esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Aftershocks Space Distribution, Temporal Evolution and Focal Mechanism  
The analysis of the aftershock sequence following the 2014 Ludian _Ms_ 6.5 earthquake elucidates a distinct L-shaped conjugate distribution, suggesting a compound fracture orientation primarily along the east-west (E-W) and northwest-southeast (NW-SE) axes. The E-W oriented section spans approximately 23 km in length and 5 km in breadth, while the NW-SE extension measures about 18 km in length and similarly 5 km across. In terms of depth, the aftershocks predominantly clustered within a stratum extending from 5 to 15 km beneath the surface. The intersection of the conjugate faults marks the zone of maximal hypocentral depth, where the concentration of aftershocks is notably dense. Moving laterally from this central intersection, there is a discernible gradation towards more superficial seismic events (Figure 7).  
The aftershocks also exhibited distinct spatiotemporal variation characteristics. Initially, within the first 2 to 5 h post-main shock, the aftershocks predominantly aligned in a northwest-southeast (NW-SE) orientation, forming a strip-like distribution. This pattern underwent a notable shift approximately 5 h later, with the emergence of a northeast-southwest (NE-SW) directional trend in the aftershock distribution. This NE-SW orientation became increasingly pronounced over the subsequent 24 h. Remarkably, after five days, the aftershock sequence evolved to display an asymmetric conjugate distribution, further illustrating the dynamic nature of the seismic event's aftershock activity.  
The focal mechanism solutions for the two nodal planes, as published by the Global Centroid Moment Tensor (GCMT) [32; 33], were consistent with the dominant orientations of the L-shaped conjugate distribution of aftershocks. On the other hand, this spatiotemporal patterning of the Ludian aftershock sequence concurred with the observations presented in the works of [PERSON] et al. [25] and [PERSON] et al. [34]. The consistency of these findings across independent studies lends credence to the interpretation of the tectonic behavior in the  
Figure 6: Comparison of frequency magnitude distribution. The solid circles and solid triangles represent the cumulative and non-cumulative distribution, respectively. These indicate the relationship between the number of earthquakes equal to or greater than a certain magnitude. The red symbols represent the CERP catalog in this study, the orange symbols represent the earthquake catalog by [PERSON] et al. [25], and the blue symbols represent the earthquake catalog from the China Earthquake Administration’s station network, which overlaps with the fixed stations used in this study.  
aftermath of the Ludian earthquake and reinforces the understanding of seismic dynamics in conjugate fault systems.
# FedOpt: Towards Communication Efficiency and Privacy Preservation in Federated Learning
## 6 Related Work and Discussions
### Functional Comparison  
Several communication efficient and privacy preservation distributed approaches have been proposed recently including Practical Secure Aggregation (PSA) [41], Federated Extreme Boosting (XGB) [42], Efficient and Privacy-Preserving Federated Deep Learning (EPFDL) [43] and Privacy-preserving collaborative learning (PPCL) [44] as listed in Table 3. Specifically, PSA and XGB utilise collaborative training in order to resist collusion among adversaries, but both approaches do not guarantee communication efficiency. Compared with EPFDL, the proposed FedOpt is not only communication efficient but also serves for collaborative training. Meanwhile, PPCL serves most of the security features by sharing the same decryption key among all the users, which is not desirable in real-time applications. On the contrary, FedOpt considers these real-time applications, where adversaries may act as honest parties but colludes due to the shared parameters. Table 3 shows that, none of the state-of-the-art approaches completely address the challenges of privacy preservation and communication efficiency. On the other hand, the proposed FedOpt not only mitigates the above attacks by utilising differential privacy, but also provides communication efficiency via compression algorithm.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Functionality** & **PSA** & **XGB** & **EPFDL** & **PPCL** & **FedOpt** \\ \hline Communication Efficient & & & & & \\ \hline Collaborative Training & ✓ & ✓ & & ✓ & ✓ \\ \hline Non-IID Support & & & ✓ & & \\ \hline Gradient Confidentiality & ✓ & & ✓ & ✓ & ✓ \\ \hline Attack Resilience & & ✓ & & & ✓ \\ \hline Post-Quantum Security & & ✓ & ✓ & & \\ \hline Collusion Resistance & ✓ & & ✓ & ✓ & ✓ \\ \hline Fast Convergence Speed & ✓ & & ✓ & & ✓ \\ \hline Application Aware & & ✓ & & ✓ & ✓ \\ \hline Algorithm Complexity & ✓ & ✓ & & ✓ & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Functionality comparison with existing FL approaches.
# Finite Difference Algorithm on Non-Uniform Meshes for Modeling 2D Magnetotelluric Responses
###### Abstract  
A finite-difference approach with non-uniform meshes was presented for simulating magnetotelluric responses in 2D structures. We presented the calculation formula of this scheme from the boundary value problem of electric field and magnetic field, and compared finite-difference solutions with finite-element numerical results and analytical solutions of a 1D model. First, a homogeneous half-space model was tested and the finite-difference approach can provide very good accuracy for 2D magnetotelluric modeling. Then we compared them to the analytical solutions for the two-layered geo-electric model; the relative errors of the apparent resistivity and the impedance phase were both increased when the frequency was increased. To conclude, we compare our finite-difference simulation results with COMMEMI 2D-0 model with the finite-element solutions. Both results are in close agreement to each other. These comparisons can confirm the validity and reliability of our finite-difference algorithm. Moreover, a future project will extend the 2D structures to 3D, where non-uniform meshes should perform especially well.  
finite-difference algorithm; magnetotelluric; 2D structures; modeling +
Footnote †: journal:
# Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories
## 1 Introduction
In addition, new wavelength combinations for leaf water status (WC and MC) are constantly being proposed using the above model expression [2; 7]. However, the relationships between the wavelength bands and water status indicators (WC and MC) have not been well investigated to date. Furthermore, the study of different objects requires the use of different wavelength combinations that correspond to them. Based on the aforementioned background, the main objectives of this research were to (1) perform statistical analysis on the leaf water status (WC and MC); (2) investigate the impact of hyperspectral data preprocessing on improving the model accuracy, and identify the best combination of wavelength bands for leaf water status (WC and MC) assessment based on the NDVI and two-band index (TBI); (3) evaluate the performance of the leaf water status model based on various types of indices; and (4) establish an approach for obtaining the tomato leaf water status (WC and MC) in real time using HSI technology for tomato plant irrigation management in plant factories.
# Zero-Shot Recognition Enhancement by Distance-Weighted Contextual Inference
## 3 Method
### Visual Inference  
The visual inference process aims to classify an unseen object using only its individual visual feature as input. As a result of that process, the classifier predicts a visual score for each unseen class label as output. In general, classifiers use a model trained with fully supervised learning for seen class labels or an indirectly trained model based on semantic relations between seen and unseen class labels. Because the main concept of this paper is the additional use of context, we adopt several existing ZSR models [6; 9; 22; 23] for the visual inference function \(G:I\to E\) and use them as baselines in the comparison of models with and without the contextual inference in Section 4. The \(G\) functions used by the baseline methods to measure the visual scores are specified in Table 1.  
Structured Joint Embeddings (SJE) [6] trains an image-semantic embedding matrix \(W\in\mathbb{R}^{d_{i}\times d_{e}}\) and infers a class label of an unseen object by measuring the distance between an embedded image feature of the object and unseen semantic embeddings. Latent Embedding (LatEm) [22] tries to relax the limitation of linearity in SJE by using multiple image-semantic embedding matrices. LatEm proposes a nonlinear compatibility function with \(K\) indexes over the latent choices.  
The convex combination of semantic embeddings (ConSE) [23] uses a pretrained image classifier trained on seen class labels with full supervision. For the inference, softmax-output values \(p\) of the classifier for an input unseen image are used to create its representation vector by weighting the semantic vectors of seen class labels \(e_{c}\). An unseen class label for the semantic vector nearest to the representation vector is then predicted as an appropriate class label.  
A multi-layer GCN model [9] begins by training an image classifier in the same manner, but it uses classifier weights from the image classifier as ground-truth to learn predicted classifier weights with semantic embeddings for class labels and their adjacency matrix as input. At test time, the visual inference for the GCN conducts a dot-product estimation between an image feature vector \(i\) and a predicted classifier weight \(\hat{w}\) for unseen class labels.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Function \(G(i,e)\)** \\ \hline SJE & \(i^{T}\,We\) \\ LatEm & \(\max_{1\leq j\leq K}\,i^{T}\,W_{j}\,e\) \\ ConSE & \(\cos\left(\frac{1}{Z}\sum_{c\in S}p(c|i)\cdot e_{c},\,e\right)\) \\ GCN & \(\hat{w}_{e}\cdot i\) \\ \hline \hline \end{tabular} Notations: SJE (Structured Joint Embeddings), LatEm (Latent Embedding), ConSE (Convex Combination of Semantic Embedding), GCN (Graph Convolutional Network), \(W\) (image-semantic embedding matrix), \(K\) (latent variable, \(K\geq 2\)), \(Z\) (normalization factor, \(Z=\sum_{c\in S}p(c|i)\)), \(\hat{w}\) (predicted classifier weight).  
\end{table}
Table 1: Visual inference functions of baseline zero-shot recognition (ZSR) models.
## 2 System Model
### Communication Model
#### 2.2.2 Cooperative Communication Model
The SINR of the merged data may be expressed as: \[SINR_{MRC}=\frac{h_{bn}\left(1-\alpha_{mn}\right)P_{n}}{ah_{bn}\alpha_{mn}P_{n} +N_{0}}+\frac{h_{bn}P_{m}}{N_{0}}\] (8) After receiving the data from \(DT_{n}\), \(DR_{n}\) separates the \(DT_{n}\)'s and \(EU_{m}\)'s data with the aid of SIC technology. The SINR of the \(DT_{n}\)'s data may be expressed as: \[SINR_{D2D}=\frac{h_{nn}\alpha_{nm}P_{n}}{bh_{nn}\left(1-\alpha_{mn}\right)P_{n} +N_{0}}\] (9) where \(h_{nn}\) represents the channel gain of the D2D link. Coefficient \(b\) represents the relationship between the power \(P_{mn}^{D}\) and \(P_{mn}^{B}\), which may be given by: \[b=\left\{ \begin{array}{ll}1&P_{mn}^{B}\leq P_{mn}^{D}\\ 0&P_{mn}^{B}>P_{mn}^{D}\end{array} \right.\] (10)
# No-Reference Blurred Image Quality Assessment by Structural Similarity Index
## 2 Related Works
### Holistic NR IQA Algorithms  
Holistic IQA algorithms are designed for measuring distortion of unknown type. Holistic models extract common features of various distortions or establish various models for different distortions. BIQI [14] assumes an image is subjected to a wavelet transform over three scales and three orientations using the Daubechies 9/7 wavelet basis [15], and assesses image quality with a two-step framework which estimates the presence of a set of distortions and evaluated the quality of the image along each of these distortions. BLIND-II [3] is a multiscale but single-stage algorithm through machine learning that operates in the DCT domain, where a number of features, i.e., scale and orientation selective statistics, correlations across scales, spatial correlation and across orientation statistics, are computed from a natural scene statistics (NSS) model of block DCT coefficients. BRISQUE [4] details the statistical of locally normalized luminance coefficients in the spatial domain. Considering images are naturally multiscale, BRISQUE captures 36 features from two grayscales to identify image distortions and activate distortion-specific quality assessment. A support vector regressor (SVR) [16] is used to build a regression module to perceive quality score. NIQE [17] is founded on perceptually relevant spatial domain NSS features extracted from local image patches that capture the essential low-order statistics of natural images. NIQE takes the distance between the quality-aware NSS feature model and the MVG fit to the features extracted from the distorted image as quality score.  
The up-to-date NR IQA algorithm NR-CSR [18] applies convolutional sparse representation (CSR) to simulate the entire image as a sum over a set of convolutions of coefficient maps, which has the same size as the image. NR-CSR uses a low-pass filter to obtain the sparse coefficient and calculating the gradient value to score the sharpness. Meanwhile, artificial neural network method has already been used by novel NR IQA algorithms. For example, [PERSON] [19] proposed an NR IQA algorithm based on multi-expert convolutional neural networks (MCNN) and [PERSON] [20] proposed an IQA model for realistic blur image-based semantic feature aggregation (SFA).  
It should be noticed that the state-of-the-art NR IQA algorithms like BRISQUE and BLIIND-II perform well at adapting HVS but lose computation convenience due to the training by distorted images in advance. Moreover, deep learning methods like MCNN suffer heavy time cost and high-level hardware requirements.
## 5 Discussion of the Results  
### Buckling Mode Shapes  
Figures 10-13 show the buckling mode shapes of perforated plates strengthened with FRP under uniaxial compression with different \(e_{x}/b\), \(\beta\), \(d/b\) and BCs. The following are found:  
When the hole center is in the middle of the plate, the buckling mode shapes transform from two half-waves to three half-waves as \(d/b\) is enlarged from 0 to 0.7 (seen from Figure 10), a similar phenomenon occurs in all plates with \(e_{x}/b=1\) and 2.  
* From Figures 10a and 11a, it can be seen that when \(d/b=0.1\), the buckling mode shapes are very close to plates without hole; and as \(d/b\) is enlarged, the max deformations of the plate are firstly centered near the hole then gradually transferred between hole and loading edge for 4S (seen from Figures 10b-d and 11b-d).
* From Figure 12, it can be seen that as \(\beta\) increases, the deformations of the existing part have been basically unchanged, and the deformations of the adding part are very small.
* From Figure 13, it can be seen that the mode shapes are essentially unchanged as \(d/b\) is enlarged for plates with BCs of 3S1F.  
Figure 10: The model shapes of plates with \(e_{x}/b=1\), \(\beta=2\) and BCs of 4S. (**a**) \(d/b=0.1\); (**b**) \(d/b=0.3\); (**c**) \(d/b=0.5\); (**d**) \(d/b=0.7\).  
Figure 9: Finite element (FE) models converge using mesh refinement.
# Survey of Finite Element Method-Based Real-Time Simulations
## 1 Introduction  
Model building belongs to basic but most important engineering activities in which various options are explored to reach the solution that offers a compelling compromise between the computational effort and reached accuracy of performed simulations. This procedure has progressed from rather simple sketches in the sand, via manual computations and graphical solutions performed on the paper by means of simplified models, to high fidelity solutions performed on modern computers based on models of various levels of complexity and involving significant computational effort. Engineers combine the available knowledge of physical processes with innovation and creativity to shape the world in ways that are sometimes breathtaking. The modern engineering tools, hardware, and software are the key factors that enable highly demanding computations that engineers could only dream of just a couple of decades ago. The power of modern hardware components allows to analyze different physical processes at practically any level of detail.  
Focusing on the field of structural analysis, the finite element method (FEM) has imposed itself as currently the most powerful tool that enables highly efficient modeling and simulation of structures characterized by complex geometry and exposed to arbitrary boundary and initial conditions. As a numerical method, it substantially relies on the usage of modern hardware. In typical engineering tasks, the FEA computations are performed as \"off-line\" computations. This practically implies a sequenced use of all three essential parts of a FEA program--a model is prepared in the preprocessor; the solver provides the solution of the problem with all required quantities; finally, the postprocessor offers tools to observe and analyze the obtained solution. Hence, setting external excitations (done in the preprocessor) and observing the structural response (done in the postprocessor) are two processes separated by a substantial amount of time.  
On the other hand, the remarkable pace of hardware development in the past couple of decades was the crucial impetus for a number of researchers to raise the question about the possibility ofreal-time simulations based on FE models. Such an intriguing option opens up the door to a whole new virtual world, thus offering unforeseen possibilities in various fields of work ranging from engineering tasks, via training simulators, to entertainment applications. It is no wonder that it has attracted a significant number of researchers to provide their ideas on how to make FEM-based real-time simulation feasible.  
The authors believe that, although still relatively young, the field has reached a level of maturity that deserves a not necessarily exhaustive but representative overview of the work conducted in the field, with the primary idea to address various directions of development. Prior to that, the term \"real-time simulation\" will be addressed together with various means to achieve it.
## 2 Materials and Methods
### Questionnaire
#### 2.2.1 Ad Hoc Questionnaire Design  
The structured ad hoc questionnaire was designed to assess attitudes towards PA focusing on various aspects related to motivation, perceived barriers, and the benefits of PA, as well as the demographic information and anamnesis elements of the participants. The questionnaire was divided into two sections.  
The first section of the questionnaire collected demographic data (age, sex, and geographical origin) and anthropometric characteristics (weight and height). Specific data regarding the participants, such as the level of SCI (thoracic, cervical, or lumbar), degree of injury (complete or incomplete), date and cause of injury (traumatic or non-traumatic), and data on information received from healthcare providers regarding PA, were also collected.  
The structure of the ad hoc questionnaire was informed by the theory of planned behavior (TPB). The 5 domains of the questionnaire were as follows: (1) physical domain: perceived importance of PA for health and physical well-being (9 items); (2) psychological domain: emotional and psychological benefits associated with PA (7 items); (3) motivational domain: motivation and enjoyment derived from engaging in PA (3 items); (4) sociality domain: social aspects and autonomy related to PA (5 items); and (5) barrier domain: perceived barriers to adopting an active lifestyle (8 items). These domains correspond to key components of the TPB.  
Specifically, attitude towards the behavior was assessed through items related to physical and psychological domains. Subjective norms were reflected in the sociality domain, capturing perceived social support and expectations. Perceived behavioral control was addressed through the barrier and motivational domains, evaluating individuals' perceptions of obstacles to engaging in PA.  
For each item, the participants indicated their level of agreement with a series of statements (items) using a 5-point Likert scale (where 1 corresponded to \"strongly disagree\" and 5 to \"strongly agree\") [24; 25].  
For each domain, the mean score was calculated by summing the individual responses and dividing them by the number of items included in the domain. The questionnaire was distributed through the Microsoft Forms platform (Microsoft Corporation, Redmond, WA, USA, 2020).
## 2 Research Methods  
### Participants  
This study included healthy right-handed male and female participants with normal cognitive function. The Korean version of the Perceived Stress Scale (PSS) test [8] was administered prior to the experiment to classify the participants into moderate- or high-stress groups.  
The Perceived Stress Scale (PSS) is a commonly used assessment tool for describing an individual's level of perceived stress. Numerous studies have investigated the PSS in the diagnosis of stress, with overall results suggesting that the scale is a reliable measure of perceived stress with a good internal consistency and test-retest reliability [8; 9]. One study found that the PSS had a high internal consistency, with a Cronbach's alpha of 0.84, indicating that the items on the scale measured the same underlying construct of perceived stress [8]. Another study found that the PSS has a good test-retest reliability, with a correlation coefficient of 0.85 over a two-week period [9].  
For the PSS test, the total score ranges from 0 to 40, with higher scores indicating a greater level of perceived stress. In our study, the moderate-stress group was defined as having a PSS score ranging from 15 to 18, whereas the high-stress group was defined as having a score of 19 or greater.  
The exclusion criteria included individuals with metal objects in their bodies, such as pacemakers or metal implants, due to potential interference during magnetic resonance imaging (MRI). Those with claustrophobia were also excluded, as they might have experienced discomfort during the MRI scans. Additionally, individuals with a history of neurological or psychiatric disorders, including depression, anxiety, or epilepsy, were excluded to avoid confounding factors affecting brain activation. Participants currently taking psychoactive medications or substances that could alter their cognitive or neural responses were not included. Individuals with a history of substance abuse, cardiovascular diseases--given the potential health risks posed by stress-induced tasks--and those reporting irregular sleep patterns, as sleep quality can significantly impact stress levels and brain function, were also excluded. The protocol and rationale of the study were described to the participants prior to enrolment, and individuals were instructed to refrain from activities that may affect their brain activation, including smoking, alcohol, and coffee. All participants were right-handed, according to the revised Edinburgh Reading Test. All participants gave their written informed consent before any study related procedures were performed.  
The protocol for the research project was approved by the Institutional Review Committee of Konkuk University, within which the work undertaken conformed to the provisions of the Declaration of Helsinki (IRB number: 7001355-202010-HR-405).