esa-sceva/satcom-chunk-collection huggingface dataset:
# Interpretable Machine Learning for Legume Yield Prediction Using Satellite Remote Sensing Data
###### Abstract  
Accurate crop yield prediction is vital towards optimizing agricultural productivity. Machine Learning (ML) has shown promise in this field; however, its application to legume crops, especially to lupin, remains limited, while many models lack interpretability, hindering real-world adoption. To bridge this literature gap, an interpretable ML framework was developed for predicting lupin yield using Sentinel-2 remote sensing data integrated with georeferenced yield measurements. Data preprocessing involved computing vegetation indices, removing outliers, addressing multicollinearity, normalizing feature scales, and applying data augmentation techniques to correct target imbalance. Subsequently, six ML models were evaluated representing different algorithmic strategies. Among them, XGBoost showed the best performance (\(R^{2}=0.8756\)) and low error values across _MAE_, _MSE_, and _RMSE_ metrics. To enhance model transparency, SHapley Additive exPlanations (SHAP) values were applied to interpret the feature contributions of the XGBoost model. The Enhanced Vegetation Index (\(EVI\)) and Normalized Difference Vegetation Index (\(NDVI\)) were found to be key predictors of crop yield, both showing a positive correlation with higher values reflecting greater vegetation vigor and corresponding to increased yield. These were followed by \(B03\) (green) and \(B12\) (short-wave infrared), which captured key reflectance properties associated with chlorophyll activity and water content, respectively. Both of them substantially influence photosynthetic efficiency and plant health, ultimately affecting yield potential.  
precision agriculture; lupin cultivation; spectral reflectance data; vegetation indices; explainable artificial intelligence; SHAP values +
Footnote †: journal: Applied sciences
# Measuring Gas Flaring in Russia with Multispectral VIIRS Nightfire
## 3 Results
### Comparison with the Reported Gas Flaring and Oil Extraction Data
Figure 8: Scatterplot VIIIRS Nightfire estimates (BCM) vs. reported gas flaring volumes (BCM) by the major oil and gas companies in Russia available in their annual reports or reports on sustainable development (directly or as a result of APG extraction volumes and gas utilization rate) in 2012–2020. Note that both Nightfire and reported data include only oil upstream facilities. 1 The Rosenft reported data and the VIIIRS Nightfire data include TNK-BP and Slavneft assets starting in 2013 and Bashneft in 2016. The Rosneft data include shares in joint ventures. 2 The Gazprom Neft reported data and the VIIRS Nightfire data exclude shares in joint ventures. 3 Due to the event described in Section 4.1, Lukoil’s 2016 data are excluded from the graph.
## 3 Numerical Examples
### Numerical Validation  
In order to compare the dispersion relation of circumferential thermoelastic waves with that of a hollow cylinder of silicon nitride in the literature [21], the same material as well as geometrical dimensions were used in this paper for the calculations. The geometrical dimensions in the literature are an inner radius of 9 m and a tube thickness of 1 m. Where Figure 2a,b show the result plots in the references, the result of this paper as a term is taken as M = 8, and the results are shown in Figure 2c,d. From Figure 2, it can be seen that the results of this paper are consistent with those in the literature, i.e., it proves that the theoretical analysis and calculation in this paper are correct.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Units** & **Quantity** \\ \hline \(c_{11}\) & \(10^{9}\) N\(\cdot\)m\({}^{-2}\) & 574 \\ \(c_{12}\) & \(10^{9}\) N\(\cdot\)m\({}^{-2}\) & 127 \\ \(c_{13}\) & \(10^{9}\) N\(\cdot\)m\({}^{-2}\) & 127 \\ \(c_{23}\) & \(10^{9}\) N\(\cdot\)m\({}^{-2}\) & 195 \\ \(c_{22}\) & \(10^{9}\) N\(\cdot\)m\({}^{-2}\) & 433 \\ \(c_{33}\) & \(10^{9}\) N\(\cdot\)m\({}^{-2}\) & 433 \\ \(c_{44}\) & \(10^{9}\) N\(\cdot\)m\({}^{-2}\) & 119 \\ \(c_{55}\) & \(10^{9}\) N\(\cdot\)m\({}^{-2}\) & 108 \\ \(c_{66}\) & \(10^{9}\) N\(\cdot\)m\({}^{-2}\) & 108 \\ \(\rho\) & \(10^{3}\) kg\(\cdot\)m\({}^{-3}\) & 3.2 \\ \(C_{c}\) & J-kg-deg\(\cdot\)m\({}^{-1}\) & 670 \\ \(\beta_{1}\) & \(10^{6}\) N\(\cdot\)deg\({}^{-1}\cdot\)m\({}^{-2}\) & 3.22 \\ \(\beta_{2}\) & \(10^{6}\) N\(\cdot\)deg\({}^{-1}\cdot\)m\({}^{-2}\) & 2.71 \\ \(\beta_{3}\) & \(10^{6}\) N\(\cdot\)deg\({}^{-1}\cdot\)m\({}^{-2}\) & 2.71 \\ K\({}_{1}\) & W/W\(\cdot\)K & 55.4 \\ K\({}_{2}\) & W/m\(\cdot\)K & 43.5 \\ K\({}_{3}\) & W/m\(\cdot\)K & 43.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties of silicon nitride.  
Figure 2: Dispersion curves of silicon nitride circumferential thermoelastic waveguides: (**a**,**b**) show results in the literature; (**c**,**d**) display the authors’ results.
# DTGCF: Diversified Tag-Aware Recommendation with Graph Collaborative Filtering
## 5 Experiments
### Ablation Study  
In this section, we perform ablation studies to investigate the effect of the designed components in DTGCF, including personalized category-boosted negative sampling, adversarial learning, and two-way TransTag regularization. Specifically, we compare our model with the basic tag-aware GCN without any additional component and the tag-aware GCN with only one component. In addition, we also compare the performance with DPP and DGCN. Table 3 lists the comparison results on the Movielens 10M dataset, from which we have the following observations:  
1. In terms of accuracy, our basic GCN without any additional component performs substantially better than DPP and DGCN, which demonstrates the effectiveness of the proposed GCN architecture for tag-aware recommendations. Nevertheless, the diversity of the basic GCN is not good compared to DPP and DGCN, resulting in pool user satisfaction;
2. Each component plays an obvious positive role in improving diversity or accuracy. After introducing personalized category-boosted negative sampling into the tag-aware GCN, the recommendation diversity is significantly promoted while keeping comparable accuracy performance. The utilization of adversarial learning can achieve an impressive increase in diversity, but make a large sacrifice on accuracy, which confirms the existing accuracy-diversity dilemma. In particular, the basic GCN with adversarial learning has competitive diversity performance compared to DPP and DGCN. By adding the two-way TransTag regularization function, the model achieves obvious improvement on accuracy and slight enhancement on diversity;
3. In terms of the trade-off metrics, the proposed DTGCF is consistently superior to all the variant methods, which indicates that, by integrating three components of different characteristics within a joint learning framework, the proposed DTGCF can provide more satisfactory recommendations with respect to both accuracy and diversity.
# Enhancing Production Efficiency Through Digital Twin Simulation Scheduling
###### Abstract  
Flexible custom manufacturing is becoming increasingly important, and, in the near future, it will serve as a key method to counter growing competition and meet market demands across most industrial sectors. This situation necessitates the substantial reorganization of companies' material and information flows, as traditional planning approaches focused on serial production and longer time horizons are gradually losing their effectiveness. An integrated digital twin system that unifies production and logistics planning is emerging as a promising solution. The proposed approach entails implementing a digital twin directly within custom manufacturing, enabling the continuous monitoring and real-time adjustment of production plans based on instant data from sensors and information systems. The system architecture is designed around multiple modules responsible for data collection and processing, scheduling, simulation, statistical analysis, and effective communication between the system and its users. By leveraging these components, the solution can flexibly adapt to any deviations or changes as they occur. Within the scope of this research, attention is devoted not only to the handling of dynamic and random data but also to the prioritization of individual orders. Equally emphasized is the role of intelligent communication tools, which promptly inform us about shifts in the production process and allow for rapid plan modifications to ensure the highest possible levels of efficiency and reliability.  
custom manufacturing; digital twin; production planning; real-time adaptation; dynamic data +
Footnote †: journal: Applied sciences  
Article
## 2 Methodologies  
### Fault Diagnosis  
Critical machines regularly have permanently installed vibration sensors and transducers, and the acquired vibration signal is used to diagnose the machine for maintenance [31; 32]. Many vibrations are directly linked to cyclical events in the machine equipment's operation, such as rotating shafts gear-teeth. Such events occur often and give a direct indication of the source from the frequency; hence, many powerful diagnostic techniques are usually using frequency analysis.  
However, critical industrial machinery always has a complicated configuration and operates under mixed non-stationary modes. The various vibration signals of these machines are complex and are often contaminated by other interference and noises. The collected signals are analyzed with traditional signal-processing methods, such as wavelet analysis and envelope spectrum analysis [33]. More importantly, the traditional fault-diagnosis accuracy is low considering the massive and multiple data. Therefore, it remains a challenge to accurately and intelligently diagnose machine faults using the recorded vibration signals.
# Laser Transverse Modes with Ray-Wave Duality: A Review
## 2 Ideal Spherical Cavity
### Experimental Results and Theoretical Confirmations
For \(Q\to\infty\), the summation in Equation (20) can be rewritten as the integration:  
\[\Psi_{0,N}^{\pm}(\mathbf{r})=\frac{1}{2\pi}e^{-i\Theta^{\pm}(\mathbf{r})}\psi_ {0}(\widetilde{x})\int_{0}^{2\pi}g(\widetilde{y},v^{\pm})\,e^{i\,N\theta}d\theta \tag{21}\]  
with  
\[v^{\pm}(\theta)=\sqrt{N}\,\exp\left\{-i\left[\theta+\phi_{y}\pm\theta_{\text{G }}(z)\right]\right\} \tag{22}\]  
Applying the orthogonal property  
\[\frac{1}{2\pi}\int_{0}^{2\pi}e^{i(n-m)\theta}d\theta=\ \delta_{n,m} \tag{23}\]  
to Equation (14), we can obtain  
\[\frac{1}{2\pi}\int_{0}^{2\pi}g(\widetilde{y},v^{\pm})\,e^{iN\,\theta}\,d \theta=\ a_{N}\,e^{-iN\phi_{y}}\,e^{\mp i\,N\theta_{\text{G}}(\widetilde{z})} \,\psi_{N}(\widetilde{y}) \tag{24}\]  
Equation (24) implies that the Gaussian wave packet \(g(\widetilde{y},v^{\pm})\) can be used to express the eigenmodes \(\psi_{N}(\widetilde{y})\) with an integral transformation. Substituting Equation (24) into Equation (21), we can obtain  
\[\lim_{Q\to\infty}\Psi_{0,N}^{\pm}(\mathbf{r})=a_{N}\,e^{-i\,\Theta^{\pm}( \mathbf{r})}\ e^{-iN\phi_{y}}\,e^{\mp i\,N\theta_{\text{G}}(\widetilde{z})}\, \psi_{0,N}^{(HG)}(\widetilde{x},\widetilde{y}) \tag{25}\]  
where \(a_{N}=N^{N/2}e^{-N/2}/\sqrt{N!}\). Equation (25) means that the origin of HG eigenmodes is related to a nondegenerate cavity under the off-axis local pumping. Figure 6 shows the calculated patterns for \(\left|\Psi_{0,N}^{\pm}(\mathbf{r})\right|^{2}\) and \(\left|\psi_{0,N}^{(HG)}(\widetilde{x},\widetilde{y})\right|^{2}\) with different values of \(Q\) and \(P=1\) for \(N=6\) to manifest the asymptotic property in Equation (24). Besides the planar rays, the representation in Equation (19) can be extended to the nonplanar tracings. The calculated results by using Equation (19) with \(N_{x}=N_{y}=50\) for the transverse patterns on the flat mirror are shown in Figure 7, all the cases one-to-one corresponding to the nonplanar geometric modes shown in Figure 3. The calculated results are in good agreement with the geometric spots for all cases. Similar to the planar modes, if the transverse orders \(N_{x}\) and \(N_{y}\) are less than \(Q\), the wave intensity cannot be localized on the geometric rays. For \(N<Q/2\), the state \(\Psi_{N,N}^{\pm}(\mathbf{r})\) with \(\phi_{x}-\phi_{y}=\pi/2\) can be found to exhibit the spatial pattern very close to the LG mode. Figure 8 shows the calculated transverse patterns of \(\Psi_{N,N}^{\pm}(\mathbf{r})\) on the flat mirror with \(P/Q=2/7\) and \(\phi_{x}-\phi_{y}=\pi/2\) for \(N=1,3,14\) to reveal the variation from the LG modes to the geometric modes. The present demonstration can be extended to the elliptical mode by considering \(\phi_{x}-\phi_{y}\
eq\pi/2\).
# Partial Fake Speech Attacks in the Real World Using Deepfake Audio
## 3 Background  
### User Identification Based on Voice  
**Speaker Voice Recognition by Humans.** Humans consistently and accurately identify individuals by their voices, particularly when there is a high degree of familiarity with the person, such as close acquaintances or public figures. Often, a brief non-linguistic cue, like a laugh, is sufficient for us to recognise a familiar person [31]. Although human speaker identification is not perfect, it is highly accurate and has inspired the creation of speaker recognition systems for security purposes.  
**Automatic Speaker Recognition (ASR).** Speech signals reveal different speaker characteristics, such as their origin, identity, gender, and emotion. This unique feature of speech allows speaker profiling through speech-based techniques, which can be used in different areas like forensics and recommendation systems. Speaker recognition is a widely researched subject with two primary objectives: automatic speaker identification, which involves determining the speaker's identity, and automatic speaker verification, which involves confirming the claimed identity [32].  
**Automatic Speaker Identification (ASI).** Automatic speaker identification is a technology that uses algorithms and computational techniques to automatically identify the speaker using an audio sample [33]. ASI systems analyse various voice features, such as pitch, tone, rhythm, and spectral characteristics, to create a unique user voiceprint or template.  
**Automatic Speaker Verification (ASV).** Automatic speaker verification is a technology that uses computational methods to automatically verify whether a speaker's claimed identity matches their actual identity based on their voice characteristics. ASV is often used in applications where secure access or authentication is required.
# Power Losses Investigation in Direct 3 \(\times\) 5 Matrix Converter Using MATLAB Simulink
## 5 Simulation Results
### Simulation of Power Losses in SiC-Based Bidirectional Switch  
In the next simulation, the matrix converter with SiC bidirectional switches was investigated. The simulation parameters and conditions were the same as for previous models. The distribution of the power losses in the SiC MxC is shown in Figure 8.  
The columns in Figure 8 show a significant reduction in the power losses of the bidirectional switches because MOSFET can conduct current in the reverse direction, thus effectively eliminating internal body diode losses, which were significantly higher than losses in the conducting channel. The total power losses at maximum output power were 56.6 W, from which switch losses formed 27.6 W, direction diodes were 18 W, and filter losses were 11 W. The total output power of the simulation was 945 W. The output power was higher due to the lower power losses in the power part of the matrix converter. The Simscape library was used to properly implement power losses models of the used semiconductors. Subsequently, m-file script was used to extract all data from the simulation and calculate average values of power losses used in Figures 4, 6 and 8. The total output power with every type of switch is shown in Figure 9.  
As we can see, the total output power was very similar, but the output power was higher with the SiC simulation model, due to the lower power losses. This is because the power losses were lowest in this configuration, and were transferred to the output. The efficiency of the simulation model is shown in Figure 10.  
Figure 8: Distribution of power losses in SiC-based MxC.  
Figure 9: Output powers of the simulation models.  
As can be seen in Figure 10, the highest efficiency of 94.35% was again reached at the maximum value of the q\({}_{\text{INV}}\).
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 3 Thermoplastic Resin Toughening Method
### Thermoplastic Resin Toughening Mechanism  
The toughening of epoxy resin with thermoplastic resins primarily involves mechanisms such as the crack pinning mechanism and bridging constraint effect. The crack energy dissipation mechanisms include crack deflection, crack bridging, hard particle debonding, interparticle rupture, shear band formation in the matrix, and void growth [59].  
In the 1970s, [PERSON] et al. [60] first proposed the crack pinning mechanism. This mechanism suggests that, under stress, as a crack tip with unit length propagates through the cured material, it encounters a series of curing particles tightly bonded to the matrix. At these particles, the crack path deviates, creating secondary cracks. In the initial stage, the formation of secondary cracks causes the crack front to exhibit non-linear characteristics, and this new crack front can absorb and store energy.  
Based on [PERSON]'s crack pinning mechanism, [PERSON] et al. [61] proposed a bridge-crack pinning theory, which is well-suited for describing toughening in epoxy resins with thermoplastic resins. Thermoplastic resins often possess an elastic modulus comparable to that of epoxy resins and a much higher elongation at break than the matrix. This allows their particles to bridge across cracks in the brittle epoxy resin, constraining and closing the cracks. The bridging effect of thermoplastic resin particles not only restrains the overall propagation of the crack front but also acts as a pinning force on the crack, resulting in a wavy, bow-shaped crack front.
## 7 Cross-Layer Design Schemes
### Application and MAC, Physical Layer Schemes
The work addressed the issues of end-to-end packet losses due to excessive path loss, interference, handoffs, congestion, and system load. It was observed that when the signal contains redundancies, the packet losses can be treated as lossy compression performed by the channel. Lost packets are identified at the application layer via a sequence number field in the packet header of the lower layers. The research observed that reconstruction accuracy degrades gracefully as packet loss rate increases. ECG signals can be recovered with high fidelity even in high packet loss conditions with 99% heartbeat detection accuracy at packet loss rates of 20% with a constant latency of less than 2.5 s.
## 2 Related Work
Machine learning and artificial intelligence are gaining popularity in wireless networks to optimize the utilization of resources and user QoE. In case of multiple network accesses, they are used to optimize network access selection and switching or handover processes. The SARA (Smart Aggregated RAT Access) method, which uses multi-agent reinforcement learning, is proposed in [36] to optimize network access capacity in a multi- RAT (Radio Access Technology) environment. Reinforcement learning to optimize the selection of non-terrestrial links with multipath routing in non-stationary multipath 5G non-terrestrial networks is proposed in [37]. Deep reinforcement learning for multipath routing in LEO mega constellation networks is proposed in [38]. An AI (artificial intelligence)-based framework for multiple network access that, besides terrestrial network access, considers LEO satellites is presented in [39]. To evaluate the performance of new methods or algorithms, measurements can be conducted on real networks or testbeds designed using real devices [40]. The existence of open-source 5G network core solutions like Free5 GC or OpenAirInterface enables the development of testbeds in labs. For example, an MP-TCP ATSSS testbed using Free5 GC as a network core and UERANSIM for user equipment is presented in [41]. A 5G-MANTRA testbed for 4G/5G and WiFi multipath evaluation is presented in [16]. However, this is not always possible or feasible. Simulations can provide a means to inspect and analyze performance in a more economical way. A very detailed survey on network simulators, emulators and testbeds is given in [17]. In [42], the authors conducted their own simulation experiments to analyze the performance of their proposed multiservice-based traffic scheduling for 5G ATSSS. In [43], an ns3 simulator is used to analyze the performance of the proposed intelligent access traffic management for hybrid 5G-WiFi networks. A comprehensive list and discussion on 5G simulation techniques is given in [44], where link-level and system-level simulators are also covered. Based on the related work analysis, there is a lack of simulators that focus of on hybrid wireless network access, especially for cases where one of them is satellite access. Also, most of the existing simulation solutions are focused on a narrow set of use cases, like 4G or 5G in combination with WiFi. Hardware components are typically used in real testbeds and represent actual devices without the possibility for meaningful modifications, for example, hardware acceleration. For these reasons, we designed and proposed a distributed AI-based simulation framework that should overcome the aforementioned shortcomings and provide users a flexible environment to test existing and novel solutions such as AI models, channel models, and multipath protocols under different scenario settings.