esa-sceva/satcom-chunk-collection huggingface dataset:
# First In-Orbit Validation of Interferometric GNSS-R Altimetry: Mission Overview and Initial Results
## 2 Mission Overview
### Inter-Satellite Cross-Validation Method
#### 2.5.4 Crossover Data Calculation Method  
In this study, we selected the near-observation point interpolation method for crossover data calculation [50]. This approach effectively mitigates inaccurate or erroneous assessments that may arise from imprecise positioning measurements at crossover points. To match the 40 km spatial resolution of the iGNSS-R altimeter SSH products, we applied an approximately 40 km smoothing filter to consecutive 1 Hz altimeter measurements prior to interpolation.  
Rigorous data preprocessing was conducted, including temporal and spatial matching with radar altimeter data within a 3-day time window for SSH, a 0.2 h time window for ocean tides, pole tides, solid Earth tides, and DACs, and a 0.5 h time window for dry/wet tropospheric delay corrections.  
In this study, we evaluated the bias, standard deviation (STD), root mean square error (RMSE), and correlation coefficient (R) of sea surface height, as well as several error correction parameters, including ocean tide, polar tide, solid Earth tide, DAC, and dry and wet tropospheric correction terms. It should be noted that the assessment results for the dry and wet tropospheric correction terms are only applicable to the case where the incidence angle is 0\({}^{\circ}\). For other incidence angles, the assessment results at a 0\({}^{\circ}\) incidence angle should be divided by the cosine of the incidence angle to obtain equivalent evaluations.  
The statistical parameters were calculated using the following equations:  
\[Bias=\frac{1}{N}\sum_{i=1}^{N}(R_{i}-A_{i}) \tag{2}\]  
\[STD=\sqrt{\frac{\sum_{i=1}^{N}\left(R_{i}-A_{i}-\overline{R-A}\right)^{2}}{N- 1}} \tag{3}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}\left(R_{i}-A_{i}\right)^{2}}{N}} \tag{4}\]  
\[R=\frac{\sum_{i=1}^{N}\left(R_{i}-\overline{R}\right)\left(A_{i}-\overline{A }\right)}{\sqrt{\sum_{i=1}^{N}\left(R_{i}-\overline{R}\right)^{2}\sum_{i=1}^{ N}\left(A_{i}-\overline{A}\right)^{2}}} \tag{5}\]  
where \(N\) is the number of collocations, \(R_{i}\) is the GNSS-R SSH measurements, and \(A_{i}\) is the radar altimeter measurement. Together, these four statistical parameters are capable of characterizing the error property of the iGNSS-R measurements.
## 2 Materials and Methods
### Time-Series MODIS Data Processing
#### 2.3.1 Calculation of the Two-Band Enhanced Vegetation Index (EVI2)  
Instead of NDVI, the two-band enhanced vegetation index (EVI2; [33]) was used in this study for crop yield estimation, as it is less susceptible to saturation at high biomass [34; 35; 33], which is the case in the study region. [PERSON] et al. [36] reported that EVI2 performed slightly better than NDVI for yield estimation. EVI2 can be calculated from the red (\(\rho_{R}\)) and near infrared (\(\rho_{N}\)) band reflectance as is shown below:  
\[EVI2=2.5(\rho_{N}-\rho_{R})/(\rho_{N}+2.4\rho_{R}+1). \tag{1}\]
# Photonic Packaging: Transforming Silicon Photonic Integrated Circuits into Photonic Devices
## 2 Optical Packaging
### Fiber-to-PIC Coupling  
The main challenge associated with transferring light between a telecom single-mode fiber (SMF) and the typical waveguides on a Si-PIC is the large difference between the mode-field diameter (MFD) of the two material systems. At telecom wavelengths (1260-1650 nm), the MFD in the fiber is approximately 10 \(\upmu\)m and circularly symmetric, while in the PIC it is typically elliptical and 0.5 \(\times\) 0.3 \(\upmu\)m in size [13]. Consequently, in addition to an order of magnitude difference in the mode sizes between the two waveguides, there can also be a strong polarization dependent loss (PDL), if the unknown and unstable direction of the electric-field in the SMF-mode does not align with the fundamental polarization of the PIC waveguide (usually TE (transverse-electric) for telecoms and datacoms applications, and TM (transverse-magnetic) for sensing applications). As a result, it is often necessary to implement some form of 'polarization management' to ensure stable Fiber-to-PIC coupling--either directly in the coupler-element itself (as in the case of a 2D grating-coupler [14]), or at a later stage in the PIC (such as a wave-guide element that filters and rotates the polarization as necessary [15]). As more fully described in the following three sub-sections, there are essentially three approaches to Fiber-to-PIC coupling--edge-coupling, grating-coupling, and most recently evanescent-coupling--each having their own performance advantages and limitations, and so suiting particular photonic applications. A summary of the typical insertion-loss, bandwidth, and alignment tolerance for these different Fibre-to-PIC coupling approaches is given in Table 1.  
Figure 2: Examples of packaged photonic modules; (**a**) Single-channel PIC with side-by-side electronic integration; (**b**) Optical Network Unit (ONU) of the FP7-FABULOUS project; (**c**) Single-channel in/out photonic module with vertically integrated electronic chip; (**d**) 8-Channel Transceiver for the FP7-PLAT4M project.
## 3 Identified Research Gaps, Issues, and New Directions  
### Research Gaps and New Directions  
One of the good ideas for absorbing impact energy is to use negative Poisson's ratio (NPR) structures. One of the NPR structure cells is shown in Figure 12. If the inner cell structure of a future seat cushion is properly designed to be similar to the structure cell shown in Figure 13, the low-frequency vibration and impact energy of the seating suspension system will be absorbed and reduced, and the seat ride comfort will be improved.  
Figure 11: The second proposed NSMS Stewart platform vibration isolator prototype [66].  
Figure 12: Modelling approaches of the 3D re-entrant cell. (**a**) 3D solid mesh using 19830 C3D10M elements, (**b**) simplified mesh using 160 beam elements, (**c**) simplified mesh using 96 beam elements [67].  
Figure 13: The blast absorbing structure of the negative Poisson’s ratio cell [68].  
If the double-diamond structure isolator is proved to be a good isolator or to have very small vibration transmissibility, it should be used as one of the cells/units/elements of the 3D-printed seat cushion structure. If the conventional passive seating suspension system added with the 3D-printed seat cushion is able to reduce the vibration transmissibility of the seating suspension system, the new system will be superior to the active and semi-active seating suspension system in terms of the cost and implementation readiness.  
Very little research work has been conducted on a 3D-printed seating cushion built with the QZS or NPR cell/unit/element structure such as the double-diamond isolator element structure for reducing vibration transmissibility and improving ride comfort. Therefore, the seating suspension system integrated with a 3D-printed seat cushion, which consists of a QZS or NPR unit/element structure for vibration control, is identified as the research gap. The study of the 3D-printed seat cushion built with the QZS or NPR unit/element structure for vibration isolation is a new research area or direction to be focused on.
## 5 An Optimization Framework for Parcel Locker Locations with Randomly Sampled Passenger Trip Data
### The Optimization Framework for Parcel Locker Locations  
We attempt to use the taxi trip data and the candidate parcel locker location data as inputs to solve the optimal solution to the proposed PM model. However, given the NP-hard nature of the \(p\)-median models, the computational time needed to obtain the global optimal solution increases exponentially with the number of decision variables. Inputting all 26,698 trips into the model would result in a number of 26,698 \(\times\) 421 + 421 = 11,240,279 (421 is the number of candidate locations) decision variables, which is computationally inefficient. Therefore, we propose an optimization framework based on random sampling. The core idea of this framework is to randomly select \(N\) trips from the 26,698 trips as inputs to the PM model and solve for the optimal solution, which contains the information of the selected candidate locations (the optimized \(y_{j}\)) and passenger-locker matching (the optimized \(x_{ij}\)). Random selection is repeated for a pre-determined number of times (denoted as \(S\)), and the total frequency for each candidate parcel locker location to be matched with passengers among all the selections is calculated. These frequencies are then used to rank the candidate locations, with the top \(P\) locations chosen as the final output of the optimization framework. The detailed steps of the optimization framework are shown in Table 2. For a clearer presentation, we also present the steps of the optimization framework in the flowchart in Figure 6.  
We note that since the proposed optimization framework is based on random sampling, the computational time needed to solve the PM model for a sample is, without doubt, shorter than that needed to solve the PM model using all the trip requests as inputs. Therefore, the optimal solution outputted by the optimization framework can be seen as an approximation to the \"real solution\", i.e., the optimal solution obtained by using all the trip requests as inputs to the PM model. One should see that if the samples are not properly generated, the optimal solution obtained by the optimization framework may deviate from  
Figure 5: Spatial distribution of candidate parcel locker locations.  
the real solution. It therefore remains a question of how to balance the computational time and the gap between the optimal solutions.
# Multi-Domain Communication Systems and Networks: A Tensor-Based Approach
###### Abstract  
Most modern communication systems, such as those intended for deployment in IoT applications or 5G and beyond networks, utilize multiple domains for transmission and reception at the physical layer. Depending on the application, these domains can include space, time, frequency, users, code sequences, and transmission media, to name a few. As such, the design criteria of future communication systems must be cognizant of the opportunities and the challenges that exist in exploiting the multi-domain nature of the signals and systems involved for information transmission. Focussing on the Physical Layer, this paper presents a novel mathematical framework using tensors, to represent, design, and analyze multi-domain systems. Various domains can be integrated into the transceiver design scheme using tensors. Tools from multi-linear algebra can be used to develop simultaneous signal processing techniques across all the domains. In particular, we present tensor partial response signaling (TPRS) which allows the introduction of controlled interference within elements of a domain and also across domains. We develop the TPRS system using the tensor contracted convolution to generate a multi-domain signal with desired spectral and cross-spectral properties across domains. In addition, by studying the information theoretic properties of the multi-domain tensor channel, we present the trade-off between different domains that can be harnessed using this framework. Numerical examples for capacity and mean square error are presented to highlight the domain trade-off revealed by the tensor formulation. Furthermore, an application of the tensor framework to MIMO Generalized Frequency Division Multiplexing (GFDM) is also presented.  
Keywords:tensors; multi-domain communication systems; [PERSON] product; tensor partial response signaling; capacity; tensor channel +
Footnote †: journal:  
_Article_  
A Tensor-Based Approach.
# XSQ-Learning: Adaptive Similarity Thresholds for Accelerated and Stable Q-Learning
## 2 Related Work
### Current Limitations  
Despite their demonstrated advantages, existing similarity-based Q-learning approaches exhibit several fundamental limitations that constrain their practical application.  
First, computational complexity remains a significant bottleneck. Methods relying on exhaustive similarity searches [20] face \(\mathcal{O}(|S|^{2})\) time complexity for pairwise state comparisons, becoming prohibitive in large or high-dimensional state spaces. This overhead persists even with approximate similarity metrics, as the comparison operations themselves scale quadratically with the number of states. Moreover, these methods typically lack mechanisms to prioritize or filter updates based on their expected utility or trustworthiness, limiting their scalability.  
Second, the static nature of similarity thresholds and update parameters limits adaptability. Approaches like QL-Smooth [7] employ fixed thresholds (\(\delta_{s}\), \(\delta_{a}\)) and smoothing factors (\(\beta\)) that cannot adjust to local variations in state space density or problem dynamics. This rigidity often leads to either insufficient generalization (when thresholds are too strict) or over-propagation of updates (when thresholds are too lenient). Furthermore, such parameters remain constant throughout the learning process, making it difficult to accommodate heterogeneous regions of the state space or evolving dynamics as training progresses.  
Third, uncontrolled update propagation risks policy degradation. QSLearning's [8] strategy of updating all state-action pairs with \(\sigma>0\) can introduce harmful interference, particularly when  
* The similarity function \(\sigma\) imperfectly captures true task-relevant similarities;
* The state-action space contains regions with fundamentally different dynamics;
* The learning rate \(\alpha\) remains high during later training stages.  
These problems are exacerbated by the fact that existing methods do not incorporate mechanisms to scale updates based on the confidence in value estimates, nor do they adapt similarity propagation in response to learning progress.  
Additionally, current methods share two subtle but important weaknesses:  
* They lack mechanisms to dynamically adjust similarity measures based on learning progress;
* Their update strategies do not account for the reliability of similarity estimates.  
Our _XSQ-Learning_ algorithm, detailed in Section 3, addresses these limitations through three distinctive components: (1) adaptive similarity thresholds that evolve in response to the agent's current performance and temporal-difference signals, (2) confidence-weighted update propagation that scales similarity-based updates based on the stability of each Q-value estimate, and (3) a local similarity clipping mechanism that limits propagation to highly reliable regions. These design choices maintain the generalization benefits of similarity-based learning while mitigating over-propagation, improving convergence stability, and enhancing scalability to more complex environments.
## 3 State Estimation and Prediction
### Data Fusion  
A track-to-track fusion approach is employed in this study for combining high-level data from multiple sources. The data processing for each sensor system is performed individually at the sensor level in a high-level fusion system. Each sensor system outputs one or more tracks based on the sensor measurements, and the state estimates from multiple sensor tracks are associated and combined with a track-to-track fusion algorithm. A high-level fusion of multiple sensors has been successfully implemented in many studies dealing with automotive applications [58; 59; 60; 61; 62]. Important advantages of the high-level fusion approach lie in spatial and temporal alignment, modularity, and communication overhead [63; 64]. A simple block diagram for the high-level fusion system architecture [63; 65] is shown in Figure 4.  
Figure 3: Operation of the unbiased converted measurement Kalman filter algorithm.  
One of the most widely used algorithms for track-to-track fusion is the convex combination algorithm [65; 66; 67; 68], and it has been used extensively for its simple implementation. Two state estimates \(x_{i}\) and \(x_{j}\) with the corresponding covariance \(P_{i}\) and \(P_{j}\), respectively, can be fused to obtain the state estimate \(\tilde{x}\) by  
\[\tilde{x}=P_{j}\big{(}P_{i}+P_{j}\big{)}^{-1}x_{i}+P_{i}\big{(}P_{i}+P_{j} \big{)}^{-1}x_{j}=\tilde{P}\Big{(}P_{i}^{-1}x_{i}+P_{j}^{-1}x_{j}\Big{)} \tag{8}\]  
where \(\tilde{P}\) is the covariance associated with the fused estimate, which is given by  
\[\tilde{P}=P_{i}-P_{i}\big{(}P_{i}+P_{j}\big{)}^{-1}P_{i}=\ P_{i}\big{(}P_{i}+P_{j} \big{)}^{-1}P_{j}=\ \Big{(}P_{i}^{-1}+P_{j}^{-1}\Big{)}^{-1}. \tag{9}\]
# Generative Adversarial Networks in Histological Image Segmentation: A Systematic Literature Review
## 5 Results
### GANs in Histological Image
#### 5.4.1 Data Augmentation  
Employing GANs for data augmentation enables the generation of histological images with significant morphological diversity, thereby enhancing the reliability and adaptability of segmentation, classification, and detection models [96]. For example, in a study by [PERSON] et al. [86], PGGAN was utilized on an RTX 3090 GPU to produce synthetic images, achieving FID = 34.96 and IS = 2.91, indicating satisfactory visual fidelity and diversity. Conversely, [PERSON] et al. [73] utilized CycleGAN but with lower performance metrics (MSE = 5.664, SSIM = 0.204), showcasing the variation in image quality depending on the GAN architecture and implementation specifics.  
Noteworthy is the study by [PERSON] et al. [76] which combined CycleGAN and RRAGAN, achieving superior results with SSIM = 0.933 and PSNR = 24.109, utilizing Google Colab for training. These outcomes underscore how the selection of architecture and computational resources impacts the effectiveness of data augmentation tasks. Apart from enhancing model performance, data augmentation with GANs aids in preventing overfitting, enhancing class balance, and facilitating deep neural network training in resource-constrained settings, thereby serving as both a technological advancement and a solution to systemic challenges in medical data availability. This positions data augmentation as a fundamental application of GANs in the realms of digital pathology and biomedical imaging.
## 3 Modeling Methodology
### Data Collection and Preparation
#### 3.1.3 Factors Importance  
To check for the significance of the factors identified for landslide modeling in the Nam Dan commune, we used the One-R feature selection technique [66]. The One-R technique works based on correlation analysis among the conditioning factors and the historical landslides to select the most significant factors for the modeling process [66]. Using the One-R technique, each factor is assigned a weight that indicate the average merit (AM) of the factor in landslide modeling. Based on the AM, one can more accurately focus on the crucial factors and remove unnecessary factors to increase the predictive performance of the models.
## 3 Results
### Operation and Maintenance  
The energy consumption during the operation and maintenance phases can be categorized into eight sources: fugitive emissions, station energy and lighting, electric traction, diesel traction, base energy and lighting (workshop and administration), control center energy and lighting, transportation, and materials. Tables 2-6 provide a detailed description of the scope of each source, serving as a reference for future life cycle assessment (LCA) studies on urban railway transportation systems.  
are around 23%. This differs from most railway systems around the world, which are predominantly powered by electricity (Figure 3).  
Although the emissions are not directly generated by the electricity consumed during operation, the LCA calculations encompass greenhouse gas emissions arising from energy generation and transmission processes. In 2021, the average annual electricity consumption for train traction in Pernambuco's railway transportation during the operation phase reached 51.7 million kWh. The corresponding greenhouse gas emissions from electricity utilized for train traction amounted to 3774.70 t CO\({}_{2}\)e. For the diesel train system, which consumed 578,978 L of diesel fuel in 2021, the calculated greenhouse gas emissions were 1385.34 t CO\({}_{2}\)e.  
Through aggregating emissions from other energy sources, the total annual greenhouse gas emissions from operation and maintenance summed up to 6170.54 t CO\({}_{2}\)e. Assuming a total service life of 50 years, the estimated total greenhouse gas emissions from the life cycle of operation and maintenance in Pernambuco's railway system amounted to 308,550 t CO\({}_{2}\)e.
###### Abstract  
Traditional manual inspection methods for tunnel lining leakage are subjective and inefficient, while existing models lack sufficient recognition accuracy in complex scenarios. An intelligent leakage identification model adaptable to complex backgrounds is therefore needed. To address these issues, a Vision Transformer (ViT) was integrated into the UNet architecture, forming an SE-TransUNet model by incorporating SE-Block modules at skip connections between the encoder-decoder and the ViT output. Using a hybrid leakage dataset partitioned by k-fold cross-validation, the roles of SE-Block and ViT modules were examined through ablation experiments, and the model's attention mechanism for leakage features was analyzed via Score-CAM heatmaps. Results indicate: (1) SE-TransUNet achieved mean values of 0.8318 (IoU), 0.8304 (Dice), 0.9394 (Recall), 0.8480 (Precision), 0.9733 (AUC), 0.8562 (MCC), 0.9218 (F1-score), and 6.53 (FPS) on the hybrid dataset, demonstrating robust generalization in scenarios with dent shadows, stain interference, and faint leakage traces. (2) Ablation experiments confirmed both modules' necessity: The baseline model's IoU exceeded the variant without the SE module by 4.50% and the variant without both the SE and ViT modules by 7.04%. (3) Score-CAM heatmaps showed the SE module broadened the model's attention coverage of leakage areas, enhanced feature continuity, and improved anti-interference capability in complex environments. This research may provide a reference for related fields.  
image recognition; semantic segmentation; tunnel engineering; secondary lini sunder seepage; vision transformer; squeeze-and-excitation block +
Footnote †: journal: Applied Sciences  
1
# A Photometric Stereo Using Re-Projected Images for Active Stereo Vision System
## 2 Related Works
### Photometric Stereo  
Photometric stereo is widely used for applications which require the surface detail of a target object to be obtained [11; 12; 13]. Photometric stereo requires one camera and three or more light sources, which must not be located in one straight line, as shown in Figure 2a. This method is a technique for acquiring normal vector through the object's reflection characteristics by using images taken from a single camera with different light positions (Figure 2b-d). The normal vector (Figure 2e) acquired are relatively accurate, but a depth map obtained with the same technique is not accurate. This is because the depth map is obtained by continuously integrating the normal vector, causing accumulated error.  
Figure 1: (**a**) Our active stereo vision system configuration; (**b**) Image taken from left camera: structured light is projected onto the target object; (**c**) Image taken from right camera: structured light is projected onto the target object; (**d**) Acquired 3D scan data.  
Figure 2: (**a**) Our photometric stereo system configuration; (**b–d**) Images taken under different positions of external light source; (**e**) Acquired Normal map.