esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
We proposed an integrated mobile VLCP scheme capable of simultaneously integrating VLC and VLP into a single system. By employing DFCE for accurate estimation of time-varying CSI, we enhanced the performance of both communication and positioning under mobile receiver scenarios. Our approach considers VLCP for both MISO and MIMO scenarios, utilizing multipath channels for CSI extraction in MISO and employing STBC for CSI estimation in MIMO. Comprehensive simulations were conducted to evaluate performance. The results showed that in the MISO system, when utilizing DFCE, the BER is 3.8 \(\times\) 10\({}^{-3}\) and 1.0 \(\times\) 10\({}^{-2}\), with 90% of positioning errors less than 5.8 cm and 7.5 cm when the receiver moves along Route 1 and Route 2, respectively. In the MIMO system, with DFCE employed, the BER is 3.3 \(\times\) 10\({}^{-4}\) and 1.3 \(\times\) 10\({}^{-2}\), with 90% of positioning errors less than 4.0 cm and 16.5 cm in Route 1 and Route 2, respectively. Furthermore, optimizing weight coefficients and increasing PD distance for MIMO receivers can effectively enhance positioning accuracy. In future work, we will consider 3D positioning and receiver tilt.  
Conceptualization, X.Y.; methodology, R.W., Y.S. and X.Y.; software, R.W., Y.S. and Z.L.; validation, R.W. and Y.S.; formal analysis, R.W. and Y.S.; investigation, R.W. and Z.L.; resources, M.G. and X.Y.; data curation, R.W.; writing--original draft preparation, R.W. and X.Y.; writing--review and editing, M.G. and X.Y.; visualization, R.W. and Z.L.; supervision, X.Y.; project administration, X.Y.; funding acquisition, X.Y. All authors have read and agreed to the published version of the manuscript.  
Figure 12: Comparison of mean positioning errors with DFCE when using different weight coefficients: (**a**) MISO scenario; (**b**) MIMO scenario.  
Figure 13: Comparison of mean positioning errors in MIMO with different PD distances.  
**Funding:** This research was funded by the National Natural Science Foundation of China (62001319), Suzhou Science and Technology Bureau-Technical Innovation Project in Key Industrial (SYG202112), and Open Fund of IPOC (BUPT) (IPOC2020A009).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Trajectory-Planning Method
### Calculation of Fuel Consumption during Transfer (TCFC)  
When the launch vehicle adopts the transfer orbiting mode after the power system failure occurs, the fuel consumption from the transfer orbit to the target orbit needs to be calculated and set aside. The capacity of the launch vehicle to enter the target orbit is characterized by the flight capability of the launch vehicle entering into the transfer orbit with the remaining fuel. The velocity pulse is used to enter the target orbit when the launch vehicle flies to the apogee of the transfer orbit, and the velocity increment can be expressed as:  
\[\Delta v=\sqrt{\frac{2\mu(h_{a}+R_{e})}{\big{(}h_{p}+R_{e}\big{)}\big{(}h_{a} +h_{p}+2R_{e}\big{)}}}-\sqrt{\frac{2\mu\big{(}h_{pt}+R_{e}\big{)}}{(h_{at}+R_{ e})\big{(}h_{at}+h_{pt}+2R_{e}\big{)}}} \tag{10}\]  
where \(h_{pt}\) and \(h_{at}\) represent the perigee altitude and apogee altitude of the transfer orbit, respectively. The fuel consumption corresponding to the velocity increment when adopting the transfer orbiting approach can be solved iteratively by [PERSON]'s formula, and the solution process is shown in Figure 4.  
geocentric angle between the failure point and the original target orbit-injection point be \(\Delta\phi_{\text{ref}}\), the initial value of the target orbit geocentric amplitude angle after the failure occurs is:  
\[\phi_{f}=\phi_{\text{rc}}+\frac{\Delta\phi_{\text{ref}}}{\kappa} \tag{12}\]  
According to the principle of optimal control, the analytical solution of iterative guidance in the coordinate guidance system [9] can be written as Equation (13):  
\[\left\{ \begin{array}{l}\varphi_{\text{ocf}}=\overline{\varphi}_{\text{ocf}}+ K_{\varphi 2}t-K_{\varphi 1}\\ \psi_{\text{ocf}}=\overline{\psi}_{\text{ocf}}+K_{\psi 2}t-K_{\psi 1}\end{array} \right. \tag{13}\]  
where \(\overline{\varphi}_{\text{ocf}}\) and \(\overline{\psi}_{\text{ocf}}\) are the primary attitude angles used to ensure terminal velocity, and \(K_{\varphi 2}\), \(K_{\varphi 1}\), \(K_{\psi 2}\), and \(K_{\psi 1}\) are coefficients of the secondary attitude angles for terminal position deviation correction, which can be expressed as follow [11]:  
\[\left\{ \begin{array}{l}\overline{\varphi}_{\text{ocf}}=\arctan\frac{V_{ \text{ocf}}-V_{\text{ocf}}-\bar{y}_{\text{ocf}}t_{\text{g}}}{V_{\text{scf}}-V _{\text{scf}}-\bar{y}_{\text{scf}}t_{\text{g}}}\\ \overline{\psi}_{\text{ocf}}=-\arcsin\frac{V_{\text{scf}}-V_{\text{scf}}- \bar{y}_{\text{ocf}}t_{\text{g}}}{\|V_{\text{ocf}}-V_{\text{ocf}}\|-\bar{y}_{ \text{ocf}}t_{\text{g}}\|}\end{array} \right. \tag{14}\]  
\[\left\{
# Deep Activation Pooling for Blind Image Quality Assessment
## 3 Experiments
### Performance on Individual Distortion Types  
To evaluate the proposed method more thoroughly, we present the results on individual distortion types in Table 5. The experiments are conducted on the CSIQ and the LIVE databases. From Table 5, we can see that the proposed HPSC+DAP achieves the best results on the CTD, PGN, JPEG and JPEG2000 distortion types. On the distortion types of GB, FF and AWN, the proposed HPSC+DAP can also achieve competitive results. Hence, the proposed HPSC+DAP is powerful for opinion-unaware BIQA.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**IQA Models**} & \multicolumn{2}{c}{**CSIQ**} \\ \cline{2-3}  & **SRCC** & **PRCC** \\ \hline CORNIA [27] & 0.663 & 0.764 \\ \hline BRISQUE [14] & 0.557 & 0.742 \\ \hline BLIINDS2 [26] & 0.577 & 0.724 \\ \hline NIQE [17] & 0.627 & 0.716 \\ \hline IL-NIQE [19] & 0.815 & 0.854 \\ \hline Proposed HPSC+DAP & **0.828** & **0.867** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation results of different methods when trained on the LIVE databases and tested on the CSIQ database. The best results are highlighted in bold.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Databases**} & \multirow{2}{*}{**Distortion Types**} & **BRISQUE [14]** & **BLIINDS2 [26]** & **CORNIA [27]** & **NIQE [17]** & **IL-NIQE [19]** & **Proposed HPSC+DAP** \\ \hline \multirow{6}{*}{CSIQ} & AWN & **0.925** & 0.801 & 0.746 & 0.810 & 0.850 & 0.863 \\ \cline{2-6}  & GB & 0.903 & 0.892 & **0.917** & 0.895 & 0.858 & 0.869 \\ \cline{2-6}  & CTD & 0.024 & 0.012 & 0.302 & 0.227 & 0.501 & **0.523** \\ \cline{2-6}  & PGN & 0.253 & 0.379 & 0.420 & 0.299 & 0.874 & **0.891** \\ \cline{2-6}  & JPEG & 0.909 & 0.900 & 0.908 & 0.882 & 0.899 & **0.916** \\ \cline{2-6}  & JPEG2K & 0.867 & 0.895 & 0.914 & 0.907 & 0.906 & **0.921** \\ \hline \multirow{6}{*}{LIVE} & FF & - & - & - & **0.864** & 0.833 & 0.841 \\ \cline{2-6}  & GB & - & - & - & **0.933** & 0.915 & 0.929 \\ \cline{1-1} \cline{2-6}  & JPEG2K & - & - & - & 0.919 & 0.894 & **0.920** \\ \cline{1-1} \cline{2-6}  & JPEG & - & - & - & 0.941 & 0.942 & **0.947** \\ \cline{1-1} \cline{2-6}  & AWN & - & - & - & 0.972 & **0.981** & 0.980 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The SRCC of BIQA models on each individual distortion type. The best results are highlighted in bold.
# Optimizing Geospatial Data for ML/CV Applications:
###### Abstract  
Massive image datasets are often required for the proper functioning of Machine Learning (ML) and Computer Vision (CV) applications. This paper offers a solution to computational challenges in the Image Processing of satellite imagery, by proposing an optimization procedure. The presented approach is verified by an exemplary Python implementation, constituting a standalone tool for automating the dataset creation and labeling, including the extraction of road network data from the national satellite cartography provider. The collected data include detailed road maps along with the parcel information obtained via WebMapService endpoints. The method presented in this paper involves three basic steps: road segmentation (using the Shapely module) to facilitate handling high-resolution orthoimagery, and then a modified Region-of-Interest approach, i.e., removing irrelevant areas, with only roads remaining. This results in obtaining file sizes that are significantly smaller. The presented algorithm also involves asynchronous tile downloading, which, combined with the masking of irrelevant areas, improves not only the efficiency but surprisingly also the accuracy of subsequent ML/CV procedures. The research results of the paper reveal substantial file size reduction, and improved processing efficiency, thus making the optimized geospatial graphical data more practical for ML/CV applications, while still maintaining the original data quality and relevance of the analyzed parcels or infrastructure.  
satellite imagery; road segmentation; geospatial data optimization; dataset creation and labeling; machine learning; image processing
## 4 Discussion
### Clinical Implications of Learning-Curve Behaviors in ML Models
#### 4.1.1 Analysis of Problematic Learning-Curve Behaviors  
The analysis of problematic learning-curve behaviors reveals several key patterns that can indicate issues in ML model training, each with distinct causes and clinical implications (summarized in Table 3). When the validation curve is higher than the training curve, it often points to data leakage, where information from the validation set inadvertently influences the training process. This can also occur if the training data is overly simplistic or lacks diversity. Clinically, such a model may appear to perform well during validation but fail catastrophically when exposed to real-world data, leading to misdiagnoses or inappropriate treatment recommendations. Non-converging curves, where the training and validation curves fail to converge, indicate underfitting. This issue arises from insufficient model complexity, poor feature engineering, or inadequate training data. Clinically, an underfitted model is incapable of capturing the underlying patterns in the data, resulting in low diagnostic accuracy and unreliable predictions. On the other hand, non-plateauing curves, where the curves converge but fail to plateau, suggest overfitting to noise or artifacts in the data. This behavior implies that the model is learning spurious patterns rather than clinically relevant features, which could lead to unreliable predictions, particularly in edge cases or less common conditions. A large gap between the training and validation curves is a hallmark of overfitting. This occurs when the model is too complex relative to the amount of training data, allowing it to memorize the training data rather than generalize. Clinically, this results in a model that performs well on training data but poorly on unseen data, undermining its utility in real-world settings. Oscillating curves, characterized by significant fluctuations in
# OTFS: A Potential Waveform for Space-Air-Ground Integrated Networks in 6G and Beyond
## 2 Related Work
MIMO-OTFS has been explored for its high spectral and energy efficiency and has been shown to convert a doubly dispersive channel into an almost non-fading channel in the DD domain through a series of two-dimensional transformations, making it robust to channels with high-Doppler fading [18]. Despite its advantages, OTFS still faces some challenges, particularly in massive MIMO systems, where channel estimation becomes computationally complex due to the large number of antennas at the base station. To address this, researchers in [19] proposed a 3D-structured orthogonal matching pursuit algorithm, improving channel estimation efficiency. Similarly, in [20], a low-complexity message passing (MP) detection algorithm was introduced, leveraging the inherent channel sparsity in large-scale OTFS systems. This MP detection technique enhances OTFS performance in Doppler channels by mitigating inter-Doppler interference (IDI), which arises from fractional Doppler paths misaligned with the Doppler taps. Further, studies such as [21; 22] have explored the broad applications of OTFS in integrated sensing and communication (ISAC), satellite communications, underwater acoustics, and UAV-based networks. While OTFS offers numerous advantages in these domains, the literature also highlights key challenges, including increased computational complexity, hardware implementation difficulties, and integration with ISAC systems. The concept of Space-Air-Ground Integrated Networks (SAGINs) has also gained significant attention as an enabler for 6G due to its potential to support diverse applications and use cases seamlessly and efficiently. The potential of OTFS technology in improving the performance of SAGINs, particularly in high-Doppler scenarios, has been investigated. OTFS has been proposed as a modulation technique for Reconfigurable Intelligent Surface (RIS)-assisted SAGIN systems, transforming time-varying channels in the TF domain to time-invariant channels in the DD domain. The study showed that OTFS-based RIS-assisted SAGINs outperform the OFDM alternative in doubly selective high-Doppler scenarios [23]. Furthermore, OTFS has been demonstrated to be resilient to channel-induced Doppler shifts in high-mobility and extreme wireless environments, making it a promising technology for future communication networks [21]. Overall, the literature surrounding OTFS modulation demonstrates its versatility and potential for improving the performance of communication systems in various challenging scenarios. Further research is required to fully explore the potential of OTFS in SAGINs for 6G and beyond, and this paper seeks to address that.
# Monitoring Changes in Water Use Efficiency to Understand Drought Induced Tree Mortality
## 2 Materials and Methods
### Ecosystem Water Use Efficiency  
Water use efficiency is recognized as an important characteristic of productivity in various natural scientific disciplines and has been used recently at the ecosystem level [8; 16; 47; 48; 49; 50; 51], Ecologists commonly use the ratio of ecosystem fluxes such as NPP [8; 16; 52], net ecosystem productivity/exchange (NEP/NEE) [49; 50], or gross primary productivity (GPP) [48] to water loss (ET or transpiration; [19; 48; 53] as a measure of WUE.  
We obtained MODIS GPP (MOD17) [54; 55] and ET (MOD16) [56] data that were processed and made available by the Numerical Terradynamic Simulation Group at the University of Montana to calculate WUE [8] (Table 1). A comparison of annual WUE (2007-2014) derived from MODIS layers and WUE measured with the eddy covariance method at three tower sites in California forests showed that MODIS WUE captured temporal trends in WUE (\(R^{2}\) = 0.40; \(p\)! 0.001), although gross primary productivity (\(R^{2}\) = 0.78; \(p\)! 0.001) was overestimated and ET was underestimated (\(R^{2}\) = 0.90; \(p\)! 0.001) [8]. Differences between tower WUE and MODIS WUE were likely due to the limited sampling period in Eddy covariance data (2007 to 2014 for most tower sites). Additional limitations of this comparison are that tower sites represent just three forest types in the Sierra Nevada's (oak pine forest, ponderosa pine forest, and Sierra mixed conifer forests), the sample size was small (\(n\) = 16), there were large differences in the sampling area for tower sites (hundreds of meters) and MODIS pixels (4 km), and comparisons occur mostly during drought years preventing an extensive evaluation of how tower based WUE varies overtime in comparison to MODIS WUE.  
Deviations in WUE in 2014 (WUE\({}_{\text{Severe\, Drought}}\)) from WUE\({}_{\text{BASELINE}}\) (i.e., \(\Delta\)WUE) were used to explore patterns in maximum tree mortality rates (2013-2016) for all forested areas in California. We defined WUE\({}_{\text{BASELINE}}\) by extracting values of annual WUE under normal conditions (i.e., mean annual scPDSI between \(-\)0.04 and 0.04) from 2002-2013. Values were averaged across climate ecoregions ([[http://data.fs.usda.gov/geodata](http://data.fs.usda.gov/geodata)]([http://data.fs.usda.gov/geodata](http://data.fs.usda.gov/geodata))) to fill gaps in forested locations that were not under normal conditions from 2002-2013 [8]. The lag between 2014 AWUE and maximum tree mortality rates in 2015-2016 will aide our ability to capture drought induced mortality, which is often delayed [4].
# Distributed Joint Source-Channel Coding Using Quasi-Uniform Systematic Polar Codes
## 5 Simulation Results
Since the conventional puncturing scheme is not applicable in this situation, \(64\cdot 11\) parity bits of source \(V_{2}\) are randomly punctured.
## 3 Results  
We consider two test cases to illustrate the relevance of the selection ensemble Kalman algorithms presented in Section 2. The model, common to both test cases, is based on the diffusion equation.  
The test cases are designed such that it will be opportune to consider bi-modal initial distributions. In the first test case, we compare the SEnKF to the traditional EnKF with a focus on predicting the diffusivity field that contains a high diffusivity channel. In the second test case, we compare the SEnKS to the traditional EnKS with a focus on evaluating the initial temperature field that is divided into two distinct areas where the initial temperature is substantially higher in one than in the other.
# Single-Task Joint Learning Model for an Online Multi-Object Tracking Framework
## 2 STJL Model Applied to Online Multi-Object Tracking
### Data Association  
The Kalman filter refines the predicted object state by linking it to the detection results using the Hungarian algorithm. After prediction, the object's location is estimated, and feature vectors are extracted. The squared Mahalanobis distance, \(d^{(1)}(i,j)=(d_{j}-y_{i})^{T}S_{i}^{-1}(d_{j}-y_{i})\), measures the positional distance between the \(i\)-th tracker and the \(j\)-th detection box, incorporating the filter's prediction. The inverse covariance matrix, \(S_{i}^{-1}\), is derived from the error covariance matrix using Cholesky decomposition. Distances within the 95% confidence interval of a chi-square distribution are considered probable links, with the connectivity matrix \(b_{ij}^{(1)}\) indicating valid connections when the Mahalanobis distance falls below the threshold \(t^{(1)}\).  
When target dynamics are reliable, Mahalanobis distance is used to assess tracker-detection box linkage, but its accuracy diminishes with rapid camera movement. To compensate, cosine distance based on appearance is proposed. Feature vectors \(r\) for each detection box are normalized, and each tracker's feature history is stored in a gallery \(\mathcal{R}_{kg}=\{r_{kg}^{(i)}\}_{kg=1}^{L_{kg}}\). \(\mathcal{R}_{kg}\) is composed of the feature vectors from the most recently linked \(L_{kg}\) detection boxes. The cosine distance, \(d^{(2)}(i,j)=\min\{1-r_{j}^{T}r_{kg}^{(i)}\Big{|}r_{kg}^{(i)}\in\mathcal{R}_{i} \}\), calculates the similarity between the \(i\)-th tracker and the \(j\)-th detection box. Connections are deemed valid if the cosine distance falls below threshold \(t^{(2)}\), represented by the connectivity matrix \(b_{ij}^{(2)}\).  
By combining Mahalanobis distance, which evaluates short-term position dynamics, with cosine distance, which accounts for appearance over longer occlusions, a comprehensive connection cost \(c_{i,j}=d^{(1)}(i,j)+d^{(2)}(i,j)\) is derived. The final connectable indicator \(b_{i,j}\) integrates both metrics, as shown in \(b_{i,j}=\prod_{m=1}^{2}b_{i,j}^{(m)}\). In multi-object tracking, linking trackers and detection boxes is treated as a series of multiple subproblems. To improve robustness, \"matching overlap\" prioritizes trackers with consistent visibility, linking detection boxes with the lowest distance cost while accounting for the tracker's \"tracking age\", \(A\), which measures the time since its last link.
## 2 Proposed Routing Scheme
### The Routing Scheme  
The proposed scheme is made up of two phases.  
1. The setup phase. It consists of allocating nodes in 4 Regions as well as election of the cluster heads.
2. The steady phase. It relates to how data are transmitted among the nodes within the four regions. The energy involved in this data transmission is mostly determined in this phase.
# Overlap Avoidance of Mobility Models for Multi-UAVs Reconnaissance
## 4 Performance Evaluation  
We evaluated performance comparison with previous reconnaissance mobility models and those with overlap avoidance. We simulated ten UAVs reconnoitering the scanning area of 30 km \(\times\) 30 km for two hours, where each UAV follows the model in Table 1. We developed the mobility model simulator using C++ language and designed those models in the simulator. We verified the average performance by repeating 20 times in each model.  
We measured the coverage rate and the inter-arrival time of scanning in each unit region for overall scanning performance. We also measured the overlapped scanned area in order to show the effect of overlap avoidance scheme. The following subsections show various simulation results.
# Advanced Medical Image Segmentation Enhancement:
## 3 Materials and Methods
### Dataset Selection
#### 3.1.2 Chest X-ray (COVID-19) Dataset [82]  
The chest X-ray dataset encompasses a larger collection of 3616 images. These images are chest X-rays (Figure 3)--a fundamental diagnostic tool widely used in clinical practice. This dataset reflects a diverse range of medical conditions, including those related to COVID-19 [83], and includes a variety of anatomical regions within the chest.  
As mentioned above, we chose datasets with different characteristics and challenges to evaluate our method's performance. The chest X-ray dataset, for instance, consists of 2D X-ray images of the chest. These images introduce a unique set of challenges, characterized by lower spatial resolution and the presence of overlapping structures such as ribs and organs. Moreover, these images, relevant to COVID-19 diagnosis, might exhibit specific patterns associated with viral pneumonia, adding another layer of complexity to the segmentation process.
## 3 Results and Discussion
### Principal Component Analysis  
Principal Component Analysis (PCA), as a multivariate statistical method, can reduce the dimensionality of data to eliminate unnecessary information, thereby examining the correlation between different variables [27; 33]. To further analyze the differences among the yak milk powder samples, we conducted PCA analysis using the content data of the 48 identified compounds, and the results are shown in Figure 4. Similar previous studies suggested that, when the cumulative contribution rate of Principal Component 1 (PC1) and Principal Component 2 (PC2) is greater than 80%, the PCA model can be considered relatively reliable as a separation model [30; 34].  
As seen in Figure 4, the contribution rate of PC1 was 65.72%, and that of PC2 was 17.55%, with a cumulative contribution rate of 83.27% for PC1 and PC2. Therefore, the PCA model was considered reliable. In the PCA plot, the higher the clustering degree of sample points, or the closer the distances, the smaller the differences between the samples. Conversely, greater distances indicate larger differences. Thus, the PCA plot can reflect the variability between samples from a global perspective [35]. In Figure 4, the data points for  
Figure 4: Principal Component Analysis (PCA) of volatile flavor compounds in yak milk across different lactation periods.  
YCSP, YMMP, and YEMP showed high clustering within each group, while the data points among the three groups were far apart, indicating significant distinctions. In conclusion, there were clear differences in volatile compounds among YCSP, YMMP, and YEMP, and PCA analysis could be used to visually differentiate the volatile compounds in yak milk powders made from the milk collected during different lactation periods.