esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Magnitude and Patterns of Global GPP  
The generation of global SIF maps with almost continuous global coverage by directly aggregating original OCO-2 SIF soundings could only be achieved at very coarse spatial resolution (e.g., 1-2\({}^{\circ}\)) and temporal resolution (e.g., monthly). To illustrate the value of our finer-resolution GPP maps, we directly aggregated the OCO-2 soundings to coarse-resolution (1\({}^{\circ}\)) maps at 8-day intervals, and also converted the SIF values to GPP estimates based on one SIF-GPP relationship as an example (site-level, universal, non-zero intercept relationship: slope = 20.04, intercept = 0.89 g C m\({}^{-2}\) d\({}^{-1}\)/W m\({}^{-2}\)\(\mu\)m\({}^{-1}\) sr\({}^{-1}\); see Table 1). We then compared these coarse-resolution OCO-2 SIF-based GPP with the finer-resolution GPP maps that we produced using our finer-resolution SIF estimates in both spatial patterns and seasonal cycles in three geographical regions (East Asia, Australia, and United States).  
With the gridded GPP estimates based on the universal SIF-GPP relationship, we assessed the spatial patterns and seasonal changes in GPP from January to December throughout 2016 to determine whether GOSIF GPP captured the expected patterns in terrestrial photosynthesis. We also assessed how the universal versus biome-specific relationship influences GPP estimates. For better visualization of the difference among relationships, we provided the difference maps of annual GPP in 2017 derived from the universal SIF-GPP relationship against the biome-specific relationship as well as the non-zero intercept relationship against the zero-intercept relationship developed at the site-level as examples.  
We then used the ensemble mean GPP estimates to examine the magnitude and spatial patterns of annual mean and maximum GPP at the global scale and the ensemble standard deviation to assess the variability of GPP among the eight sets of GPP estimates based on different SIF-GPP relationships. We also calculated the global annual GPP values (Pg C yr\({}^{-1}\)) from our gridded ensemble GPP estimates, and compared our ensemble estimate with global GPP estimates from the literature based on both non-process-based and process-based approaches.
# Metabolic Potential of Halophilic Filamentous Fungi--Current Perspective
## 5 Biomolecules Produced by Halophilic Fungi  
Fungi from saline habitats are a source of an abundance of biocompounds with potential industrial applications, which have unique features such as salt tolerance, stability, and activity in presence of organic solvents and under low water activity conditions [29; 49].  
Halophilic fungi produce various enzymes, mainly known for their hydrolytic and ligninolytic properties [50; 51]. Defense mechanisms against oxidative stress make halophiles a rich source of the compatible solutes or osmolytes including glycerol, arabinitol, erythiritol, mycosporines, and mycosporine-like amino acids (MAAs) [27; 34; 52]. The saline environments represent the wide natural resources of fungal biosurfactants and surface-active proteins such as hydrophobins [53; 54]. Halophilic microorganisms, including fungi, can also produce pigments. The black yeast-like and related melanized fungi accumulate melanin in their cells to protect the cell from harsh conditions [47]. Furthermore, they are considered producers of antimicrobial and anticancer compounds of pharmaceutical importance and antioxidants for food prevention and the cosmetics industry [20; 43; 55; 56]. Halophilic fungi are still being discovered, and new compounds are described all the time.
# GeoBERT: Pre-Training Geospatial Representation Learning on Point-of-Interest
## 2 Related Work
### Pre-Trained Models  
Due to the self-supervised learning tasks and huge models parameters, PTMs, represented by GPT (2018) [8] and BERT(2018) [7], have achieved great success in the past few years. By pre-training on massive unlabelled data, PTMs can effectively capture the underlying knowledge in the text and store it into the huge parameters. With simple fine-tuning on quite a few samples, the rich knowledge implicitly encoded in huge parameters can be transferred to various downstream tasks. After GPT and BERT, more and more efforts are devoted to the field of large-scale PTMs. Subsequently, RoBERTa [23], XLNet [24], ALBERT [25], GPT-3 [26], and other variation models constantly refresh the SOTA in NLP. As the effects continue to improve, the parameter scale and data size used are also becoming larger, which are increasing by 10 times per year [27]. Most of these PTMs are Transformer-based [28] structures, and the optimization targets are MLM (mask language model). The paradigm of \"pre-training +fine-tuning\" has also been transferred from the field of natural language to image. Computer vision pre-trained models such as VIT [29], MAE [30], and BEiT [31] have brought widespread attention.  
In addition to general fields, domain PTMs have also been proposed recently, mainly in the fields of healthcare [32; 33], biomedical [34; 35; 36], and academic research [37]. Most studies learn domain-specific knowledge by pre-training on domain-specific corpora through self-supervised learning tasks. OAG-BERT [38]is an academic PTM that uses heterogeneous structural knowledge in the academic knowledge graph.  
The pre-trained model closest to the model proposed in this paper is ERNIE-GeoL [39; 40], which is proposed by Baidu. ERNIE-GeoL is a geography-and-language pre-trained model designed and developed for improving the geo-related tasks at Baidu Maps, such as POI retrieval. It builds a heterogeneous graph based on POIs in user search history. The core idea is to learn the text attributes of a geographical entity and focuses on the relationship between \"geography location\" and \"language\". In this study, we focus on learning the universal representation of urban space for wider application. Map retrieval services are not part of our research. There are significant differences between these two studies in terms of research objectives and methods. To some extent, the foundation of ERNIE-GeoL is still language, while our study is completely based on urban space.  
Previous NLP methods used in extracting urban information have proved the similarity between natural language and geographic space and that many methods are transferable. However, existing work has mainly focused on certain areas. Urban spaces involving complex functional semantic information can greatly benefit from large-scale urban data. A large-scale pre-trained model for geospatial representation learning that can effectively support various downstream tasks in the urban domain is urgently required. Our research aims to bridge this gap.
# Topological Interference Management for K-User Downlink Massive MIMO Relay Network Channel
## 1 Introduction
### Summary of Contribution  
The main contributions of this paper are summarized as follows:  
* We consider a three-user MIMO interference channel with a shared relay, where no more than two transmitters can be active over the same time slot. At the receiver-end, the interference signals from an unknown transmitter affect the performance of the downlink relay network channel considerably.
* The introduction of an additional (unintended) receiver incorporates one relay signal and two unknown receiver signals received from the two active transmitters aid to improving the recovered signal strength drastically at the intended receiver-end.
* The proposed R-STIA technique assists the interference signals to align in both the time and space domains under limited CSIR knowledge by using the R-STIA transmission technique. The proposed technique also recovers and reconstructs the transmitter and relay signals at the unintended receiver-end by considering the cooperation between the receivers.  
* The motivation behind the proposed scheme is to reduce the transmitter power consumption largely by not activating more than two transmitters over the same time slot. The proposed scheme can also reduce the decoding complexity due to the presence of the additional receiver and less interference misalignment at the intended receiver-end.
* The Alamouti space-time transmission technique and the MMSE linear precoder are employed at the transmitters and relay to detect the presence of interference signals at the unintended receiver, which, in turn, aid in improving the BER performance of the proposed scheme.
* Numerical results confirm that the proposed R-STIA technique significantly improves the sum-rate and BER performance compared to the existing broadcast channel schemes [17; 18].
# A Novel Indoor Localization Method Based on Image Retrieval and Dead Reckoning
## 5 Experiments and Results  
The proposed fusion positioning system was tested in typical indoor scenarios. To verify the advantages of the proposed V-PDR algorithm compared with the traditional PDR algorithm in indoor positioning and to verify the feasibility of the V-PDR algorithm in three-dimensional space, the experiment was divided into two parts. The first was to verify the effectiveness of our proposed VPR model in indoor positioning. The second was to verify the advantage of the proposed V-PDR model compared to the traditional PDR model, which included comparing the positioning results between the V-PDR and PDR algorithms during driving and verifying that our V-PDR algorithm works properly in a cross-floor environment.
# Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS
## 5 Scheme Implementation
### Registration and Verification Phase
#### 5.2.3 Verification  
When a DU wishes to access medical data stored in the system, identity verification and access control are required based on different roles and scenarios. When initiating a data access request, the DU needs to provide \(pf_{DUL}\), \(VK_{DUL}\), and \(uid_{DU}\).  
If \(Exist(uid_{DU})=true\), the chaincode proceeds to authenticate the identity of the DU and verify the correctness of \(pf_{DUL}\). If \(Verify(VK_{DUL},proof_{DU})=true\), the verification result is returned to the data client. Then, the system determines the legitimacy of the user's request. Otherwise, the verification fails, and the data usage request is rejected.
## 4 Abstract Concept Modeling with RANs
### Step 1: Concept Identification (CI) Process  
Concept identification is the first step in RANs modeling. The objective of the CI procedure is to appropriately identify each instance within the data as a distinguished member of various underlying convex groups. This is realized by categorizing the input data based upon their geometrical relationship, i.e., distance, conforming to the theory of conceptual spaces (see Section 3.2). Here, we also recognize data points that are the most probable representative of each identified group, complying with the prototype theory (see Section 3.2). These identified data points are termed as Cluster Representative Data Points (CRDP) and are used in Step 3 for learning the relationship between two adjacent layers (see Section 4.4).  
The process of CI instantiates after preprocessing the input data. Initially, an input layer is formed, with dimension equal to the size of the input data feature vector. Step 1 in Figure 4 shows the Layer-0 with two nodes which is like the magnitude of the input vector of 2-Dimensional Toy-data. At Layer-0, clustering methods are used to determine geometrical relation among the several input data instances and identify the underlying categories within the data. Thus, K-means [57] clustering algorithm is applied to Toy-data to identify five classes (Class-1,..., Class-5) by assigning a value 5 to 'K' in K-means clustering algorithm (Note: The 'K' value in the K-means algorithm is to provided manually but in unlabeled datasets, the best value of 'K' can be determined using the Elbow method). Figure 3 shows the plot of 2-D data points obtained after performing concept identification operationusing the K-means algorithm. Figure 3 also displays the centroids (C\({}_{1}\),..., C\({}_{5}\)) of all the clusters, recognized as CRDPs of all five classes and will be used in Inter-Layer Learning (ILL) in Step 3 (see Section 4.4).  
Any clustering algorithm can act as a concept Identifier in RANs modeling if it suffices two basic requirements. First, the algorithm can determine convex categories based upon their geometric relationship among the data instances. Second, the algorithm recognizes CRDPs of all the identified clusters. This flexibility of choosing a suitable method for the concept Identification process in RANs modeling is demonstrated by a separate experiment using Affinity propagation [58] clustering algorithm, in Section 5.1.
# Automated Pneumothorax Segmentation with a Spatial Prior Contrast Adapter
## 3 Materials and Methods
### Spatial Prior Contrast Adapter
#### 3.4.3 Probabilistic Atlas  
In medical image segmentation, the use of a probabilistic atlas has proven to be an effective approach for detecting and delineating pneumothorax in chest X-ray radiography. Given the clinical importance of precise pneumothorax segmentation for accurate assessment and decision-making, this method provides valuable spatial priors to guide the segmentation process. A probabilistic atlas is constructed from a dataset of annotated chest X-rays representing various stages and manifestations of pneumothorax. This enables the model to capture the spatial distribution and visual characteristics of pneumothorax-related features, such as air pockets and the lung-chest wall boundary. By leveraging the ground truth masks from the CANDID-PTX dataset, a probability map is generated for each X-ray, where each pixel is assigned a statistical likelihood of belonging to the pneumothorax region (Figure 6). This approach effectively addresses challenges such as anatomical variability, inconsistent image quality, and the heterogeneous presentation of pneumothorax. The application of a probabilistic atlas not only enhances segmentation robustness and diagnostic accuracy but also reduces the time required for evaluation, making it especially valuable in time-sensitive clinical settings such as emergency care.
# A Semi-Random Elliptical Movement Model for Relay Nodes in Flying Ad Hoc Networks
## 5 Performance Evaluation
### Sensitivity Analysis of Key SREM Parameters  
To evaluate the robustness and adaptability of the proposed SREM model, we conducted a sensitivity analysis focusing on three key parameters: the semi-major axis length \(a\), the aspect ratio \(b/a\), and the angular increment \(\delta\). Each parameter governs the spatial structure and movement behavior of UAV nodes, which directly affects the model's performance in relay-based FANET scenarios.  
We conducted a sensitivity analysis of the semi-major axis parameter \(a\) while maintaining a fixed aspect ratio of \(b/a=0.5\). As shown in Figure 9, communication performance varies noticeably depending on the geometric configuration of the elliptical trajectory.  
When \(a=500\,\mathrm{m}\), the SREM model achieves optimal performance in terms of PDR, EED, and CT. This is because the major axis of the ellipse aligns exactly with the \(1000\,\mathrm{m}\) straight-line path between the source and destination nodes, with the center of the ellipse located at \((500,500)\). Such geometric alignment increases the concentration of UAVs along the relay route, enabling efficient packet delivery with fewer hops and lower delay. In contrast, values of \(a\) shorter or longer than \(500\,\mathrm{m}\) either constrain node distribution or disperse it excessively, weakening relay availability and degrading overall communication performance.  
An analysis of the aspect ratio \(b/a\) of the ellipse revealed that optimal communication performance--measured in terms of PDR, EED, and CT--was achieved when \(b/a\approx 0.4\). This ratio provides an effective balance between path alignment and vertical dispersion, preventing excessive node clustering near the center while maintaining strong alignment with the relay axis. When \(b/a\) is smaller than \(0.4\), nodes tend to concentrate excessively around the center, limiting coverage. Conversely, when \(b/a\) exceeds \(0.4\), the elliptical shape approaches a circle, causing nodes to disperse too widely from the main relay path, which degrades connectivity and transmission efficiency.  
Lastly, we analyzed the effect of the angular increment \(\delta\in[\theta_{\mathrm{min}},\theta_{\mathrm{max}}]\), which determines the spacing between waypoints along the elliptical path, on communication performance. The experiment was conducted using three angular ranges: \(\left[\frac{\pi}{\theta},\frac{\pi}{2}\right]\), \(\left[\frac{\pi}{2},\frac{\pi}{6}\right]\), and \(\left[\frac{\pi}{2\mathcal{U}},\frac{\pi}{12}\right]\). Although smaller angular increments result in tighter adherence to elliptical trajectory, they did not lead to meaningful differences in key performance indicators such as PDR, EED, and CT. Extremely small increments may reduce path diversity and limit adaptability in dynamic environments, while excessively large increments can disrupt trajectory consistency. Therefore, the angular increment should be configured to balance path alignment and route diversity.
## 4 Discussion  
### Sensitivity Analysis of the Water-Use Efficiency (WUE) and Conductance Methods  
To explore what influences the relationship between SIF and T, we analyzed the sensitivity of variables in the WUE and conductance methods. The scatterplots between SIF and T\({}_{\text{WUE}}\) for the four study sites are shown in Figure 4 (a, b, c, and d for hourly scale; e, f, g, and h for daily scale). T is the product of SIF and VPD in the WUE method, which means that SIF and VPD interact with each other closely and the effect of the SIF is modified by the VPD. The parameters k4 at the four sites were 0.33, 0.46, 1.00, and 0.11, respectively, at the hourly scale; and 0.10, 0.68, 1.00, and 0.25 at the daily scale. With the increase in VPD, the slopes of SIF-T became steeper and the points became denser, while under low VPD condition, the points were relatively sparse, which indicates that the relationship between SIF and T is more linear under high VPD. In particular, the relationship between SIF and T was strongly influenced by VPD at the NR site with evergreen needle leaf plants, but at the HF site, the SIF-T relationship was less sensitive to the VPD.  
Atmospheric dryness also plays an important role in modeling the stomatal behavior by SIF. For C\({}_{4}\) plants, relative humidity is used to describe the response of stomatal conductance to air dryness in the empirical Ball-Berry model. For C\({}_{3}\) plants, we investigated the sensitivities of different variables in the gs model \(\mathbf{\mathcal{F}}\) by RBD-FAST (Random Balance Designs-Fourier Amplitude Sensitivity Test) [58]. The results showed that \(\mathbf{\mathcal{F}}\) was sensitive to VPD, \(\lambda\), SIF, and ql (supplementary Figure S1). VPD exhibited the highest relative sensitivity of 0.34, which was much higher than SIF with a value of 0.17. The marginal water-use efficiency \(\lambda\) also played an important role with a sensitivity value equaling 0.32. The fraction of open Photosynthesis II reaction centers ql is as important as SIF (relative sensitivity of 0.17), which is due to the fact that the electron transport rate J is the product of SIF and ql (Equation (8)). Furthermore, the gs model is not sensitive to \(\Gamma\) and C\({}_{\text{a}}\).  
Figure 3: Scatterplot of hourly (first row) and daily (second row) LE estimates from the three approaches (linear method, WUE method, and conductance method) versus LE (observed from eddy covariance) with the 1:1 line (black line), along with coefficients of determination (R\({}^{2}\)) and root-mean-square error (RMSE, W/m\({}^{2}\)). Subfigures (**a**,**d**) refer to the linear method, (**b**,**e**) to the WUE method, and (**c**,**f**) to the conductance method.
## 3 Data Collection
### Data Preprocessing  
The experimental dataset is divided into two parts: Raw eye movement data recorded at 60 frames per second and recordings of feature variables at each lane change. The raw eye movement data are converted into a time buffer value for each frame using the AttenD algorithm. We used the coordinates of the driver's gaze points for each frame to filter out the time buffer data when the driver's gaze fell into the blind spot. These values were then matched with other feature variables using timestamps. Each data entry represents the moment the driver looked into the blind spot and the corresponding status. The time buffer values were evenly categorized into three levels: \"low\", \"medium\", and \"high\", based on their numerical magnitude.
## 4 Results
### Analyzing the Near-Field Polarization
Quantitatively, the recursive fit enables the determination of the sample's permittivities at two different wavelengths as summarized in Table 2, which are close but not perfectly matching the values found in literature. Deviations might be related to the spherical tip shape assumed here as part of the point-dipole model. Especially, inaccuracies regarding the out-of-plane electric field enhancement are reported to impact the quality of the model [27]. The fit might be improved further by implementing more realistic tip shapes [23]. However, compared to the investigation of incident polarization only as summarized in Table 1, the advanced polarization-point-spectrum fit improved the  
Figure 4: Polarization distribution of the near-field signal for a 3C-SiC sample measured as a function of analyzer rotation \(\phi\) (\(\lambda=10.6\) μm, \(\theta=45^{\circ}\)). (**a**) Overall distribution and fit; (**b**) p-polarized (blue), (**c**) s-polarized (orange), and (**d**) mixed contributions (green), calculated and extracted from the fit showing a strong coupling even for small s-polarized contributions due to component mixing (see Equation (11)). For comparison, the fitted overall distribution from (**a**) is reproduced in gray in (**b–d**). Figure 5: Normalized polarization point spectrum of 3C-SiC at 10.6 μm. (**a**) The polarization point spectrum was measured by rotating the analyzer (\(\phi\)) by \(360^{\circ}\) for each illumination polarization angle (\(\theta\)). (**b**) The 2D fit of the polarization point spectra matches well with the experimental results and is used to extract the local sample permittivity (see Table 2). obtained Re(\(\varepsilon\)) values significantly, demonstrating the relevance of a complete mapping of near-field signal polarization. Overall, the successful extraction of local permittivity data from polarization point spectra demonstrates the potential of polarization-controlled s-SNOM for local material characterization.
## 5 Analysis and Result
### Machine Learning Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Time** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline SVM & 0.0744 & 90.91\% & 90.35\% & 85.14\% & 87.67\% \\ Bagging & 0.2501 & 95.45\% & 96.88\% & 96.88\% & 96.88\% \\ KNN & 0.0926 & 90.91\% & 89.76\% & 85.85\% & 87.76\% \\ DA & 0.0809 & 89.77\% & 89.84\% & 84.00\% & 86.82\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of the four learners in terms of time, accuracy, precision, recall and F1 score. In the alumina and cristobalite materials, the FRF results of the samples with normal data and boundary data (C1-C4) or normal data and unusual data (A1-A3) confirmed that the first negative peak shifted to a low frequency, and the FRF waveform was distributed higher than normal. These results are similar to those of the FRF of the defect test specimens. Moreover, because the damage cannot be confirmed in appearance, it may be caused by sudden breakage due to continuous loads during the insulator manufacturing process or as a result of excess voltage conditions; it may be necessary to repair or replace it in these cases.