esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 1 Introduction
### Network Authentication  
There are distinct ways to provide access control to a network. There are different systems whose aim is to authorise the end devices to send and receive traffic on the network. Wireless home personal networks can use different network authentication protocols for controlling connectivity. Although there has been an evolution in the protocols, from WEP to the last WPA3-Personal, they still rely on long-term passwords to authenticate users.  
Usually, corporate networks have other solutions. The Extensible Authentication Protocol (EAP) with IEEE 802.1X can control the connectivity at the link level. This system  
Figure 1: User personal computer compatible with FIDO2: W3C WebAuthn and FIDO CTAP standards. The web application interacts with the OS via the WebAuthn API of the web browser. The personal computer can have a compatible credential storage for WebAuthn credentials, or interact with a security key via FIDO CTAP1/2.  
is used by different systems, like the EDUROAM project. They can control access on Ethernet and Wi-Fi networks. However, like wireless home protection, the most common corporate EAP systems still rely on passwords. EAP methods such as EAP-MD5 and LEAAP or some tunneled methods based on PEAP or EAP-TTLS, like MSCHAPv2 or PAP, use password-based credentials.
# Design and Evaluation of an Acoustic Modern
## 2 System Design
### Software Design  
As the software design of the transmitter is relatively straightforward for the community, we briefly introduce the software design of the AD sampling rate adjustable receiver to further illustrate the main contribution of this paper. The software flowchart of the receiver is presented in Figure 5, from which one can see the specific implemental procedures of the AD sampling rate adjustment.  
At the beginning of the flowchart, successful detection of the first LFM signal indicates the capturing of synchronization and then triggers the detection of the second LFM signal. Afterward,  
Figure 4: Illustration of the demodulation processing of the receiver.  
Figure 5: Software flow chart of the receiving.  
the real interval \(T_{pp}\) of two correlation peaks associated with these two LFM signals can be measured to facilitate Doppler estimation and timer parameter calculation. Then, the flowchart enters the timer interruption to trigger AD sampling, with the sampling rate determined by the timer parameter. With the adjusted AD sampling rate, the Doppler effects induced by the underwater acoustic channel can be compensated to guarantee the following demodulation procedures.  
To ensure real-time implementation of synchronization capturing and Doppler estimation, a dual-buffer manner is adopted by dividing the memory space into a 'ping' buffer and a 'pong' buffer. With one buffer receiving data via ADC, the other buffer performs the cross-correlation calculation to detect and measure the arrival times of two LFM signals. The role of the two buffers will switch between 'ping' and 'pong' once the receiving buffer is full to guarantee real-time implementation.  
Moreover, as the internal correlation function provided in the STM32 Digital Signal Processing function library will automatically save the end part of the previous correlation result to assemble with the current correlation result, continuity of the cross-correlation result can be ensured, even when the LFM signal is more or less equally spread over both buffers.
# Efficient and Safe Strategies for Intersection Management: A Review
## 3 Efficiency
### Green Light Optimized Speed Advisory (GLOSA)
#### 3.2.1 Evaluation  
Based on experience in actual testing and related work, [PERSON] et al. define a comprehensive set of newly created metrics for the technical evaluation of the GLOSA system, including latency, end-to-end delay, message delivery ratio, packet delivery ratio,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Model** & **Main Technology** & **Advantages** & **Need To Be Improved** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Using prediction \\ algorithm \\ \end{tabular} } & \multirow{4}{*}{AUTM [31]} & Map and transfer; & \multirow{4}{*}{Congesting forecasting more than 89\%} & Considering more realistic features in AUTM; applying dynamic strategy of traffic light; short of real-time \\ \cline{2-5}  & & & & \\ \cline{2-5}  & \begin{tabular}{c} An advanced \\ dynamic traffic \\ network prediction \\ and navigation \\ model [32] \\ \end{tabular} & \begin{tabular}{c} Equilibrium Markov \\ chain model; \\ time-dependent \\ navigation algorithm \\ \end{tabular} & Average speed improved & \multirow{4}{*}{\begin{tabular}{c} Network adaptation and \\ robustness; making the \\ network into real-time; \\ self-update \\ \end{tabular} } \\ \cline{2-5}  & \begin{tabular}{c} A real-time queue \\ length estimation \\ method [35] \\ \end{tabular} & \begin{tabular}{c} Using Markov model \\ to estimate the \\ real-time CV; the \\ real-time queue \\ length estimation \\ \end{tabular} & \
\begin{tabular}{c} Good performance in \\ handling the randomness; \\ high accuracy in real-time \\ queue length estimation \\ \end{tabular} &
\begin{tabular}{c} The impact of queue \\ estimation accuracy caused \\ by different CV distributions \\ in the queue \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of methods in congestion avoidance.  
Figure 6: Green wave speed guide scene.  
stability of the prediction, and information distance. They cover all relevant system components required for GLOSA system functionality, including system and communication performance, as well as application-related measures and infrastructure aspects. Together, they allow for a comprehensive assessment and analysis of GLOSA performance [38].  
The above criteria are technical standards proposed for algorithm optimization, but in actual simulation, we tend to look at more intuitive standards: CO\({}_{2}\) emission, fuel consumption, and travel time. Even in high-density traffic conditions, the paper [39] points out that GLOSA can improve fuel economy and reduce carbon dioxide emissions by an average of about 10%. HOwereer not every time GLOSA can work, the simulation results show that when the traffic per hour exceeds 400, GLOSA will lose the effect we want and be less environmentally friendly than conventional vehicles.
## 5 The Case Study
### Results Comparison and Validation  
The validation of the proposed co-simulation architecture was verified by comparing the obtained numerical results with the experimental measurements reported in the referred article [15].  
The requested simulation time for the single analysis was around 35 min with an 11 th Gen Intel Core i7 CPU, NVIDIA GeForce GTX 1650 GPU and DDR4 32 Gb RAM.  
Figure 11 depicts on two different graphs (a) the laser-based displacement measure obtained at different levels of applied current and (b) the numerical trends of the estimated deflection amplitude in a 0.8 s time window for a circulating current equal to: [0.2; 1.0] A. For each one of the reported configurations, the trends show an initial increasing monotone transition phase and, subsequently, a maintenance phase into a deformed configuration. The final maintenance values were very similar to what was reported by the experimental measurements. It is important to underline that the greatest deviation between measurements and numerical estimations occurred for current values equal to 1.0 A. This response could come from non-linear elastic behavior obtained for such large strain deformation.  
To emphasize the capabilities and the wide range of simulation possibilities provided by the proposed multi-domain architecture, a series of results obtained from the previously described actuator model are reported below. In detail, we studied the dynamic response of the device through a variable frequency-modulated excitation. The signal provided by the microcontroller equivalent generator (see Figure 12) commanded the MOSFET gate according to a PWM voltage characterized by three main frequencies ([80; 90; 100] Hz) and duty cycles ([50%; 60%; 70%]).  
Figure 13 shows the dynamic response of the device. It is important to notice how the estimated deflection strictly follows the PWM frequency changes occurring at intervals of 200 ms. After 600 ms, the excitation signal ended, and the device returned to the quiescent configuration through a damped oscillatory motion.  
Figure 11: (a) Co-simulation numerical results; (b) referred experimental measurements.  
In detail, Figure 14 depicts the characteristic trend of the electro-magnetic actuation force. The first graph shows the trend during the entire actuation window, while the lower one shows a detailed magnification of the characteristic waveform. Figure 15, instead, represents the time history of the voltage measured on the MOSFET drain in a time interval of [0.18; 0.22] s, in which are clearly visible the under- and over-shoot (see the graph below) characteristics of a real MOSFET Spice model.  
The reported series of results highlights how, although on a simple but extremely representative case study, the presented co-simulation methodology allows researchers to investigate in depth the strongly coupled phenomena that characterize general electro-magnet-mechanical systems actuated by a firmware control logic.
# Semantic Attention Guided Hierarchical Decision Network for Age Estimation
## 3 Hierarchical Age Estimation with Semantic Attention
### Local Feature Learning and Semantic Attention Mechanism  
#### 3.2.1 Semantic Attention Mask Generation  
Supposing the size of global feature maps from the last feature layer is \(C\times W\times H\), and global age estimation probability is \(\widehat{p}\in R^{L}\), we firstly exploit a linear transformation layer to transform \(\widehat{p}\) to a semantic guided feature weighting vector. Specially, it can be achieved by a fully connected layer that mapping the overall age label probability to a semantic guided feature channel weighting vector.  
\[h=W_{s}\widehat{p}+b_{s} \tag{3}\]  
where \(W_{s}\in R^{C\times L}\) and \(b_{s}\in R^{C}\) are model parameters. As a result, this global prediction guided semantic feature weighting vector \(h\in R^{C}\) provide an attention that denotes the importance of feature channels from global feature map.  
In order to transfer the semantic attention to local age estimation, the intuition is to convert the feature weighting vector to a regional attention, which aims to select most age-sensitive local regions. For this purpose, we introduce an attention map \(A\in R^{C\times W\times H}\) by performing the \(1\times 1\) convolutional layer on the global feature map. The attention map \(A\) can represent the visual patterns of facial patches and can be further used to produce the regional attention. By reshaping the size of attention map \(A\) to \(R^{C\times N}\), where \(N=W\times H\), the regional attention mask \(m\in R^{W\times H}\) is computed as follows:  
\[m=\sigma\Big{(}A^{T}h\Big{)} \tag{4}\]  
where \(\sigma\) is the sigmoid function. The generated regional attention mask \(m\) reflects the correlations between the local patches and the feature channels, and also describes the importance of the regions for age estimation. It can be easily propagated to the second local age estimation stage for fine-grained feature learning.  
Specifically, given the regional attention mask \(m\in R^{W\times H}\) obtained in Equation (4) we first broadcast it along the channel dimension to form \(\widehat{M}_{s}\in R^{C\times W\times H}\). The broadcasted mask matches the shape of each local feature map \(F^{\prime}_{i}\) and is applied through element-wise multiplication:  
\[F^{\prime}_{i}=\widehat{M}_{s}\odot F_{i} \tag{5}\]  
where \(F^{\prime}_{i}\) denotes the attention-enhanced local feature and \(\odot\) denotes element-wise multiplication. This spatial reweighting explicitly highlights age-sensitive areas, such as eye corners, forehead wrinkles and nasolabial folds, thus guiding the subsequent local branch to focus on the most informative regions for robust age estimation. This design is further supported by the attention heatmaps in Figure 3, which show that the semantic attention mechanism consistently emphasizes these key facial regions, confirming its effectiveness in guiding local refinement.
# Non-Destructive Detection of Golden Passion Fruit Quality Based on Dielectric Characteristics
###### Abstract  
This study pioneered a non-destructive testing approach to evaluating the physicochemical properties of golden passion fruit by developing a platform to analyze the fruit's electrical characteristics. By using dielectric properties, the method accurately predicted the soluble solids content (_SSC_), _Acidity_ and pulp percentage (_PP_) in passion fruit. The investigation entailed measuring the relative dielectric constant (\(\epsilon^{\prime}\)) and dielectric loss factor (\(\epsilon^{\prime\prime}\)) for 192 samples across a spectrum of 34 frequencies from 0.05 to 100 kHz. The analysis revealed that with increasing frequency and fruit maturity, both \(\epsilon^{\prime}\) and \(\epsilon^{\prime\prime}\) showed a declining trend. Moreover, there was a discernible correlation between the fruit's physicochemical indicators and dielectric properties. In refining the dataset, 12 outliers were removed using the Local Outlier Factor (LOF) algorithm. The study employed various advanced feature extraction techniques, including Recursive Feature Elimination with Cross-Validation (RFECV), Permutation Importance based on Random Forest Regression (PI-RF), Permutation Importance based on Linear Regression (PI-LR) and Genetic Algorithm (GA). All the variables and the selected variables after screening were used as inputs to build Extreme Gradient Boosting (XGBoost) and Categorical Boosting (Cat-Boost) models to predict the _SSC_, _Acidity_ and _PP_ in passion fruit. The results indicate that the PI-RF-XGBoost model demonstrated superior performance in predicting both the _SSC_ (R\({}^{2}\) = 0.9240, RMSE = 0.2595) and the _PP_ (R\({}^{2}\) = 0.9092, RMSE = 0.0014) of passion fruit. Meanwhile, the GA-CatBoost model exhibited the best performance in predicting _Acidity_ (R\({}^{2}\) = 0.9471, RMSE = 0.1237). In addition, for the well-performing algorithms, the selected features are mainly concentrated within the frequency range of 0.05-6 kHz, which is consistent with the frequency range highly correlated with the dielectric properties and quality indicators. It is feasible to predict the quality indicators of fruit by detecting their low-frequency dielectric properties. This research offers significant insights and a valuable reference for non-destructive testing methods in assessing the quality of golden passion fruit.  
Keywords:golden passion fruit; dielectric properties; feature screening; model establishment; non-destructive testing +
Footnote †: journal: Applied Sciences
## 1 Introduction
While DT-based ML algorithms have been used for energy management in electric city buses [39], internal temperature monitoring in a greenhouse [40], variable selection and its application to gas-liquid two-phase CO\({}_{2}\) flow measurements [41], identifying the effects of the built environment's characteristics on driving distance [42], predicting soil drainage classes [43], developing a CO\({}_{2}\) emission benchmark [44], GHG emission reduction in a shared micro-mobility system [45], and water table modeling of shallow groundwater [46], its use in GHG emission prediction requires more attention. In Saudi Arabia in particular, DT-based ML has been applied for tracking CO\({}_{2}\) fronts in carbonate reservoirs [47]; finding the relationship between disaggregated energy use, economic growth, and environmental quality [48]; forecasting energy production [49]; groundwater mapping [50]; air quality index creation [51]; and the prediction of power in a wind farm [52]. However, to the best of the authors' knowledge, DT-based ML has not been used to predict GHG emissions in Saudi Arabia. Here lies this paper's primary contribution. The article is divided into the subsequent sections in the following sequence: in Section 2, a method is presented, which covers topics such as model development statistics and forecasting strategy. The results related to the GHG emission projection models are illustrated in Section 3. The conclusion of the paper can be found in Section 4.
## 4 Approaches of Optimizing Geolocation Measurements
### Maximum Likelihood Estimator (MLE)  
Maximum Likelihood Estimator (MLE) is an approach for estimating the probability distribution parameters by maximizing a likelihood function. The maximum point of the parameter space is called the maximum likelihood estimate [84].  
In [85], the problem of multi-static sonar with a transmitter has been addressed. Two solutions based on TOA and AOA measurements to estimate the object were proposed in that study. In addition, the localization parameters were updated by MLE, and an approach, as well as precise propagation speed, can be obtained from MLE proposed. Figure 13 illustrates the geometry of sonars, the transmitter, and the object. The simulation results exhibited that the proposed algorithm can produce the optimal solution to the MLE problem. Furthermore, an accurate localization solution and propagation speed update was achieved using RMSE and CRLB.  
Figure 12: Distribution of sensors and emitter scenario [82].  
Figure 13: Scenario of multi-static sonar, a transmitter, and the object [85].
# Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview
## 4 CR-Based UAVs  
A CR-based UAV may be seen as a UAV with an SDR platform embedded to it. It should also contain a computational unity that interacts with the SDR platform in order to autonomously take decisions regarding the radio spectrum usage. In this section, we first discuss the need for CR-based UAVs, potential applications, and hardware/software characteristics.
# A Constrained Coding-Aware Routing Scheme in Wireless Ad-Hoc Networks
## 4 Performance Evaluation
### Results from the Grid Topology
#### 4.1.1 Effective Coding Benefit  
In this experiment, we first analyzed the encoded and decoded packet ratio of the schemes. The encoded packet ratio measures the number of encoded packets in proportion to the total number of transmitted packets. The decoded packet ratio is the proportion of the number of successfully-decoded packets to the total number of the encoded packets.  
Figure 9 shows the comparison of the encoded and decoded packet ratios of the three protocols (i.e., COPE + AODV, DCAR, and CCAR). We can see that the encoded packet ratio of DCAR was the highest, exceeding those of CCAR by 10% and AODV + COPE by 52% in the \(5\times 5\) grid topology, because the DCAR mechanism had the most coding opportunities. However, the encoded packet ratio  
Figure 8: A \(5\times 5\) grid topology.  
only represents the encoding ability. In the case of the multi-flow interference, not all encoded packets can be successfully decoded. Then, we considered the decoded packet ratio. Compared to DCAR and COPE + AODV, CCAR had the best decoded opportunity, being approximately 26% better than DCAR and 59% better than AODV + COPE, since it provided more accurate decoding by the constrained coding conditions.  
We then analyzed the effective coding benefit ratio, which shows the successfully-decoded packets in the overall transmitted packets. It is defined by the product of the encoded packet ratio and the decoded packet ratio. The effective coding benefit ratio can comprehensively reflect the abilities of coding and decoding. Figure 10 presents the effective coding benefits of the three protocols under the grid topology. The benefit of CCAR is better than for the other schemes because CCAR has the highest decoded packet ratio.
# Efficient GPU Implementation of the McMurchie-Davidson Method for Shell-Based ERI Computations
## 5 Implementations in Graphics Processing Unit (GPU)
### Triple-Buffering of Shared Memory
On the other hand, the proposed three-buffering approach requires three buffers to store the maximum number of \(R\) values needed for all relevant _batches_\(k\) (\(0\leq k\leq K\)). As such, the maximum number of \(R\) values required for any batch \(k\) is as follows:  
\[\max_{0\leq k\leq K}\frac{(k+1)(k+2)(K-k+1)}{2}.\]  
In the approach mentioned above, the _batch_ values can be computed sequentially. However, to execute Algorithm 2, the values of \(R^{0}_{t,u,v}\) computed in each _batch_ are to be preserved while avoiding loss and overwrites, implying the requirement of a separate storage. The number of \(R^{0}_{t,u,v}\) values in _batch_\(k\) is given by  
\[\frac{(k+2)!}{k!2!}=\frac{(k+1)(k+2)}{2}.\]  
Therefore, to store all \(R^{0}_{t,u,v}\) values for _batch_\(k\) (\(0\leq k\leq K\)), the following size of _shared memory_ is additionally required:  
\[\sum_{k=0}^{K}\frac{(k+1)(k+2)}{2}=\frac{(K+1)(K+2)(K+3)}{6}\]  
In order to portray the performance differences in terms of the required size of the _shared memory_, Figure 6 compares the required memory size between the proposed triple-buffering scheme and a naive implementation that considers retaining all values rendered from the recurrence \(R^{n}_{t,u,v}\)) for \(0\leq K\leq 24\)). By observing the trend and behavior on the required shared _memory size_ in Figure 6, we can observe the following facts:  
* the difference in required _shared memory_ size is trivial in configurations fulfilling \(K<7\), whereas smaller sizes achieved by the proposed triple-buffering scheme become noticeable in configuration profiles with \(K\geq 7\), and
* as \(K\) increases, the memory reduction rate becomes comparatively clear and significant, achieving a reduction to 0.344 times the original memory usage, or approximately 65% less space. The above-mentioned results portray the potential, feasibility, and versatility of implementing the proposed triple-buffering scheme across different classes of GPU devices, including those with limited _shared memory_ capacity. Figure 6: Comparison of the required size of _shared memory_ to store \(R\) values.
# Sentinel-1 GRD Preprocessing Workflow +
## 2 Workflow
### Conversion to dB  
As a last step of the preprocessing workflow, the unitless backscatter coefficient is converted to dB using a logarithmic transformation.