esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Multipath Modelling and Mathematic Tools for Analysis
### Interpolation with Grid Method  
The grid method for GNSS multipath mitigation models the spatial distribution of multipath errors by partitioning the sky into a grid structure based on azimuth and elevation angles. The method assumes that multipath errors exhibit spatial repeatability, meaning that errors observed at a particular position in the sky can be used to estimate future errors in the same or nearby locations. This approach allows for more precise error correction over time. Mathematically, the grid method can be described as follows.  
First, the sky is divided into grids with resolutions determined by azimuth angle \(\theta\) and elevation angle \(\varphi\). For example, a \(1^{\circ}\times 1^{\circ}\) grid resolution means that the sky is divided into cells of size \(\Delta\theta=1^{\circ}\) and \(\Delta\varphi=1^{\circ}\), resulting in \(N_{az}\times N_{el}\) total grid cells, where \(N_{az}\) and \(N_{el}\) are the number of divisions in azimuth and elevation, respectively.  
Then, over multiple observation days, the post-processing residuals \(l(t,\theta,\varphi)\), where \(t\) represents time, are collected for each grid cell. These residuals capture the differences between the observed and true GNSS signals, with the assumption that multipath errors dominate these residuals. For each grid cell, we compute the average residual:  
\[\bar{l}(\theta_{i},\varphi_{i})=\frac{1}{n}{\sum_{k=1}^{n}l(t_{k},\theta_{i}, \varphi_{i})} \tag{5}\]  
where \(l(t_{k},\theta_{i},\varphi_{i})\) is the residual at time \(t_{k}\) for the grid cell defined by azimuth \(\theta_{i}\) and elevation \(\varphi_{i}\), and \(n\) is the number of observations for that grid.  
During subsequent observations, the estimated multipath error for each satellite position is corrected by subtracting the corresponding average residual from the raw GNSS observation. For a satellite positioned at azimuth \(\theta_{i}\) and elevation \(\varphi_{i}\), the corrected observation \(O_{corr}\) is  
\[O_{corr}(t)=O_{obs}(t)-\bar{l}(\theta_{i},\varphi_{i}) \tag{6}\]  
where \(O_{obs}(t)\) is the raw observation at time \(t\).  
In summary, the grid method mitigates GNSS multipath errors by constructing a spatial model of the errors through grid partitioning and residual averaging. This mathematical framework helps to reduce multipath-induced inaccuracies in GNSS observations by using past data to predict and correct future signal distortions.
# Potential of a SAR Small-Satellite Constellation for Rapid Monitoring of Flood Extent
## 1 Introduction
That is, the required observation performance (such as the revisit time, spatial coverage, swath, spatial resolution, and spectral bandwidth) should be specified in advance for a functional implementation. Now several space companies such as QPS, Synspective, ICEYE, Capella Space, and Umbra Lab are planning to realize the constellation of SAR-equipped small satellites. Most of these plans considered flood monitoring as one of the main targets, but they did not discuss the required observation performance in the application of effective flood monitoring. Identification of this requirement needs an integrated approach that simulates the satellites orbiting in space and river flooding on the ground to investigate how flood extent would be temporally and spatially observed with a SAR small-satellite constellation. Therefore, in this study, we combined the design of constellations and the hourly outputs of a flood inundation simulation to conduct a virtual experiment of how a constellation of SAR small-satellites would observe the fluctuation of flood extents in spatial and temporal scales, using two flood events in Japan as case studies.
## 4 Proposed Approach for Forecasting the Wind Power Intervals
### Wind Power Forecasting Model Based on CNN
#### 4.1.1 Wind Power Data Preprocessing by VMD and PSR  
The matrix data in a CNN are similar to the data in image pixels. For one-dimensional wind power data, the convolution and pooling operations require preprocessing (as described in Sections 2.1 and 2.2) to reduce the complexity of the original wind power data, uncover additional information in the data, and extract high-dimensional matrix information from one-dimensional wind power data. This meets the requirements of the first-layer CNN sub-model for input data, and provides more-accurate modes of forecasting values.
## 4 Experiments
### Ablation Study  
In this section, we will investigate the effect of each component of the KDAS-ReID algorithm after an extensive experiment on the Market-1501. In addition, we show the results in Table 1.  
As shown in Table 1, compared to traditional manual architecture and backbones, which are searched on CIFAR-10 such as DARTS [15] and GDAS, our work has competitive performance. In our ablation experiment, we make several observations as follows:  
* Baseline. Directly searching on the Re-ID dataset with differentiable architecture search strategy and adopted the search space of Auto-ReID [4]. The loss is computed by the cross-entropy function and the triplet function.
* Baseline + Distillation. Based on Baseline, we use the objective loss (Equation (9)) which is composed of triplet loss and distillation loss instead of the original loss function. Considering the effect of the distillation loss heavily relying on a well-performance teacher model, we use the official ResNet-50 network which pre-trains on the ImageNet as the backbone of the teacher model. In addition, the temperature \(T=2\) is retained during the whole search stage.
* Distillation + Dynamic Temperature. To break the limit of the teacher model, we introduce the distillation loss with dynamic temperature \(T\). We start searching with a high value of temperature \(T=2\) and decrease evenly; finally, the temperature \(T\) will decrease to 0 at the 190 th epoch.  
All of the above experiments ran four times under the same training configuration to ensure fairness. Although we can find that the CNNs searched with distillation loss has a better performance than the CNNs searched only with triplet loss, there is still room for improvement. We have found the best architecture through the method \"Distillation + Dynamic Temperature\" called KDAS-ReID. In addition, extensive experiments in Section 4.4 showed that the KDAS-ReID not only has the best performance but also broke the limit of the teacher model.
## 2 Results and Discussion
### C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) Mag-dSPE Procedure and Its Optimization
#### 2.2.2 Optimization of Adsorbent Dosage  
The control of the adsorption dose has a great influence on the adsorption effect of the target substance. If the adsorption dose is too small, the target can not be completely adsorbed, and if the adsorption dose is too much, there will be too many irreversible points on the surface of the adsorbent so that the adsorbed target can not be eluted successfully, thus affecting the elution efficiency. In this paper, adsorbent dosages such as 10 mg, 20 mg, 30 mg, 40 mg, and 50 mg are selected for research. According to the experiment, the recoveries of 11 kinds of food additives were obtained. As shown in Figure 9, the trend of recovery was obvious when the amount of adsorbent was in the range of 10-50 mg. The adsorption effect was best when the amount of adsorbent was 40 mg, and the recoveries of 11 food additives were 88.2-105.2%. When the amount of adsorbent increased to 50 mg, the recovery did not change significantly. Therefore, the adsorbent of 40 mg is the best dosage.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **Gross Mass (g)** & **N\%** & **O\%** & **Epoxy Group** & **N, N-** \\  & & & & **Content (mmol)** & **Dimethyloctadecylamine** \\ \hline PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) & 1.0 & / & 12.23 & 2.546 & / \\ C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) & 1.0 & 1.45 & 8.46 & 1.766 & 1.035 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nitrogen analysis of C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\).  
Figure 8: Influence of different concentrations of the ammonium acetate–methanol solution on the recovery (\(n=6\)).  
Figure 7: Effect of the type of desorption solvent on the recovery (\(n=6\)).
# A Survey on Underwater Acoustic Sensor Network Routing Protocols
## 5 The Cross-Layer Design Method
### Intelligent Algorithm-Based Methods
#### 5.2.4 Particle Swarm Optimization-Based Methods  
The particle swarm optimization is a kind of algorithm based on swarm intelligence which was proposed by [PERSON] and [PERSON] in 1995 [20]. The particle swarm optimization is inspired by the foraging behavior of birds. In particle swarm optimization, every optimization solution is a bird in search space, which is called a particle. Every particle has a fitness value decided by the optimal function. The speed vector decides the direction and the distance of the particle. Then the other particles will follow the optimal particle to search for an optimal solution in the solution space. The initialization of the particle swarm optimization is a set of random solutions; then the algorithm will find the optimal solution by iteration. In each round of iteration, the particle updates itself by following two extrema. The first extremum is the optimal solution which is found by a particle, called private best. Another extremum is the present optimal solution for the whole swarm, which is the global best. In recent years, the PSO algorithm has been widely used in function optimization, neural network training, fuzzy logic control, or any research area in which genetic algorithms can be used.  
The authors in [88] use a two tier particle swarm algorithm to configure a network for achieving an optimal energy consumption and packet delivery rate performance. A hybrid technique is proposed in [89], which integrates both the PSO algorithm and the GA algorithm together. In this algorithm, the constraint HC (Hybridization Cofficient) is used to express the population percentage. When HC is zero, it means the procedure is a PSO algorithm; when HC is 1, it means the procedure is a GA algorithm. A PSO-based routing and clustering protocol has been proposed in [90]. In this algorithm, the gateway collects information from nodes and sends the information to a base station. Once the base station receives the information from the gateways, it will execute PSO-based routing and clustering algorithms to form clusters. In [91], the PSO algorithm will be used to judge different paths and choose the best optimized path according to the fitness value. The fitness value represents the energy consumption in this algorithm. The endocrine cooperative particle swarm optimization (ECPSOA)-based routing recovery method is proposed in [92]. In ECPSOA, the multi-swarm strategy guides particles flying in better directions, and the endocrine mechanism yields a high diversity of particles to increase search space, which can jump out of the local optimization and improve the searching capability.
# Vegetation Productivity Losses Linked to Mediterranean Hot and Dry Events
## 2 Materials and Methods
### Vegetation Productivity Losses  
The assessment of impacts on vegetation productivity was performed by means of the annual accumulation of monthly GPP values over the considered affected area for the selected episodes (Table 1). The monthly cumulative GPP values for each event were compared with the historical median and the 25-75% variability range of affected pixels. The process was repeated annually, allowing to evaluate the functional recovery of vegetation productivity following a given extreme event.  
For the year of the ecological extremes and the two subsequent years, spatial averages of monthly anomalies were determined in the affected pixels, as well as the annual productivity balance relative to the median. The end of the ecological extreme is defined as  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Areas** & **Study Cases** & **Affected Area [km\({}^{2}\)]** \\ \hline Iberian Peninsula (IB) & 2005 & 250,655 \\  & 2012 & 75,221 \\ \hline Eastern Mediterranean (EM) & 2003 & 121,484 \\  & 2007 & 76,112 \\ \hline Western Europe (WE) & 2003 & 45,626 \\  & 2006 & 76,766 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected extreme events for each study area that had major impacts on vegetation productivity between 2001 and 2019. The affected area corresponds to the regions under vegetative stress for 5 or more months, described in km\({}^{2}\).  
the moment when the values of GPP reached the historical median values. This analysis follows the perspective of [55] and provides an observation of functional productivity recovery over-stressed vegetation.  
SM and LST monthly fields were used by computing, for each point, the monthly mean value to further study the dynamics of the relationships between vegetation productivity, soil moisture and land surface temperature over the stressed pixels. Monthly anomalies of LST and SM were also determined per pixel, as a departure from the monthly median of respective variables between 2001 and 2019, and for each study area.  
The analysis of the influence of SM and LST on vegetation productivity was performed through the computation of spatial averaged seasonal cycles of pairs GPP-SM and GPP-LST and were compared with the corresponding monthly median cycle of each variable. Also, the relationships of GPP with both SM and LST over the stressed pixels for different types of vegetation were assessed through the computation of Pearson correlations between the correspondent anomalies of the variables.
# A Novel Cooperative Opportunistic Routing Scheme for Underwater Sensor Networks
## 3 Opportunistic Void-Avoidance Routing Protocol
### Problem Description  
During the packet forwarding, if a relay node cannot find any qualified node with a positive progress towards the destination, the packet may be dropped, even though there exists a topologically-valid path from the sender to the destination. This phenomenon is called the local maxima or void problem. The performance of greedy-based routing protocols significantly degrades in the presence of a void area. The characteristics of an underwater sensor network can make the problem even more challenging. The mobility of most underwater nodes and three-dimensional holes in the routing path can lead to more packet failures [11]. Moreover, some moving objects, like a ship, can temporarily create a void area by blocking the communication between two parts of the network [22]. In dense networks, void areas can arise temporarily and with small volumes in some regions. On the contrary, sparse networks include many void areas, which severely affect routing performance. Some existing protocols often ignore void handling in their routings or employ high overhead methods to mitigate its effect.  
On the other hand, the underwater environment has higher path loss and more ambient noises in comparison to the terrestrial physical layer. The main sources of the noise include turbulence, shipping, waves and thermal noise [13]. Moreover, packet loss depends on the traversed distance and the transmission power of the underwater acoustic signal [14; 34]. Thus, packet forwarding is more likely to be successful if packets are relayed over multiple short distances instead of traversing over long distances. These factors can influence the design of underwater sensor protocols, which are not properly resolved in the majority of the proposed protocols. Applying some simplistic methods, such as increasing the number of forwarding nodes or increasing the transmission power, mostly lead to a waste of energy overall.  
Energy consumption is another major concern in UWSNs, because it is hard to replace or recharge the sensor batteries in the harsh underwater environment. In the underwater acoustic networks, the energy consumed by the sensors is much more than what is consumed by the regular sensors in the terrestrial networks [35]. Therefore, energy efficiency is an essential requirement of routing protocols in UWSNs. To this end, it should be noted that the energy consumed by data processing is significantly less than that of data transmission [36; 37].
## 4 Application and Discussion of the Methodology
### Application of the HFACS
#### 4.1.2 Discussion of Distributions  
It can be seen from Figure 3 that Level 2 has the most significant frequency ratio, reaching 47.33%, followed by Level 4, reaching 18.20%. Moreover, from Figure 4, in the sub-level, the frequency of unsuitability of 'Quality and ability' is the highest, reaching 19.45, the second is 'Organizational process', reaching 14.36%. Moreover, 'Quality and  
Figure 4: The distribution of HFACS–EMAs sub-categories.  
Figure 3: The distribution of the HOFs in EMAs.  
ability' belongs to Level 2, 'Organizational process' belongs to Level 4. From a macro level, it is not difficult to think that Level 2 and Level 4 are important potential factors that cause EMAs.  
As can be seen from the Appendix A, at a more detailed level, 'Inadequate guardian/supervision' has the highest frequency, reaching 80 times, followed by 'Paralyzed thinking, irresponsible', 'Low-security awareness', 'On-site safety production management is not in place'. Therefore, the detailed factors that lead to the occurrence of EMAs are more concentrated on the employees themselves and organizational management.
## 4 Results and Discussion
### Discussion
#### 4.5.2 Synergistic Effects  
The integration of RCCF and AM-HDGAT within AttenFlow creates synergistic effects that enhance overall system performance. RCCF's confidence estimates guide AM-HDGAT's graph construction, while AM-HDGAT's classification results provide semantic context for RCCF's retrieval decisions, creating a feedback loop that continuously improves system performance.
# Optical Flow Odometry with Panoramic Image Based on Spherical Congruence Projection
## 4 Experiments and Discussion
### Ablation Experiments on FOV  
To analyze how the camera's field of view (FoV) influences the odometry performance of our proposed SCP-VIO system, we conducted experiments using five different horizontal FoV settings: 165\({}^{\circ}\), 140\({}^{\circ}\), 120\({}^{\circ}\), 100\({}^{\circ}\), and 80\({}^{\circ}\). The results are summarized in Table 8, which reports the RMSE and standard deviation of translational error (RPEt), rotational error (RPEr), and absolute trajectory error (ATE).  
The experimental results indicate that wider FoVs generally lead to better odometry accuracy. Specifically, when the FoV was reduced from 165\({}^{\circ}\) to 80\({}^{\circ}\), the RPEt increased from 1.831% to 2.523%, and the ATE increased from 0.095 m to 0.226 m. This degradation can be attributed to the reduced parallax and decreased visual overlap between frames under narrower FoV settings, which in turn affects the quality of optical flow tracking and depth estimation.  
To further analyze whether the odometry error is spatially biased within the image field, we visualize the local optical flow estimation quality across the FoV in Figure 14. As shown in the zoom-in regions, although minor flow discrepancies appear in both central and peripheral areas, these errors are primarily associated with local feature ambiguity (e.g., textureless regions or occlusion boundaries) rather than the spatial position on the sphere.  
This observation confirms that our spherical projection model ensures a relatively uniform distribution of motion information across the image plane. Unlike traditional pinhole or narrow FoV systems where peripheral degradation is often observed, the SCP-VIO benefits from spherical geometry to maintain consistent tracking quality in all directions. Consequently, the increase in error under smaller FoV settings is primarily due to the overall reduction in visual cues rather than location-dependent bias.  
Figure 13: The continuity of feature points among different subregions.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \multirow{2}{*}{**Field of View**} & \multicolumn{2}{c}{**165\({}^{\circ}\)**} & \multicolumn{2}{c}{**140\({}^{\circ}\)**} & \multicolumn{2}{c}{**120\({}^{\circ}\)**} & \multicolumn{2}{c}{**100\({}^{\circ}\)**} & \multicolumn{2}{c}{**80\({}^{\circ}\)**} \\  & **RMSE** & **Std** & **RMSE** & **Std** & **RMSE** & **Std** & **RMSE** & **Std** & **RMSE** & **Std** \\ \hline RPEt (\%) & 1.831 & 1.299 & 2.064 & 1.422 & 2.088 & 1.457 & 2.341 & 1.669 & 2.523 & 1.789 \\ RPEr (deg/m) & 2.648 & 2.271 & 2.777 & 2.374 & 2.624 & 2.213 & 2.610 & 2.190 & 2.650 & 2.293 \\ ATE (m) & 0.095 & 0.037 & 0.117 & 0.052 & 0.112 & 0.046 & 0.103 & 0.042 & 0.222 & 0.141 \\ \hline \end{tabular}
\end{table}
Table 8: Experimental results on the effect of camera field of view on SCP-VIO.
# Speech Enhancement Based on Unidirectional Interactive Noise Modeling Assistance
## 3 Experimental Setup
### Baselines
#### 3.4.2 BiInterNet  
BiInterNet follows the existing bidirectional interactive speech and noise modeling-based dual-branch network topology. Compared to UniInterNet, the causal speech branch in BiInterNet remains unchanged, while the noise branch is also converted to a causal version. This is because the noise branch of BiInterNet needs to be deployed during model inference. Meanwhile, similar to the previous work [29], the information flow between the speech and noise branches in BiInterNet is bidirectional. The intermediate features of the speech and noise branches flow to each other after being processed by the interaction module, as proposed in Section 2.2.3.
## 4 Experimental Results and Analysis
### Traffic Parameter Extraction Results
#### 4.1.3 Average Travel Time  
Travel time represents the most directly perceived level-of-service indicator for travelers and serves as a key input parameter for signal coordination control. Figure 8 illustrates the distribution characteristics of travel times across various links:  
Travel time variability correlates highly with speed fluctuations yet exhibits distinct characteristics. Links '4,3', '1,9', '21,22', and '16,22' display substantial travel time variations. These fluctuations not only reflect traffic state instability but may also relate to signal control discoodination. Particularly for link '1,9', as part of a major arterial, its high travel time variability may severely impact arterial coordination effectiveness.  
The distribution patterns of travel times provide additional insights. Several links exhibit pronounced right-skewed travel time distributions, characterized by a small number  
Figure 8: Box plot of link average travel times.  
of extreme values. These extreme values typically correspond to travel times under severe congestion conditions, and while their occurrence frequency may be low, their impact on user experience is substantial. Signal optimization should consider not only minimizing average travel times but also performance under extreme conditions.  
Travel times calculated using both instantaneous speed and time difference methods (mttime and mttime_dt) demonstrate good consistency across most links, validating the reliability of the calculation approaches. However, substantial differences between methods exist on certain specific links, typically areas with complex traffic states or data quality issues, requiring special attention in practical applications.