esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Theory of Signal Noise Reduction Method
### Wavelet Noise Reduction Principle
#### 2.1.3 Wavelet Threshold Selection  
The wavelet threshold determination method includes the default, penalty, Birge-Massart, rigrsure rule, sqtwolog rule, heursure rule and minimaxi rule, and the threshold value can be defined by the soft or hard strategy [20]. Based on the experimental investigation, the Birge-Massart is applied in this paper.
# CF-mMIMO-Based Computational Offloading for UAV Swarms: System Design and Experimental Results
## 2 System Design
### Cloud Tier  
In this tier, the cloud server performs the following core functions: algorithmic model training, real-time inference, task scheduling, and comprehensive monitoring of resources, tasks, and network status. The algorithmic model training relies on the robust computational capabilities of cloud-based servers, where optimization is achieved through advanced artificial intelligence (AI) models or iterative algorithms [32; 33; 34; 35]. Real-time inference leverages pre-trained models to generate predictions promptly upon receiving new data, requiring low latency and high throughput [36; 37; 38]. The task scheduling mechanism dynamically orchestrates resource allocation to maximize parallelism and efficiency in multi-task execution, while integrating fault-tolerant strategies to ensure resilience against potential failures [39; 40]. The real-time resource monitoring system tracks resource usage to dynamically adjust allocations. The task monitoring system ensures timely handling of faults by real-time tracking of task status. The network monitoring evaluates performance in real-time to guarantee efficient and secure data transmission. Data and command exchange is facilitated between the core network and edge-tier CPUs through bidirectional interaction. These functionalities are interdependent, collectively forming a highly efficient and reliable system.
## 2 Model and Methods
### Lyapunov Exponents
Figure 1 is especially useful when one tries to understand the relative positioning of the coexisting attractors; it is also an instant visual of their general dynamics. AI and AII have special distinction from the other attractors in that they appear to be the scaffolding upon which other attractors coexist, except for AVII and AVIII which coexist with chaos. WI and WII interrupt chaos. AI brings LIS to life for low values of the injected signal, while AII has the largest attractor domain and guides the system into injection locking. Note, that some \(Y\) values label period-doubling episodes: for example, in AI and AII there is a \(2^{n}\) period-doubling sequence and in AVII a \(5\times 2^{n}\) doubling sequence. The transition from \(6P\) to \(12P\) identified in AIV refers to the two dominant limit cycles of a period-doubling sequence, \(6\times 2^{n}\) into chaos. The callouts for AI, AIV, AII, AVII, and AVIII magnify some details of the attractor dynamics. For example, inverse bifurcation sequences are identified in the callouts AII and AVII. Different theoretical tools provide information about an attractor or window dynamics and that includes chaos, but it is the exploration of the LEs as a function of the system's control parameter that reveals the detailed, universally predictive information about each attractor [37] or window. In addition, the study of LE patterns leads ultimately to understanding how an attractor can be accessed reliably under experimental conditions without changing the parameters of the system itself. It is called a power shift [34].
## 4 Power Management for the Energy Router
### Design of the Fuzzy Logic Controller
#### 4.1.1 Fuzzification of the Controller Input and Output  
Based on previous analysis, the fuzzy logic controller has a two-dimensional structure. The inputs of the controller can be derived by the following equations,  
\[\Delta P =P_{\text{DG}}-P_{\text{load}} \tag{14}\] \[\Delta SOC =SOC-SOC_{\text{avr}} \tag{15}\]  
where \(SOC_{\text{avr}}\) is the average value of the \(SOC\). The charging or discharging power \(P_{\text{bat}}\) is chosen as the output of the controller. Table 1 shows the quantitative results of the input and output.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Basic Domain** & **Fuzzy Domain** & **Quantization Factor** & **Fuzzy Subset** \\ \hline \(\Delta P\) & \([-3.75\) kW, \(3.75\) kW & \([-2,-1,0,1,2]\) & \(2/3.75\) & \([\)NB, NS, ZO, PS, PB\(]\) \\ \(\Delta SOC\) & \([-20\%,20\%]\) & \([-2,-1,0,1,2]\) & \(2/20\) & \([\)NB, NS, ZO, PS, PB\(]\) \\ \(P_{\text{bat}}\) & \([-2.5\) kW, \(2.5\) kW & \([-2,-1,0,1,2]\) & \(2.5/2\) & \([\)NB, NS, ZO, PS, PB\(]\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative results of the input and output.  
Figure 6: Charging and discharging control of the self-energy storage system.  
The basic domains of the input and output vary according to different design requirements. In this paper, \(SOC_{\text{max}}\) is set to 90% and \(SOC_{\text{min}}\) to 50%. The maximum charging or discharging power is set to 2.5 kW. Five linguistic states, NB (negative big), NS (negative small), ZO (zero), PS (positive small) and PB (positive big), are considered for the controller input and output. Based on the quantitative results, the triangular membership functions of the input and output are established as shown in Figure 7.
# A Subcell Finite-Difference Time-Domain Implementation for Narrow Slots on Conductive Panels
## 2 Narrow Slots in FDTD
### DMMA Model  
The DMMA model does not directly address the geometry of the aperture. Instead, this method employs the slot admittance [17] as an equivalent model of its behavior. Through this formalism, the admittance is transformed into an artificial material with effective dispersive permittivity and permeability.  
\[\varepsilon_{\text{eff}}=\frac{C_{\text{pu}}}{\varepsilon_{0}}\;\;\text{and}\; \;\mu_{\text{eff}}=\frac{1}{\epsilon_{\text{eff}}} \tag{1}\]  
where \(C_{\text{pu}}\) is the capacity per unit of length [5; 18]. Generally, \(C_{\text{pu}}\) depends on the frequency. However, for reducing the computational cost and simplicity, the capacity is taken as constant in frequency and, as a result, the relative effective parameter is also constant in frequency [7; 19].  
The aperture is determined by the cells centered on the magnetic-field components. Consequently, the electric fields are aligned across the slot (\(E_{w}\)), and the magnetic fields are arranged transverse to its gap (\(H_{t}\)), as depicted in Figure 1.
## 2 Problem Formulation
### System Model  
Figure 1 shows the system model diagram of our scheme, VSSE. Within VSSE, four entities are involved: _Data Owner_, _Cloud Server_, _Data User_ and _Block Chain_.  
_Data Owner_: These entities aim to store the collected extensive and critical data on a cloud server. Specifically, _DO_ encrypts each file and constructs secure indexes, which are then uploaded to _CS_. Furthermore, _DO_ is tasked with generating verification information and subsequently uploading it to the Blockchain _BC_.  
_Cloud Server_: _CS_ is tasked with executing the search protocol and returning the corresponding search results to the blockchain for verification. Specifically, upon receiving the search traps sent by _DU_, _CS_ uses secure index to execute search query. Then, _CS_ also has to determine which users have the authority to access which files. In addition, due to the potential of the server returning inaccurate search outcomes to _DU_, _CS_ is required to transmit the results to _BC_ for verification.  
_Data User_: These entities would like to obtain reliable search results. Specifically, _DU_ generates search trapdoor based on the keywords and transmits them to _CS_. Any legitimate _DU_ will receive search results and verification information, while unauthorized _DU_ will not be able to obtain any results.  
_Block Chain_: The responsibility of verifying the outcomes to determine the reliability of search results obtained by _DU_ lies with _BC_. Specifically, upon receiving the search results, _BC_ utilizes a verification contract to validate the search results and returns it along with the verification identifier to _DU_.
# Data Privacy and Ethical Considerations in Database Management
## 3 Best Practices
### Ethical Considerations in Data Retention  
When implementing data retention policies, several ethical considerations must be taken into account:  
* Right to be forgotten: As per GDPR Article 17, data subjects have the right to request the erasure of their personal data. Database designs must incorporate mechanisms to efficiently and completely remove an individual's data upon request [31].
* Purpose limitation: Data should only be collected and retained for specified, explicit, and legitimate purposes, as outlined in GDPR Article 5(1)(b) [32].
* Data minimization: Organizations should limit the collection and retention of personal data to what is necessary for the specified purposes, in line with GDPR Article 5(1)(c) [33].
* Storage limitation: Personal data should be kept in a form which permits the identification of data subjects for no longer than is necessary for the purposes for which the personal data are processed, as per GDPR Article 5(1)(e) [34]. These ethical considerations ensure that data retention policies not only comply with legal requirements but also respect the rights and privacy of data subjects.
## 1 Introduction  
Advancements in diagnostic ultrasound have allowed for a rapid expansion of the quantity and quality of non-invasive information that clinical researchers can acquire from cardiovascular physiology. As a primary application, quantification of blood flow through Doppler ultrasound is useful for the identification of early disease states or diagnosis of pathological conditions either as consequence, or root cause, of altered hemodynamics [1,2]. These applications range across multiple organ systems, for instance, grading conduit artery stenoses by flow jet velocity [3,4], estimating mitral valve inflow for left ventricular diastolic dysfunction [5], or identifying locations at risk for atherosclerotic plaque development by low/oscillatory wall shear stress [6]. However, much of the current technology is limited by assumptions of laminar flow and slow-moving blood flow in a small acoustic window, which restrict most applications to imaging with fixed insonation angles and simple flow patterns.  
In recent years, high frame rate ultrasound (HiFRUS; also termed 'ultrafast' ultrasound) imaging techniques have been developed to address the above limitations by insonating a large area with unfocused beams through either spherical or plane wave emissions [7]. Rather than being limited by line-by-line pulse-echo imaging, HiFRUS techniques acquire full-field data at very high frame rates (e.g., 1000-10,000 frames per second) for excellent temporal resolution, and allow beamforming in any direction for angle-independent blood velocity estimations. These advancements enable accurate quantification of high-velocity non-laminar flows, demonstrated both in vivo and in geometrically-realistic phantoms at vessel bifurcations [8; 9; 10], aneurysms [11], and even 3D structures such as the left ventricle [12; 13]. However, the ability for basic science or clinical researchers to access the vast amount of biological information has so far been limited, with recent HiFRUS reviews touting only the perceived potential of these advances in imaging [7; 14; 15; 16].  
The major challenges for mainstream adoption of HiFRUS technology stem from the lack of large sample human data, as well as descriptive studies of normal and pathological physiology. In this review, we provide a practical framework for basic science researchers conducting preliminary in vivo studies with the long-term objective of clinical relevance. Here, we propose a guideline for standards in technical reporting, and provide considerations for investigations in humans, data management, and storage, as well as a narrative example of how studies could be designed for in vivo observations in basic science questions. Although this review focuses on relevant cardiovascular applications, we encourage the readers to extend these principles to other areas of research which may benefit from HiFRUS imaging.
# IoT Firmware Emulation and Its Security Application in Fuzzing: A Critical Revisit
## 4 State-of-the-Art Firmware Emulation and Its Application in Fuzzing
### Solutions to Peripheral Interaction Support
#### 4.2.3 Partial Emulation  
For research on specific vulnerabilities or code snippets in firmware where full system emulation is not required, researchers [37; 38; 39] propose slicing the code snippet without peripheral interactions. For instance, \(HEAPSTER\)[37] focuses on detecting heap vulnerabilities within firmware by emulating only a limited number of functions related to heap operations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Years** & **Tools** & **Fidelity * ** & **Automation** \\ \hline Hardware-in-the-loop & 2014 & _Avatar_[40] & High & Low \\  & 2022 & _AutoMap_[41] & High & Low \\ \hline High-level Replacement & 2020 & _HALucinator_[24] & Moderate & Low \\  & 2023 & _SAFIREFUIZZ_[20] & Moderate & Low \\ \hline  & 2020 & _Laelaps_[30] & Moderate & Low \\  & 2021 & \(\mu\)_Emu_[31] & Moderate & Moderate \\  & Symbolic & 2021 & _Jetest_[32] & Moderate & Moderate \\  & Constraints-based & 2022 & _Fuzzware_[33] & Moderate & High \\  & 2022 & _MetaEmu_[19] & Low & Low \\  & 2024 & _AIM_[42] & Moderate & Moderate \\ \hline Peripheral & Hardware & 2019 & _Pretender_[25] & Moderate & Moderate \\  & Record-based & 2021 & _Conware_[26] & Moderate & Moderate \\ \hline Human & 2020 & \(P^{2}\)_IM_[27] & Moderate & Moderate \\  & Heuristic-based & 2021 & _DICE_[28] & Moderate & Moderate \\  & 2022 & _FirmWire_[29] & Moderate & Low \\ \hline Fuzzing & 2023 & _Ember-IO_[34] & Moderate & High \\  & Feedback-based & 2023 & _SEmu_[35] & High & Low \\ \hline Manual-based & 2022 & _Perry_[36] & High & Low \\ \hline Driver-based & 2024 & _[PERSON]_[36] & High & Low \\ \hline  & 2019 & _Gerbil_[38] & Low & Low \\  & 2022 & _HEAPSTER_[37] & Moderate & Low \\  & 2022 & _SFuzz_[39] & Low & Low \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of peripheral interaction emulation solution.
## 3 Sequential Restoration Strategy
### Optimal Restoration Approach  
We will propose a new sequential restoration strategy, as shown in Figure 5. First, if a blackout occurs, the restoration process is started. We set the recovery time \(t=0\) and the restoration step \(i=1\). In the next stage, the black start unit is started, and we confirm the generator that can be restored in the next step. If all generators are restored, the flow is terminated. Otherwise, the flow is continued to determine the generator start-up sequence (GSS), and the start-up time of each generator to be restored is calculated. At this time, the time taken to re-energize the transmission lines and transformers is calculated. To consider this, we search for the shortest path to the generator by considering the charging currents as a weighting factor. The time taken to restore the transmission lines and transformers is calculated by counting the amount of equipment in the path and multiplying it by a certain value. Afterwards, we compute the time to re-start the generator in accordance with the generator characteristic data. By adding the respective times that are calculated, the generator with the shortest restoration time is selected for the next unit. During the process of restoration, the constraints  
Figure 4: Generator capability curve.  
expressed in Equations (5)-(7) must be satisfied. Unless the constraints are satisfied, the corrective actions such as power generation or load adjustment should be performed. The restoration of the next unit is started, and the load is restored in order to maintain the system balance and stability. At this time, the sum of the time to re-energize transmission lines and transformers and the time until the generator can supply the power is added to the recovery time. In the same way, the flow of the GSS is repeated until the available generators exist in the systems.
# Dynamic Graph Convolutional Crowd Flow Prediction Model Based on Residual Network Structure
## 3 Proposed Method  
### Problem Definition  
The crowd flow problem is a typical time series prediction problem. In this study, we learn from the observation of historical crowd flow data to achieve the prediction of future crowd inflow and outflow at each node of the dynamic space-time graph (DSTG).  
Definition: The DSTG is defined as an undirected graph \(G=[V,A]\), \(V\) is the set of nodes, \(|V|=N\) is the number of nodes, which can be described as \(V=\{v_{i}|i=1,2,3,\ldots,N\}\), \(A\) denotes the dynamic adjacency matrix of nodes, \(A_{t}\in R^{N*N}\). In any time interval \(t\), the historical data can be described as \(X_{t}\in R^{N*2}\), where 2 is the inflow and outflow attributes.  
Problem description: Let there be a graph \(G=[V,A]\), and node history data \(\{X_{t,i,j}|t=1,2,3,\ldots,t|i=1,2,3,\ldots,N\Big{|}j=1,2,3,\ldots,N\Big{\}}\), to forecast the result of the next step \(\{X_{t+1,i,j}\Big{|}i=1,2,3,\ldots,N\Big{|}j=1,2,3,\ldots,N\Big{\}}\), where \(v_{i}\in V\) denotes an outflow nodeand \(\_v_{j}\in V\) denotes an inflow node. The crowd flow prediction algorithm is formulated as function \(Y=f(x)\), i.e.,  
\[\hat{X}_{t+1,i,j}=f\big{(}X_{t,i,j}|t=1,2,3,\ldots,t|i=1,2,3,\ldots,N|j=1,2,3, \ldots,N\big{)} \tag{1}\]  
In building the DSTG, this paper treats the historical OD flows as the edges of the DSTG, as shown on the left side of Figure 2a. The paper divides days into two categories: workdays and holidays, and divides each day into several time intervals. A spatio-temporal graph needs to be built for each time interval to form dynamic OD flows at different times. Afterwards, the average values of the OD flows obtained directly within the same time period for workdays and weekends are taken as the OD flow patterns X, respectively [11]. The normalized adjacency matrix at time interval t can be obtained with the following Equation (2):  
\[A^{t}=\hat{D}^{-\frac{1}{2}}X_{t}\hat{D}^{-\frac{1}{2}} \tag{2}\]  
within it, \(\hat{D}^{i,i}=\sum_{j}X_{t,i,j}\), we further add a self-loop to \(A^{t}\), forming \(\hat{A}^{t}\), as shown in Equation (3):  
\[\hat{A}^{t}=I+A^{t} \tag{3}\]  
where \(I\) is the identity matrix, \(\hat{A}\in R^{VsV}\) is the normalized adjacency matrix with self-loops at time interval t. Although the edges are for time intervals, the graph structure remains dynamic within each day, with a topology that changes over time. At the same time, we obtain the node attributes through the inflows and outflows \(X_{n}\) within each time interval. Based on the above operations, the construction of the DSTG with dynamic graph structure and node attributes is completed.
# Image Degradation Modeling for Real-World Super Resolution via Conditional Normalizing Flow
###### Abstract  
In recent years, deep-learning-based super-resolution (SR) methods have obtained impressive performance gains on synthetic clean datasets, but they fail to perform well in real-world scenarios due to insufficient real-world training data. To tackle this issue, we propose a conditional-normalizing-flow-based method named IDFlow for image degradation modeling that aims to generate various degraded low-resolution (LR) images for real-world SR model training. IDFlow takes image degradation modeling as a problem of learning a conditional probability distribution of LR images given the high-resolution (HR) ones, and learns the distribution from existing real-world SR datasets. It first decomposes the image degradation modeling into blur degradation modeling and real-world noise modeling. It then utilizes two multi-scale invertible networks to model these two steps, respectively. Before applied into real-world SR, IDFlow is first trained supervisedly on two real-world datasets RealSR and DPED with negative log-likelihood (NLL) loss. It is then used to generate a large number of HR-LR image pairs from an arbitrary HR image dataset for SR model training. Extensive experiments on IDFlow with RealSR and DPED are conducted, including evaluations on image degradation stochasticity, degradation modeling, and real-world super resolution. Two known SR models are trained with IDFlow and named as IDFlow-SR and IDFlow-GAN. Testing results on the RealSR and DPED testing dataset show that not only can IDFlow generate realistic degraded images close to real-world images, but it is also beneficial to real-world SR performance improvement. IDFlow-SR achieves 4\(\times\) SR performance gains of 0.91 dB and 0.161 in terms of image quality assessment metrics PSNR and LPIPS. Moreover, IDFlow-GAN can super-resolve real-world images in the DPED testing dataset with richer textures and maintain clearer patterns without visible noises when compared with state-of-the-art SR methods. Quantitative and qualitative experimental results well demonstrate the effectiveness of the proposed IDFlow.  
Keywords:image reconstruction; super resolution; image degradation modeling; deep learning; normalizing flow +
Footnote †: journal: Applied Sciences
# Study of Green Synthesis of Ultrasmall Gold Nanoparticles Using _Citrus Sinnensis_ [PERSON]
## 3 Results and Discussion
### Elements Contained Analysis of AuNPs by EDS  
Energy-dispersive spectroscopy (EDS) analysis was performed to detect the elements contained in AuNPs solution when pH = 11. As shown in Figure 2, a strong signal is observed around 2.2 keV, which conforms the existence of Au in the solution where the redox reaction happened between Au\({}^{3+}\) and the fruit peel extract. Meanwhile, C, O, and Na elements are observed on the fabricated AuNPs EDS spectra. These elements exist in the cellulose and salinity in _C. sinensis_ peel extract. NaOH solution is the other source of Na atoms.
# STHarDNet: Swin Transformer with HarDNet for MRI Segmentation
## 4 Proposed Method: STHarDNet
### STHArDNet: Combination of Swin Transformer with HarDNet  
To combine the convolution with the vision transformer, this study proposes a STHarDNet model structure, as shown in Figure 6. STHarDNet consists of (1) the HarDNet with the encoder-decoder structure of the U-Net form and (2) the Swin Transformer used in skip connection that connects the encoder and the decoder.  
Figure 5: Architecture of the Swin Transformer.  
The Swin Transformer uses a hierarchical transformer to extract hierarchical feature maps and uses the shifted windows approach to calculate the relationship between patches in the whole feature map. Therefore, it has a more suitable model structure for segmentation than the transformer. However, the hierarchical transformer down-samples the feature map's size by merging adjacent 2 \(\times\) 2 patches to generate a hierarchical feature map. Therefore, if the input feature map's size is small, the Swin Transformer cannot extract a deep hierarchical feature map.  
Conventional combined models of CNN and transformer used the transformer in the bottleneck of the encoder and the decoder; however, the bottleneck has a small size (7 \(\times\) 7) because its input is the feature map output at the end of the encoder. This is not suitable for the purpose of using the hierarchical transformer of the Swin Transformer in this study. Therefore, the Swin Transformer was not used at the bottleneck of the encoder and the decoder in this study, but used it in the first skip connection, where the size of the feature map was the largest among skip connections.