esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards a Real-Time Description of the Ionosphere:
## 7 Validation Results for _Inm_F2 Based on Ground-Based Ionosonde Observations  
### Statistics on the Full Dataset  
The full _Inm_F2 ionosonde dataset comprises 10,133,987 measurements obtained cumulating time series recorded from 2000 to 2019 at the 40 considered ionospheric stations. This dataset has been used to calculate the two-dimensional density plots of IRI-_Inm_F2 vs. ionosonde-_Inm_F2 and IRTAM-_Inm_F2 vs. ionosonde-_Inm_F2, along with the histograms of corresponding residuals, as shown in Figure 24.  
Figure 24: Density plots between measured (\(x\)-axis) and modeled (\(y\)-axis) _Inm_F2 values, for **(a)** IRI and **(c)** IRTAM. Measured values are those from the entire ionosonde dataset. The black solid lines represent the best linear fit. Corresponding slope and intercept values, along with the correlation coefficient, are reported in the upper left box of each plot. Statistical distributions of the resultant residuals for **(b)** IRI and **(d)** IRTAM. Corresponding _Res. Mean_, _RMSE_, and _NRMSE_ values, along with the total number of counts on which the statistics is based, are reported in the upper left box of each plot. The comparison between IRI and IRTAM highlights that the IRTAM model slightly improves the \(hm\)F2 modeling accuracy. In fact, the IRTAM and IRI statistical distributions of residuals around the zero value are very similar and, on the other hand, _Res. Mean_\({}_{\text{IRTAM}}=-1.619\) km is just a little bit smaller than _Res. Mean_\({}_{\text{IRI}}\) = 1.804 km. Additionally, the comparisons \(RMSE_{\text{IRTAM}}\) = 27.965 km vs. \(RMSE_{\text{IRI}}\) = 32.993 km and \(NRMSE_{\text{IRTAM}}\) = 10.097% vs. \(RMSE_{\text{IRI}}\) = 11.912% testify a slight improvement of the \(hm\)F2 modeling precision made by IRTAM. This point is also supported by the IRTAM density plot and the correlation coefficient (\(R_{\text{IRTAM}}\) = 0.845 vs. \(R_{\text{IRI}}\) = 0.766). Compared to the _fo_F2 results (ionosonde dataset, Figure 3), in this case the improvement made by IRTAM is lower. However, we have to consider that, for what concerns \(hm\)F2, IRTAM cannot be considered as a direct updating of IRI because of the Brunini procedure [53] for mapping \(hm\)F2 (see Section 2.2) and, more importantly, because we applied the current default \(hm\)F2 IRI option, i.e., that of [PERSON] et al. [44]. Hence, IRI and IRTAM \(hm\)F2 models have to be considered as completely unrelated. Overall, the statistical results reported in Table 5 show that IRTAM models \(hm\)F2 better than IRI in terms both of accuracy and precision, although the degree of improvement is lower than that obtained for _fo_F2 (Table 2).
# Flexural Behavior of Corroded High-Speed Railway Simply Supported Prestressed Concrete Box Grider
## 3 Analytical Model
### Impact of Strand Corrosion
#### 3.1.3 Concrete Cracking  
Corrosion of prestressing strands results in volume expansion, creating many microcracks in the surrounding concrete. The presence of internal microcracks leads to a deterioration in the concrete compressive strength in the tensile zone. It is noted that corrosion has a minor effect on the concrete strength in the compression zone. Thus, the reduced concrete strength in the tensile zone can be calculated by adopting the formula proposed by [PERSON] [37]:  
\[f_{\text{ck,\rho}}=\frac{f_{\text{ck}}}{1+K\varepsilon_{1}/\varepsilon_{ \text{c}}} \tag{11}\]  
where \(f_{\text{ck}}\) and \(f_{\text{ck,\rho}}\) are the standard values of the concrete axial compressive strength before and after strand corrosion, respectively; \(K\) is a coefficient related to strand diameter and roughness [38], and here has a value of 0.1; \(\varepsilon_{\text{c}}\) is the concrete peak compressive strain (when \(f_{\text{cu}}\leq 50\) MPa, \(\varepsilon_{\text{c}}=0.002\)) [19]; and \(\varepsilon_{1}\) is the average concrete tensile strain that is perpendicular to the prestressing force direction, which can be calculated as follows:  
\[\varepsilon_{1}=nw_{\text{cr}}/b \tag{12}\]  
where \(n\) is the number of steel wires to be considered for corrosion; \(b\) is the section width at the position of the strand; and \(w_{\text{cr}}\) is the width of the crack at a certain corrosion depth.  
Assuming that all corrosion products are incompressible, the crack width \(w_{\text{cr}}\) equals the increase in circumference (as shown in Figure 6). Therefore, Equation (13) can calculate the width of the crack:  
\[w_{\text{cr}}=\sum_{i}u_{i}=\frac{2}{3}[2\pi(\mu_{\text{rs}}-1)p_{a}] \tag{13}\]  
where \(\mu_{\text{rs}}\) is the volume ratio of corroded oxide to the strand subject to corrosion, recommended to be 2.78 [39].  
After the concrete compressive strength in the tensile zone has been calculated, it is then possible to estimate the concrete tensile strength (\(f_{\text{tk,\rho}}\)) by taking 1/10 th of the compressive strength as suggested [40].  
Figure 6: Corrosion crack width \(w_{\text{cr}}\) calculation diagram: (**a**) corrosion product accumulation around a seven-wire strand; (**b**) the increase in one external wire’s circumference.
## 3 Methodology
### System Design
#### 3.3.2 Stress Information Feedback  
In the CoManager system, stress information is presented through two devices, one for the child and one for the parent. As shown in Figure 2, a vaporizer device (based on atomizer plates: 5 V, 300 mA, 110 KHz, 2 W) that presents information for children in a subtle manner during the learning process, minimizing distractions while allowing the child to be aware of their stress levels. The vaporizer uses pure water without additives, which is converted into fine mist vapor. This process does not involve any chemical reactions or generate new substances. The vaporizer has two outlets: one operates when the stress level is relatively low, and both operate when the stress level is high, providing an increased level of alerts. Each outlet is connected to the Arduino NANO ATMEGA (Zhangzhuo Technology, Shenzhen, China) via Dupont wires. NANO ATMEGA boards are connected to the Arduino UNO R3 mainboard. The Arduino UNO R3 (Emakefun, Shenzhen, China) mainboard is powered by an external battery pack (9 V, PKCELL) to ensure adequate power supply for all modules. The Arduino UNO R3 is connected to a computer via a USB interface. After the heart rate data are collected by the PPG sensor and transmitted to the computer, they are processed through an algorithm to generate stress values, which then control the vaporizer and user graphical interface for stress information display. A graphical interface for parents, developed using the PROCESSING program on the computer, allows the parent to view the baseline stress value (measured before the experiment and not changing during the process), real-time stress data, and stress status interpretation.  
Figure 1: The working principle of the CoManager system.
## 2 Materials and Methods  
### Description of Gas Analysers  
The tested gas analysers for NH\({}_{3}\) in this study included an INNOVA Photoacoustic Gas Monitor (LumaSense Technologies A/S, Ballerup, Denmark), a RosemountTM Quantum Cascade Laser Gas Analyser (Rosemount Inc., Shakopee, MN, USA), a GasmetTM Fourier Transform Infrared Gas Analyser (Gasmet Technologies Inc., Vantaa, Finland), a Picarro Cavity Ring-down Gas Concentration Analyzer (Picarro Inc., Santa Clara, CA, USA), and an Akertris Tunable Diode Laser Gas Detector (Axetris AG, Kagiswil, Switzerland). These anaysers are hereafter referred by their respect brand or trademark names. A summary of the analyser specifications is given in Table 1. Different optic gas sensing techniques are employed by the analysers, including photoacoustic spectroscopy (PAS), laser absorption spectroscopy (LAS), cavity ring-down spectroscopy (CRDS), and Fourier transform infrared spectroscopy (FTIRS). These techniques rely on a common underlying mechanism that the gas molecules of a given species are capable of absorbing electromagnetic radiation at specific wavelengths. Extensive reviews of optic sensing principle and instrumentations can be found in [14; 15].  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline \multirow{2}{*}{**Analyser**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Accuracy a**} & \multicolumn{3}{c}{**Calibration**} \\ \cline{4-6}  & & & & **Range (ppm\({}_{\text{v}}\))** & **Execution** & **Interval b** \\ \hline INNOVA & 1314 & not specified & 0–44 & Manufacturer & 3 \\ Picarro & G2103 & 5\% reading + 0.5 ppb\({}_{\text{v}}\) & 0–50 & Manufacturer & 18 \\ Rosemount & CT5100 & 2\% full-scale & 0–52 & Manufacturer & 12 \\ Gasmet & CX4000 & 2\% full-scale & 0–150 & Manufacturer c & 9 \\ Akertris & LGD F200-A & 2\% full-scale & 0–69 & Authors & 7 \\ \hline \hline \end{tabular}  
* Based on the product datasheet with purchase. b Number of month since the most recent calibration.
* Zero-calibration with dry nitrogen gas performed daily.  
\end{table}
Table 1: Summary of the specifications of gas analysers for measuring ammonia (NH\({}_{3}\)).  
All of the tested analysers are closed-path systems. That is, air samples are actively drawn to the analyser and analysed in a measurement cell to determine the NH\({}_{3}\) concentrations. INNOVA takes air samples in subsequent batches, and from each batch one concentration measurement is taken. The other analysers perform measurements from a continuous gas flow. In this study, the Picarro and Axetris analysers measured at their default frequency, which fluctuated between 0.5-1 Hz. The measurement interval of Rosemount was set to 5 s. The measurement cycle of Gasmet was set to 60 s according to the setting recommended by the manufacturer, and each measured value represented the average gas concentration in the air flow over the 60 s duration. In INNOVA, the data interval was approximately 130 s because of a more complex sampling procedure. Calibration of the gas analysers was carried out independently either by the corresponding manufacturer or the authors.
## 3 The Processing Method for Integrated Radar-Communication Signals
### Symbol Time Offset and Carrier Frequency Offset
STO is located at the point where the difference between the CP sampling block and the data sampling block is minimized, or at the point where their correlation is maximized. Figure 6: CFO synchronization scheme using pilot signals. Figure 8 shows the CFO estimation using the phase difference between CP and the corresponding OFDM symbols at the end, as well as the CFO estimation based on the phase difference between two repeated preambles. We can observe that the mean squared error (MSE) of the CFO estimation using pilot signals is smaller. As the signal-to-noise ratio (SNR) increases, the MSE of CFO estimation continues to decrease.
## 4 Proposed Method
### Multi-Task Learning Mechanism  
The proposed method adopts a multi-task learning framework to enhance steganalysis performance. The primary task focuses on classifying cover and steganographic samples, which is essential for detecting hidden information. Meanwhile, the regression task captures continuous variations in embedding rates, helping to model the underlying distribution of steganographic modifications. By jointly optimizing both tasks, the model learns more expressive representations, improving classification accuracy and robustness against diverse embedding strategies.
## 4 The Experimental Results Discussion
### Regular Crack Detection Using the Flood-Filling Algorithm  
In order to inspect the flood-filling algorithm of crack detection under the threshold range \(\Theta\) = [60; 80] (the mid-value of threshold range \(\Theta\) is 70), the measurement accuracy experiments of different crack widths were conducted under different detection distances and different zoom ratios. The detection distances were 0.5 m, 1 m, 1.5 m, and 2 m, and the camera zoom times were 1, 3, 5, 7, and 9, respectively. The regular crack images were photo-taken using a standard crack detection card with a smartphone (type: Huawei Mate-20 Pro). The results from the authors' studies (the literature from [29; 30]) showed that the accuracy of APP detection was high when the detection distance was within 2 m for cracks with widths from 1 mm to 3 mm. The relative measurement error was generally less than 10%, and the measurement accuracy of a high zoom ratio was higher than 95%. However, the APP detection conditions were limited for cracks; the reason was that some fine cracks less than 1 mm wide could not be detected with the increase in detection distance. In order to ensure accuracy and measurement, a high zoom ratio should be used within a close detection distance. The suggested detection distances are shown in Table 1 based on the threshold range \(\Theta\) = [60; 80]. That was the applicable range of the APP detection method for different crack widths of (0-3) mm, and the recommended zoom ratio at different detection distances are shown in Table 1[29; 30].  
Figure 3: The results of the threshold range experiment.
## 4 System Architecture and Graph Modeling
To mirror real-world AV deployment constraints, the state vector \(s_{t}\) input to the reinforcement learning (RL) agent is defined as  
\[s_{t}=[v_{t},\rho_{t},\eta_{t}], \tag{3}\]  
where  
* \(v_{t}\) is the vehicle's current node location in \(V\);
* \(\rho_{t}=[\rho_{1}(t),\ldots,\rho_{n}(t)]\) is the ST-GCN-predicted risk vector;
* \(\eta_{t}\) represents AV-specific telemetry signals, including
* GNSS confidence score \(\gamma_{t}\in[0,1]\);
* Visual entropy \(\phi_{t}\) (e.g., edge-map confidence or frame illumination);
* Operational factors: fuel level \(f_{t}\), speed \(s_{t}^{\text{veh}}\), scan mode indicator \(m_{t}\). The RL agent selects an action \(a_{t}\in\mathcal{A}\) (i.e., move to neighboring node \(v_{j}\)) to maximize cumulative expected reward:  
\[\pi^{*}=\arg\max_{\pi}\mathbb{E}_{\pi}\left[\sum_{t=0}^{T}\gamma^{t}r_{t}(s_{t },a_{t})\right], \tag{4}\]  
where \(\gamma\) is the discount factor and \(r_{t}\) encodes a risk-aware reward function, penalizing both excessive low-risk zone repetition and failure to visit predicted hotspots. This architecture enables the AV to act as an intelligent agent that adaptively reroutes based on predicted spatial risk and uncertain localization, rather than relying solely on GNSS or map priors. It inherently supports the following:  
* Dynamic replanning in GNSS-impaired or partially observable regions. * Risk-sensitive patrol strategies modulated by contextual sensor data. * Generalization across heterogeneous spatial morphologies (e.g., narrow alleyways, blocked roads).
## 4 Interference-Aware Clipping Scheme
### SIR, SCR, and Effective SINR
This is due to the fact that, as Iter increases, the distortion power also increases. Adopting adjustable factor in the nonlinear process is very general,  
Figure 4: Normalized distortion power versus clipping ratio (CR), \(\
u\) (dB). Figure 3: Visualized example of inter-user interference and clipping distortion level (\(\
u=2,3,4\), \(Nt=400,U=40\)). and the approximation of the oversampled complementary cumulative distribution function (CCDF) also has been successfully applied [44]. As observed, the analytical results are well-matched with the simulation results. We set the number of iterations as 5 in the scheme to show the numerical analysis and validity. This number of iterations can be reduced in any kind of situation. The amount of peak-regrowth is also heavily dependent on the number of iterations. We assume that five number of iterations is reasonable as both latency and peak-regrowth view points. The reasonable number of iterations can be chosen based on real implement situations. Figure 5 gives the effective SINR versus SNR (dB), and the analytical results are also well-matched with simulation results. In particular, when \(\
u\) is above 4 dB, the analytic results more closely match the results of simulation, and this is a very desirable result because most practical systems use the \(\
u\) above 4 dB as evidenced from the EVM requirement.
# Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control
## 4 Results
### Average Delay  
The average delay is the waiting time to receive data requests from any respective zone or network. We tracked the average delay time by adopting 50 and 150 vehicles in this simulation results. We check our proposed system based on these two main parameters to evaluate the results. The graph in Figure 11 shows the results explained easily to find the final results from the proposed methodology. The trends rise at the initial stages, then fill for the average delay to summarize the results. The results explain the end methodology and the results to simulate effective management. Initially, the cached content on the different zone for content is null; with the simulation time, the simulation ends with an effective proposed methodology. Over time RPCC, MLCP, CCMP, and PCZS+PCNS compared the results with the proposed technique. These results effectively enhance the working functionality of the proposed technique. Figure 12 shows that network latencies with different degrees effectively reduce and enhance effective collaborative results.  
The existing techniques provide a lack of effective results of the proposed technique. The updating mechanism is compared with the compared approach. The dynamic adaptability from the network analysis shows that it provides results comparisons as earlier peaks and with the decline with time. The proposed technique provides less average delay than the existing techniques in dynamic adaptability.  
They are adding more vehicles--up to 150--with the network's average delay of the RPCC, MLCP, and PCZS+PCNS. The average delay increases when the number of vehicles is continuously added to the existing network. Road traffic and network traffic increased by adding more vehicles, and under this condition, our proposed system performs well and shows minimum average delay compared to existing techniques. The number of vehicles increased the prediction accuracy to provide the content pre-caching to enhance the operational performance of the proposed system. The prediction process contains the network traffic latency and shows the minimum content information regarding other vehicles' traffic flow. The pre-caching is evolved by developing the proposed mechanism to enhance and provide the pre-caching accuracy values.  
Figure 10: Pre-caching Prediction Accuracy of the proposed technique.
# Design and Implementation of a Smart Intercom System through Web Services on Web of Things
## 4 Software Framework  
The system integrates an embedded and instant messaging platform to develop various functions through the web services on the WoT. The embedded system is used as a host for the intercom and as a chatbot server for this system. The functionality of the instant messaging software on smartphones is designed through an instant messaging platform, and communication between the platform and the software takes place through messages. The embedded system and the instant messaging software platform collaborate through the webhook mechanism. The software framework of this system is shown in Figure 4.  
Figure 3: Hardware architecture of smart intercom system.  
The developed functions of the embedded system use a common programming language that provides a touch and display function, a door lock control function, a camera function, and an alarm function. The system functions by integrating the embedded system's web proxy server and wireless network module with cloud services such as image hosting services and instant messaging platforms to provide web services for the residents and visitors.  
The embedded system provides visitors with an intercom interface that uses touch screens instead of the traditional pushbutton switches. Visitors click on the touch screen as if they were using a keyboard or a mouse, and the system is programmed to activate the relevant software functions in an event-driven manner. The system not only allows visitors to send messages and photos to the residents' smartphone instant messaging software after pressing the floor number, it also allows residents to use the instant messaging software to generate a standardized interface through the instant messaging platform to trigger the chatbot server to control various devices, such as door locks, cameras, buzzers, etc. The touchscreen layout of this system is shown in Figure 5.  
Any apps on smartphones need to be installed before use. If written as a web application, there are concerns about information security, such as anonymous attacks on web pages. In this study, the instant messaging software already installed on the smartphone successfully integrates with the embedded system to control the WoT devices through the instant messaging platform, thus enhancing the smart control capability of the residents' smartphones without needing to install an app. The system uses web services as the integration interface, activates the webhook service in the channel of the instant messaging platform, and then integrates the embedded system's chatbot server to ensure the system's security. The system mainly uses the chatbot of the instant messaging platform and designs  
Figure 4: The software framework of this system.  
Figure 5: The touch screen layout of this system.  
\"Open Door\", \"Watch\", \"Alarm\", and \"Busy\" menus for the resident to use by the instant messaging software of a smartphone. The proposed functional flow is described as follows.
###### Abstract  
Research on emotion recognition based on electroencephalogram (EEG) signals is important for human emotion detection and improvements in mental health. However, the importance of EEG signals from different brain regions and frequency bands for emotion recognition is different. For this problem, this paper proposes the Capsule-Transformer method for multi-region and multi-band EEG emotion recognition. First, the EEG features are extracted from different brain regions and frequency bands and combined into feature vectors which are input into the fully connected network for feature dimension alignment. Then, the feature vectors are inputted into the Transformer for calculating the self-attention of EEG features among different brain regions and frequency bands to obtain contextual information. Finally, utilizing capsule networks captures the intrinsic relationship between local and global features. It merges features from different brain regions and frequency bands, adaptively computing weights for each brain region and frequency band. Based on the DEAP dataset, experiments show that the Capsule-Transformer method achieves average classification accuracies of 96.75%, 96.88%, and 96.25% on the valence, arousal, and dominance dimensions, respectively. Furthermore, in emotion recognition experiments conducted on individual brain regions or frequency bands, it was observed that the frontal lobe exhibits the highest average classification accuracy, followed by the parietal, temporal, and occipital lobes. Additionally, emotion recognition performance is superior for high-frequency band EEG signals compared to low-frequency band signals.  
EEG; emotion recognition; Transformer; capsule network; brain region; frequency band