esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Re-Identification by Estimating and Utilizing Diverse Uncertainty Types for Embeddings
## 3 Uncertainty Estimation in Neural Networks -- Mathematics and Algorithm
### Uncertainty Estimation for Embedding Vectors
#### 3.3.1 Bayesian Module
```
Input: Extracted feature tensor \(F\) according to Equation (3)  Weight tensors \(\widehat{\theta}\)  Number of weight samples \(K\)  Drop-probability \(p\) for DropConnect Output: Output features of the Bayesian module \(\left\{{}^{k}F\right\}_{k=1}^{K}\)
1 for\(k\in\{1,\dots,K\}\)do
2\({}^{k}\theta\leftarrow\widehat{\theta}\)// Initialize \({}^{k}\theta\) with weights of Bayesian module // \({}^{k}\theta\) contains the weights of several tensors // but is viewed as vector here for simplicity for\({}^{k}\theta_{i}\in{}^{k}\theta\)do
3 Sample \(\beta\sim\text{Bernoulli}(1-p)\) \({}^{k}\theta_{i}\leftarrow\beta\cdot{}^{k}\theta_{i}\)// Apply DropConnect
4\({}^{k}F\leftarrow\psi_{\theta}(F)\)// Apply Bayesian module using sampled weights
5 return\(\left\{{}^{k}F\right\}_{k=1}^{K}\)
```  
**Algorithm 1** Bayesian Module  
Discussion of the Number of Weight Samples during Training  
In preliminary experiments, we investigated the effect of using more than one weight sample during both training and inference. In this case, we consistently observed a small decrease in the re-identification quality. Why is a single weight sample beneficial during training? In the case of \(K=1\) during training, the model is required to produce an accurate embedding distribution based on the single weight configuration it has access to. The model must learn to produce these, for any given weight perturbation it may encounter. However, in the case of \(K>1\) during training, the prediction of the embedding distribution results from the average over the output for all \(K\) weight configurations. There is no requirement that any individual output is perfect or fit any particular weight configuration perfectly. It is sufficient for the resulting average to be a good prediction. Consequently, the quality of the output for each single weight configuration can decline, or become less well-adapted.
# Area of Operation Planning for Free-Floating Car Sharing Systems
## 3 Case Study for Vienna
### Mobility Demand Forecast
#### 3.2.2 The Taxi Data Set  
This data set consists of over 400,000 taxi trips within Vienna, which are based on actual trips that took place within four different weeks of the year 2014.  
Although taxi and car sharing services do not necessarily share the same customer base and trip characteristics--round trips are, for instance, less common among taxi rides-- taxi trips have already been successfully used to derive car sharing demand forecasts in other studies on the topic (see, e.g., [11]). Car sharing can be regarded as a competitor since the client base is similar. While the taxi covers a wider serviced area and comes with a driver, car sharing is a cheaper self-service alternative.
# Application of Compound Terrain Factor LSW in Vegetation Cover Evaluation
## 2 Mechanisms of LSW Factors on Vegetation Cover  
### Response of LSW Factors to Hydrologic Conditions  
The current slope and length factor (LS) can describe the shape of the slope's vertical profile and are important indicators of hydrological connectivity and soil erosion. However, the multiplicative fusion method has a tendency to compress information related to topographic features [28; 29], which can lead to distorted representations. In contrast, the LSW factor originated from LS, while creatively integrating LS with the slope width (W) factor in the field of engineering applications. Due to the late introduction of LSW, the application field of this factor needs to be further explored. Theoretically, the slope width integrally represents the hydrodynamic law of the slope: the larger the slope width, the stronger the confluence capacity of the slope where the grid cell lies compared to other slopes [30; 31]. Based on the transverse profile analysis, the slope section's ductility range expands with increasing slope width, which is compatible with the water convergence conditions. According to [PERSON] et al. [32], the horizontal movement of the water flow is mostly influenced by the matrix potential gradient and gravitational potential gradient in the soil. Its horizontal motion is also more apparent when the slope is well ductile. Considering that slope width can more accurately express hydrological connectivity, regions with large slope width suggest that there is abundant surface water flow, which controls solute exchange and streambed sediment interactions [33], ensuring high permeability sediment thickness in the area. This creates a beneficial cycle for hydraulic exchange and eventually encourages the growth and development of nearby plants.
## 2 Materials and Methods
### Effect of MWCNT on Methane Production by Anaerobic Sludge  
Anaerobic granular sludge was obtained from an anaerobic digester of a brewery's wastewater treatment plant, located in the municipality of Vila Franca de Xira (Lisbon district, Portugal). Sludge samples were collected by filling several 1.5 L bottles previously flushed with N\({}_{2}\), and were immediately transported to the laboratory at 4 \({}^{\circ}\)C. In the laboratory, the various samples were combined in a single container, flushed with N\({}_{2}\) and preserved at 4 \({}^{\circ}\)C. The granules were mechanically disrupted and added to each vial at a final volatile solids (VS) concentration of 3 g L\({}^{-1}\). Assays were prepared as detailed by [PERSON] et al. [36], using the basal medium described by [PERSON] et al. [37] with Na\({}_{2}\)S (1 mmol L\({}^{-1}\)) instead of cysteine as reducing agent. Methane production was evaluated in 25 mL serum bottles containing 12.5 mL basal medium, in the presence of MWCNT (0.5 g L\({}^{-1}\) or 1.0 g L\({}^{-1}\)) or in its absence, with acetate (30 mmol L\({}^{-1}\)), ethanol (30 mmol L\({}^{-1}\)) or butyrate (15 mmol L\({}^{-1}\)) as substrate (Figure 1). Substrates were added after overnight incubation (37 \({}^{\circ}\)C, 120 rpm), and the excess pressure was vented. The assays with butyrate received two substrate additions, since very low activity was detected after the first butyrate addition (more than 10 days were necessary for complete butyrate degradation). For that, after day 10 of incubation, the headspace of the bottles was flushed with N\({}_{2}\)/CO\({}_{2}\) (80:20% _v/v_) under sterile conditions, and 15 mmol L\({}^{-1}\) of butyrate were added again. Blank assays, without any added substrate, were also performed. Another set of assays was prepared similarly, using 70 mL serum bottles and 12.5 mL basal medium (Figure 1), with H\({}_{2}\)/CO\({}_{2}\) (80:20% _v/v_, 2.0 \(\times\) 10\({}^{5}\) Pa) as carbon and energy source, and N\({}_{2}\)/CO\({}_{2}\) (80:20% _v/v_, 2.0 \(\times\) 10\({}^{5}\) Pa) as blanks. All assays were performed in triplicate, and incubated at 37 \({}^{\circ}\)C, in the dark, with agitation (120 rpm). Biogas production was monitored with a pressure transducer, which measured pressure variations in the closed bottles [37; 38; 39]. These measurements were performed every hour, during the first eight hours of incubation. For acetate and butyrate, since their degradation was slower, these measurements were continued, at larger time intervals, until the recorded values of methane production reached more than 50% of the theoretical (calculated based on the stoichiometry of the reactions presented in Table S1). Methane percentage in the biogas and VS concentration were determined at the end of the assays. Specific methane production rate (SMPR) was calculated from the highest slope of the linear initial region of the cumulative methane production curve, divided by the mass unit of sludge (in VS), and expressed as mL g\({}^{-1}\) d\({}^{-1}\). The values obtained were corrected for standard temperature and pressure (STP) conditions [39].
## 2 Materials and Methods  
### Reagents and Materials  
The chemicals employed in the experiments were of analytical grade or superior. Commercial CNC, characterized by a needle-like morphology with dimensions of 10-20 nm in width and 50-400 nm in length, was generously provided by Cellulose Lab (Fredericton, NB, Canada). CTAB (cetyltrimethylammonium bromide) with a purity of over 99.0% was sourced from Sigma-Aldrich (St. Louis, MO, USA). The CNC modification using CTAB followed the method described by [PERSON] et al. (2023) [13]. CBG isolate (purity >98%) was obtained via fermentation from Amyris (Lot: 146754; Emeryville, CA, USA). Phytocannabinoid Mixture 10 comprising cannabidiolic acid, cannabigerol, cannabidiol, tetrahydroocannabinolic acid A, \(\Delta\)9-tetrahydroocannabinol, \(\Delta\)8-tetrahydrocannabinol, and (\(\pm\))-cannabichromenewas sourced from Cayman Chemical (Ann Arbor, MI, USA). Trolox ((\(\pm\))-6-hydroxy-2,5,7,8-tetramethylchromane-2-carboxylic acid) and ascorbic acid (>99%) were obtained from Sigma-Aldrich.
## 5 Discussion and Future Research Directions
### Concluding Remarks on Future Directions  
In summary, predictive maintenance systems combine diverse data sources, advanced signal processing, and cutting-edge machine learning techniques to enable proactive management of industrial assets. While these systems offer significant benefits, including reduced downtime and optimized performance, they also face challenges related to data shortages, evolving operational conditions, computational overhead, and explainability. Addressing these gaps will require ongoing innovation in algorithmic design, infrastructure optimization, and user-centred solutions.  
A viable path forward consists of four key strategies. First, scalable data sharing and synthetic augmentation methods can enrich datasets without compromising sensitive information, thereby widening the scope of model training. Second, adaptive and hybrid models offer a means to handle concept drift while improving interpretability by incorporating domain knowledge into learning pipelines. Third, synergistic edge-cloud architectures could distribute computational loads effectively, ensuring faster local decisions while maintaining a robust historical analysis in the cloud. Finally, a human-in-the-loop perspective  
Figure 14: Industry 5.0 implementation framework integrating physical assets, edge/fog AI, cloud learning, and the digital thread with human-in-the-loop feedback.  
aligns with Industry 5.0 values, fostering greater trust, transparency, and sustainability. Through these avenues, predictive maintenance can evolve from proof-of-concept implementations to core industrial practices, ultimately contributing to safer, more efficient, and sustainable operations.
# Research on Compressed Sensing Spectrometry Based on Electro-Optical Transmittance Coding
## 5 Conclusions  
In summary, a new method of compressed sensing spectrometry based on electro-optical transmittance coding is presented. A LiNbO\({}_{3}\) electro-optic modulator driven by sinusoidal AC control voltage was taken as the core to build the transmittance coder. The coding compression measurements were directly accomplished in the spectral dimension. The principle of compressed sensing spectrometry was established. The measurement system was built, and the transmittance coding and compressed sensing spectrum measurement experiments were carried out. The experimental results reveal that the spectral  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Compressed Ratio** & \multirow{2}{*}{**Spectral Resolution**} & \multirow{2}{*}{**Ref.**} \\  & **M/N** & & \\ \hline Aperture & - & 10 nm & [6] \\ transmittance coding & - & 10 nm & [8] \\ LCVR coding & 1:10 & 0.4 nm & [8] \\ FP coding & 1:13 & 1 nm & [9; 10] \\ Thin film coding & 1:14 & 1.4 nm & [12] \\ Metasurface coding & 1:24 & 0.5 nm & [13] \\ Electro-optic & 1:29 & 0.4 nm & Present work \\ transmittance coding & - & 0.2 nm & Ocean Optics HR4000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with reported spectrometry methods.  
reconstruction relative error is in the order of \(10^{-2}\), the FWHM value of the spectral measurement is as high as 1.2 nm, and the spectral resolution can reach approximately 0.4 nm. The method of compressed sensing spectrometry has a high compressed ratio (\(M/N\) = 1:29), and the spectral signal measurement time does not exceed 0.25 ms. The proposed method demonstrates high precise, high-speed, high compressed ratio, and integrated and wide spectral range hyperspectral measurements.  
Methodology, S.W.; software, S.W. and K.L.; formal analysis, K.L. and Z.W.; validation, S.W.; investigation, K.L. and Z.W.; resources, X.H. and Z.W.; data curation, S.W.; writing--original draft preparation, S.W. and K.L.; writing--review and editing, S.W.; supervision, X.H. and Z.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China: 62205309; National Natural Science Foundation of China: 62205310.  
Not applicable.  
The authors declare no conflict of interest.
# DIFshilling: A Diffusion Model for Shilling Attacks
## 5 Experiments
### Ablation Study (RQ4)  
To evaluate the necessity and effectiveness of each component in DIFshilling, we conduct an ablation study by isolating the forward noising and reverse denoising mechanisms. Specifically, we analyze the attack performance and adversarial robustness of two DIFshilling variants:  
* **Forward Process Only:** This variant applies the noise-adding step without incorporating any learning or reverse denoising, generating fake users solely through random perturbations.
* **Backward Process Only:** This variant begins with predefined Gaussian noise and applies the reverse denoising process to recover fake user profiles, without explicitly injecting noise in the forward phase.  
We evaluate these variants on two datasets (ML100K and FilmTrust) using seven victim RS models, assessing both attack performance and adversarial robustness. The results are presented in Table 5 and Figure 8. The results from Table 5 indicate that the forward process only variant leads to a more pronounced decline in attack performance compared to backward process only. For instance, in ML100K, SHR@10 drops from 5.4083 in DIFshilling to 1.3786 for BPR, with similarly low values observed across other models. This suggests that noise addition alone is insufficient to generate effective attack profiles. Conversely, backward process only demonstrates moderate attack effectiveness (e.g., SHR@10 reaches 3.4995 for BPR in ML100K), yet it remains significantly weaker than the full DIFshilling model. This finding suggests that initiating the attack from pure noise without a forward noising phase constrains the model's ability to generate highly effective adversarial users. However, the trend is reversed when considering adversarial robustness. The forward process only variant, which introduces irregular random noise, generates attack profiles that are more difficult to detect. In contrast, the backward process only  
Figure 7: Different percentages of fake user injection in the FilmTrust latent space.  
variant follows a structured denoising process, making the generated attack profiles more identifiable. Ultimately, the full DIFshilling model outperforms both ablation variants across all metrics, demonstrating that the combined use of forward noising and reverse denoising is essential for maximizing attack effectiveness while maintaining stealth against detection mechanisms.
## 5 Application of MSWOA in Fault Diagnosis of Analog Circuits  
In this section, we utilized the Multisim14 software to simulate two well-known filter circuits, namely the Sallen-Key band pass filter circuit and the four-op-amp biquad high-pass filter circuit. For a start, we set the maximum deviation of components to be 40% for the soft faults. In each experiment, only one component was selected as the fault component at a time, and the parameters of other components were within the tolerance. Then, the Monte Carlo simulation analysis was conducted on each state of the test circuit. We extracted the features using a wavelet packet and then randomly divided those data into the training set and testing set. After that, the kernel parameter \(\gamma\) and the penalty parameter \(C\) of SVM were optimized by the proposed MSWOA method, while the diagnosis accuracy was defined as the fitness function of the MSWOA during the optimization process. Finally, we applied the trained SVM to the fault diagnosis of analog circuits.  
We conducted a total of two sets of diagnosis experiments in this literature. Two different optimization methods, namely, PSO and WOA, were compared with the proposed MSWOA in each group of tests. The overall process of fault diagnosis of analog circuits based on SVM and MSWOA is illustrated in Figure 14.  
Figure 14: The overall process of analog circuits fault diagnosis.
# Demystifying DFT-Based Harmonic Phase Estimation, Transformation, and Synthesis
## 2 Robust DFT-Based Phase Estimation of Individual Sinusoids
### DFT and the Sine and Shifted Hanning Windows
First, the side lobe attenuation decay (not shown in these figures) is stronger for the sine window, when compared to the rectangular window [5]. Second, because the main lobe of the frequency response of the sine window is wider than the main lobe of the rectangular window, this means that when \(\Delta_{\ell}\) is such that the magnitudes of the spectral lines \(k=k_{\ell}-1\) and \(k=k_{\ell}\) become comparable, or the magnitudes of the spectral lines \(k=k_{\ell}\) and \(k=k_{\ell}+1\) become comparable, these magnitudes are closer to the maximum value they can reach (which takes place for \(\Delta_{\ell}=0.0\)) than what happens for the rectangular window. These concurrent reasons make it that relative to the case of the rectangular window, phase estimation using the sine window is likely to be more immune to the noise influence and to suffer fewer leakage effects. This can be confirmed by making a simple study on the phase estimation error, as it was described in the previous subsection for the rectangular window. Using the same simulation conditions, and the same phase estimation function (Equation (10)), we obtain the cumulative phase estimation error results that are illustrated in Figure 7.  
Figure 6: When \(\Delta_{\ell}\in[-0.5,0.5]\), the magnitude spectrum of the DFT of a sine-windowed sinusoid exhibits a local maximum for \(k=k_{\ell}\). The plots illustrate the case when \(\Delta_{\ell}=-0.5+\epsilon\) (**left**), when \(\Delta_{\ell}=0.0\) (**center**), and when \(\Delta_{\ell}=0.5-\epsilon\) (**right**), where \(\epsilon<0.5\) is a small real positive number. When compared to the results in Figure 5, it can be concluded that phase estimation appears to be more accurate when the sine window is used, especially when the SNR is high, which is a natural consequence of the smaller leakage caused by this window. Results are similar if the shifted Hanning window is considered instead. As we shall see in Section 2.5, more informative conclusions will emerge from a study of the phase estimation error variance.
## 3 Wireless Channel Analysis
### Distributed Antenna System  
The DAS is an explicit way with the merits of easy to deploy and low cost of equipment. As shown in Figure 1, the Hyperloop is equipped with MIMO antenna array for wireless connection to the ground AAUs. The on-board terminals connect with the inside-train antennas via WiFi technology, these antennas converge all terminals signal and forward them to the outside MIMO antennas via cable or vice versa. This two-hop relay structure avoids a radio wave penetration attenuation of the train body, guaranteeing a relatively stable received amplitude. As such, the wireless channel between ground antennas and train antennas is crucial to the communication system performance.  
In [15], we investigated the wireless channel characterization of this access scheme based on the propagation-graph channel modeling method [30] and obtained some interesting results. In the simulation, the transmitter (Tx) is installed at the inner side of the tube and the receiver (Rx) is set at the top of the train body. To be compatible with existing public mobile cellular communication system, we consider the 5G communication standard and set the bandwidth to 100 MHz and carrier frequency to 4.85 GHz. The shape and size of the tube is set according to [5]. The Hyperloop proceeds at a speed of 1000 km/h traveling across the ground antenna (Tx) located at the middle of the tube.  
Figure 1(a) plots the emulated normalized channel impulse responses (CIRs) at different positions. The strongest received power appears when the Rx arrives at the middle of the tube, i.e., the shortest distance between Tx and Rx. In addition, the number of the effective taps increases as the Hyperloop approaches the ground antenna. The Doppler effect reflects the relative motion between Tx, Rx, and surroundings, which is extremely significant to the channel characterization for the Hyperloop.  
Figure 1: The sketch of the distributed antenna system.  
Figure 2b shows the Doppler power spectrum considering the LOS, single bounce, and double bounce components. Notably, the shape of the Doppler spectrum presents a feature of central symmetry with the symmetry point lies at the position of ground Tx. The main variation tendency of the LOS component is like as a \(Z\) shape with a turning angle close to \(90^{\circ}\), whereas the tendency of the multipath component is like a S shape. In addition, their symmetry points vary along with the vactrain's position. In summary, the severe Doppler effect using the DAS can lead to a drastic amplitude variation of the received signal.
# Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops
## 3 Validation Methodology
### Validation of the Decametric LAI Product and Reference LAIs
#### 3.1.2 Generation of Empirically Based LAI  
For comparison, the hierarchical up-scaling validation approach proposed by CEOS WGCV was also adopted [15]. The empirical transfer function between LAINet LAI and Sentinel-2 reflectances was established based on an iteratively re-weighted least squares algorithm (IRLS) [56; 57; 58]. IRLS is a way of mitigating the influence of outliers in an otherwise normally distributed data set and used to find the maximum likelihood estimates of a generalized linear model. IRLS provides lower weight to unreliable ESUs that do not fit well; therefore, the resulting transfer function is less sensitive to outliers in the data as compared with ordinary least squares regression [59].