esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Behavioral Tests  
In the FST, immobility times of the SZS-extract-treated groups, E100 and E300, were decreased compared to C, but not significantly, while those of N were significantly increased compared to C (\(p<0.05\)) and those of P were significantly decreased compared to N (\(p<0.01\); Figure 2). In the TST, immobility times of E100 and E300 were significantly decreased compared to N (\(p<0.01\)), with a dose dependency (Figure 2). Moreover, the immobility time of N was significantly increased compared to C (\(p<0.05\)), and that of P was significantly decreased compared to N (\(p<0.01\)). The results of immobility times between N, P, E100, and E300 in both tests were statistically significant (\(p<0.001\)).  
In the OFT, the total distances of E100 and E300 were significantly higher than those of N (\(p<0.01\)) with a dose-dependent relationship close to that of C, and the results of the distances between N, P, E100, and E300 were statistically significantly different (\(p<0.001\); Figure 3). Zone distances of E100 and E300 were higher than those of P, with a dose-dependency (\(p<0.01\); Figure 3). However, times in zone of E100 and E300 were ineffective (i.e., lower than those of N) (Figure 3).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Compounds**} & \multicolumn{4}{c}{**Residual Activity (\%) at 40 \(\upmu\)M**} & \multicolumn{4}{c}{**IC\({}_{50}\) (\(\upmu\)M)**} \\ \cline{2-10}  & **MAO-A** & **MAO-B** & **AChE** & **BChE** & **MAO-A** & **MAO-B** & **AChE** & **BChE** \\ \hline SZS\({}^{\text{a}}\) & 79.2 \(\pm\) 2.53 & 67.9 \(\pm\) 1.81 & 58.4 \(\pm\) 5.26 & 79.5 \(\pm\) 0.71 & - & - & - & - \\ Spinosin & 86.9 \(\pm\) 0.00 & 67.9 \(\pm\) 3.02 & 50.2 \(\pm\) 1.05 & 83.5 \(\pm\) 7.78 & \(>\)80 & 63.3 \(\pm\) 0.075 & 39.8 \(\pm\) 0.40 & \(>\)80 \\ Toloxatone & & & & 1.08 \(\pm\) 0.025 & - & - & - \\ Lazabemide & & & & - & 0.063 \(\pm\) 0.015 & - & - & - \\ Tacrine & & & & - & - & 0.27 \(\pm\) 0.019 & 0.014 \(\pm\) 0.0043 \\ \hline \multicolumn{10}{c}{Results are expressed as means \(\pm\) standard errors of triplicate experiments. Values for reference compounds were determined after preincubation for 30 min with enzymes. \({}^{\text{a}}\) Residual activities at 50 \(\upmu\)g/mL.} \\ \end{tabular}
\end{table}
Table 1: Inhibitions of recombinant human monoamine oxidase (MAO) enzymes, acetylcholinesterase (AChE), and butyrylcholinesteraseterase (BChE) by Semen Ziziphi Spinosae (SZS) extract and spinoin.  
Figure 1: The overall procedure of animal behavioral experiments. FST: forced swimming test; TST: tail-suspension test; OFT: open field test; CUMS: chronic unpredictable mild stress.
# A Web-Based Application for Smart City Data Analysis and Visualization
## 4 Experimental Protocol: System Implementation
### Data Layer  
Our system's backend is developed in Python using the Flask web framework, which serves static HTML pages and dynamic API endpoints for handling data ingestion, transformation, and delivery. At the same time, its modular structure, with clear boundaries between data logic and visualization endpoints, separates data processing from content rendering, ensuring a maintainable and scalable architecture. This facilitates long-term maintenance and enables future extensions, such as including new sensor data types or integration with real-time sources. We focus on robust preprocessing, hybrid imputation, and scalable caching mechanisms to ensure a reliable data delivery pipeline for the interactive web-based frontend.  
Our backend, in particular, defines individual routes for different types of sensor data. HTML templates are rendered through Flask's built-in engine, which organizes user-facing pages and backend services. Dedicated endpoints are responsible for processing specific types of data. Missing data are imputed using the proposed hybrid strategy. Once imputation is completed, the backend aggregates the sensor data. It computes descriptive statistics, which are subsequently returned as structured JSON responses. Another endpoint generates a correlation matrix across variables from different sensor types, supporting multidimensional exploratory analysis of urban dynamics. It is worth noting that to improve responsiveness, we integrate Flask-Caching and cache computation-heavy endpoints, such as those performing imputation or correlation analysis, to prevent redundant processing and reduce latency. A custom logging utility is employed to reduce the execution time of each route, facilitating runtime diagnostics and performance tuning. Our implementation uses a suite of scientific libraries, including Pandas and NumPy, for efficient data manipulation and numerical analysis. Scikit-learn is employed for \(k\)-NN-based imputation; Statsmodels is used for time-series forecasting via SARIMAX; and Flask-Caching manages the temporary storage of processed results to improve API responsiveness.
# Spatially-Precise Video Editing with Reference Imitation: A Region-Aware Cross-Modal Framework
## 3 Materials and Methods
### Deformation Field Propagation  
After completing the editing of the canonical image, in order to efficiently and accurately propagate the editing results to all frames of the entire video, we use the Deformation Field to achieve the conversion process from the canonical image to the video frames.  
The deformation field \(D(X,t)\) ensures that each pixel in the video can precisely obtain the edited color value from the canonical image by describing the mapping relationship between each pixel \(X\) in the video frame and the corresponding coordinate \(X^{\prime}\) in the canonical image. Through the inverse mapping \(D^{-1}(X^{\prime},t)\) of the deformation field, we can propagate the content of the edited canonical image back to each frame of the video:  
\[I^{\prime}_{t}(X)=I^{\prime}_{c}(D^{-1}(X,t)) \tag{11}\]  
\(I^{\prime}_{t}(x)\) represents the color value of the pixel \(X\) in the video frame ; \(I^{\prime}_{c}(x^{\prime})\) represents the color value of the pixel \(X^{\prime}\) in the edited canonical image; \(D^{-1}(X,t)\) is the inverse mapping of the deformation field, which maps the pixel coordinate \(X\) in the video frame to the coordinate \(X^{\prime}\) in the canonical image.  
This mapping process ensures that each pixel in the video frame can obtain the edited content through the canonical image, and can avoid the jumping or flickering phenomena between frames.  
However, in cases where the object undergoes drastic shape or appearance changes, the current method may become less stable. In such cases, we suggest that future work could incorporate additional constraints or adaptive warping techniques to improve temporal consistency. These techniques are not applied in the current implementation.
# Evaluating the Performance of Lightweight Ciphers in Constrained Environments--The Case of Saturnin
## 2 Background
### The Saturnin Cryptographic Suite  
Saturnin constitutes a family of cryptographic functions, being able to provide both authenticated encryption as well as hashing operations [7]. More precisely, Saturnin is a 256-bit block cipher with a 256-bit key and an additional 9-bit parameter for domain separation, which can provide the following cryptographic services:  
1. Authenticated encryption, through either the so-called mode _Saturnin-CTR-Cascade_, which is an authenticated cipher using the counter mode and a separate MAC, or the so-called _Saturnin-Short_ mode which is intended for messages with length strictly smaller than 128 bits.
2. A 256-bit hash function, via the so-called _Saturnin-Hash_ mode.  
The Saturnin cipher is inspired by the AES, operating on 256-bit blocks and using a 256-bit internal state. Similarly to AES, in which the internal 128-bit state can be seen as a \(4\times 4\) square of bytes, the state value in the Saturnin can be viewed as a \(4\times 4\times 4\) cube of 4-bit nibbles [7]. Therefore, it can be somehow seen as an extension of AES into 3 dimensions. Saturnin is being considered--in a broad sense--as a SPN cipher. A detailed description of Saturnin, as well as its security analysis, are provided in [7]. As it is stated therein, its security analysis follows naturally from that of the AES, providing a high security level even in the presence of quantum computers.  
It should be pointed out that _Saturnin-CTR-Cascade_, being actually an AEAD, requires two passes over the data (and, as stated in Section 1, this was the reason that Saturnin is not one of the 10 finalists in the NIST's competition). As the authors state in [10], this is the case because it is hard to achieve security against superposition queries with one pass--and security against such queries seems to be the right way to achieve post-quantum security in symmetric primitives. However, as it is stated in [10], the authors were in the phase of finalizing an one-pass mode, being called QCB.  
The original submission of Saturnin included three constant-time implementations: in portable C, in assembly for the ARM Cortex M3 and in assembly for the ARM Cortex M4. Some performance results, in terms of cycles per bytes (cpb), are given in [7]--e.g., for the encryption operation, the costs of the above implementations are 250 cpb, 144 cpb and 147 cpb, respectively. An estimation of the performance of a constant-time AES/GCM implementation on an ARM Cortex M4 is about 161 cpb, as it is described in [10]. Such a comparative study is further elaborated in our present work.
## 5 Mechanisms for Triggering \(\sigma\) Factor Release from Anti-\(\sigma\) Factors
### Direct Sensing  
In some cases a signal is sensed directly by the anti-\(\sigma\), causing a conformational change that releases the \(\sigma\) factor. The first-described example of this class was the \(\sigma^{\mathrm{R}}\)-RsrA system of _S. coelicolor_[83]. As mentioned above, RsrA is a member of the ZASD family but lacks membrane spanning or extracytoplasmic domains, and is cysteine-rich. In response to oxidative stress, RsrA forms anintramolecular disulphide bond between two cysteines including one that occurs in the ZASD motif [84]. This causes the expulsion of zinc and the stabilisation of a form of RsrA that is unable to interact with \(\sigma^{\mathrm{R}}\)[84] (Figure 4b). The induction of the \(\sigma^{\mathrm{R}}\) regulon is transient, partly through the recycling of RsrA following its reduction by members of the \(\sigma^{\mathrm{R}}\) regulon such as thioredoxin [57, 58, 83]. It should be noted that most members of the ZASD family are unlikely to sense redox change, and instead use zinc in a purely structural role. However, other redox sensing anti-\(\sigma\) factors have been identified, including the integral membrane protein OsrA that modulates an oxidative stress response by \(\sigma^{\mathrm{Ecrf}}\) in _Bradyrhizobium jabonicum_[85]. Mutation of a single periplasmically-located cysteine residue removed the ability of OsrA to release \(\sigma^{\mathrm{Ecrf}}\) in response to oxidative stress, suggesting that the mechanism for \(\sigma\) activation involves the modification of this residue. Conformational changes in anti-\(\sigma\) factors that do not involve redox change have also been proposed. In _C. metallidurans_ the binding of cobalt or nickel to the periplasmic domain of the sensor CnrX is thought to cause a conformational switch in the associated anti-\(\sigma\) CnrY, which then releases \(\sigma^{\mathrm{Cnrf}}\)[86]. Finally, in _B. subtilis_ activation of \(\sigma^{\mathrm{V}}\) in response to lysozyme treatment appears to occur via a combination of direct sensing and RIP mechanisms [87]. The anti-\(\sigma\) RsiV binds directly to lysozyme, which is thought to invoke an allosteric change that makes RsiV susceptible to site-1 proteolysis. Remarkably, the site-1 protease appears to be a signal peptidase responsible for general protein secretion, whereas site-2 proteolysis is catalysed by RasP, as is the case for RsiW (see above).
# Do Pores Exist?--Foundational Issues in Pore Structural Characterisation
## 2 Are Pores Real?
### Can Pores Be Observed?
Figure 1: (**a**) FIB-SEM (focused ion beam-scanning electron microscope (SEM)) image of mesoporous silica. Reprinted with permission from Ref. [9]. 2017, Elsevier. (**b**) SEM image of a ceramic foam structure [1, 10]. Reprinted with permission from Ref. [1]. 2020, Springer. [PERSON] has responded that, while the distinction between observable and unobservable may be vague, it is still valid and usable to make the anti-realist case that unobservables do not necessarily exist, since one can present clear-cut examples of observables, like chairs, and unobservables, like electrons [6,11]. [PERSON] also suggested that the distinction between observable and unobservable can be made based upon specifically taking 'observation' as meaning that observation was obtained unaided both conceptually and instrumentally (i.e., no instruments and no conceptual foreknowledge) [6,11]. Hence, [PERSON] would allow entities seen with telescopes, binoculars and spectacles to count as observable, but not those observed via microscopes (and the like), such as small pores. [PERSON] claims microscopes (and the like) only allow detection, which means that something has been noted which also requires interpretation or explanation. This interpretative element means the detection needs inferential reasoning to conclude that it is this or that kind of entity, for example, that it is a spherical-shaped mesopore. However, entities such as porous materials with void spaces with a proper continuum of features ranging in size from hundreds of microns, visible to the human eye, down to mesopores, only visible with electron microscopes, present a clear challenge to this argument. Many porous materials, such as some carbonate rocks, can possess void spaces with fractal geometry, which exhibit the property of self-similarity over length scales that range from well within the observable range but also down to the unobservable range [1,12]. The difference between this sequence and that proposed by [PERSON] [7] is that there is a mathematical fractal-scaling law describing the incidence of pores at different length scales that extends well below the (vague) unobservable boundary. One can then ask why one should make a distinction about why the fractal scaling law should not also apply below the observable limit, as this cutoff does not feature anywhere in the mathematics. There is, thus, no distinction in making predictions about the nature of observable, large macropores in similar rocks yet to be cored, and unobservable nanopores in the rocks known to possess a fractal-scaling law that extends to unobservable mesoporous. It is the same problem of induction, based upon observed regularities, in both cases. Hence, the issue of whether mesopores and micropores actually exist is no different to using the concept of (macro)porosity to explain the, say, capillarity behaviour of previously unencountered rocks with potentially observable pores.
## 4 Discrete Element Numerical Model and Analysis Index  
### Modeling and Analysis Index  
According to the field rock mass geological report, a numerical analysis model (the ideal state without considering the influence of geological structure) based on UDEC (Version 7.0) constructed, as shown in Figure 9. The properties and size parameters of the rock strata in the model are consistent with the actual borehole histogram data and the description of rock strata in the operation procedures. The size parameters and position relationships between the roadways, coal pillars, and goat are in line with the actual situation on-site. Through this model, the stress evolution law and failure characteristics of the lower seam under different pillar widths and roof states are systematically studied, which provides support for the subsequent formulation of the control scheme of roof-cutting in the original roadway. In the model joint division, to avoid the influence of different pillar widths on the joint division, the seam is divided by random Thiessen polygons (the edge maximum is 0.5 m-1 m), and the remaining rock layers are divided by conventional horizontal and vertical cross joints. The surface contact constitutive model (area contact) is used between the joints, and the new contact is set as the default constitutive model to better simulate the contact between the original joint surface of the rock layer and the contact between the cavity blocks.  
The model's size is set to \(x\times y\) is 180 m \(\times\) 67.67 m, and the left, right, and bottom boundary are fixed. Based on the upper boundary of the seam burial depth calculation model, 3.99 MPa of distributed load is applied to simulate the load value of the overlying strata. The bulk density is 26 kN/m\({}^{3}\), the lateral pressure coefficient is 1.3, and the gravity acceleration is 9.81 m/s\({}^{2}\). The Mohr-Coulomb criterion is applied to simulation, and the mechanical parameters of the coal and rock are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Rock Stratum**} & **Bulk** & **Shear** & **Internal Friction** & **Cohesion** & **Tensile** & **Poisson** & **Density** \\  & **Modulus** & **Modulus** & **Angle** & **(MPa)** & **(MPa)** & **(MPa)** & **Ratio** & **(kg/m\({}^{3}\))** \\ \hline Silty sandstone & 9.44 & 7.08 & 40 & 4.10 & 2.60 & 0.20 & 2550 \\ Mudstone & 8.64 & 5.69 & 29 & 2.40 & 1.56 & 0.23 & 2550 \\ Fine-grained sandstone & 9.14 & 7.14 & 36 & 3.80 & 2.40 & 0.19 & 2650 \\ Sandy mudstone & 8.93 & 6.15 & 32 & 2.50 & 1.62 & 0.22 & 2608 \\ Coal seam & 8.67 & 5.20 & 24 & 1.80 & 1.17 & 0.25 & 1440 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mechanical parameters of coal and rock mass.  
Figure 9: Discrete element numerical calculation model: (**a**) 4 m; (**b**) 6 m; (**c**) 8 m; (**d**) 10 m; (**e**) 12 m.
# Orbit Determination Method for BDS-3 MEO Satellites Based on Multi-Source Observation Links
###### Abstract  
Research on augmentation and supplement systems for navigation systems has become a significant aspect in comprehensive positioning, navigation and timing (PNT) studies. The BeiDoub-3 navigation satellite system (BDS-3) has constructed a dynamic inter-satellite network to gain more observation data than ground monitoring stations. Low Earth orbit (LEO) satellites have advantages in their kinematic velocity and information carrying rate and can be used as satellite-based monitoring stations for navigation satellites to make up for the distribution limitation of ground monitoring stations. This study constructs multi-source observation links with satellite-to-ground, inter-satellite and satellite-based observation data, proposes an orbit synchronization method for navigation satellites and LEO satellites and verifies the influence thereof on orbit accuracy with different observation data. The experimental results under conditions of real and simulated observation data showed the following: (1) With the support of satellite-based observation links, the orbit accuracy of the BDS-3 MEO satellites could be improved significantly, with a 78% improvement with the simulation data and a 76% improvement with the real data. When the navigation satellites leave the monitoring area of the ground monitoring stations, the accuracy reduction tendency of the orbit prediction could also be slowed down with the support of the LEO satellites and the accuracy could be maintained within centimeters. (2) Comparing the orbit accuracy with the support of the satellite-to-ground observation links, the orbit accuracy of the MEO satellites could be improved by 65.5%, 73.7% and 79.4% with the support of the 6, 12 and 60 LEO satellites, respectively. When the observation geometry and the covering multiplicity meet the basic requirement of orbit determination, the improvements to the orbit accuracy decrease with the growth of LEO satellite numbers. (3) The accuracy of orbit determination with the support of the LEO satellites or the inter-satellite links was at the centimeter level for both, verifying that inter-satellite links and satellite-based links can be used as each other's backups for navigation satellites. (4) The accuracy of orbit determination with the multi-source observation links was also at the centimeter level, which was not better than the results with the support of the satellite-to-ground and inter-satellite links or the satellite-to-ground and satellite-based links.  
BeiDoub-3 navigation satellite system (BDS-3); medium Earth orbit (MEO) satellite; multi-source observation; inter-satellite link 2024  
[PERSON], [PERSON], [PERSON] Orbit Determination Method for BDS-3 MEO Satellites Based on Multi-Source Observation Links  
1 Beijing Satellite Navigation Center, Beijing 100094, China 1  
Shanghai Astronomical Observatory, Shanghai 200030, China; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS]
# Development and Evaluation of an Image Processing-Based
## 2 Materials and Methods
### Data Analysis  
We used descriptive analysis in this study. It is an analysis technique in which the conceptual structure of the research is clearly determined in advance. The primary purpose of descriptive analysis is to quantify qualitative data, analyze descriptive possibilities with frequencies, and transfer the findings to the reader in an organized and interpreted manner. Finally, the findings were enriched and interpreted with relevant texts according to their importance in answering the research questions, and the findings were supported with direct quotations. In order to increase the reliability of the determined themes and codes, more than one researcher independently marked the interview forms and the interview coding key by marking the appropriate option for the interviewed students' opinions on the relevant interview coding key. To determine the consistency of the markings by the researchers on the interview coding key, we checked the answers to each question one by one. We marked them as \"Consensus\" or \"Disagreement\", and the calculation was made using [PERSON]'s reliability formula P (Percentage of Consensus) = Na (Consensus)/Na (Consensus) + Nd (Disagreement) \(\times\) 100 to ensure validity and reliability(reference).  
The SPSS 18.0 (Statistical Package for the Social Sciences) package program was used in the statistical analysis of the quantitative data. Descriptive statistics such as the percentage, frequency, and arithmetic mean were used to analyze the quantitative data collected at different stages of the study. The repeated measures ANOVA test was applied to determine the differences according to group and time interaction. A value of \(p\) < 0.05 was taken as a significance value. A partial \(\eta^{2}\) was used to determine the effect size. For the partial \(\eta^{2}\) values, 0.01 was considered a low effect size, 0.06, a medium effect size, and 0.14, a large effect size. Pre-test and post-test proportional (%) differences in the groups were calculated by the following formula: ((post-test \(-\) pre-test)/pre-test \(\times\) 100).