esa-sceva/satcom-chunk-collection huggingface dataset:
# Improving Air Pollution Prediction System through Multimodal Deep Learning Model Optimization
## 4 Deep Learning Models
### LSTM AutoEncoder  
The LSTM [11] model solves the problem related to information from the past cannot be transmitted up to the end in a long time series structure, which is a disadvantage of RNN [11]. The LSTM model has many variants, such as bidirectional-LSTM.  
The multimodal deep learning model proposed in this paper combines the features of the numerical dataset, which are processed by the LSTM-based model, with the features of the image dataset, processed by the CNN-based model. As a result, the number of features increases, making learning difficult due to increased dimensions. To solve this problem, we use the LSTM AutoEncoder that efficiently encodes the high-dimensional data. We already showed that LSTM AutoEncoder outperforms the vanilla LSTM model in our problem [2].  
The LSTM AutoEncoder has a layer on top of LSTM layers to extract features by reducing input data dimension [12] and creating original data based on the extracted features. In general, the AutoEncoder is widely used for self-supervised learning or unsupervised learning, where the input and the label are the same, but in our previous work [2], the AutoEncoder is used for supervised learning. Figure 3 shows the LSTM AutoEncoder implemented in our previous work [2], where the encoder has two LSTM layers for input and dimensionality reduction, and the decoder also has two LSTM layers for output and dimensionality recovery. The original input value of the constructed model, data from the past 5 h, goes through the encoder and decoder to predict PM\({}_{10}\) values after 1-8 h from the present.  
Figure 2: Cropped satellite image of the Korean Peninsula.
# Bandwidth-Based Wake-Up Radio Solution through IEEE 802.11 Technology
###### Abstract  
IEEE 802.11 consists of one of the most used wireless access technologies, which can be found in almost all consumer electronics devices available. Recently, Wake-up Radio (WuR) systems have emerged as a solution for energy-efficient communications. WuR mechanisms rely on using a secondary low-power radio interface that is always in the active operation mode and is in charge of switching the primary interface, used for main data exchange, from the power-saving state to the active mode. In this paper, we present a WuR solution based on IEEE 802.11 technology employing transmissions of legacy frames by an IEEE 802.11 standard-compliant transmitter during a Transmission Opportunity (TXOP) period. Unlike other proposals available in the literature, the WuR system presented in this paper exploits the PHY characteristics of modern IEEE 802.11 radios, where different signal bandwidths can be used on a per-packet basis. The proposal is validated through the Matlab software tool, and extensive simulation results are presented in a wide variety of scenario configurations. Moreover, insights are provided on the feasibility of the WuR proposal for its implementation in real hardware. Our approach allows the transmission of complex Wake-up Radio signals (i.e., including address field and other binary data) from legacy Wi-Fi devices (from IEEE 802.11n-2009 on), avoiding hardware or even firmware modifications intended to alter standard MAC/PHY behavior, and achieving a bit rate of up to 33 kbps.  
IEEE 802.11; Wake-up Radio; green networks IEEE 802.11; Wake-up Radio; green networks IEEE 802.11
## 4 Experiments
### Evaluation Metrics
The average degradation is computed as  
\[SRS=Acc_{full}-\frac{1}{C}{\sum_{c=1}^{C}Acc_{masked}^{(c)}} \tag{25}\]  
Lower values of \(SRS\) suggest greater tolerance to absent or degraded spectral inputs, which indicates an enhanced capability of cross-modality generalization. For assessing the feasibility of deployment, we report the model parameter count \(P\), floating-point operations (FLOPs) in a single forward pass, and inference latency \(T\) on GPUs as well as edge devices. These measurements indicate the burden the model imposes with real-time responsive constraints:  
\[P=\sum_{t=1}^{L}dim(W_{l}),\ \ T=mean(runtime\ per\ sample) \tag{26}\]  
where \(W_{l}\) are the trainable weight matrices in layer \(l\). Together, these metrics provide a comprehensive view of HUE-Net's capabilities--from recognition accuracy and confidence calibration to operational efficiency and spectral robustness--highlighting its suitability for real-world, safety-critical applications. To ensure the reproducibility and statistical validity of our results, all reported metrics--including top-1 Accuracy, the SRS, ECE, and the Brier score--are averaged over five independent training runs with different random seeds. Each run uses identical training protocols and dataset splits. For each metric, we report the mean \(\pm\) standard deviation, reflecting the inherent variability due to stochastic training factors such as initialization and dropout. All experiments were conducted using PyTorch 2.1 on an NVIDIA RTX 2080 GPU. The network was trained from scratch using the Adam optimizer with an initial learning rate of 0.001, decayed by a cosine annealing scheduler. The batch size was 64, and training was run for 150 epochs. We employed standard data augmentation techniques (random crop, horizontal flip, brightness/contrast jitter) applied uniformly across all modalities (RGB, NIR, event). We used an 80/10/10 split for training, validation, and testing, ensuring no identity overlap between sets. To validate repeatability, we performed 5-fold cross-validation on the N-SpectralFace dataset and report average metrics across runs (\(\pm\)standard deviation). PMB-VM was configured with 5 stochastic branches, each using a dropout rate \(p=0.3\) and variational priors initialized as isotropic Gaussians.
# SMT Assembly Inspection Using Dual-Stream Convolutional Networks and Two Solder Regions
## 6 Convolutional Neural Network (CNN) Structure
### Training  
The training step is a process of adjusting the weight of the convolution and the fully connected layer so that the CNN is able to classify the defect type of the chip component image. The training consists of five steps.  
1. Training data consisting of a full chip component image and a one-hot encoded ground truth is loaded.
2. Two solder regions \(S_{1}\) and \(S_{2}\) are extract via the vertical and horizontal projection of full chip component images (Section 5). Use two solder regions \(S_{1}\) and \(S_{2}\) as the input of each stream of the dual-stream CNN.
3. The solder region is converted into a feature vector of the same size as the one-hot encoded ground truth, using the convolution layer, pooling layer, and inception layer of the dual-stream CNN.
4. The cross-entropy loss between the output of dual-stream CNN and the ground truth is calculated.
5. The weight of the dual-stream CNN is updated by backpropagation [32], and the cross-entropy loss is optimized using the Adam [33] algorithm.
6. Steps 2-5 are repeated in order to the desired epoch.
# Optimal Design of a New Rotating Magnetic Beacon Structure Based on Halbach Array
## 6 Conclusions  
In this paper, the magnetic field expression of a single rectangular permanent magnet is derived based on the surface current method, and the magnetic field expression of a multivariate magnetic array is obtained by superposition, which is modeled by MATLAB  
Figure 21: Comparative data plot of the mathematical modeling and simulation model.  
Figure 22: Error analysis.  
software according to the magnetic field expression. Firstly, the ternary magnetic array is adjusted for the flux direction, and the computational analysis yields the best overall magnetic field aggregation effect and is weak when \(\alpha_{1}=45^{\circ},\alpha_{2}=180^{\circ},\alpha_{3}=-45^{\circ}\), which means the aggregation effect on the negative half-axis side of the Y-axis is better, and the weakening effect on the positive side of the Y-axis is the best. Then, a seven-element magnetic array is designed and optimized according to the principle of phase-type angle measurement and the Halbach array model, and using the particle swarm algorithm. The optimized structure is obtained to produce the most characteristic magnetic field: width of each permanent magnet is \(w=2\) cm; height of each permanent magnet is \(h=2\) cm; lengths of each permanent magnet are \(l_{1}=l_{7}=1.55\) cm, \(l_{2}=l_{6}=1.95\) cm, \(l_{3}=l_{5}=2.2\) cm, \(l_{4}=2.6\) cm; position difference between adjacent permanent magnets in the Z-axis \(\Delta\)h = 1.5 cm; the angles between the direction of magnetization of each permanent magnet and the positive direction of the Z-axis are \(\alpha_{1}=0\), \(\alpha_{2}=45^{\circ}\), \(\alpha_{3}=90^{\circ}\), \(\alpha_{4}=180^{\circ}\), \(\alpha_{5}=-90^{\circ}\), \(\alpha_{6}=-45^{\circ}\), \(\alpha_{7}=0\). Finally, the optimized structure parameters were input to COMSOL physics simulation software to verify the feasibility of the structure. Through analysis and comparison, the optimized data obtained by the MATLAB software are basically consistent with the simulation data obtained by the COMSOL physical simulation software, where the error is controlled at 0.0008 T 0.00115 T. Therefore, the seven-element magnetic array designed in this paper can generate magnetic signals that satisfy the principle of phase-type angle measurement, which provides a new magnetic source design idea for the study of artificial magnetic beacon positioning.  
Methodology, B.L.; Writing--original draft, B.L.; Writing--review editing, B.Y.; Investigation, F.X. and J.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Not applicable.  
The authors declare no conflict of interest.
# Low-Thrust Transfer to Quasi-Synchronous Martian Elliptic Orbit via Nonlinear Feedback Control
## 3 Nonlinear Orbit Control  
In this section, nonlinear orbit control is introduced and described. It is aimed at identifying the thrust direction and magnitude to inject a spacecraft in a specified elliptic Martian orbit. The initial parking orbit is a typical ESA 4-Sol orbit [12], associated with the following orbit elements:  
\[a=51,547\ \text{km}\ \ \ e=0.928\ \ \ i=92.3\ \text{deg}\ \ \ \Omega=64.7\ \text{deg}\ \ \ \omega=342.4\ \text{deg} \tag{20}\]  
At the initial epoch \(t_{0}\), set to 16 April 2025 at 0:00 UTC, the true anomaly is assumed equal to 0 deg.
# Numerical Simulation Study on the Volumetric Efficiency Loss of CO2 Swash Plate Axial Piston Pumps
## 6 Calculations and Analyses
Due to the effect of the pressure difference, the leakage of the liquid film increased, and the leakage curve showed periodicity; (2) The average leakage volume loss of the liquid film of a single plunger pair was multiplied by the number of plunger pairs to obtain the leakage volume loss of the liquid film of the total plunger pairs. The total leakage volume loss of the plunger pairs was 1.5 L/min. According to Formula (7), the leakage volume loss of the plunger pairs accounted for 0.5% of the volumetric efficiency. Figure 11: Relationship between total leakage volume loss of the distributing vice and the sub-leakage loss at each outlet boundary. Figure 10: Compression stall volume loss for displacement pumps. Figure 13 shows the leakage volume loss of the slip shoe sub-film for two motion cycles. From the figure, the following can be seen: (1) The leakage volume loss showed periodic changes with time. At 0-0.03s, the leakage volume was almost zero. At this time, the sliding shoe rotated in the low-pressure region, the import and export pressure were the same, and there was no leakage. As the inlet pressure increased, the leakage volume gradually increased and tended to stabilize; (2) The average value of the leakage volume loss of the liquid film of a single sliding shoe vice was multiplied by the number of sliding shoe vices to obtain the total leakage volume loss of the liquid film of the sliding shoe vice, which was 9.8L/min. According to Formula (7), the leakage volume loss of the sliding shoe vice accounted for 3.4% of the volumetric efficiency.
# Assessing Ambisonics Sound Source Localization by Means of Virtual Reality and Gamification Tools
## 3 Materials and Methods
### Experimental Setup  
The tests took place at the Arts and Technology Lab (LATe) at Universidad Austral [27]. The experimental setup involved an original VR 3D audio environment designed in UE for graphics rendering and Max software with _Sput_ libraries for higher-order Ambisonics (HOA) audio processing.  
During the task, participants wore an HMD and were asked to determine the direction from which an auditory stimulus originates. The experiment was conducted using 12 Genelec 8330 loudspeakers (Genelec, Isialmi, Finland) arranged in a hemispheric array configured for Ambisonics reproduction. Objective performance measures, including accuracy of sound localization, were recorded during the task. Additionally, subjective measures such as participant feedback and perceived task difficulty were collected through a post-task informal interview.  
The VR environment was created using UE's Blueprints visual scripting language (Unreal Engine version 5.3), enabling communication via OSC with a Max patch for stimulus playback. In the first part of the test, the VR environment was designed to replicate the LATe lab (Figure 0(a)), featuring accurate spatial representations of the Genelec 8330 loudspeakers arranged according to the virtual source distribution shown in Figure 2.  
Figure 1: _Cont._  
In the second part of the test, a wetland 2 nd-order Ambisonics field recording was played in the background to enhance the immersive experience for listeners and add realism to the virtual environment as shown in Figure 1b.  
The designed virtual environments integrated various gamification elements to improve task engagement and comprehension among participants. These features included interactive buttons for controlling stimulus playback, a progress bar to indicate task progress, and VR avatar elements like virtual hands for interacting with the environment, as shown in Figure 3.  
Figure 1: (a) VR environment as a replica of the LATe lab at Universidad Austral de Chile. (b) VR environment of a wetland in the city of Valdivia, Chile.  
Figure 2: Spatial representations of the loudspeakers, arranged according to the virtual source distribution. (a) Horizontal distribution at 0\({}^{\circ}\) elevation. (b) Horizontal distribution at 0\({}^{\circ}\) and 35\({}^{\circ}\) elevation.
# Automated Processing of fNIRS Data--A Visual Guide to the Pitfalls and Consequences
## 1 Introduction
### Motion Correction  
It is also crucial to identify and remove artifacts that appear in the signal due to subject motion. Several groups have performed thorough investigations of the available motion correction algorithms ([PERSON] and colleagues, 2010; [PERSON] and colleagues, 2010; [PERSON] et al., 2011). Results from all three comprehensive comparisons support the use of the wavelet filtering method ([PERSON] and colleagues, 2010), particularly for dealing with high-frequency spikes, although the spline interpolation method ([PERSON] and colleagues, 2010) may be better at correcting baseline shifts [PERSON] and colleagues (2010); [PERSON] et al. (2011).  
Interestingly, a recently developed method that combines spline interpolation with a smoothing filter (similar in concept to a wavelet filter), appears to improve motion correction of data with both high-frequency spikes and baseline shift components [PERSON] and colleagues (2010). It is important to note that motion correction algorithms are very powerful tools that have the ability to distort results if used incorrectly. Many of these techniques have numerous adjustable parameters, and the defaults may not be appropriate for all datasets. Even the advanced methods mentioned here may introduce artifacts if called with the wrong parameters. Further, in some cases the efficacy of a motion correction algorithm differs depending on the characteristics of the motion artifact ([PERSON] and colleagues, 2010; [PERSON] et al., 2011). For these reasons a visual inspection of the algorithms' performance on a subset of data from a given study is recommended instead of assuming the motion correction algorithm you implement with default settings will automatically be correct. In this study we provide a visual comparison of two similar wavelet-based motion correction algorithms (example performance shown in Figure 2) available in the HOMER2 ([PERSON] et al., 2010) software as well as no motion correction, to highlight the importance of knowing how your motion correction method performs on your data. For a comprehensive analysis of other available motion correction methods, see the studies referenced above ([PERSON] and colleagues, 2010; [PERSON] et al., 2011; [PERSON] et al., 2011).  
Figure 2: Motion correction. An example of motion correction applied to a signal to successfully remove large spike artifacts. (**A**) Uncorrected; (**B**) motion corrected with the Homer Wavelet motion correction (MC#1) method.
## 3 Discussion
In this work, we calibrated the gene expression at each fruit stage, also to avoid possible confounding factors related to the variability in expression of housekeeping genes at different developmental and maturity stages. The comparative analysis implies that transcriptional regulation of the AsA biosynthesis, especially for genes of the L-galactose pathway, is a relevant factor for the differential accumulation in our pepper varieties [25; 39; 41]. GGP and GGP/VCT2 were overexpressed in PEP1 during the whole maturation process, consistent with their key role in regulating the AsA pool content reported in different plant species [22; 41]. Similarly, MADHR1, whose involvement in AsA accumulation was studied in blueberry and tomato [42; 43], was steadily upregulated in PEP1. The expression of all these genes significantly correlated with the AsA content in fruit. On the other hand, although AKR was also overexpressed in PEP1 during fruit development, its expression level did not correlate to AsA content, which can be explained considering a genotype-specific contribution of the L-galactonic acid pathway to the AsA content of PEP1 fruits. The relative quantification also indicated that GME differential expression declined over maturation, being absent at the R stage. The expression of this gene weakly correlates with either tAsA or dAsA content. The GDP-D-mannose is a key enzyme connecting ascorbate synthesis to the primary cell wall metabolism in higher plants, and differences in the expression at IG and MG could be related to cell wall biosynthesis or more generally, to different fruit growth [44]. Accordingly, the expression of GME in leaves of the PEP10 was much lower than in PEP1 and not correlated with metabolites, consistent with a different tissue-specific regulation between varieties, as observed in celery [45]. The regulation of AsA concentrations during fruit ripening is a complex phenomenon that includes both transcriptional and post-transcriptional regulatory processes, also comprising feedback inhibition [22; 25]. The higher expression of genes involved in ascorbic acid biosynthesis and regeneration is an important explanation for the observed higher AsA content in pepper fruits, as is also shown in other plant species for some genes. While transcript level is only one of the factors that modulate AsA abundance, by using a linear model approach, we found biologically meaningful gene-metabolite pairs that strongly correlated. The overexpression of the genes in the higher accumulating varieties and their clustering according to their correlation with tAsA also opens the possibility that the observed phenotype-specific relationships could rely on a single co-regulated molecular pattern [46]. The gene expression-metabolite correlations, although supported by the biochemical literature, do not necessarily imply causation, and further studies will have to strengthen the observed phenotype-gene relations in pepper and elucidate possible bio-modules of co-regulated genes.
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 2 Materials
### Data Preprocessing  
To ensure spatial consistency and model-readiness, both ground-based and satellite data underwent the following preprocessing procedures:  
(1) Ground Truth Preprocessing: Carbon stock values were standardized to Mg/ha and spatially aligned to the center of a 32 \(\times\) 32 pixel region (640 m \(\times\) 640 m) extracted from the Sentinel-2 mosaic. This patch size provides sufficient spatial context for learning forest structure. Only plots with high data quality and complete field attributes were retained. Plots with evident outliers or georegistration errors were excluded.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Description** & **Band** & **Wavelength (nm)** & **Resolution (m)** & **Resampled (m)** \\ \hline Blue & B2 & 490 & 10 & 20 \\ Green & B3 & 560 & 10 & 20 \\ Red & B4 & 665 & 10 & 20 \\ Red-edge 1 & B5 & 705 & 20 & 20 \\ Red-edge 2 & B6 & 740 & 20 & 20 \\ Red-edge 3 & B7 & 783 & 20 & 20 \\ NIR & B8 & 842 & 10 & 20 \\ Narrow NIR & B8A & 865 & 20 & 20 \\ SWIR 1 & B11 & 1610 & 20 & 20 \\ SWIR 2 & B12 & 2190 & 20 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Spectral bands of Sentinel-2 MSI used in this study.  
(2) Sentinel-2 Imagery Preprocessing: All satellite imagery was preprocessed using the following steps:  
* Resampling: All 10 selected spectral bands were resampled to a uniform 20 m resolution via bilinear interpolation.
* Cloud and Shadow Masking: The Scene Classification Layer (SCL) was used to mask cloud and shadow pixels. Only classes corresponding to vegetation, bare soil, or water (SCL classes 4-7) were retained.
* Mosaicking: A median composite was generated from four cloud-free Sentinel-2 scenes to produce a radiometrically consistent mosaic.
* Normalization: Pixel values were normalized to the [0, 1] range using min-max normalization:  
\[x^{\prime}=\frac{x-x_{\text{min}}}{x_{\text{max}}-x_{\text{min}}} \tag{1}\]  
(3) Patch Extraction: From the preprocessed Sentinel-2 mosaic, a 32 \(\times\) 32 \(\times\) 10 cube was extracted for each sample plot, covering an area of 640 m \(\times\) 640 m with 10 spectral bands. These patches were used as input features, with the corresponding carbon stock values serving as regression targets. All preprocessing was conducted in Python 3.9 using the rasterio, GDAL, and NumPy libraries. The final dataset was split into training (70%), validation (15%), and test (15%) subsets through stratified sampling to ensure balanced representation across forest types and elevations.
# Scalable Photonic Digital-to-Analog Converters
## 3 Alignment of the Resonances and the Dynamic Range
We chose 1550 nm as the operating wavelength. To determine the dynamic range of the MRM, a reverse bias voltage sweep is performed between 0 V and 7 V at 0.1 V increment steps. To find the intensity change contributed by an individual bit, the other ring modulator is kept aligned with the operating wavelength. In other words, the resonance associated with the other MRM is located at the center wavelength C. Figure 4d shows the change in the optical intensity for the MSB and LSB. As the reverse bias is increased from 0 V to 7 V, the MSB contributes approximately 2.8 dB of extinction ratio, while the LSB contributes to a 1 dB difference. Figure 4: The 2-bit PDAC tuning: (**a**) the resonance alignment of the PDAC ring modulators, manifested by a single resonance with a large extinction ratio, while the wavelength is set to the operating wavelength O. (**b**) The transmission of a CW signal versus the bias voltage of the MSB branch phase shifter (MSB–PS) and (**c**) LSB branch phase shifter, while MSB–PS is biased at 4.4 V. (**d**) Output transmission of an operational 2-bit PDAC versus the reverse bias voltage applied to MRMs, showing the change in the signal intensity as the bias voltage is varied between 0 V to \(-7\) V. This graph provides information about the MRMs dynamic range of the LSB and MSB branches.