esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Active Disaster Response System (ADRS)
### Overview of ADRS  
Figure 1 shows a use scenario of ADRS. When a natural disaster happens, the Central Weather Bureau (CWB) publishes CAP warning messages. The messages are received and interpreted by Tibbo embedded boards inside a building (Step 1). If a warning message indicates that a strong earthquake is coming, these components of ADRS will automatically perform emergency tasks, such as opening doors, cutting off power and gas, and so on (Step 2), in order to prevent the loss of lives, to reduce the  
chance of injuries and to minimize economic losses. After the earthquake, ADRS configures Tiboo EM1000-TEV embedded boards [6] to construct a temporary network (Steps 3 and 4). After the network connections to the outside world are recovered, Tibbo will deliver these messages to relief workers nearby or the server of a command center, so that rescue actions can be well planned (Steps 5 and 6).
## 4 Discussion  
In this section, the variations in accumulative deformation and pile bending moment under long-term cyclic loading are further compared and discussed for different skirt diameters and lengths. Subsequently, load-displacement curves are generated based on the measured loads and displacements. Each pile type's horizontal cyclic stiffness development is thoroughly examined, with a focus on the load-displacement relationship.  
We examined the skirt-pile foundation's accumulated deformation by examining the rotational change. Displacement meters were installed at the top of the pile and at the mud surface along the loading direction to measure the pile turning angle under the influence of horizontal cyclic loads. The horizontal displacement at the corresponding position was measured, and the pile rotation could be obtained by calculating the horizontal displacement difference using the vertical spacing of the two displacement meters. The cumulative angle of rotation can be calculated using Equation (1).  
\[\theta=tan^{-1}\frac{\Delta y}{\Delta s} \tag{1}\]  
where \(\theta\) is the rotation of the pile, \(\Delta y\) is the vertical distance of two displacement meters (J1 and J2), and \(\Delta s\) is the horizontal distance of J1 and J2.  
Strain gauges were strategically placed along the pile body to measure both tensile and compressive strains at various elevations. This approach allows for an assessment of the variation in the pile bending moment along the burial depth under long-term cyclic loading, employing the elastic foundation beam theory. Then, the pile bending moment is calculated by Equations (2) and (3):  
\[I=\frac{\pi D^{4}}{64}\left[1-\left(\frac{d}{D}\right)^{4}\right] \tag{2}\]  
\[M_{e}=\frac{EI}{\text{D}}(\varepsilon_{te}-\varepsilon_{ce}) \tag{3}\]  
where \(I\) is the moment of inertia of the pile section; \(E\) is the modulus of elasticity of the pile foundation, taking the value of 71 Gpa; \(M_{e}\) is the pile bending moment; and \(\varepsilon_{te}\) and \(\varepsilon_{ce}\) are the tensile and compressive strains on both sides of the pile base, respectively.
# The Impact of Optogenetics on Regenerative Medicine
## 3 Biomedical Applications of Optogenetics
### Applications in Ophthalmology  
Degenerate retinal disorders affect 1/3000 people worldwide. Early diagnosis and gene therapies have good chances to successfully treat most such pathologies [29]. Although the retinal disorders severely impact the sight, many neural retinal connections remain active and functional for a long time; this residual function is the main target of optogenetics. The therapeutic approach is based on intravitreal vectors carrying ChR2 to retinal ganglion cells. The main limitation of this technique is that high frequencies of light and prolonged exposures to it can saturate the response of the retinal cells [30]. In 2018, [PERSON] et al. [31] described in vitro and in vivo experiments performed on blind mice, providing rhodopsin GPCR vectors, and applying the optogenetic technique. It was possible to characterize how the visual recovery was different, based on the exposure to specific wavelength, and on the movement and the duration of exposition to pulsed light [32].
## 1 Introduction
### This Study  
Although the dynamic elements of dwell times have been acknowledged and have been the subject of previous research, dwell times are typically scheduled based on generalized assumptions [10] and in a static way. As a result of this, actual dwell times exceed planned times regularly [24], thus raising the question of how we can schedule dwell times more appropriately. One aspect that is relevant to understand is whether the frequency of the different delay sizes changes under different passenger volumes. Although previous studies have indicated a linear relationship between the volume of passengers and dwell times, this is potentially not reflected in the frequency of delay sizes. This study aims to provide an insight into how the volume of passengers influences the frequency of different delay sizes. We try to answer the following questions:  
1. Does the frequency of delays change as a result of passenger volumes?
2. At which point do frequencies change significantly and how strong is this effect?
3. What can be done in terms of policy measures for dwell times to reduce dwell time delays?  
To answer these questions, we made use of automatic passenger count data collected on commuter trains in Stockholm at multiple stations. To understand how passenger volumes relate to the frequency size dwell time delays, we performed Chi-square goodness of fit tests to compare the frequency distribution of different delay sizes under several passenger volumes to an unconditional distribution of dwell time delays. We then performed post hoc tests to understand the changes in the frequency of dwell time delays. The remainder of this paper is structured as follows: Section 2 provides an overview of the data used in this study, Section 3 describes the method, Section 4 describes our findings, and Section 5 provides a discussion and future study directions.
## 2 Twin Rotor \(Mimo\) System
### Transfer Function Model  
To calculate the transfer function of the \(TRMS\), the nonlinear model has been linearized around the equilibrium point \(\mathbf{x}_{0}\) by applying Jacobian linearization:  
\[A_{ij}=\left[\frac{\partial\dot{x}_{i}}{\partial x_{j}}\right],\quad B_{ij}= \left[\frac{\partial\dot{x}_{i}}{\partial u_{j}}\right].\]  
which gives  
\[\dot{\mathbf{x}} =\mathbf{A}\cdot\mathbf{x}+\mathbf{B}\cdot\mathbf{u}\] \[\mathbf{y} =\mathbf{C}\cdot\mathbf{x}+\mathbf{D}\cdot\mathbf{u}\]  
where \(\mathbf{y}=[\psi,\phi]^{T}\) represents the pitch and yaw angles, and  
\[\mathbf{A}=\begin{bmatrix}0&1&0&0&0&0\\ -\frac{M_{g}}{I_{1}}&-\frac{B_{1\psi}}{I_{1}}&0&0&\frac{b_{1}}{I_{1}}&0\\ 0&0&0&1&0&0\\ 0&0&0&-\frac{B_{1\psi}}{I_{2}}&-\frac{K_{c}\cdot T_{0}}{T_{p}\cdot I_{2}}&\frac {b_{2}}{I_{2}}\\ 0&0&0&-\frac{T_{10}}{I_{11}}&0\\ 0&0&0&0&0&-\frac{T_{20}}{I_{21}}\end{bmatrix},\quad\mathbf{B}=\begin{bmatrix} 0&0\\ 0&0\\ 0&0\\ 0&0\\ \frac{K_{1}}{I_{11}}&0\\ 0&\frac{K_{2}}{I_{21}}\end{bmatrix}\]Substituting above the values given in Table 1, these matrices become:  
\[\mathbf{A}=\begin{bmatrix}0&1&0&0&0&0\\ -4.7059&-0.0882&0&0&1.3588&0\\ 0&0&0&1&0&0\\ 0&0&0&-5&1.6170&4.5\\ 0&0&0&0&-0.9091&0\\ 0&0&0&0&0&-1\end{bmatrix},\quad\mathbf{B}=\begin{bmatrix}0&0\\ 0&0\\ 0&0\\ 1&0\\ 0&0.8\end{bmatrix},\]  
\[\mathbf{C}=\begin{bmatrix}1&0&0&0&0&0\\ 0&0&1&0&0\end{bmatrix}\qquad\qquad\mathbf{D}=\begin{bmatrix}0&0\\ 0&0\\ 0&0\\ 1&0\\ 0&0.8\end{bmatrix},\]  
Subsequently, the linearized state model is converted into a transfer function matrix:  
\[\mathbf{G}(s)=\mathbf{C}\cdot(s\cdot\mathbf{I}_{6}-\mathbf{A})^{-1}\cdot \mathbf{B}+\mathbf{D}=\begin{bmatrix}G_{11}(s)&G_{12}(s)\\ G_{21}(s)&G_{22}(s)\end{bmatrix}=\begin{bmatrix}\frac{1,359}{s^{3}+0.9973\cdot s ^{2}+4.786\cdot s+4.278}&0\\ \frac{1,617}{s^{3}+5.909\cdot s^{2}+4.546\cdot s}&\frac{3.6}{s^{3}+6\cdot s^{2} +5.5}\end{bmatrix} \tag{6}\]  
where \(\mathbf{I}_{n}\) represents the identity matrix of dimensions \(nxn\). In this transfer function matrix, \(G_{11}(s)\) is the transfer function in the vertical plane, \(G_{22}(s)\) is the transfer function in the horizontal plane, and \(G_{12}(s)\) and \(G_{21}(s)\) are coupling access transfer functions.  
The time delay of our \(TRMS\) platform is derived from sensor data and is influenced by the cascading effects of multiple dynamic elements, including the effect of a teleoperated mode. As a result, the same delay is introduced in both input signals \(u_{1}\) and \(u_{2}\). Consequently, the time delay in the \(TRMS\) is represented by  
\[\mathbf{L}(s)=\text{diag}\Big{(}e^{-L\cdot s},e^{-L\cdot s}\Big{)}=\mathbf{I} _{2}\cdot e^{-L\cdot s} \tag{7}\]  
where \(L=0.4\) s.  
Finally, the complete linearized model of our \(TRMS\) is  
\[\hat{\mathbf{G}}(s)=\mathbf{G}(s)\cdot\mathbf{L}(s) \tag{8}\]
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 4 ViMindXAI: A Scalable and Cognitive AI Platform for VBDA
### Layered Architecture and Functional Components  
#### 4.2.1 Data Acquisition Layer (DAL)  
The DAL serves as the foundational tier of the ViMindXAI platform. It is responsible for ingesting, integrating, and preprocessing video data from diverse sources, including surveillance cameras, drones, dashcams, IoT sensors, and streaming services. Its main objective is to support low-latency, on-device filtering before forwarding data to higher layers, thereby reducing bandwidth usage and enhancing system responsiveness.  
The DAL comprises two main modules: Data Collection and Edge AI Preprocessing and Optimization. The first module aggregates multi-source video and sensor inputs into a unified stream. The second module executes real-time inference, denoising, compression, and metadata extraction using lightweight models deployed on edge hardware such as NVIDIA Jetson Nano/Xavier, Intel Neural Compute Stick (NCS2), and Google Coral TPU. These embedded devices provide sufficient GPU/TPU acceleration for on-device AI tasks, enabling scalable and cost-effective inference in resource-constrained environments.  
These processes are accelerated by stream engines like Apache Flink and Spark Streaming. AI functionalities in this layer enable real-time event detection, intelligent caching, and dynamic prioritization of critical segments. FL is embedded to support local model training and inference, enabling privacy-preserving analytics at the edge.  
Figure 3: ViMindXAI: A hybrid AI-driven platform for scalable and privacy-aware VBDA.  
Technologies such as Apache Kafka and NiFi manage data ingestion, while Flink and Spark handle distributed stream computation. ONNX ensures interoperability across AI models. The output of the DAL, consisting of cleaned and enriched metadata, is forwarded to the Data Infrastructure Layer for indexing and persistent storage. It also feeds the FL module in the application layer to support collaborative updates.  
In summary, the DAL streamlines early-stage video analytics by enabling efficient edge inference, reducing transmission overhead, and facilitating scalable ingestion from diverse video sources.
## 1 Introduction  
In recent years, plasma technology has presented an alternative in the processing and development of new materials. Of the plasma processing methods developed thus far, atmospheric pressure plasma is the most economically accessible, due to the low costs of this technology. Among the first applications of plasmas in atmospheric pressure was the treatment of various polymeric materials. Plasma treatment efficiently improves the surface wettability and adhesion properties of different types of materials. This is possible because plasma parameters such as particle density, collision frequency, mean kinetic energy of the particles, and the presence of chemical active species will cause a large variety of elementary processes both within the plasma volume and on the plasma-material interface. Consequently, the quality and magnitude of the polymer surface treatment, including etching, functionalization, and crosslinking, might also be controlled [1]. Generally, the plasma used in material processing can be generated in different gases, pressure ranges, or discharge geometries.  
Another technique developed later was that of a plasma polymerization technique. Plasma polymerization is a versatile method for the deposition of films with functional properties, which are suitable for a range of applications. These plasma polymers have different properties than those fabricated via conventional polymerization: the plasma-polymerized films are usually branched, highly cross-linked, insoluble, and adhere well to most substrates [2].  
In the last decade, new applications of plasmas at atmospheric pressure have been developed, such as: plasma medicine, plasma agriculture, plasma used in food industry, in 3D printing technology, textile industry, and so on. This Special Issue attempts cover all these applications, publishing both target applications of the atmospheric pressure plasma and also the fundamental aspects that appear in the case of these types of discharge.
## 2 Materials and Methods
### Data Preprocessing and ASV Generation  
After sequencing, Cutadapt (v3.2) [22] was utilized to remove adapter and primer sequences from the raw data. Additionally, forward and reverse reads were trimmed to 250 bp and 200 bp using Cutadapt (v3.2). To generate Amplicon Sequence Variants (ASV) sequences, the reads were performed with error-correction, merging, and denoising processes with DADA2 (v1.18.0) [23]. Sequences with an expected errors of 2 or more were excluded. Erroneous reads were denoised based on an established error model. Following error correction, paired-end reads were merged by overlapping. Chimeric sequences were eliminated using the consensus method with the remove Bimera Denovo function in DADA2. ASVs with lengths shorter than 350 bp were filtered out using R (v4.0.3). Moreover, for microbial community comparison analysis, normalization was performed using QIIME (v1.9) [24]. During this process, subsampling was conducted based on the sample with the lowest read count among all samples to ensure comparability between them. The resulting ASVs were utilized for downstream analysis.
## 3 Results
Figure 7: Normalized CE Error vs. number of base station (BS) antennae. Figure 8 shows the MSE performance evaluation for the proposed system compared with the Least Square (LS) and MMSE [12]. A total of 32 subcarriers are selected through a normalized delay spread of 1/16. Sixteen QAM modulations were used with a Doppler frequency of 0.1. With QAM, the proposed system offers a SNR of above 6 dB compared to the existing LS and MMSE. Due to error transmission, the SNR goes above 20 dB. In the suggested system, 24 pilots are allocated to every source through 512 sub-carriers. The length of the CP is 8 and the symbol duration is 1.13 ms with an information ratio of 4 bits per symbol. The suggested approach was diminished with minimal computational complexity in terms of BER and MSE because the various pilots connected in our system is smaller than existing plans. Table 3 shows the complexity of the proposed system in terms of several iterations, while Table 4 shows the computational complexity analysis of the various multi-user detection and CE techniques along with our proposed approach. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Complexity** & **Description of Notations** \\ \hline EM & O(\(KL-N_{p}\)) & \begin{tabular}{c} _K_-frequency subcarriers, _L_-OFDM symbols, \\ \(N_{p}\)-pilot symbols \\ \end{tabular} \\ \hline CP & O(\(MTK\)) & \
\begin{tabular}{c} _M_-RF chain sub frames, _T_-time frames, \\ _K_-subcarriers \\ \end{tabular} \\ \hline JCCAE (Joint Channel and \\ Clipping Amplitude Estimation) & O(\(N^{3}+N^{2}+N^{2}\)) &
\begin{tabular}{c} _N_-symbols \\ \end{tabular} \\ \hline Four Bayesian inference methods & O(\(NK^{2}\)) & _N_-received symbols, _K_-transmitted symbols \\ \hline Proposed & O(\(n^{2}\)) & _n_-symbols \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Computational complexity analysis. Figure 8: Performance measure for mean square error (MSE) vs. signal-to-noise ratio (SNR).
# Segment Shards: Cross-Prompt Adversarial Attacks against the Segment Anything Model
## 6 Discussion
### Image Quality  
In this section, we will discuss the relationship between image quality and the attack success rate of adversarial examples generated by the PBA method and PA method.  
We varied the hyperparameter (the maximum perturbation value \(\mu\)) during the generation of adversarial examples to obtain adversarial examples with different image qualities and attack effectiveness. We evaluated the image quality by calculating their similarity to the original images. In these experiments, we used the structural similarity index measure (SSIM) metric to quantify the image quality of adversarial examples while assessing their attack effectiveness through the attack success rate (ASR) metric. This experiment was conducted on a dataset of 300 images, sourced from the validation set of the CBCL Street Scenes Dataset [51].  
The experimental results are presented in Table 6 and Figure 6. From Table 6, it can be observed that in most cases, the PBA method exhibits higher SSIM values and lower mIoU values compared to the PA method, especially when the perturbation is smaller (\(\mu<0.2\)). In a more intuitive manner, from the relationship graph depicted in Figure 6, it can be observed that under similar image quality conditions (equal SSIM), the PBA method demonstrates higher attack effectiveness (lower mIoU). Conversely, under incomparable  
Figure 5: Comparative analysis of mIoU, ASR, and SSIM across attack iterations and methods. To ensure consistency in the actual number of image updates, for both PA and NPA methods, the number of attack iterations is four times the number shown in the horizontal coordinate in this figure.  
attack effectiveness conditions (equal mIoU), the PBA method exhibits superior image quality (higher SSIM).  
To summarize, the experimental results from this section demonstrate that the PBA method achieves both effective adversarial attacks and high-quality image generation simultaneously.
# Performance Assessment of NAMI DANCE in Tsunami Evolution and Currents Using a Benchmark Problem
###### Abstract  
Numerical modeling of tsunami evolution, propagation, and inundation is complicated due to numerous parameters involved in the phenomenon. It is important to assess the performance of numerical codes that solve tsunami motion, as well as flow and velocity patterns. NAMI DANCE is a computational tool developed for the modeling of long waves. It provides numerical modeling and efficient visualization of tsunami generation, propagation, and inundation mechanisms and computes the tsunami parameters. In the theory of long waves, the vertical motion of water particles has no effect on the pressure distribution. Based upon this approximation and neglecting vertical acceleration, the equations of mass conservation and momentum are reduced to two-dimensional depth-averaged equations. NAMI DANCE uses finite difference computational method to solve linear and nonlinear forms of depth-averaged shallow water equations in long wave problems. In this study, NAMI DANCE is applied to a benchmark problem which was discussed in the 2015 National Tsunami Hazard Mitigation Program (NTHMP) annual meeting in Portland, USA. The benchmark problem features a series of experiments in which a single solitary wave propagates up a triangular shaped shelf which has an offshore island feature. The problem provides detailed free surface elevation and velocity time series in the vicinity of the island. The comparison of the results showed that NAMI DANCE is able to satisfactorily predict long wave evolution, propagation, amplification, and tsunami currents.  
numerical modeling; tsunami currents; depth-averaged equation; benchmark
# Recent Advances in Hydrothermal Oxidation Technology for Sludge Treatment
## 2 Results and Discussion
### Issues and Optimization Strategies in HTO of Sludge  
#### 2.5.1 Challenges in HTO for Sludge Treatment  
Although hydrothermal oxidation can enhance treatment efficiency through process optimization, its effectiveness in sludge treatment remains suboptimal, and several issues need addressing:  
1. Pollution risks: Sludge may contain harmful substances, and improper treatment can pose threats to the environment and public health.
2. Low resource recovery: Current recovery rates for sludge resources are still low, with much sludge being disposed of through landfilling or incineration.
3. Outdated equipment: Many reactors involved in HTO are outdated and unable to withstand the high temperatures and pressures required for effective processing over extended periods.
4. The cost is too high: Hydrothermal oxidation technology requires oxygen assistance and is carried out in a high-temperature and -pressure environment, which inevitably generates some equipment costs and high energy consumption. To improve the efficiency of HTO for sludge treatment, researchers must continue to explore optimization methods to achieve more effective and environmentally friendly sludge management. Currently, various strategies are being implemented to address these three challenges, which will be summarized and discussed in the following sections.
## 5 SBS in a DOF
### Numerical Optimization of Longitudinal Fiber Profile for SBS Mitigation
Thus an astute strategy to mitigate the adverse impacts of SBS in highly non-linear fibers involves selecting a modulation depth that corresponds to a Brillouin frequency shift range surpassing the acoustic linewidth. This approach promises substantial benefits in taming the effects of SBS in speciality optical fibers.