy as well as the F1-score. The proposed method had an extraordinary performance, with 94.10% accuracy and a 92.00% F1-score, with all the recent benchmark models improving by a great extent.  
It is at least 11.19 percentage points more accurate than the Attention-Based Convolutional Long Short-Term Memory (ConvLSTM) Autoencoder with Dynamic Thresholding (ACLAE-DT) (82.91% vs. 94.10%), 12.46 percentage points more accurate than the Deep Convolutional Autoencoding Memory Network (CAE-M) (81.64% vs. 94.10%), and 14.09 percentage points more accurate than Multivariate Time-Series Anomaly Detection via the Graph Attention Network (MTAD-GAT) (80.01% vs. 94.10%). Likewise, with regard to the F1-score evaluation, the proposed method outperformed the ACLAE-DT (81.93%  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classifier** & **Training Time (in sec.)** & **Detection Time (in sec.)** \\ \hline Random Forest & 4.36 & 0.0271 \\ RF + SMOTE & 5.12 & 0.0284 \\ RF + Tomek & 4.95 & 0.0279 \\ SVM & 40.52 & 0.1216 \\ SVM + SMOTE & 45.78 & 0.1302 \\ SVM + Tomek & 42.15 & 0.1258 \\ ANN & 24.81 & 0.0654 \\ ANN + SMOTE & 28.12 & 0.072 \\ ANN + Tomek & 26.75 & 0.0695 \\ W-ELM & 3.34 & 0.0057 \\ Proposed Method & 10.27 & 0.0055 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Performance comparison for computational efficiency in terms of training and detection time measures.  
vs. 92.00%) by a margin of 10.07 percentage points, the CAE-M (81.88% vs. 92.00%) by 10.12 percentage points, and MTAD-GAT (81.23% vs. 92.00%) by 10.77 percentage points.  
Glancing over the comparison, it is observed that all the benchmark models exhibit comparably similar performance metrics with respect to each other, in the range of 80.01-82.91% for accuracy and 81.23-81.93% for the F1-score, while the discussed OP-ELM achieved remarkably higher values regarding both performance metrics. It achieved a very good accuracy and F1-score with only a 2.10 percentage point difference between them, attesting to its excellent behavior overall with respect to the metrics.
# A New Scene Sensing Model Based on Multi-Source Data from Smartphones
## 2 Materials and Methods
### Complex Indoor and Outdoor Scene Classification Algorithm Based on Spatio-Temporal Features
#### 2.6.4 Assessment of Indicators  
The researchers used four metrics--accuracy, precision, F1 score, and recall--to evaluate the classification results of spatio-temporal scene recognition. The true positive (TP), false negative (FN), and true negative (TN) are parameters obtained from the confusion matrix [37]. The accuracy rate represents the proportion of all samples that can be correctly predicted, and the equation is as follows:  
\[\text{Accuracy}\;=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{TN}} \tag{22}\]  
Precision measures the proportion of positive samples that are correctly identified in the prediction results, and the equation is as follows:  
\[\text{Precision}\;=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{23}\]  
Recall measures a classification model's ability to recognize samples in the positive class (the class of interest). It is defined as the proportion of samples correctly identified as positive by the model out of all samples that are actually positive.  
\[\text{Recall}\;=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{24}\]  
F1 combines two metrics, precision and recall, in the following equation:  
\[\text{F1}=2\times\frac{\text{Precision}\;\times\;\text{Recall}}{\text{ Precision}\;+\;\text{Recall}} \tag{25}\]  
The average value of F1 for all categories is called Macro-F1, and the equation is as follows:  
\[\text{Macro-F1}=\frac{\sum_{1}^{n}(\text{F1})_{n}}{n} \tag{26}\]
# The Helical Resonator: A Scheme for Radio Frequency Plasma Generation
## 2 Geometry  
The basic geometry of a helical resonator, as defined in the context of this paper, is shown in Figure 1. The resonator consists of a helically wound conductor of length \(L\), with one end (which we shall call bottom end in the following) grounded, while the other (top end) is left open, or connected to a load. We indicate with \(b\) the helix radius, with \(H\) its height, and with \(N\) the number of turns. The helix pitch is given by the following:  
\[p=\frac{H}{N}. \tag{1}\]  
The driving voltage, labeled \(V_{in}\), is fed in an intermediate point, named \"tap point\", usually near the bottom end. The tap point axial position, normalized to \(H\), is labeled as \(\delta\). The resonator is covered by a grounded conducting shield, of radius \(c\). This shield, not present in all treatments (if absent, it may be considered at infinity) represents the second conductor of the transmission line for which the helix is the first one, according to the modeling presented in Section 4. The voltage at the top end of the resonator is labeled \(V_{out}\).  
It is useful to emphasize that the shieldless resonator is fully defined by the three parameters \((b,H,N)\), while in the more general case where the shield is present, the fourth parameter \(c\) is required. In principle, also the thickness of the helical conductor should be taken into account: however, this has only an effect on the dissipation due to the skin effect, and this effect is not so important at RF since the skin thickness is much smaller than any practical conductor thickness, so that the current is anyway flowing on the conductor surface. Therefore, we shall not consider this further parameter in our model, although one should be aware that \(p\) cannot be smaller than twice such thickness, and this puts a lower bound on the angle \(\psi\) defined below.  
It is worthwhile to introduce the pitch angle \(\psi\), defined as the following:  
\[\tan\psi=\frac{p}{2\pi b}. \tag{2}\]  
Figure 1: Schematics of the helical resonator. The terminals labeled IN and OUT are the ones where the voltages \(V_{in}\) and \(V_{out}\) are evaluated. The external rectangle represents the screen.  
This will typically be quite small, expressing the fact that the helix is tightly wound, for the reason of compactness of the device; however, nothing prevents making it large, if required. The total length of the helical conductor can be expressed in terms of the other parameters as  
\[L=N\sqrt{p^{2}+4\pi^{2}b^{2}}. \tag{3}\]  
In most practical situations, \(p<<2\pi b\), so that \(L\approx 2\pi bN\).  
It is also useful to introduce the aspect ratio, defined as \(H/(2b)\). This quantity gives an immediate idea of how elongated the coil is. In the following, we actually use, for convenience of notation, the inverse aspect ratio:  
\[\epsilon=\left(\frac{H}{2b}\right)^{-1}. \tag{4}\]  
It is useful for the following to notice that \(\cot\psi=\pi N\epsilon\).
## 3 Results and Discussion
### Relationship between Resistance Torque and Roughness Height
This partition of components is consistent with similar findings from a study by [PERSON] [25] on the resistance components of a streamlined cylindrical body, where they reported that the shape (or pressure) component accounted for over 98% of the resistance, whereas the friction component accounted for less than 2%. Clearly, the roughness height does not alter the shape (or pressure) of the resistance but only affects the friction component. Consequently, for the armature, the roughness height has a relatively minor impact on the torque resistance.