esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Modern medicine has provided physicians with many \"prodigies\" regarding the understanding and therapeutical approaches of pulmonary diseases, such as interstitial lung diseases (ILDs), idiopathic pulmonary fibrosis (IPF), or other chronic conditions with multifactorial and complex pathophysiology [1].  
The novel coronavirus disease 2019 (COVID-19) is distinguished by significantly lower understanding and less efficient therapeutic strategies in severe cases. Despite the vaccination policies adopted in many countries, COVID-19 represents a global health problem with a high disease burden on worldwide healthcare systems [2]. Moreover, a paramount concern is raised around the new variants of the virus (especially B.1.1.7 and B.1.3.5) that were already spotted in several countries [3] and seemed to render the approved vaccines inefficient [4].  
Therefore, due to the lack of robust therapeutic strategies, early prognosis of severe disease and preventive actions hang around as the mainstay in managing the SARS-CoV-2 outbreak [5]. As such, the identification of early markers of severe disease constitutes an urgent matter.  
Pathophysiology of lung injury due to SARS-CoV-2 infection is a complex inflammatory and immunological process which leads to interstitial disease and pulmonary fibrosis [6; 7; 8]. As a consequence, the pathophysiological similarities between COVID-19 and ILD/IPF were studied [9]. The overlapping of some of the COVID-19 and ILP/IPF pathways is meaningful and essential as prognostic biomarkers of ILD and IPF may also represent valuable markers for SARS-CoV-2.  
Krebs von den Lungen-6 (KL-6 or KL-6/MUC1) is a relatively new discovered high-weight transmembrane mucoprotein found on type II alveolar epithelial cells that is expressed in the blood flow, secondary to pulmonary tissue lesions [10]. KL-6 has been proposed as a biomarker in many research papers regarding ILDs, especially as a supplemental enhancer of specificity when affirming the diagnostic in cases \"out of the guidelines\" or as a predictor for disease severity [11]. It is also a demonstrated prognostic and diagnostic biomarker for the severity of acute respiratory distress syndrome (ARDS) as higher concentrations of circulating KL-6 (\(p<0.001\)) correlate positively with peak and mean airway pressure and oxygenation index potential role in ARDS diagnosis [12]. The release of KL-6 seems to result from alveolar epithelial cell damage and destruction [13]. A recent meta-analysis observed that besides good KL-6 sensitivity (0.85, 95% CI, 0.77-0.91) and specificity (0.97, 95% CI, 0.90-0.99) for interstitial lung disease, it was also associated with mortality (HR 2.95, 95% CI, 2.45-3.55, \(p=0.032\)) [10].  
Therefore, we propose a legitimate question regarding the role of KL-6 in COVID-19: is KL-6 associated with worse outcomes in case of lung damage due to SARS-CoV-2 infection? We aimed to systematically research the literature to assess the relationship between the KL-6 biomarker of lung injury and patients' outcomes related to SARS-CoV-2 infection.
# A Development and Validation of an AI Model for Cardiomegaly Detection in Chest X-rays
## 1 Introduction  
Chest X-rays (CXRs) are routinely performed during hospital admissions and checkups due to their low cost and simplicity. They are essential for screening and evaluating thoracic diseases, pulmonary and pleural effusions, and cardiomegaly [1]. Cardiomegaly is an enlargement of the heart, often caused by conditions such as hypertension and aging, which leads to an increase in the thickness of the posterior wall of the left ventricle [2]. According to data from the Health Insurance Review & Assessment Service, the number of patients with cardiomegaly increased from 19,590 in 2015 to 27,321 in 2019, representing an increase of approximately 139%. The number of patients with cardiomegaly is on the rise, and it is often asymptomatic in the early stages, making early detection difficult. Common clinical symptoms include dyspnea and chest pain even with simple activities. In patients with acute myocardial infarction, left ventricular hypertrophy is associated with a poor prognosis and requires aggressive treatment [3]. When associated with arrhythmias, cardiomegaly can lead to serious conditions such as syncope and sudden death; therefore, early detection is critical.  
Cardiomegaly can be easily detected by X-ray imaging. However, subjective judgment can affect interpretation, leading to variability among radiologists and potential oversight of mild cardiomegaly. Although 2D-CRT has proven useful in the diagnosis of cardiomegaly, manual measurement and calculation of the heart and chest areas using a mouse are cumbersome and tiring. In addition, the increasing number of chest X-rays to be interpreted is exacerbating the shortage of radiologists, resulting in longer interpretation times [4]. Toaddress these issues, research into AI-based interpretation systems is actively underway. Previous studies have shown that AI developed using large datasets of X-ray images can achieve high accuracy rates of 0.96 and 0.93, respectively. These AI systems have the potential to improve diagnostic accuracy and reduce interpretation time by image classification. Although medical data are necessary for such research, they are challenging to obtain due to the need for IRB approval. Therefore, this study used publicly available medical data, which exclude personal information (name, gender, age), for the benefit of the public [5]. This study also highlights the high level of accuracy achieved with a relatively small dataset compared to previous studies and describes the validation process to ensure proper learning of the model.
## 1 Introduction
Furthermore, in order to evaluate the transferability, generalization power, and robustness of our method, we utilized another set of data acquired from Bam city, Iran, during the 2003 earthquake. The main contributions of our study can be summarized as follows:  
* We proposed a two-stage UNet-based architecture for building localization and damage classification from pre- and post-disaster satellite images, and the performance of different backbones within our UNets was evaluated. * In order to improve the capability of our network to extract suitable features and enhance the performance of the above-mentioned two-stage framework, we introduced an SKM that adaptively adjusts the kernel sizes through our network's feature extraction process. Adaptive receptive field selection within the SKM, in three steps of Split, Fuse, and Select, enhanced the quality of the extracted features. * To further regularize the learning process of our model and cope with the challenge of limited samples in different classes, heavy data augmentation, including CutOut [41], was applied to the xBD images. * Moreover, to investigate the transferability power of our model, we used another set of data from a completely different region with different types of buildings to assess building damage after an earthquake. * Finally, we investigated the correlations between image acquisition attributes (e.g., Sun elevation, satellite-looking angle, and GSD) and the performance of the proposed model in both localizations and damage assessment stages per disaster type in detail. The remainder of this paper is organized as follows. Related studies are first discussed in the \"Related Works\" section. Then, we introduce the datasets that we used for our model development in the \"Datasets and Preparations\" section. In \"Methodology\", we introduce our proposed model architecture along with the evaluation metrics. \"Experiments and results\" provide the implementation details of our experiments and comprehensive results and comparisons that we assessed during our experiments. Finally, \"Discussion\" and \"Conclusion\" conclude the paper.
# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 2 Related Works
### Path Representation  
In the domain of path representation, the following two primary approaches have been widely adopted: the segment-based and path-based approaches.  
The segment-based approach [6,16,17] is a traditional method where travel time is estimated independently for each road segment, with these estimates then aggregated to determine the total travel time for a journey. This approach often relies on data from loop detectors and GPS-enabled floating car systems. While effective for monitoring traffic on highways and freeways, segment-based models have several critical limitations. Their reliance on segment-by-segment calculations can introduce inaccuracies by neglecting transition times between links, and they often suffer from data sparsity in urban environments. Moreover, these systems are expensive to maintain and have limited applicability in complex urban contexts where data distribution is often uneven. These shortcomings underscore the necessity for alternative methods of path representation that offer more comprehensive and accurate predictions.  
In contrast, the path-based approach provides a more holistic method of predicting travel times, addressing many of the limitations inherent in segment-based approaches. Rather than focusing on individual road segments, the path-based approach estimates the travel time for an entire journey, offering a more integrated and realistic reflection of real-world travel conditions. This approach is particularly beneficial in urban environments, where interactions between various road segments significantly influence overall travel times. For example, [PERSON] et al. [18] introduced a method leveraging average travel times from past trips, utilizing large-scale trip data. However, such methods may struggle to adapt to dynamic traffic conditions. [PERSON] et al. [7] advanced this field by developing a unified neural network approach that simultaneously estimates travel time and distance for taxi trips, providing a more nuanced and precise prediction model. Similarly, [PERSON] et al. [19] proposed a multi-task learning framework integrating prior spatiotemporal knowledge to capture various trip properties.  
Recent advancements in path-based approaches have integrated deep learning techniques. For example, [PERSON] et al. [8] combined a Convolutional Neural Network (CNN) for spatial feature extraction with a Long Short-Term Memory (LSTM) network for modeling temporal dependencies. This hybrid model effectively captures both the spatial and temporal dimensions of TTP, addressing challenges posed by fluctuating traffic patterns. Other models, such as the isolated XGBoost regression models [20] and the unified deep learning framework proposed by [21], have also emerged, demonstrating significant potential in capturing intricate spatial correlations and temporal dependencies simultaneously.
## 3 Results and Discussion
### Transmittance (T\(\%\))  
The method for determining the intensity of the yellow color in apple juice is the transmittance percentage at 440 nm, the results of which are shown in Table 3.  
It can be observed that all samples have higher T\(\%\) values (\(p<0.001\)) compared to the control sample (1.87 \(\pm\) 0.1\(\%\)). For the GranuBent (G) bentonite-based samples, exceptionally high (favorable) values were obtained. The results of the statistical analysis confirmed a significant effect of the mineral-based clarification agent (F(2;101) = 49.317; \(p<0.001\)), the protein-based clarification agent (F(2;101) = 7.216; \(p<0.01\)), and the interaction between them as well (F(4;101) = 3.878; \(p<0.01\)). Statistical analysis also indicates that gelatine is no longer performing better since vegetable proteins frequently resulted in higher transmittance values at 440 nm.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **E** & **L** & **F** \\ \hline  & & & Klar Sol 30 & \\ \hline MM & 12.4 \(\pm\) 0.1 a & 12.2 \(\pm\) 0.1 a & 12.3 \(\pm\) 0.0 a \\ HH & 12.2 \(\pm\) 0.0 a & 12.2 \(\pm\) 0.1 a & 12.2 \(\pm\) 0.1 a \\ MH & 12.2 \(\pm\) 0.0 a & 12.2 \(\pm\) 0.0 a & 12.2 \(\pm\) 0.0 a \\ HM & 12.3 \(\pm\) 0.1 a & 12.3 \(\pm\) 0.0 a & 12.3 \(\pm\) 0.1 a \\ \hline  & & GranuBent Pore-Tec & \\ \hline MM & 12.0 \(\pm\) 0.0 b & 11.8 \(\pm\) 0.1 a & 11.9 \(\pm\) 0.1 ab \\ HH & 11.9 \(\pm\) 0.0 a & 11.8 \(\pm\) 0.1 a & 11.8 \(\pm\) 0.1 a \\ MH & 11.9 \(\pm\) 0.1 a & 11.9 \(\pm\) 0.1 a & 11.9 \(\pm\) 0.1 a \\ HM & 11.9 \(\pm\) 0.0 a & 11.9 \(\pm\) 0.1 a & 11.8 \(\pm\) 0.1 a \\ \hline  & & & Seporit Pore-Tec & \\ \hline MM & 11.9 \(\pm\) 0.1 ab & 11.8 \(\pm\) 0.1 a & 12.1 \(\pm\) 0.1 b \\ HH & 11.8 \(\pm\) 0.1 a & 11.9 \(\pm\) 0.1 ab & 12.0 \(\pm\) 0.1 b \\ MH & 12.0 \(\pm\) 0.1 a & 11.9 \(\pm\) 0.1 a & 12.1 \(\pm\) 0.1 a \\ HM & 11.9 \(\pm\) 0.1 a & 11.9 \(\pm\) 0.1 a & 12.0 \(\pm\) 0.1 a \\ \hline \end{tabular} Each value is expressed as mean \(\pm\) standard deviation. The presence of different letters indicates that the groups in question are significantly different. Lower-case letter: comparison of proteins at fixed mineral clarification agents and concentrations.  
\end{table}
Table 2: Water-soluble content of apple juices in Brix.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **E** & **L** & **F** \\ \hline  & & Klar Sol 30 & \\ \hline MM & 7.6 \(\pm\) 0.6 a & 9.6 \(\pm\) 0.6 b & 11.2 \(\pm\) 0.6 c \\ HH & 7.9 \(\pm\) 0.6 a & 6.9 \(\pm\) 1.0 a & 8.9 \(\pm\) 0.0 b \\ MH & 10.1 \(\pm\) 0.6 c & 7.5 \(\pm\) 0.6 b & 5.7 The combination of bentonite and gelatin was found to be particularly effective in combination with ultrafiltration; the control sample value increased from 48.5 T% to 76.2 T%. With the bentonite-gelatin combination, they achieved a maximum of 60.1 T%. In their experiment, the standard was set at 40 T% based on the literature [37]. In the case of the McIntosh variety, after purification by electroflotation, only 43 T% could be achieved with a 30 s treatment at 20 mA/cm\({}^{2}\) without gelatin with the addition of 200 mg/L, 63.5 T% [38].
## 3 Proposed Methodology
### Data Acquisition  
As thoroughly has been explained in [21], the dataset created and used in this study is extracted from a real-time video stream, which is modulated and processed by GNU radio. GNU is a free and open-source software development toolkit that provides signal processing blocks to implement software radios. The Sol is transmitted using a Universal Software Radio Peripheral (USRP-N210) [32], which is an enhanced version of the USRP that includes a larger FPGA. In GNU radio, the modulation type and amplitude of the transmitted signal can be easily adjusted [33].  
A Satcom emulator [34] (RTLogic) is used for modeling a real-time communication channel. The programmatic control of the channel simulator is facilitated over an Ethernet connection using a control protocol or optional plugin to System Tool-Kit (STK) software. The channel simulator produces IF/RF signals with extracting signal behavior for any scenario, such as various satellite's orbit around Earth [35]. The Kratos STK plugin provides real-time, phase-continuous control of the channel simulator when playing STK scenarios [34]. Furthermore, the generated jamming signals are transmitted using a NanoBee modem and are combined with Sol by an additive combiner [21]. Finally, the combined signal is received by a MegaBee modem [21].  
A summary of the dataset characteristics is presented in Table 1. The generated dataset consists of 300 samples for each class of modulation type, namely Quadrature phase-shift keying (QPSK) and 8/16/32 asymmetric phase-shift keying (APSK).
# Symmetric Circle Configurations from Regular Skeletal Polyhedra
## 2 Basic Notions
For example, the Miquel configuration in the Euclidean plane has eight points and six circles such that each point lies on three circles and each circle contains four points; its Levi graph is the skeleton of both the rhombic dodecahedron and the Bilinski dodecahedron [2]. Our configurations will usually not be planar. The point-circle configuration in Figure 1 lies in 3-space and is associated with the dodecahedron (via the E-constructed). Most of our configurations are highly symmetric. A _symmetry_\(g\) of a point-circle configuration \(\mathcal{C}=(P,C)\) in \(\mathbb{E}^{3}\) is an isometry of \(\mathbb{E}^{3}\) which preserves \(\mathcal{C}\) (that is, \(g(P)=P\) and \(g(C)=C\)). The _symmetry group_\(G(\mathcal{C})\) consists of all symmetries of \(\mathcal{C}\). A point-circle configuration \(\mathcal{C}=(P,C)\) is said to be _point transitive_ or _circle transitive_ if \(G(\mathcal{C})\) acts transitively on the set of points \(P\) or the set of circles \(\mathcal{C}\), respectively. We call \(\mathcal{C}\)_point-circle transitive_ if \(G(\mathcal{C})\) acts transitively on the set of all incident pairs consisting of a point and a circle. A _point-circle realization_ of a combinatorial configuration \((\mathcal{P},\mathcal{B})\) in \(\mathbb{E}^{3}\) is a point-circle configuration \(\mathcal{C}=(P,C)\) in \(\mathbb{E}^{3}\) together with bijections \(\pi:\mathcal{P}\to P\) and \(\beta:\mathcal{B}\to C\) such that \(p\in b\) holds within \((\mathcal{P},\mathcal{B})\) if and only if \(\pi(p)\) lies on \(\beta(b)\). Thus, informally speaking, the elements in \(\mathcal{P}\) are realized by actual points in \(\mathbb{E}^{3}\), the blocks in \(\mathcal{B}\) are realized by circles in \(\mathbb{E}^{3}\), and a point-block pair is incident in \((\mathcal{P},\mathcal{B})\) if and only if the corresponding point-circle pair is incident in \(\mathbb{E}^{3}\). If the underlying set of points is clear from the context, we often simply refer to a point-circle configuration as a circle configuration. A point-circle configuration is said to be _isometric_ if all its circles have the same radius. Every circle-transitive point-circle configuration is isometric, but vice versa, isometric point-circle configurations may not be circle-transitive.
# Integrated Haptic Feedback with Augmented Reality to Improve Pinching and Fine Moving of Objects
## 2 Materials and Methods  
### Pinching Gestures Detection  
HL2 is a head- mounted display (HMD), manufactured by Microsoft and MicroVision Inc., USA, with onboard computation and tracking that projects digital information into real physical space. By integrating it with a Mixed Reality Toolkit (MRTK 2), users can interact with 3D virtual models coherently displayed in their environment. These devicesoffer many functionalities, including hand tracking that captures 26 joints for each hand, such as the palm and the tip of the index finger, allowing for precise tracking of hand movements [15] (see Figure 1). By leveraging these capabilities, the pose of the user's index finger is detected and mapped as a rigid body. This enables the recognition of primary gestures, such as touching, through collision detection with virtual objects. Assigning mass properties to the index finger's rigid body enables users to simulate physical interactions, such as pushing and moving virtual objects. As detailed in the introduction, one notable gesture to simulate is the act of pinching an object between the index finger and thumb, commonly referred to as the pincer grasp. This gesture enables precise movements of objects in six degrees of freedom (6 DOF), as during precision assembly tasks.  
The recognition of a pinch by the MRTK works on the boundary box around the objects, but our goal is to notify the pinch only when the object is between the index and the thumb, as in real life. For this reason, we detect the gesture by monitoring both the position and the curling motion of the index finger toward the thumb, based on the relative positions of its three phalangeal joints. When the fingers meet at a defined threshold, the application recognizes this as a pinch event, enabling more accurate and controlled interactions. We have defined a value range from 0 to 1, where 0 indicates a fully curled finger and 1 signifies a fully open and extended finger. Through practical experimentation, we found that a value between 0.6 and 0.8 effectively captures the pinch event. We have chosen a threshold of 0.75, as it strikes a good balance between accuracy and reliability [16; 17; 18]. The threshold value of 0.8 has been used by the Leap Motion company (Ultraleap) for their hand-tracking device to detect pinch gestures [19]. In our experiments, we found that using a threshold close to 0.8 reduces the likelihood of accidental pinch detections, making the system more reliable.  
While it is technically possible to lower the threshold to around 0.6 for more precise detection, this often leads to inconsistencies, especially when users wear haptic gloves that cover their entire hands. Through our empirical tests, we determined that a threshold value of 0.75 provides the most precise and reliable results for pinch detection.
## 3 Results and Discussion
### Macroscale Optimization and Characterization
#### 3.1.2 Characterization of Amyloid-Beta Conformation as Oligomers and Fibrils  
The characterization of A\(\beta\) using dynamic light scattering (DLS) and zeta potential analysis confirmed the successful preparation of fibrillar structures, which were subsequently used in optical experiments. To validate the successful preparation of the fibrillar structures, dynamic light scattering (DLS) and zeta potentials were analyzed, as described elsewhere [32]. Given that A\(\beta\) aggregates typically exhibit non-spherical morphologies, the measured hydrodynamic diameter represents an average diffusion-based size, through their light scattering. Therefore, this value serves as an approximation rather than an exact representation of the particles' geometric dimensions, indicating their apparent hydrodynamic size and possible size growth. Table 1 summarizes the hydrodynamic diameter and zeta potential values obtained for A\(\beta\) oligomers and fibrils. Oligomeric A\(\beta\) exhibited an average hydrodynamic diameter of 483 nm, which is in the range between oligomers and protofibrils [33], with a zeta potential of \(-\)19.2 mV, indicative of its relatively smaller and dispersed structure. In contrast, fibrillar A\(\beta\) demonstrated a substantial increase in hydrodynamic diameter (2179 nm) and a marked reduction in zeta potential (\(-\)8.8 mV), consistent with the aggregation process and formation of larger fibril structures, typically with a lower solubility profile (less negative mV). These results validate the presence of A\(\beta\) fibrils, providing a reliable basis for subsequent fluorescence-based detection experiments.  
Figure 3: Fluorescence intensity spectrum at 590 nm excitation (counts per seconds, c.p.s) under different conditions, examining probe’s behavior across different solvents, concentrations, and time stabilities: (**a**) CRANAD-2 in PBS and MeOH (0.5 uM–2 uM); (**b**) CRANAD-2 in MeOH (0.5 uM–25 uM); (**c**) CRANAD-2 in MeOH at 25 uM over a 24-h period.