esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Improved N-BEATS** & **NHiTS** & **N-BEATS** & **TCN** & **CNN-LSTM** \\ \hline MPE & \(-0.02\%\) & 0.04\% & \(-0.06\%\) & \(-0.04\%\) & \(-0.05\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Prediction biases of different models. Figure 5: Comparison of prediction performance of different models. effect of changing the loss function, demonstrating that the adoption of the Pinball-MAPE loss function reduced the model's MAPE from 0.04435 to 0.04152. The second experiment compared the impact of using original data versus employing a data augmentation strategy. The results indicated that the data augmentation strategy reduced the model's MAPE from 0.07223 to 0.04152. These findings collectively demonstrate that the Pinball-MAPE loss function and the data augmentation strategy significantly enhance the predictive accuracy and generalization capability of the improved N-BEATS model. The improved N-BEATS model adopts advanced architecture, including a nonlinear multivariate regression structure and forward-backward residual connections, significantly enhancing its adaptability to the small-scale dataset used in this study. Even in the presence of noise, the model can still identify fundamental patterns that are crucial for sequence prediction, demonstrating high robustness. This architecture not only captures shared patterns of stiffness sequences under different loading conditions but also learns the unique characteristics of each predictive sequence, thereby improving model adaptability. Additionally, the Pinball-MAPE loss function parameters can be flexibly adjusted to accommodate different prediction tasks, reducing bias and optimizing prediction accuracy. Traditional methods for predicting the stiffness degradation of wind turbine blades are primarily based on physics-driven and empirical models. While these methods provide a certain degree of theoretical interpretability, they exhibit significant limitations in practical applications. For instance, finite element analysis (FEA) offers high-fidelity local stress and stiffness analysis, but its computational cost is extremely high. Due to their strong dependence on predefined material properties and boundary conditions, these methods exhibit limited generalization capabilities under dynamically changing environments. For data-driven physics-based models, extensive experimental calibration is often required, making them highly dependent on the availability and quality of data. Furthermore, these models typically fail to capture complex multi-factor interactions, leading to reduced generalization performance when encountering previously unseen working conditions. In contrast, the improved N-BEATS model can autonomously learn degradation patterns from historical data without requiring predefined complex physical assumptions, thereby enhancing its generalization capability.
## 4 MS-FARIMA Algorithm and Orbit Extrapolation Improvement with MSEC
### MS-FARIMA Algorithm for Orbit Extrapolation Based on MSEC
#### 4.2.1 MSEC for Elimination of Ephemeris Errors
\begin{split}\delta x_{1}(t)&=f^{\prime}_{n,x}\cdot \delta n+f^{\prime}_{i,x}\cdot\delta i+\cdots+f^{\prime}_{\Omega,x}\cdot\delta \Omega\\ &\vdots\\ \delta x_{i-1}(t)&=f^{\prime}_{n,x}\cdot\delta n+f^{\prime}_{i,x} \cdot\delta i+\cdots+f^{\prime}_{\Omega,x}\cdot\delta\Omega\\ &\delta x_{i+1}(t)=f^{\prime}_{n,x}\cdot\delta n+f^{\prime}_{i,x} \cdot\delta i+\cdots+f^{\prime}_{\Omega,x}\cdot\delta\Omega\\ &\vdots\\ \delta x_{K}(t)&=f^{\prime}_{n,x}\cdot\delta n+f^{\prime}_{i,x} \cdot\delta i+\cdots+f^{\prime}_{\Omega,x}\cdot\delta\Omega\\ \end{split}  \tag{29}\]  
where \(f^{\prime}_{n,x},f^{\prime}_{i,x},\ldots,f^{\prime}_{\Omega,x}\) represent the sensitivity coefficients of the position error with respect to the SKOE error. From the above \(K-1\) equations, deviations in the X direction based on the SKOEs \(\delta n,\delta i,\cdots,\delta\Omega\) can be determined. By substituting these into the following formula, the deviation in the X direction of the ephemeris \(\delta x_{i}(t)\) for the current service satellite \(i\) received by the UE can be obtained  
\[\delta x_{i}(t)=f^{\prime}_{n,x}\cdot\delta n+f^{\prime}_{i,x}\cdot\delta i+ \cdots+f^{\prime}_{\Omega,x}\cdot\delta\Omega \tag{30}\]  
Similarly, we can obtain the deviations in the \(Y\) direction \(\delta y_{i}(t)\) and the \(Z\) direction \(\delta z_{i}(t)\)Let \(\Delta S(t)\) be the satellite orbit correction parameter. Based on the above analysis, it can be expressed as  
\[\Delta S(t)=(\delta x_{i}(t),\delta y_{i}(t),\delta z_{i}(t)) \tag{31}\]  
The corrected ephemeris can be expressed as  
\[S_{correction}(t)=S_{boardcast}(t)+\Delta S(t) \tag{32}\]  
where \(S_{correction}(t)\) is the corrected position vector and \(S_{boardcast}(t)\) is the position vector calculated from the TLE ephemeris data.
## 2 Materials and Methods
### Analysis of Volatile Compounds by GC-MS  
Volatile aroma compounds were extracted using solid-phase microextraction (SPME) fibers (DVB/CAR/PDMS, 50/30 \(\upmu\)m, Supelco, 57298-U) and measured using a gas chromatography-mass spectrometer (GC/MS) (7820A/5977E MSD, Agilent Technologies, Palo Alto, CA, USA) with an Agilent GC Sampler 120 PAL autosampler. Before analysis, the injector was conditioned by exposure to 250 \({}^{\circ}\)C for 60 min. For the extraction of volatile compounds, kimchi juice (1 g) was placed in a headspace vial (20 mL, 22.5 mm \(\times\) 75.5 mm) with 1 mL of distilled water and stirred at 300\(\times\)_g_ at 51 \({}^{\circ}\)C for 20 min. For quantitative analysis, 10 \(\upmu\)L of 100 ppm methyl cinnamate (Sigma-Aldrich) dissolved in ethanol were added to distilled water as an internal standard. Then, the volatile components were adsorbed onto the SPME fiber for 30 min, and the fiber was automatically injected into the GC injector in the splitless mode for 2 min. For the GC/MS, a DB-WAX column (50 m \(\times\) 200 \(\upmu\)m \(\times\) 0.2 \(\upmu\)m, Agilent Technologies) was used with helium flowing at a rate of 1.5 mL/min. The injector temperature was 250 \({}^{\circ}\)C, and the oven temperature was increased from 40 \({}^{\circ}\)C (5 min) to 150 \({}^{\circ}\)C (0 min) at a rate of 5 \({}^{\circ}\)C/min followed by an increment to 200 \({}^{\circ}\)C (10 min) at a rate of 7 \({}^{\circ}\)C/min. The chromatograms of volatile compounds were acquired using a scan mode of \(m/z\) 33-250 at a fragment voltage of 70 eV, and peaks were identified through a library search (NIST ver. 11).
## 4 Methodology
### Classification Models
#### 4.4.2 Deep Learning  
CNN is an extension of an artificial neural network (ANN). Similar to ANN with extra layers, such as the convolutional layer and pooling layer, which are used to extract features and downsample the input, thus reducing its computation. On the other hand, Bi-LSTM, which stands for bidirectional long short-term memory networks, consists of two LSM networks, which work in opposite directions. These DL models have been used successfully to classify several Arabic classification tasks [82; 83; 84].  
* **CNN**: is one of the most popular neural networks that consist of an input layer, hidden layer, and output layer. It uses a convolution layer that is used to transform the input data into an easier-processed form. Additionally, the pooling layer is also used to reduce the input dimensions.
* **Bi-LSTM**: is a bidirectional recurrent neural network (RNN) that takes two long short-term memory (LSTM) neural networks. The first time process the input sequence in the forward direction, and the second time process the input in the backward direction. Thus, improves the model learning and produces better accuracy.
* **CNN & Bi-LSTM**: is a hybrid model that combines CNN and Bi-LSTM. CNN is used to reduce the input dimensions. Then, the output is fed into the LSTM layer. This model uses the convolution layer to extract local features, and the LSTM layer uses the ordering of those features to learn about the text ordering of the input.
## 2 Theoretical Descriptions
### The Proposed Optimized Convex 1-D Second-Order Total Variation Denoising Algorithm
The permutation entropy (PE) of different symbol sequences in time series \(x(i)\) is defined by the form of information entropy:  
\[H_{p}(d)=-\sum_{j=1}^{k}P_{j}\text{In}(P_{j}) \tag{16}\]  
The value of \(H_{p}(d)\) indicates the degree of randomness of time series. Namely, the larger value of \(H_{p}(d)\) declares the greater randomness of time series, and vice versa. Therefore, the permutation entropy can be used to detect the randomness of time series. Naturally, the permutation entropy for random noise is large, while the permutation entropy of harmonic signal and the modulation signal is small. From Equation (8), the convex 1-D second-order total variation denoising algorithm involves two parts, the former is fidelity constraint \(\frac{1}{2}\|y-x\|_{2}^{2}\) and the latter is the total variation term \(\phi\left([\mathbf{TV}_{2}(\mathbf{x})]\right)\). It is worthy of mention that \(\lambda\) is used to adjust the weights. If \(\lambda=0\), the total variation term has no difference in the punishment, and the obtained signal \(x\) is equal to the original signal \(y\). Conversely, if \(\lambda\) tends to infinity, the total variation penalty term works and the fidelity will be poor, which may deviate from the original signal \(x\) to a great extent. It should be noted that a certain similarity between the signal after denoising and the original signal should be maintained to avoid the phenomenon of excessive noise reduction. Therefore, a new evaluation index for selecting parameter \(\lambda\) should be proposed to achieve balance in signal fidelity constraints and total variation term. To reasonably select parameters \(\lambda\), this paper proposed a weighted index based on permutation entropy \(PE\_index\) and cross-correlation \(C\_index\) as a measurement of denoising result, which can be calculated by Equations (16) and (17), respectively. \[C\_index=\frac{\text{cov}(x,y)}{\sigma_{x}\sigma_{y}}=\frac{E\left(\left(x- \overline{x}\right)\left(y-\overline{y}\right)\right)}{\sigma_{x}\sigma_{y}} \tag{17}\]  
Consequently, the final index \(PEC\_index\) is determined as the ratio of the cross correlation coefficient and entropy arrangement in Equation (18). Basically, the larger value of \(PEC\_index\) always indicates better denoising result. \[PEC\_index=\text{max}(\frac{C\_index}{PE\_index}) \tag{18}\]
# Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey
## 3 Related Work
### Adversary Models
#### 3.1.1 Global/Local  
The opponent is defined by its context to be local or regional. External adversaries usually have access to the whole network, whereas local adversaries have access to a small portion of the network. For e.g., packet sniffers have access to a limited amount of RSUs installed at road intersections [17].
## 4 Experimental Setup and Results
### Main Results Analysis
#### 4.5.2 Ablation Study  
Ablation analysis is performed to investigate the individual contributions of components within our model. To prove the impact of each main part in ML-MC, we compare ML-MC with its diverse sentence length.  
Furthermore, the ProText dataset was segmented into sentence-level units, with sentence length (Sen-Len) ranges tailored to each platform--12-120 characters for Twitter and 80-320 characters for Facebook. Each document in the dataset is tied to a single class label, under the assumption that its content is topically focused. The proposed model was trained using multi-label, multi-class (ML-MC) techniques and evaluated against a balanced dataset. Table 10 illustrates that the optimal sentence length varies by platform. For Facebook, a Sen-Len of 260 characters yielded the highest performance, with an F1 score of 0.8185 and an accuracy of 89.93%. This suggests that moderately long text segments provide richer context without introducing excessive noise. In contrast, Twitter achieved its best results at a Sen-Len of 60 characters, with an F1 score of 0.7179 and 76.08% accuracy, likely due to the inherently concise nature of tweets, where shorter inputs reduce noise and misclassification. These findings underscore the importance of platform-specific optimization in sentence length to enhance classification accuracy.  
The ablation experiment results indicate that all core components of the ML-MC model contribute significantly to its effectiveness, underscoring their importance in accurately detecting propaganda.
## 8 Numerical Results and Discussion
### Received Power in the VLC Scenario Without IRS Position Optimization  
The results in Figure 4 demonstrate the significant impact of ADR designs and signal-combining techniques on the performance of VLC systems. The baseline scenario with a single PD exhibits a maximum power of \(0.50\) dB and a minimum of \(-41.22\) dB, highlighting its limited angular coverage and significant power variation in this scenario. In contrast, the introduction of ADRs substantially improves both metrics, particularly in MRC.  
Figure 4: Received powers in the indoor VLC scenario without IRS position optimization for a single PD and different ADRs with signal selection or combination mechanisms.  
The pyramidal and hemispherical ADRs using MRC achieve maximum powers of 5.49 dB and 6.37 dB, respectively, with notable improvements in minimum power levels to \(-20.14\) dB and \(-19.59\) dB. These results underscore the advantage of combining signals from multiple PDs, with the hemispherical design being more effective due to its broader angular coverage. Meanwhile, SBC, while simpler, performs poorly compared to MRC. Both pyramidal and hemispherical ADRs with SBC yield lower maximum powers (around 0.81 dB) and higher minimum power losses (\(-23.40\) dB), as SBC does not take full advantage of the ADRs.  
In general, MRC consistently outperforms SBC, demonstrating its superiority in improving signal quality. The hemispherical ADR offers the best performance, making it ideal for scenarios with omnidirectional light distribution. These findings emphasize the importance of advanced ADR designs and combining techniques to achieve uniform and improved power distributions in VLC systems.
# Polynomial Modeling of Noise Figure in Erbium-Doped Fiber Amplifiers
## 2 State-of-the-Art  
### Overview of EDFA Devices  
EDFAs are essential components in fiber-optic telecommunications, known for their high gain, low noise, and broad wavelength support. These devices amplify input signals in optical fibers using a core doped with erbium ions. The amplification process involves a pump laser that excites the erbium ions, leading to the stimulated emission of additional photons [7]. EDFAs mainly operate in three modes:  
1. **Automatic Gain Control (AGC):** Regulates the laser source current to achieve a target gain.
2. **Automatic Power Control (APC):** Similar to AGC but targets output power.
3. **Automatic Current Control (ACC):** Delivers a constant current to the laser source.  
Commercial EDFAs allow setting the target gain (in dB) and target tilt, which indicates the slope of the peak power of active channels in the output power spectrum, usually evaluated as the difference between the power levels of the first and last channel within the bandwidth of interest (typically the optical C-band or L-band), also expressed in dB.
## 4 Results: Proof of Concept
### Identity Management Implementation  
This section focuses on the implementation of the identity module, which is a critical component for managing Decentralized Identifiers (DIDs) in IoT devices. The module includes the generation of a DID document (DDo) for the Raspberry Pi, representing the IoT device (subject), and for a trusted entity acting as the issuer. These roles simulate the basic interactions in a Self-Sovereign Identity (SSI) ecosystem.  
Since the DIDComm communication protocol, explicitly designed to enable secure and interoperable communication between entities using Decentralized Identifiers, is not yet integrated into the current framework, an alternative approach was taken to replicate these interactions. This simulation fills the gap by allowing the exploration of how entities establish trust, exchange credentials, and validate identities in a decentralized environment. The implementation highlights the key processes of creating, managing, and verifying DIDs and their associated cryptographic proofs, laying the groundwork for future integration of robust communication protocols such as DIDComm.  
This simulation also demonstrates the framework's flexibility in handling identity management tasks despite the lack of full protocol support, underscoring its potential for real-world applications in IoT ecosystems.
# A Palette of Efficient and Stable Far-Red and NIR Dye Lasers
## 4 Conclusions  
The rich and versatile chemistry of BODIPYs is a suitable tool to design a chart of photoactive dyes as efficient and stable tunable lasers within the spectral window ranging from 600 to 725 nm. Within the far-red region of the visible spectrum, conformationally restricted benzofuran-fused BODIPYs are a suitable option. Their flat and rigid aromatic framework allows a huge absorption of the pumping light and avoids non-radiative deactivation funnels. Therefore, these dyes yield efficient (approaching the 50%) and stable laser signals ranging from 600 to 650 nm. To further push the laser emission toward the NIR region, polyarylated aza-BODIPYs are more suitable. Strikingly, the chemical modification at  
Figure 6: Photostability (energy dose required to decrease 10% of the laser-induced fluorescence E\({}_{\text{dose}}\)\({}^{90\%}\)) of the \(F\)-, \(C\)-, and \(O\)-aza-BODIPYs in ethyl acetate.  
the boron atom is not a recommended strategy because the chemical and photochemical stability of the dye is reduced. Therefore, _F_-aza-BODIPYs are the best design to record notable laser emission from 650 to 725 nm, which is outstanding, mainly due to their high photostabilities.  
Both set of dyes surpass the laser performance of commercially available laser dyes working in the same spectral region. On the one hand, the benzofuran-fused BODIPYs markedly improve the brightness of the laser emission in the far-red region. On the other hand, the aza-BODIPYs greatly increase the photostability, leading to long-lasting tunable laser emissions in the NIR region.  
Furthermore, these molecular scaffolds might be envisaged as promising fluorescent probes for bioimaging purposes owing to their efficient light absorption within the biological window and ulterior display of bright fluorescent images over long exposure periods.  
Synthesis of benzofuran-fused BODIPYs, E.P.-C.; synthesis of aza-BODIPYs, A.R.A. and M.J.O.; photophysical and laser measurements, E.A.-Z., supervision of laser experiments, I.G.-M.; supervision of photophysical experiments, writing--original draft preparation, and editing, J.B.; writing--review, L.G.-R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Spanish Ministerio de Economia y Competitividad (MINECO), grant number MAT2017-83856-C3-1-P, 2-P and 3-P, and Gobierno Vasco, grant number IT912-16.  
The authors declare no conflict of interest.
# Automatic Detection of Multilevel Communities: Scalable, Selective and Resolution-Limit-Free +
## 4 Discussion  
Above, we have validated the effectiveness of our method on synthetic benchmark networks, including the hierarchical RB networks and the heterogeneous LFR networks. We also investigated its applications on real-world networks, and exhibited the consistency and discrepancy between our results and the metadata. The outperformance of our method can be attributed to two distinctive features: (1) the scalability of the community fitness function, and (2) the stability of the outputs. Next, we make some discussions on the features of our method.
# Adapting Satellite Soundings for Operational Forecasting within the Hazardous Weather Testbed
## Materials and Methods
### Methods
#### 2.2.2 Methods for Product Delivery  
During the 2019 HWT, gridded NUCAPS, modified NUCAPS, and NUCAPS-Forecast, were not available through the Satellite Broadcast Network (SBN), which is the operational data stream for NOAA NWS and external users. Instead, data were delivered online through a Local Data Manager (LDM) [29] from both the NASA Short-term Prediction Research and Transition (SPoRT) and Cooperative Institute for Research in the Atmosphere (CIRA).
## 9 Stokes Space Polarization and Field Recovery Principle
### Polarization Recovery Techniques in the Direct Detection Stokes Vector Receiver
#### 9.1.4 Analog Polarization Identification  
As previously mentioned, when no discrete constellation information is presented, some training-assisted polarization characterization is required. However, the requirement of the three polarization bases' transmission in the Stokes space might be a daunting task for the transmitter. A good instance of such scenarios is the implementation of the 3D Stokes space field recovery. In this case, irrespective of the adopted 1D field recovery scheme for the \(X\)-POL, a high CSPR is required either to guarantee a positive intensity envelope or reduce the SSBN power ratio. Consequently, the \(X\)-POL will possess a relatively higher power compared with the \(Y\)-POL, which results in an asymmetric distribution in the Stokes space. In such a condition, the associated signal will be intensely polarized towards \(X\)-POL. Based on this, the basis vector by the \(S_{1}\) axis only can be transmitted [13].  
Moreover, based on the asymmetric feature of polarization modulations, polarization axis identification is easy and digital signal knowledge is not required. Consequently, all-analog polarization recovery can be realized [13; 150]. The concept has been presented for the DSP-free COH systems' polarization recovery. In the implementation, to label the polarization state, a marker tone is appended to one polarization. It is noteworthy that the initial effort of analog polarization identification (API) in the Stokes space centers on the SCOH-SSM. In this, there is a constant carrier on the Y-POL that brings about a distribution in which the central axis orientation is towards the \(S_{1}\) axis [13]. Furthermore, if the signal distribution in the Stokes space is asymmetric, then the analog polarization identification is a polarization recovery algorithm that is modulation independent. The procedure for the Stokes space analog polarization identification realization is shown in Figure 23.  
A notable impact of the analog polarization identification is that digital clock recovery is not required by the polarization recovery. For instance, the average power \(\left\langle S_{1}\right\rangle\) of the 3D Stokes space modulation and the SCOH-SSM distribution are not dependent on proper digital sampling. Consequently, asymmetric axis identification can be realized in either the digital domain or analog domain through any sampling frequency mismatch. Moreover, the realization of polarization recovery ahead of clock recovery offers notable advantages. One is that the Stokes-space-modulated signal clock recovery can be simplified. Apart from the fact that the Stokes vector receiver will demand less DSP, it can also be DSP-free. This will eventually result in a receiver with relatively lower power consumption [13; 150].