esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Modified Non-Approximative Method for Dynamic Systems Direct Calculation
## 3 Problem Solution and Simulation Example
### Comparison with Numeric Solution  
Now, we compare the values in Tables 1 and 2 with those obtained by solving Equation system (10a,b) using [PERSON]'s implicit method.  
Equation (10a,b) was used, knowing \(u_{1}(t),\ u_{2}(t)\) and the response of the system under unit step \(U\). By substituting \(U\) with \(u_{1}(t)\) in response to Equation (14a,b), we see that the results are the same as those in Table 2, and the time waveforms are the same as those in Figure 2. However, the solution using the Laplace-Carson operator method has the advantage of showing both the transient and steady-state components in addition to the overall course of the variables \(x_{1}(t)\) and \(x_{2}(t)\).  
Equation (10a,b) can be expressed in matrix form and state-space notation:  
\[\frac{\mathrm{d}}{\mathrm{d}t}\begin{bmatrix}x_{1}(t)\\ x_{2}(t)\end{bmatrix}=\begin{bmatrix}a_{11}&a_{12}\\ a_{21}&a_{22}\end{bmatrix}\begin{bmatrix}x_{1}(t)\\ x_{2}(t)\end{bmatrix}+\begin{bmatrix}b_{11}&0\\ 0&b_{22}\end{bmatrix}\begin{bmatrix}u_{1}(t)\\ u_{2}(t)\end{bmatrix} \tag{23a}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(n\) & \(0\) & \(1\) & \(2\) & \(3\) & \(\cdots\) & \(\Psi\) \\ \hline \(x_{1}(n.t)\) & \(0\) & \(42.6942\) & \(-32.8513\) & \(28.8109\) & \(\cdots\) & \(\cdots\) & \(\pm 32.6973\) \\ \(x_{2}(n.t)\) & \(0\) & \(40.3978\) & \(-14.8257\) & \(30.3481\) & \(\cdots\) & \(\cdots\) & \(\pm 24.5804\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values of variables \(x_{1}(t)\) and \(x_{2}(t)\) at discrete time instant \(n.\tau\).  
Figure 2: Time waveforms of variables \(x_{1}(t)\) and \(x_{2}(t)\) under rectangular excitation.  
After implicit Euler forward implementation, we obtain:  
\[\begin{bmatrix}x_{1}\\ x_{2}\end{bmatrix}_{k+1}=\begin{bmatrix}x_{1}\\ x_{2}\end{bmatrix}_{k}+h\begin{bmatrix}a_{11}&a_{12}\\ a_{21}&a_{22}\end{bmatrix}\begin{bmatrix}x_{1}\\ x_{2}\end{bmatrix}_{k+1}+h\begin{bmatrix}b_{11}&0\\ 0&b_{22}\end{bmatrix}\begin{bmatrix}u_{1}\\ u_{2}\end{bmatrix}_{k} \tag{23b}\]  
In symbolic matrix form and simplification, we obtain Equation (7):  
\[\mathbf{x}_{k+1}=\operatorname{inv}(\mathbf{E}-h\mathbf{A})[\mathbf{x}_{k}+h\mathbf{B}\mathbf{u}_{k}],\] (24a) which can be further simplified as Equation ( 8 ): \[\mathbf{x}_{k+1}=\mathbf{F}_{\Delta}\mathbf{x}_{k}+\mathbf{G}_{\Delta}\mathbf{u}_{k} \tag{24b}\]  
The results are shown in Tables 3 and 4. In order to make the results comparable, the integration step should be significantly smaller than the smallest time constant of the circuit; we chose \(h=1\times 10^{-8}\) s.  
Although the accuracy seems to be sufficient for an extended target time, the computational complexity can be considerable. Therefore, in the next section, we introduce a new innovative modified method.
# Design Optimization of a District Heating Network Expansion, a Case Study for the Town of Kiruna
## 4 Results and Discussion
A deeper analysis of the optimal design solutions along to the Pareto front in Figure 6 has pointed out that they all belong to six piping layout configurations. These six layouts are shown in Figure 8 in different colours (blue, yellow, red, cyan, green and magenta), and the markers representing the optimal design solutions in Figure 6 are filled with the colours corresponding to these layout categories. It may happen that the same piping layout appears more than once in the Pareto front with the same capacity of the bio-oil boiler, this means that there must be some variations in the diameters of the pipes for the same layout. It is worth noting that piping loops are not present in any of the six layout configurations. In fact, the possible advantages of piping loops (e.g., providing alternative paths to deliver the hot water in case of some portion of the network has to be bypassed due to a failure) are not taken into account in the considered objective function, so their presence would only increase investment costs rise. The six configurations in Figure 8 show just minor differences about:  
* the way the hot water is delivered to two of the five new user areas (the second and the third from West to East), in series (from West to East in the blue and yellow configurations, from East to West in the cyan and green configurations) or in parallel (with a common pipe in the magenta configuration, with separate pipes in the red configuration);
* the way the hot water is delivered to the two east-most user areas, in parallel (blue and cyan configurations) or in series (the other four). As a final remark to the results, it is worth mentioning that the proposed methodology offers the opportunity to obtain the full spectrum of the best possible compromises between the investment and operating costs. Naturally, these two costs, which are expressed in different measurement units, could have been weighted into a single objective function according to standard accounting techniques, given the life time of the project and the fixed charge rate. However, a single objective optimization would have reduced the amount of information available to the decision makers to a single design solution (the optimal one found by the optimization procedure). The knowledge of the whole spectrum of optimal design solutions makes it possible for the decision makers to make more strategic choices based on other considerations, e.g., forecasts on the future trend of fuel cost, availability of capitals, or options for future design modifications that were not included in the present planning process.
# Uncertainty Analysis of Spherical Joint Three-Dimensional Rotation Angle Measurement
## 3 Uncertainty Analysis of Spherical Joint Space Rotation Measurement System
### Uncertainty Caused by Calibration Platform Rotation Error  
According to the technical specifications provided by the manufacturer of the 3D rotary calibration table, the RPI rotary table provides the standard rotation angle of the ball head around the X axis, \(\alpha\), and the rotation accuracy is \(\pm\)1\({}^{\prime\prime}\). The induction synchronizer shows the standard rotation angle of the ball head around the Y axis, \(\beta\), with accuracy up to \(\pm\)3.6\({}^{\prime\prime}\). The PI rotary table provides the standard rotation angle of the ball head around the spherical joint rod itself, \(\gamma\), with an accuracy of \(\pm\)2\({}^{\prime\prime}\). By three-dimensional rotary calibration, error \(\mathrm{u}_{1}\) can be caused by the assumption of uniform distribution.  
\[\mathrm{u}_{1}(\alpha)\text{--}\mathrm{U}(-1^{\prime\prime},1^{\prime\prime}) \tag{6}\]  
\[\mathrm{u}_{1}(\beta)\text{--}\mathrm{U}(-3.6^{\prime\prime},3.6^{\prime\prime}) \tag{7}\]  
\[\mathrm{u}_{1}(\gamma)\text{--}\mathrm{U}(-2^{\prime\prime},2^{\prime\prime}) \tag{8}\]
# Contributions of TOR Signaling on Photosynthesis
## 3 Conclusions and Future Prospects  
The analysis of the recent research progress and key findings on the TOR signaling pathways in plant photosynthesis suggests that TOR is a crucial signal integrator to transduce nutrient and environmental inputs into physiological, molecular, and developmental responses for growth. TOR kinase senses C nutrients and is activated by the availability of sugar produced by light-stimulated photosynthesis. Active TOR protein enhances plant photomorphogenesis and leaf development to make sure the maintenance of photosynthesis.  
Compared with measurable progress made in the TOR signaling of mammalians and yeasts, our understanding of plant and algal TOR is limited. Although these findings in the present study clearly highlight a role for TOR in plant photosynthesis, several questions remain to be answered. Do plants possess unique TOR complex involved in plant specific life process photosynthesis? How do plants sense upstream signals for sugars and light? Plant hormones auxin, ABA and BR are involved in the regulation of TOR signaling and photosynthesis. What is the molecular mechanism of these hormones on TOR? How does TOR integrated into the known canonical hormone signaling? Are other hormones such as salicylic acid, jasmonic acid and ethylene involved in TOR regulation? In addition, these findings highlight a role for TOR in leaf and chloroplast development. What are the downstream effectors that mediate TOR function in this context?  
In future studies, it is necessary to decipher the underpinning molecular mechanism of TOR in regulating plant photosynthesis. Genetic and biochemical analyses combined with currently available and future data from phosphoproteomic, transcriptomic and metabolomic are effective approaches to identify TOR functions and discover TOR up- and down-stream effectors.  
With limited agricultural land and increasing human population, it is essential to enhance crop productivity. Increasing the photosynthesis rate of plants has been recently revitalized as one of the approaches for increasing grain crop yields and solving world food crises [1; 2; 3; 4]. Understanding the regulation of TOR signaling in plants photosynthesis may be essential for crop improvement in the future.  
This work is supported by grants from the National Natural Science Foundation of China (U1804231, 31972469, 31672206), State Key Laboratory of Cotton Biology Open Fund (CB2021A15), the Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences (34-IUA-02), the Sichuan Science and Technology Program (2020 JDRC0044), and local financial funds of the National Agricultural Science & Technology Center (NASC), Chengdu (NASC2020 AR08; NASC2021 KR03).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### The Signatures  
We generated multiple signature vectors for the Hermite transform of order (1,1) for each RGB channel and grayscale images using a dataset of 362,680 samples created through data augmentation. The augmentation process included five scaling percentages (100%, 95%, 90%, 85%, and 80%) and eight rotation angles (45\({}^{\circ}\), 90\({}^{\circ}\), 135\({}^{\circ}\), 180\({}^{\circ}\), 225\({}^{\circ}\), 270\({}^{\circ}\), 315\({}^{\circ}\), and 360\({}^{\circ}\)). These descriptors utilized invariance properties to translation and scaling from the Fourier and Mellin transforms, respectively. The Hilbert transform was applied for rotational invariance. Unique image signatures were computed by summing pixel values within each ring produced by Hilbert masks used as filters. The texture descriptors were then incorporated into the previously generated radial Fourier signatures.  
This process resulted in a one-dimensional representation or signature of the skin lesion digital image, as illustrated in Figures 2 and 3. The original image \(Im(x,y)\) consists of three RGB matrix channels (red, green, and blue). These were separated into their respective primary color channels for the application of the Radial Fourier-Mellin method and uniform Local Binary Pattern (_LBP_) feature extraction. Additionally, the grayscale skin lesion image was derived as a weighted sum of RGB values using the formula 0.299R + 0.587G + 0.114B.  
Figure 3: Workflow of the proposed hybrid transform-based feature extraction method. (a) RGB and grayscale channel separation of skin lesion images. (b) The Hermite transform of order (1,1) is applied to capture localized spatial features. (c) Computation of Radial Fourier–Mellin transform for translation and scale invariance. (d) Application of the radial Hilbert transform for rotational invariance. (e) Normalization of transform-based signatures. (f) Extraction of Local Binary Pattern (_LBP_) features for texture representation. (g) Final signature generation through concatenation of transform and _LBP_ descriptors.  
Figure 2: Filtering masks used in the radial Hilbert transform step. (**a**) A binary disk mask applied in the frequency domain to localize the transform. (**b**) Real component (\(H_{R}\)) of the radial Hilbert mask. (**c**) Imaginary component (\(H_{I}\)) of the mask.
# Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework
## 6 Conclusions  
This study introduced a transformer-based text classification model that achieved an 81.4% weighted F1 score for analyzing social media content, complemented by an interactive visualization system tailored to disaster response applications. By seamlessly integrating data ingestion, accurate text classification, and geographic visualization, our platform enhances situational awareness and supports timely and effective emergency coordination. This foundational implementation demonstrates the practical utility of leveraging social media data in disaster management, bridging the gap between abundant crowdsourced information and operational crisis response needs. Future research can pursue several important directions to further enhance the system's capabilities and integrate diverse data sources such as satellite imagery and Internet of Things (IoT) sensors to provide more comprehensive situational awareness. Expanding the system's multilingual support will ensure inclusivity and effectiveness across global disaster scenarios. Additionally, real-world pilot testing and collaboration with emergency response agencies will validate the system's practical utility and inform iterative improvements. Efforts will also focus on refining model accuracy, particularly for underrepresented disaster categories, and enhancing model transparency to foster trust and accountability in automated crisis response decision-making.  
Conceptualization, D.H.; methodology, C.H.; software, C.H.; validation, C.H. and D.H.; formal analysis, D.H.; investigation, D.H.; resources, C.H.; data curation, C.H.; writing--original draft preparation, C.H.; writing--review and editing, D.H.; visualization, C.H.; supervision, D.H.; project administration, D.H.; funding acquisition, D.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the US National Science Foundation (NSF) via Grant 2346936.  
Not applicable.  
The original data presented in the study are openly available at [[https://crisisnlp.qcri.org/humaid_dataset](https://crisisnlp.qcri.org/humaid_dataset)]([https://crisisnlp.qcri.org/humaid_dataset](https://crisisnlp.qcri.org/humaid_dataset)) (accessed on 20 March 2025).  
The authors gratefully acknowledge the support from NSF and Kennesaw State University. Any opinions, findings, recommendations, and conclusions in this paper are those of the authors and do not necessarily reflect the views of NSF and Kennesaw State University.  
The authors declare no conflicts of interest.
While several storage media such as Optisol, Eusol, Cornisol, and Life 4 \({}^{\circ}\)C are considered safe and effective, having their use approved by regulatory agencies such as the American Food and Drugs Administration (FDA), these products do not necessarily present the same performances and qualities of preservation, especially regarding longer preservation times. In this sense, several studies have investigated the effects of different culture media on the quality of stored corneas, employing different qualitative and quantitative parameters [13]. However, there is no comprehensive and systematic comparison between the effects of the different culture media on corneal preservation that gathers important parameters of corneal quality assessment, such as corneal transparency (CT) and endothelial cells (EC) morphology; quantitative parameters, e.g., endothelial cell density (ECD); central corneal thickness (CCT); and EC mortality. Therefore, this study aimed to systematically review the literature regarding the effects of the use of different cold corneal storage media compared with the use of Optisol-GS on the quality of stored corneas and to answer the PECO focused research question: what are the effects of different cold preservation media versus Optisol-GS on corneal quality preservation? This review assessed the main characteristics of the different novel storage media and the relationship between their chemical formulation and performance compared with the up-to-date gold standard in transplantation and discussed the relevance of standardization of the available parameters for the quality of stored corneas. The comparison of these different solutions may fill a gap in the literature on keratoplasty regarding the choice of inputs for cornea preservation and contribute to the scientific knowledge of decision makers in this critical step of corneal transplantation and eye bank management. The remainder of the paper is structured as follows. The methodology implemented to conduct this systematic review is described in detail in Section 2, while the results are reported in Section 3 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [12]. Section 4 shows a narrative discussion of the literature findings. Lastly, Section 5 concludes the paper.
## 5 Numerical Simulation Results Analysis  
### Seismic Wave Propagation Characteristics  
As illustrated in Figure 7, the propagation time for the P-wave is recorded at 0.22 s. Furthermore, the S-wave initiates its propagation 0.12 s subsequent to the onset of the P-wave, with a total duration of 0.501 s. Figure 13 presents the velocity slice diagram for P-waves and S-waves, as obtained from dynamic calculations. This diagram is utilized to examine the attenuation characteristics of vibrational waves across different media. The vibrational wave demonstrates spherical radiation characteristics within a homogeneous medium, indicating that the vibrational energy propagates uniformly in all directions. Monitoring locations have been established at both the direct floor and the foundational floor of the source region, as depicted in Figure 14a. Figure 14b presents the velocity time history curves for the two measurement points. It is apparent from the figure that the overall vibration patterns at both locations exhibit similarities, with the primary distinction being the amplitude of the vibrations.  
As demonstrated in Figure 14b, both measurement points display identical peak directions and timings of vibration; however, the recorded amplitudes differ. This variation may be ascribed to the differing geological contexts of measurement points 1 and 2, which likely result in varying degrees of attenuation of the vibrational waves during their propagation. Specifically, as the seismic wave passes through the No. 7 coal seam on its way to measurement point 2, phenomena of partial transmission and reflection occur, leading to a substantial reduction in the energy of the seismic wave. In the process of vibration wave propagation, the degree of attenuation of velocity and energy is influenced by the density, elastic modulus, and porosity of the rock. Hard rock exhibits a strong propagation  
Figure 14: Position and curve of the measuring point: (**a**) measurement point position; (**b**) speed–time curve.  
Figure 13: Propagation law diagram of vibrational waves in different media: (**a**) is the 0.03 s propagation diagram; (**b**) is the 0.08 s propagation diagram; (**c**) is the 0.13 s propagation diagram; (**d**) is the 0.29 s propagation diagram.  
capability due to its high density and elastic modulus. In contrast, coal seams possess low density and elastic modulus, coupled with high porosity, which results in rapid attenuation of vibration waves and a limited influence range. As illustrated in Figures 13d and 14b, the disparity between the two measurement points during the propagation of S-waves shows a significant increase. This observation indicates that S-waves undergo greater attenuation than P-waves, especially in the context of weak rock strata, as well as in relation to transmission and reflection phenomena.
## 4 Datasets
### ThaiNet Dataset  
#### 4.2.1 Steps for Generating Sample Transportation Network  
The steps include collecting the central location of each region, constructing adjacency relationships based on regional boundaries, and generating an adjacency matrix for network analysis, as illustrated in Figure 1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Network** & **Type** & **Description** \\ \hline Dolphins & Social & Bottlenose dolphin social interactions in New Zealand waters \\ USAir & Transportation & Undirected transportation network representing the US Air route system from 1997 \\ EEC & Communication & European researchers exchange of emails \\ Email & Communication & Email communication network where users send emails and communicate with each other \\ Euroroad & Transportation & European road network system spanning multiple countries \\ Blogs & Social & A communication network of blogs \\ Karate & Social & Social network of friendships between 34 members of a karate club at a US university in the 1970s. \\ GDciting & Communication & A network of meeting articles that appeared in 1994–2000 \\ Celegan & Biological & Caenorhabditis elegans molecular interaction networks \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The name, type, and description for each network in public real networks dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & \(\mathbf{N}\) & \(\mathbf{E}\) & \(\langle\mathbf{k}\rangle\) & \(\langle\mathbf{d}\rangle\) & \(\langle\mathbf{c}\rangle\) & \(\mathbf{\beta_{c}}\) \\ \hline Dolphins & 62 & 159 & 5.129 & 3.357 & 0.259 & 0.172 \\ USAir & 332 & 2126 & 12.807 & 2.738 & 0.625 & 0.023 \\ EEC & 986 & 16,064 & 32.584 & 2.587 & 0.407 & 0.014 \\ Email & 1133 & 5451 & 9.622 & 3.606 & 0.220 & 0.057 \\ Euroroad & 1174 & 1417 & 2.414 & 2.418 & 0.017 & 0.500 \\ Blogs & 1222 & 16,714 & 27.355 & 2.738 & 0.320 & 0.012 \\ Karate & 34 & 78 & 4.588 & 2.408 & 0.571 & 0.148 \\ GDciting & 259 & 640 & 4.942 & 1.525 & 0.230 & 0.144 \\ Celegan & 248 & 468 & 3.774 & 1.580 & 0.071 & 0.208 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Basic properties of public real networks, including number of nodes (\(N\)), edges (\(E\)), average degree (\(\langle k\rangle\)), shortest path length (\(\langle d\rangle\)), clustering coefficient (\(\langle c\rangle\)), and epidemic threshold (\(\beta_{c}\)). Region Locations Data Collection  
Data collection begins with identifying the central location for each region. Then, latitude and longitude coordinates of these central points are collected and compiled into a CSV file, which includes the names and coordinates of each location. Adjacency Region Pair Construction  
The adjacency of regions is determined through a series of algorithms. Initially, boundary overlap is checked to filter out non-adjacent district pairs quickly (see Algorithm 2). Subsequently, detailed proximity checks are performed to confirm adjacency (see Algorithm 3). These validated adjacencies are then used to construct pairs for further analysis (see Algorithm 4).