esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modeling the Relationship between Field AGB and Satellite Data
### Geographically Weighted Regression  
Originally proposed by [PERSON] et al. (1998), GWR is a powerful approach for modeling spatially heterogeneous processes at a local scale ([PERSON] et al., 2000; [PERSON] et al., 2001; [PERSON] et al., 2002). It estimates the individual parameters for each estimation location, and the parameter estimation at any location obeys the distance decay. In other words, the closer to the location of an observation, the greater the weight that is allotted for the observation. The GWR form is regularly expressed as ([PERSON] et al., 2000):  
\[\overset{\wedge}{y}_{i}=\beta_{0}(u_{i},v_{i})+\sum_{k=1}^{p}\beta_{k}(u_{i},v _{i}){x^{*}}_{ik}+\varepsilon_{i} \tag{1}\]  
where \(\overset{\wedge}{y}_{i}\) is the dependent variable value of observation \(i\) considered in the parameter estimation at the location (\(u_{i}\), \(v_{i}\)); \(\beta_{0}(u_{i}\), \(v_{i})\) is the intercept; \(\beta_{k}(u_{i}\), \(v_{i})\) is the coefficient of \(k\) explanatory variables, indicating a parameter estimate that explains the relationship around location (\(u_{i}\), \(v_{i}\)), which varies with the location; \(x^{*}\)\({}_{ik}\) represents the independent variables of observation \(i\); \(p\) is the total number of explanatory variables; and \(\varepsilon_{i}\) is the error term that is generally assumed to be explanatory and normally distributed with zero mean and constant variance. The parameter estimator \(\beta_{k}(u_{i},v_{i})\) is identical to the weighted least squares regression, where the weights are computed based on the distance between the observations ([PERSON] et al., 2000). The parameters are estimated as ([PERSON] et al., 2000):  
\[\overset{\wedge}{\hat{\beta}}(u_{i},v_{i})=\ (X^{T}W(u_{i},v_{i})X)^{-1}X^{T}W(u_{i},v _{i})Y \tag{2}\]  
where \(X\) is the matrix formed by \({x^{*}}_{ik}\); \(Y\) is the vector formed by values of the dependent variables; \(W(u_{i}\), \(v_{i})\) is a weight matrix to ensure that those observations near the point \(i\) have more influence on the results than those farther away; and the weights are calculated based on a kernel weighting scheme such as fixed Gaussian, fixed bi-square, adaptive bi-square, and adaptive Gaussian ([PERSON] et al., 2000). In this study, we used the fixed Gaussian kernel as ([PERSON] et al., 2000):  
\[W(u_{i},v_{i})=\ e^{-0.5(d_{i}(u_{i},v_{i})/h)^{2}} \tag{3}\]  
where \(d_{i}(u_{i},v_{i})\) is the distance between the observation \(i\) and the location (\(u_{i}\), \(v_{i}\)); and \(h\) is a quantity called bandwidth, which controls the effect of the distance on the weight value.  
GWR was conducted using GWR (version 4.0, Ritsumeikan University, Kyoto, Japan), by which the weight function (a geographic kernel type) and the minimum value of the corrected Akaike information criterion (AICc, small sample bias corrected AIC) are determined to find the optimal bandwidth by a golden section search ([PERSON] et al., 2000).
## 3 Measurement Techniques of the Electro-Optic Coefficient
### Second Harmonic Generation
We point out that in the two-index simplified notation, \(d_{ij}\) is proportional to \(r_{ji}\). SHG measurement evaluates independently \(d_{31}\) and \(d_{33}\), so the technique can be used to check the ratio \(\frac{r_{33}}{r_{13}}=\frac{d_{33}}{d_{31}}\) and, indirectly, verify if the assumption \(r_{33}\approx 3r_{13}\), used in the nonlinear ellipsometric technique is valid. By continuously measuring the SHG signal, it is possible to perform faster measurements that can be usefully used in monitoring the time decay of the electro-optic coefficient as we will show later.
# Deep Multi-View Clustering Based on Reconstructed Self-Expressive Matrix
## 4 Experiments
### Evaluation Metrics  
To evaluate the clustering performance of various methods, we employ three evaluation metrics: Accuracy (ACC), Normalized Mutual Information (NMI), and Adjusted Rand Index (ARI). ACC measures the ability of a model to correctly assign instances to their respective classes, giving an overall assessment of its performance. It can be calculated as follows:  
\[ACC=\max_{m}\frac{\sum_{i=1}^{n}\{y_{i}=map(p_{i})\}}{n} \tag{8}\]  
where \(y_{i}\) represents the ground-truth label, \(p_{i}\) donates the predicted cluster and \(map(\cdot)\) is a map function. The optimal one-to-one mapping is obtained using the Hungarian algorithm. NMI is a measure of the similarity between two clusters of a dataset. Specifically, NMI measures the amount of information shared between two clusters, normalized by the total amount of information present in the two clusters, which is defined as:  
\[NMI=\frac{\mathbf{I}(y;p)}{\max\left\{H(y),H(p)\right\}} \tag{9}\]  
where \(\mathbf{I}(y;p)\) donates the mutual information between the ground-truth \(y\) and the predicted label \(p\), and \(H\) represents the entropy. NMI takes values between 0 and 1, where 0 indicates no similarity between the two clusters, and 1 indicates perfect similarity. ARI is a measure of the similarity between two clusters of a dataset. It is a modified version of the Rand  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset Name** & **Sample Number** & **Class Number** & **View Number** & **Image Size** \\ \hline Fashion-MNIST & 2000 & 10 & 2 & \(28\times 28\times 1\) \\ COIL-20 & 1440 & 20 & 2 & \(32\times 32\times 1\) \\ COIL-100 & 7200 & 100 & 2 & \(32\times 32\times 1\) \\ YTF & 10,000 & 41 & 3 & \(128\times 128\times 3\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed information of the experimental datasets.  
Figure 3: The visualization of COIL-20 and YTF dataset. The COIL-20 dataset has two views, including the original color and edge feature images. The YTF dataset has three views, including color images, gray images and edge images.  
Index (RI), taking into account the expected agreement between two random clusters. It can be defined as follows:  
\[ARI=\frac{RI-E(RI)}{\max(RI)-E(RI)} \tag{10}\]  
The Adjusted Rand Index takes values between \(-1\) and \(1\), where \(-1\) indicates complete disagreement between the two clusterings, \(0\) indicates the amount of agreement expected by chance and \(1\) indicates perfect agreement between the two clusterings.
# View Synthesis with Scene Recognition for Cross-View Image Localization
## 3 System Design
### Style Transfer  
Style transfer is a computer vision technique that involves transferring the style of one image to another. This can be achieved by using deep neural networks, such as convolutional neural networks (CNNs), to learn an image's texture and appearance and then apply this information to another image to give it a similar style. A style transfer algorithm usually involves two parts: the content and the style. The content of an image is the basic structure or shape of the image, while the style is its texture, color, and appearance. The process of style transfer involves preserving the content of one image and applying the style of another image to it, resulting in a new image that combines the content of one image with the style of another. There are several variations of style transfer, each with its own set of algorithms and techniques. Some popular style transfer algorithms include neural style transfer, which uses a deep neural network to transfer the style of one image to another, and fast style transfer, which uses a simplified neural network architecture to transfer the style of an image in real-time.  
In the case of a swapping autoencoder proposed in the paper [13], the network was trained to reconstruct an image by first encoding it into a compact representation, called a latent code, and then decoding the latent code back into an image. The key difference with a swapping autoencoder is that the network is trained to swap the latent codes between two images and then decode the swapped codes to produce a new image. This allows the network to transfer the style or appearance of one image to another. There are three pre-trained models provided in [13], i.e., the mountain model, the church model, and the bedroom model, which are trained by LSUN Church, Bedroom [31], and Flickr Mountains, respectively. Figure 4 shows the view synthesis results when we attempted to transfer the weather/light information extracted from a forest image to a Google Street View Image with each pre-trained model.  
The transformation can be expressed in Equation (2), where \(ST(\oplus)\) is a function transferring the style from a patch image (\(I_{P}\)) to a reference image (\(I_{R}\)) with the pre-trained model \(M_{p}\). \(I_{O}\) denotes the synthesized view after the style transformation.  
\[ST\big{(}I_{P}\oplus I_{R},\ M_{p}\big{)}=I_{O} \tag{2}\]  
Figure 4: Forest images are combined with a reference image with three pre-trained models with swapping autoencoder [13].
## 2 Characterization of the Microbiota in Dogs  
The microbiomes of humans and pet animals have similarities [26], and the microbiota play a critical role in health and diseases among pet animals [27; 28]. Previous bacterial-culture-based studies showed that healthy dogs contain a \(10^{2}\) to \(10^{11}\) CFU/g bacterial load in their gastrointestinal (GI) tracts. Recently, high-throughput DNA sequencing techniques were implemented to determine the microbiomes of canines [29]. Current molecular methods revealed that the microbial loads of healthy dogs can range from \(10^{12}\) to \(10^{14}\)[30; 31; 32]. [PERSON] et al. [33] reported that Firmicutes was the predominant phylum, followed by Actinobacteria, Proteobacteria, and Bacteroidetes, which were identified in the fecal microbiome of healthy dogs. Fusobacteria and Acidobacteria were the least abundant phylum compared to the other identified phyla in the fecal microbiome of healthy dogs [33]. [PERSON] et al. [34] reported the same set of phyla as the predominant one found in the fecal samples of dogs [34].  
The microbial load of each segment in the GI tract varies based on that section's physiological functions [35; 36]. For instance, the small intestine is rich in aerobic and facultative anaerobic bacteria, while the colon harbors anaerobic bacteria [35]. Individual variations in the microbiota of different intestinal compartments (such as higher diversity indices for the colon, rectum than those of duodenum, jejunum, and ileum within individual dogs) may also be present, and these variations must be taken into account to explain the microbiota of healthy dogs [36]. The results can fluctuate based on the sequencing method and depth of the study. Some studies showed that Firmicutes (abundant in duodenum, jejunum, ileum,and colon), Fusobacteria (abundant in ileum, and colon), Bacteroidetes (abundant in ileum, and colon) [37], Proteobacteria (abundant in duodenum [37; 38], jejunum, ileum [38], and rectum [37]), and Actinobacteria [37] are the primary phyla identified in the GI tract of the dog microbiota [37; 38].  
Clostridia is the most abundant bacterial class. Clostridium clusters XI (more abundant in the small intestine) and XIVa (more abundant in the colon) are the dominant clusters of Clostridia [37; 38; 39]. In addition to Clostridia, _Bacillus_ (genera _Streptococcus_ and _Lactobacillus_) and _Eryipelotrich_ (genera _Turicbacter_, _Catenibacterium_, and _Coproacillus_) are the dominant classes belonging to Firmicutes [39]. _Prerotella_, _Bacteroides_[39; 40], _Sutterella_[40], SMB53, Enterococcus, Fusobacterium [34], and, _Megamonas_[34; 39] are the most abundant genera among the gut microbial populations in dogs.
## 4 Discussion  
To summarize, the opinions of the participants in this study, vis a vis the olfactory and visual stimuli, were different, with some considering them equal in terms of intensity but also in terms of importance in recalling memories, while some were stimulated by the visual field more, and others by the odors.  
A common fact, accepted by all the participants, was that related to the pleasant feelings they had during the experiment that persisted even after it ended. The episodes retrieved from childhood or adolescence seem to be very strong and vivid with well-being feelings being the main characteristic of the subjective experience for all participants. This is similar to the results from other studies. For example, in one study [22], a VR system was designed that enabled highly realistic image-based presentation of familiar environments to elderly subjects with dementia, and their autobiographical memory was retrieved.  
VR technologies and interactions had a very important role in the experiment, especially through the prism of isolating the visual field and anchoring it in the suggestive images of scenes, such as chamomile and oranges. Two participants thought that a set of less artificial images would have a better effect than those used here. The VR technology used in this experiment had no auditory stimuli. Some participants noticed this, while others did not. Those who noticed considered that it might be an element worth taking into account for a future experiment.  
The participants all agreed that the VR system was well suited for autobiographical memory retrieval, with further benefits for improving the psychological state of a person. Our results are similar to other similar studies from the literature. For example, in another study, the memory performance of two groups of healthy adult subjects was compared, with one group experiencing a virtual reality (VR) and the other watching a 2D video. It was found that the VR group showed better recall success [18; 22]. Finally, another study designed a virtual reminiscence room using 3D modeling and rendering technology to reproduce life scenes from the 1970s in China and exposed patients with Alzheimer's disease to this virtual environment to test the efficacy of VR in improving autobiographical memory [33]. However, as compared with other studies, and according to the opinion of the participants, integrating odors and the possibility of interacting with the VR environment created more immersion and had a stronger effect than just using one technology in isolation. This integration and immersive interactions are novel aspects of the research presented in this paper.
## 4 Results
### Results on External Validation Datasets  
The results obtained on the external validation datasets are shown in Table 2. The proposed model exhibited consistent performance across both the UPENN and TCGA cohorts, confirming its generalization ability. Notably, both datasets confirmed the model's strong performance on ET and WT regions, with ET Dice scores of 0.85 \(\pm\) 0.11 on UPENN and 0.88 \(\pm\) 0.12 on TCGA and WT scores of 0.90 \(\pm\) 0.09 and 0.94 \(\pm\) 0.10, respectively.  
As observed in the internal evaluation, segmentation of the non-enhancing core (NCR) proved more challenging, with a slightly lower Dice score (0.76 \(\pm\) 0.19 on UPENN and 0.74 \(\pm\) 0.22 on TCGA) and reduced recall, likely due to its limited size and lower contrast. Nevertheless, the precision for NCR is relatively high on both datasets, suggesting that necrotic regions were accurately identified when predicted.
## 3 Discussion of Experimental Results
### Discussion of Experimental Results
#### 3.3.4 Optimization of Injection Volume  
As can be seen from Figure 12a-c, the morphology of the NMR curves changes more obviously during the production process as the injection pressure increases. According to the change in the signal amount of the NMR T2 spectra (Table 8), after simmering the well from an injection pressure of 15 MPa to 20 MPa to 25 MPa, the extraction degree changed from 5.37% to 8.69% to 9.61%, in that order, i.e., the increase in the injected amount of percolant can improve the extraction degree.  
By converting the injection volume into PV volume, the recovered oil volume and the recovery degree under different injection volumes can be obtained, and the results are shown in Table 9 and Figure 16. The CO2 injection volumes of 0.33 PV, 0.44 PV, and 0.542 PV
## 3 System and Security Model
### System Model
#### 3.1.1 Key Generation Center  
The KGC provides a partial private key for the data owner and data users. It is wholly trusted. Different from the traditional certificateless encryption, we use the signature generated by the user to demonstrate that the identity and the public parameters belong to the same user.  
Figure 3: Data Sharing in Cloud.
# CycleGAN-Driven MR-Based Pseudo-CT Synthesis for Knee Imaging Studies
## 2 Materials and Methods  
### Dataset  
This study was conducted at Hospital Universitario Ramon y Cajal. The institutional review board approved this study (protocol 297/23); the need for written informed consent was waived due to the retrospective nature of the study.  
A total of 44 patients were retrospectively retrieved from the PACS of the hospital. For inclusion in this study, subjects were required to have both an MR scan and a CT scan of the same knee within a reasonable time frame. Exclusion criteria included the following: (1) images (either MR or CT) with poor image resolution, preventing the identification of the anatomical structures of the knee; (2) severe knee disease, such as soft tissue or bone tumors; (3) surgical procedures in between the MR and CT acquisitions, changing the anatomy distribution between images; and/or (4) extremely different field of view and positioning of the knee between the MR and the CT scans due to the high degrees of freedom of the knee, hindering matching between MR and CT image structures.  
Following eligibility, data quality check, and availability, 16 patients were finally included in this study (45.9 \(\pm\) 13.2 y/o, range 20-70; 8 females), providing us with 16 MR-CT image pairs. MR T1-weighted images and CT scans differed depending on the scanners (images were acquired in different scanner models). Different protocols were used during image acquisition, leading to different image resolutions as well as diverse field of views. Moreover, a range of coil configurations with different numbers of channels were used for MR image acquisition, resulting in a highly heterogeneous database. Figure 1 shows an example of the MR and CT images of an eligible patient. Table 1 summarizes the demographic details for all subjects included in our dataset, as well as their corresponding MR and CT scans vendors, models, and technical details.  
Figure 1: Example axial (**left**), coronal (**center**), and sagittal (**right**) slices of the MR (**top row**) and CT (**bottom row**) images of an eligible patient in our dataset. Notice the different field of view and the different positioning of the knee between the MR and CT images.
# Jamming Detection and Suppression Technique for OTFS Systems in an AWGN Channel
## 1 Introduction
A smoothing window technique was also employed to provide stable samples for interference detection, effectively reducing the false alarm rate. Finally, a low-complexity improved FCME algorithm was proposed. The remainder of this paper is organized as follows. In Section 2, the system model is introduced. Section 3 shows the improved FCME algorithm based on overlap windowing for jamming detection. Section 4 shows that the proposed jamming detection and jamming suppression has better performance against single-tone and partial-band jamming than conventional ones through simulations. Section 5 draws the conclusions.
## 5 Results
### Spectral Unmixing Results
#### 5.2.2 Landsat-8/OLI  
The Landsat-8/OLI_rhyodacite_ abundance map (Figure 5b) included few pixels with high abundances northwest of Lofos Dome and north of the hydrothermal area of the central sector craters (Phlegethon, Mikros, and Megalos Polybotes craters) and south-southeast of Stefanos and Kaminakia craters. The spatial distribution of _basaltic andesites_ was very limited and similar to the spatial pattern of rhyodacites (e.g., southeast of the central sector craters), as shown in Figure 5e. On the other hand, _the alluvial deposits_ covered the largest part of the study area (Figure 5h). The highest abundances were observed at Lofos dome, the central sector craters, inside Stefanos crater, and the eastern part of the study area. _Hydroxyl-bearing alteration_ (Figure 5k) showed a similar pattern with alluvial deposits (Figure 5h) and followed the same characteristic three-center pattern that was observed in the ASTER-corresponding result. High abundances were observed at Lofos dome and the related hydrothermal area of the central sector craters (Phlegethon, Mikros, and Megalos Polybotes craters), within Stefanos crater, and at the outer rim of Kaminakia craters. Finally, _sulfur_ high abundances (Figure 5n) were mainly observed within the hydrothermal areas of the central sector craters (Phlegethon, Mikros, and Megalos Polybotes craters), east of and inside Stefanos and the outer rim of Kaminakia craters.
## 3 Materials and Methods  
This section aims to present a practical example of understanding vulnerabilities in code generated by an AI model (i.e., ChatGPT 3.5). The methodology covers the creation of a database, the definition of a table, the insertion of sample user data, and the secure storage of passwords. To do this, the resources involved a database for a web application, MySQL queries for database operations, and PHP for server-side logic.  
Examples of industrial applications utilizing the components include inventory management systems, production tracking systems, and quality management systems, where web-based applications, SQL databases, and PHP queries play a significant role. For example, inventory management systems efficiently track stock levels and manage suppliers, leveraging SQL for database operations and PHP for server-side logic. Similarly, production tracking systems monitor schedules and manage inventory levels, using SQL for data extraction and PHP for dynamic functionalities. Quality management systems ensure product compliance, using SQL for data analysis and PHP for robust server-side logic.  
In the context of self-configurable systems, these resources assume particular significance. That is, they not only facilitate data management and user interaction, but also have the capability to dynamically adapt to the evolving needs of industrial operations, thanks to the use of agents [33]. For instance, the web server can automatically adjust resource allocation to handle sudden traffic spikes, while the PHP code can adapt to modify existing behavior in response to changing system requirements. Similarly, the SQL database can be reconfigured to optimize performance or ensure data integrity in highly dynamic production environments.
# Characterization of Inter-System Biases in GPS + BDS Precise Point Positioning
## 4 Characterization of ISB  
According to Equation (6), the ISB estimated by PPP contains the satellite clock datum and the receiver-code bias. In the following, undifferenced and uncombined PPP was adopted to analyze the influence factors of ISB. The technical features of the receiver and antenna types mentioned in the experiments are available in the official websites, TRIMBLE ([[https://geospatial.trimble.com](https://geospatial.trimble.com)]([https://geospatial.trimble.com](https://geospatial.trimble.com))), LEICA ([[https://leica-geosystems.com](https://leica-geosystems.com)]([https://leica-geosystems.com](https://leica-geosystems.com))), SETPENTRIO ([[https://www.septentrio.com](https://www.septentrio.com)]([https://www.septentrio.com](https://www.septentrio.com))).
# Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN
## 2 Vehicular Named Data Networking (VNDN)
### Forwarding Information Base (FIB)  
Forwarding Information Base (FIB) is a data structure used in the VNDN architecture. FIB generates a routing table that links content names to interfaces. A name-prefix-based routing protocol populates the FIB, which might have many output interfaces for each prefix.  
Figure 1: VNDN data forwarding architecture.