esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion: Statistical Approach
### Multiple-Regression Analysis
#### 4.3.4 Analysis of Other Studies: Validation  
Final verification of the validity of the model for estimating the modulus of elasticity of the SCC containing RA when the compressive strength and the UPV index were known was conducted through the few other studies found in the literature that measured these three concrete properties [56; 57]. Equation (6) was considered, as it provided the most accurate estimates of the mechanical properties of the SCC mixes from which the models were developed (Table 6).  
It was found that the modulus of elasticity could be estimated in 87% of the mixes with mechanical properties within the range of the mixes used to develop the model, with a deviation of less than \(\pm 20\%\), while the deviation was less than \(\pm 10\%\) in 47% of the cases (Figure 8). When the modulus of elasticity was not correctly estimated, it was mainly due to unusual values of the compressive strength or the UPV. In those cases, higher values were obtained for the compressive strength and/or the UPV than those expected according to the trends of the mixtures through which the models were developed. The obtained results underline the utility of the model and its accurate predictions on the basis of UPV readings of two mechanical properties (modulus of elasticity and compressive strength) related to the compressive behavior of SCC containing RA.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Calculation Step**} & \multicolumn{1}{c}{**Value**} \\ \hline Known SCC properties & Compressive strength (MPa) 1  & 45.6 \\  & UPV (km/s) & 4.22 \\ Standardized properties & Compressive strength & 0.7177 \\  & UPV & 0.6179 \\ Standardized modulus of elasticity with Equation (6) & \(ME_{S}=\frac{10\mu V_{S}-1.246\times CS_{S}}{0.384}\) & 0.8034 \\ Standardized modulus of elasticity with Equation (7) & \(ME_{S}=\frac{2\times UPV_{S}^{2}}{0.894}-CS_{S}\) & 0.6646 \\  & (Equation (2) and Table 4) & \(ME=ME_{S}\times(ME_{u-95}-ME_{l-95})+ME_{m}\) & 37.2 \\  & (Equation (2) and Table 4) & \(ME=ME_{S}\times(ME_{u-95}-ME_{l-95})+ME_{m}\) & 35.5 \\  & Modulus of elasticity: Experimental value (GPa) & See Figure 3b & 36.4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Calculation of the modulus of elasticity of mix L0 with the proposed models.
## 5 Results Analysis and Discussion
### Model Robustness and Edge Case Analysis  
Our analysis of urban road travel time data reveals several challenging scenarios that test model robustness and highlight important considerations for real-world deployment. The data distribution demonstrates extreme variability, with typical travel times concentrated in the 25-32 s range representing 75% of observations, while extreme values can reach up to 10 times normal values. This variability reflects the severe impact of traffic signals, accidents, and other non-recurrent events on urban road networks, creating challenging conditions that differentiate urban traffic prediction from highway scenarios.  
Missing data patterns present another significant robustness challenge, with substantial gaps occurring during nighttime low-traffic periods between 3:00 and 5:00 AM. Our forward-filling preprocessing approach, while necessary for model training, may introduce systematic biases during these periods where traffic patterns differ fundamentally from peak hours. Deep learning models demonstrated varying stability across different network configurations, with larger configurations experiencing frequent convergence failures when processing highly volatile traffic data. The inherent randomness in deep learningmodels became particularly problematic under these challenging conditions, reinforcing the necessity of our multi-run training approach.  
The stark performance differences between MAPE and RMSE metrics reveal how different models handle edge cases. Models optimized for MAPE tend to excel at predicting typical traffic conditions but may struggle with extreme values, while models performing well on RMSE appear more capable of handling traffic incidents and signal delays, though with larger relative errors during normal conditions. This metric inconsistency highlights the importance of understanding model behavior across the full range of operating conditions rather than focusing solely on average performance.
## 2 Vibration Data and Early Data Processing  
### Gearbox Data  
The vibration signal used in this study is collected from the GPS and then uploaded to OpenEI data storage [22]. The GPS simulates a real gearbox device and has configurations with different options and working behaviors. Based on these configurations, the GPS can simulate gearbox working behavior, condition monitoring, and vibration data for further study.  
The basic GPS includes replaceable parts that are combined for gearbox operation simulation as follows:  
* one shaft test gearbox with two parallel stages;
* different torsion and radial loadings;
* replaceable gears with large spaces for additional devices;
* parallel gearbox that can support rolling element bearings or sleeve bearings;
* option for installing intentional error gearing to study the changes in the vibration signature;
* modular design that keeps the simulation easy to understand and doable;
* different mounting locations; and
* gearbox fault simulation and diagnosis methods.  
Based on these characteristics, the GPS can be customized to handle and examine heavy loads. GPS is also designed with a large reserve space so that the users can place, set up, and install new monitoring devices. In this paper, we collected data in the four directions: \(g\_x,g\_y,g\_z\), and \(g\_t\). In Figure 2, the GPS is set at 50% of the load condition, and we record the vibration signal under normal conditions and the broken tooth condition. The GPS data includes 450 s of normal machine vibration signal and 400 s of broken machine vibration signal.
## 2 The Normal-Mode Helical Antenna Design Considerations
### LNA Design and Implementation
\begin{table}
\begin{tabular}{c c} \hline \hline
**Frequency (MHz)** & **Impedance (\(\Omega\))** \\ \hline
20 & 7.3–368 \\
21 & 6.9–392 \\
22 & 6.3–1337 \\
23 & 7.0–322 \\
24 & 6.9–322 \\
25 & 6.9–1294 \\
26 & 6.0–1285 \\
27 & 4.8–1274 \\
28 & 5.5–1263 \\
29 & 5.8–1255 \\
30 & 4.8–1245 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Common-drain amplifier input impedance values. Figure 6: The equivalent circuit of the common-drain amplifier. Figure 7: The input impedance of the common-drain amplifier before impedance enhancement. From Table 1, we can infer that the total gate-to-source capacitance value of the BF256B, acting as a source follower amplifier, is approximately 18 pF. This value is considered in Figure 5 for simulation purposes as capacitor C6. To eliminate the capacitive reactance, a 2.2 uH negative feedback inductor (L1) was installed in parallel to the internal capacitor C6. This negative feedback inductor effectively neutralizes the parasitic capacitances at the input of the amplifier by creating a high-impedance tank circuit in parallel with the input of the amplifier. This assertion was further validated through Multisim software, as shown in Figure 8, where the input impedance of the CDA significantly increased. According to Figure 8, the input impedance of the CDA is in the order of K\(\Omega\). Table 2 depicts the vector analyzer impedance measurements of the helical antenna. Based on these measurements, the high-impedance JFET low-noise amplifier's performance was simulated using Multisim software. According to the simulation results, presented in Table 2, it is evident that the aforementioned neutralization technique significantly enhanced the bandwidth of the helical antenna when applied to the high-impedance low-noise amplifier (LNA). This enables the helical antenna to function efficiently as an active antenna across a wide frequency range as discussed in the following section.
# Contribution of Atmospheric Fallout to the Soil-Root-Leaf Transfer of PAHs in Higher Plants
## 4 Discussion
### Comparative Assessment  
Comparing the accumulated concentrations of \(\sum\)PAHs in different parts of rocket, it should be noted that studies addressing actual field contamination report values in a very broad range. In our test, the concentration of \(\sum\)19 PAHs in the leaves (edible parts) of rocket was 62.5 \(\mu\)g/kg in the case of the soil-aerial parts pathway and 333 \(\mu\)g/kg in case of the air-aerial parts pathway. In European context, [PERSON] et al. [71] measured the concentration of 15 PAHs in cabbage collected both in urban and rural areas in Romania. In urban samples, the measured value was 10 \(\mu\)g/kg, while in the rural samples it was 8.1 \(\mu\)g/kg. [PERSON] et al. [72] reported the accumulation of total PAHs in cabbage samples collected from Southern Moravia (Czech Republic) in the range of 12.34 and 78.09 \(\mu\)g/kg. A German study detected PAH contamination at 120 ug/kg in kale [73]. Nevertheless, Chinese studies have found higher PAH concentrations, most likely due to heavier contamination levels. [PERSON] et al. [34], e.g., reported average concentration of PAHs reaching as high as 1052 \(\pm\) 73 ug/kg in _Brassica chinensis_ samples collected in Xi'an city (northwestern China). In the study of [PERSON] et al. [74], the average concentration of PAHs in the same species was 950 ug/kg, and 1790 ug kg\({}^{-1}\) in _B. juncea_ (mustard).  
Rocket, however, is consumed in very small quantities and only occasionally, which makes human health risk assessment rather irrelevant. Soil PAH level, on the other hand, can give a more reliable estimation. After the treatment, the concentration of B(a)p was 3.6 ug/kg, which was well below the 0.02 mg/kg BaP level considered acceptable for the purpose of crop production [4].
# Forest Volatile Organic Compounds and Their Effects on Human Health: A State-of-the-Art Review
## 3 Results
### Evidence about the General Effects of Forest VOCs on Health  
From a pharmacokinetic point of view, when visiting a forest, monoterene VOCs such as limonene and pinene are mainly absorbed through inhalation, their blood levels rapidly rise after exposure, and they are mostly eliminated unchanged both in exhaled air and in the urine [58]. Accumulation in the adipose tissue has been demonstrated for some BVOCs like limonene [59], as well as some degree of metabolization for terpen like pinene [60]. Their general activity on inflammation, stress, sleep, and psychological behavior is described below. In particular, among monoterenes, limonene and pinene are important for their biological activities even on human subjects [36; 58] and, therefore, their health properties have been described in dedicated paragraphs.
# MUSIC: Cardiac Imaging, Modelling and Visualisation Software for Diagnosis and Therapy
## 2 Architecture
### MUSIC cardio
#### 2.3.5 Pipelines  
Processing workflows can be executed through a pipeline system included in the MUSICradio application. This allows us to script complex process sequences in order to simplify the work of the operators, make the results more robust and obtain the output data fast. The pipeline scripts were developed in Python, allowing researchers and operators to create and adapt their own customised pipelines.  
For instance, within the VT MUSIC consortium, clinicians and expert operators at Liryc IHU use the pipeline system to segment CT and MR images, and to generate a 3D structural model comprising a detailed anatomy of the whole heart, myocardial scar maps from CT-derived wall thickness or late-enhancement MRI analysis, structures at risk during SBRT (stereotactic body radiotherapy) procedures, or other structures such as coronaries, left phrenic nerve, atrioventricular node, ICD (implantable cardioverter defibrillator) lead tip, GI (gastrointestinal) tract, etc. The 3D models are used to treat refractory ventricular tachycardia. EP maps (electrophysiology data) are registered in the same geometrical coordinates to allow the user to perform the 3D rendering of combined structural and EP data.  
Pipeline scripts can handle automatic segmentation using Deep Learning-based algorithms that have been developed by our team. This allows us to obtain accurate and quick automatic segmentations of the anatomical heart structures for a more precise diagnosis and treatment of cardiac diseases.
# Hg\({}^{2+}\) Optical Fiber Sensor Based on LSPR with PDDA-Templated AuNPs and CS/PAA Bilayers
###### Abstract  
An optical fiber localized surface plasmon resonance (LSPR) sensor was proposed and experimentally demonstrated to detect Hg\({}^{2+}\) ions by functionalizing the optical fiber surface with gold nanoparticles (AuNPs) and chitosan (CS)/poly acrylic acid (PAA) bilayers. A flame-brushing technology was proposed to post-process the polydimethyl diallyl ammonium chloride(PDDA)-templated nanoparticles, avoiding the aggregation of AuNPs and achieving well-dispersed AuNPs arrays. LSPR stimulated by the AuNPs is sensitive to changes in the refractive index induced by Hg\({}^{2+}\) ions absorption on the CS/PAA bilayers. Experimental results demonstrated that the LSPR peak wavelength linearly shifts with the concentrations of Hg\({}^{2+}\) ions from 1 to 30 \(\mu\)M with a sensitivity of around 0.51 nm/ppm. The sensor also exhibits good specificity and longtime stability.  
optical fiber sensor; localized surface plasmon resonance; gold nanoparticles; mercury ion sensor detection +
Footnote †: journal: Applied Physics Letters
# Planetary-Gearbox Fault Classification by Convolutional Neural Network and Recurrence Plot
## Experiments and Discussion
### Results and Discussion  
Three comparative methods were utilized in this study. The RP of the time domain, continuous wavelet transform (cmor4-4), and the first six maximal peak values of the intrinsic mode function (IMF) generated by the empirical mode decomposition (EMD) of the experiment data [22] were employed to validate the performance of the proposed method. The comparative results of the four approaches are shown in Figure 5. Figure 5a shows the valuation loss, which was used to measure the extent of prediction error. The closer the prediction probability to 1 was, the closer the loss value to zero was. For the time-domain recurrence-analysis (green line) and EMD (blue line) methods, loss values were significantly higher than those of other methods (loss value stabilizes at 0), with more obvious fluctuations. Figure 5b shows valuation accuracy, which is defined as the ratio of the true number to the total number. The higher the accuracy is, the better the classification of the model. According to Figure 5b, the valuation accuracy of the proposed method (red line) and CWT (black line) was up to 100%. For the same CNN model, the valuation loss and accuracy of the IMF were fluctuant, which explains why this method failed to effectively extract fault features. Comparing the results of recurrence analysis, the proposed method was better than that in time domain due to eliminating the rotational fluctuations. Although both angular-domain recurrence analysis and CWT could obtain effective results, convergence speed was different. The valuation accuracy of the proposed method reached 100% and valuation loss was about 0% with epoch = 15, which demonstrated that convergence speed was better than that of the CWT.  
To eliminate the random error, each approach was executed 10 times. Results of four different methods are listed in Table 2. The confusion-matrix plots of the four methods are shown in Figure 6. Evaluation indicators demonstrated that the proposed approach and the CWT could effectively identify the faults of planetary-gear sets, and all evaluation indicators could reach 100%. As can be seen, standard variance was about 0%, which shows that performance was robust. Comparing the confusion matrix of the time domain RP with the artificially selected IMF plots, the true classification number was higher than that of handcrafted features, which concludes that adaptive feature extraction was better than artificially selected features. Therefore, the proposed approach is effective for fault classification of a planetary gearbox under various rotating speeds.
# Efficient Learning of Healthcare Data from IoT Devices by Edge Convolution Neural Networks +
## 4 Data Augmentation
### Data Generation  
In this section, four types of ECG data and randomly generated noise sequences are used as input of DCGAN, which is used for training and data generation. The input of the generator consists of a series of data sequences, each of which consists of 250 noise points. The output is the generated ECG sequence, whose length is set to 250. The input of the discriminator is the result produced by the generator and the real ECG data.  
The learning rate of the Adam optimizer in the experiment is 0.002. Since the BN mechanism is used, the batch size for each batch training is set to 64. According to the data characteristics of ECG, one-dimensional convolution and deconvolution methods are used in the process of training using tensorflow. Each iteration operation sets an event tracking to record its discriminative network loss value and generative network loss value. After 50 epochs of training, the generating samples of the four types of ECG beats are shown in Figure 6. They are the examples of atrial premature beats, ventricular fusion heartbeat, pacing mixed heart rate, and ventricular flutter. It can be seen in the figure that, although the generated pattern is not as smooth as the original data, the important basic features of the original data have been reflected.  
In the whole training process, the change trend of the loss value d_loss of discriminative network and the loss value g_loss of the generative network is shown in Figure 7. It can be seen that, in the whole process, the discriminative network and the generative network are undergoing alternating and iterative optimization, which is in line with the idea of zero-sum game. The optimization goal  
Figure 5: The structure of DCGAN: (**a**) The architecture of generative network, (**b**) The architecture of discriminative network.  
of GAN is mainly composed of two parts: the probability of judging whether the real sample is true by the discriminative network and the probability of judging whether the generated sample is true by the generative network. This makes them have a confrontation game in the process of training. The generative network tries to generate more realistic data to deceive the discriminative network and the discriminative network tries to distinguish the real samples from the generated samples. Therefore, in terms of optimization goals, the discriminative network wants \(V(G,D)\) to be as large as possible, while the generating network wants \(V(G,D)\) to be as small as possible. This leads to the phenomenon of iterative optimization in the training process.  
Figure 6: Sample ECG beat generation:(**a**) Atrial premature, (**b**) Fusion of ventricular and normal beat, (**c**) Fusion of paced and normal beat, (**d**) Ventricular flutter beat.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 8 Challenges and Opportunities
### Interoperability and Standards
#### 8.3.2 Fragmentation  
_Fragmentation_ refers to multiple, incompatible systems and protocols within the metaverse, leading to isolated digital environments that cannot seamlessly interact with each other [56]. Product fragmentation arises when different developers and platforms adopt unique standards for creating and managing virtual worlds, avatars, digital assets, and user interactions. Without a unified approach, users face significant challenges when navigating between different parts of the metaverse, as their digital identities, assets, and experiences may not be transferable across these isolated ecosystems.  
The fragmentation issue severely impacts the user experience and the overall potential of the metaverse [266]. For instance, a user might create an avatar and acquire virtual assets in one platform, only to find that these cannot be used or recognized in another platform. The lack of interoperability discourages users from investing time and resources into the metaverse, as they may be concerned about the portability and longevity of their digital assets. Moreover, developers are also hindered by fragmentation, as they must choose which standards to support or face the burden of trying to make their platforms compatible with multiple systems. The lack of universal standards stifles innovation and growth, preventing the metaverse from becoming the interconnected and expansive digital universe it aspires to be. Addressing fragmentation requires concerted efforts to establish and adopt common standards, ensuring that all parts of the metaverse can work together harmoniously.
## 4 Case Study
### Numerical Calculation Model  
The numerical simulation adopted the finite element software Abaqus. The excavation height of the upper bench was 3.8 m, the radius of the arc segment was 6.3 m, and the primary lining thickness was 0.24 m. In order to reduce the influence of the boundary on the calculation results, the overall model width (x-direction) was 60 m, the height (y-direction) was 80 m, and the top of the tunnel was 50 m from the free surface. The left and right of the model constrained the horizontal displacement, and the bottom constrained the horizontal and vertical displacement. The numerical model size and meshing are shown in Figure 12.  
The elastic-plastic model based on the Mohr-Coulomb strength criterion was adopted for the soil mass. The primary lining included C25 concrete, an I20b steel arch, and FLSP, which adopted the elastic model. The steel arch's stiffness was transferred into the primary lining through stiffness conversion [19]. The plane strain element CPE4 was adopted for the primary lining and surrounding rock. The spring element simulated the FLSP. The spring and the primary lining were connected by node coupling. Common nodes were adopted to connect the primary lining and surrounding rock. The material parameters are shown in Table 2.  
Equations (6) and (7) give the load-displacement relationship and the lateral stiffness of the connection part (point O in Figure 2) between the FLSP and the initial support, respectively. In order to facilitate the selection design and the realization of active bearing, the FLSP was simplified as a spring in the numerical simulation. The bottom of the spring was fixed, the upper part of the spring was coupled and connected to the lower end of the primary lining, and the active bearing effect of the foot-locking steel pipe could be achieved by pre-compressing the spring or setting a nonlinear spring.  
Figure 12: Finite element calculation model.