esa-sceva/satcom-chunk-collection huggingface dataset:
# Synthetic Fog Generation Using High-Performance Dehazing Networks for Surveillance Applications
## 2 Related Work
### GridNet for Fog Removal  
GridNet is a sophisticated deep learning architecture designed for image restoration tasks, including fog removal. Introduced by [PERSON] et al. [8], GridNet leverages a grid-like structure of convolutional layers to process multi-scale features effectively. The network employs Residual Dense Blocks (RDBs) to enhance feature extraction and combines down-sampling and upsampling operations to capture both local and global contexts. This hierarchical design allows GridNet to handle varying fog densities and complex scene structures, making it particularly suitable for surveillance applications where image clarity is critical [4,19].
# Recent Strategies for Environmental Remediation of Organochlorine Pesticides
## 5 Photocatalysts for Pesticides Degradation
### Zinc Sulfide-Based Photocatalyst for Pesticides Degradation  
The utilization of ZnS as photocatalyst has been applauded as a result of its outstanding structure. It is a polymorphous material that exists in two crystalline forms, wurtzite and zinc blende. While the wurtzite has band gap energy of 3.91 eV, zinc blend has a band gap energy of 3.54 eV. Interestingly, ZnS is not only stable to oxidation and hydrolysis, but it is also not toxic and it has high natural abundance [93]. Due to its high band gap energy, it absorbs in the UV region, but could be made to absorb in the visible light region by doping with transition elements. These properties make its maximum use as a photocatalyst in the degradation of organochlorine pesticides to be highly feasible [94]. In the studies conducted by [PERSON] et al. [95], eight miscellaneous pesticides including tolclofos-methyl, propanil, pendimethalin, pencycuron, metribuzin, metalaxyl, isoxaben and ethoprophos were destroyed by zinc oxide and zinc sulfide photocatalysts. The concentration of all the pesticides remaining was lower than 0.8 \(\mu\)g L\({}^{-1}\) at the end of the investigation. [PERSON] et al. [96] used ZnS-containing nanotube array, which was sensitized by titanium oxide, to destroy anthracene-9-carboxylic acid and 2,4-dichlorophenoxyayacetic acid. This laboratory-scale investigation showed that all the contaminants were completely removed from water after 150 min of visible light irradiation. Toxic organic pollutants have also been degraded by using nanoparticles made from zinc sulfide and chitosan as the photocatalyst [97]. Zinc sulfide-cellulose nanocomposite have been used to degrade pollutants with high degradation efficiency [98]. Thin film of zinc sulfide was also reported to degrade organic contaminants by [PERSON] et al. [99]. By using a nanocomposite composed of zinc sulfide and molybdenum sulfide as photocatalyst, under visible light irradiation, [PERSON] et al. degraded the pesticide dicofol [100]. Their result showed maximum removal efficiency. In addition, the reusability efficiency of the photocatalyst was very good. Chlorpyrifos pesticides was recently degraded along with other organic pollutant by using photocatalyst made of zinc sulfide, iron oxide and cadmium oxide [101]. Further studies on the effect of various parameters on the degradation efficiency were also carried out.
## 4 Design of Proposed Scheme: FAPR
### Algorithms and Formulation
#### 4.5.1 Broadcast Storm Suppression Algorithm  
Broadcast storm refers to a situation in which a broadcast message in the network is repeatedly received and forwarded, and circulates for a long time, leading to network congestion, increased delay, and performance degradation. In SDN, many broadcast messages exist in the network, and SDN switches cannot handle this type of message due to a lack of intelligence. As a result, suppressing broadcast storms is a very important task in SDN, and can reduce the disorderly spread of broadcast messages in the network, improving network performance and stability.  
In the FAPR scheme, we have designed a relatively simple but efficient broadcast storm suppression algorithm. The proposed algorithm does not require any extra man-made interference and does not disrupt the transmission of data packets in the network, which maximize the application value of the algorithm in real network scenarios. The complete algorithm is shown in Algorithm 1. The input to the algorithm is an ARP packet, \(p_{f}\), of a flow, and the output is a signal indicating whether to normally forward this packet. We use the following four tuples, including source and destination MAC addresses, destination IP address, and ingress port, to determine whether the current ARP packet is duplicate or not. If so, we guide the switch to drop the same packet, otherwise we forward it normally. Since we need to traverse a nested dictionary, the time complexity of Algorithm 1 is \(O(n^{2})\).  
```
0: an ARP packet \(p_{f}\);
0: a signal indicating whether to forward \(p_{f}\);
1: Get the ARP type of \(p_{f}\), denote it as \(T_{p}\);
2: Get the source mac, destination mac, destination ip and input port of \(p_{f}\), denote it as mac_src, mac_dst, ip_dst and in_port;
3:if\(T_{p}\) is ARP request and mac_dst is broadcast address then
4:if mac_src is in record then
5:if ip_dst is in record [mac_src] then
6:if value in record [mac_src, ip_dst] is the same as in_port then
7: Add a rule to drop packets that are the same as \(p_{f}\), and drop \(p_{f}\);
8:else
9: Forward;
10:endif
11:else
12: Add ip_dst and in_port to the record [mac_src], then forward;
13:endif
14:else
15: Add the key-value pair of mac_src and ip_dst to in_port as a new record;
16: Then forward;
17:endif
18:else
19: Forward;
20:endif
```  
**Algorithm 1** Broadcast storm suppression algorithm
# Smart Home Technology Solutions for Cardiovascular Diseases: A Systematic Review
## 2 Materials and Methods
### Study Selection Criteria  
Technological innovations progressively support the use of commercial smart wearable devices to monitor cardiovascular health remotely in real time; however, to date, challenges such as device accuracy, clinical validity, a lack of standardised regulatory policies and patient privacy concerns hinder the widespread adoption in clinical practice [32]. In addition, poor quality medical devices negatively affect CVD monitoring and management [33]. Hence, we have excluded studies undertaken using wearable devices for CVD monitoring and management in home settings and have considered studies using medical-grade CVD monitoring devices. On the other hand, cloud computing and machine learning (ML) could assist in monitoring the health status of heart patients [34]. The adoption of cloud computing in healthcare could improve healthcare delivery quality and reduce the economic burden, enabling governments to address healthcare challenges quickly [35]. A healthcare cloud architecture comprises ambient and wearable sensors capturing physiological data that are compressed and transferred to the cloud for further analytics, triggering alerts to healthcare and care providers according to the anomalies in the recordings [36]. However, since the cloud adoption rate is slow, governments could consider converting client-server models to web-based applications, quickening the cloud computing adoption rate [35] to reap the technical benefits of cloud computing. Hence, we have considered studies using clinically accepted devices integrated to function as client-server, web-based or cloud applications. Among CVD patients, reviews have been undertaken to evaluate the effectiveness of telemedicine [37; 38] and telemonitoring [39; 40; 41; 42]. Hence, we have not considered such studies. Additionally, we used a set of selection criteria as mentioned in Table 1 to narrow down the selection of articles that would fulfil the main objectives of the systematic review. Accordingly, Figure 1 illustrates an SHT for CVD management and vital signs monitoring. Vital recordings such as HR, BP, ECG, steps walked, sleep, body temperature, and body weight are captured from the individual and transferred to the server for processing. After processing the information in the server, the data are transferred to healthcare providers for analysis and to initiate necessary care and intervention. Once the healthcare providers initiate alerts and interventions, the patients receive necessary notifications, including alerts and interventions to upkeep their health and well-being. Based on the selection criteria and the defined architecture of SHT, we have conducted this review to be unique and add knowledge by providing relevant future directions for CVD monitoring.
# Dynamic Similitude Design Method of the Distorted Model on Variable Thickness Cantilever Plates
## 3 Similitude Design of the Variable Thickness (VT) Cantilever Plate  
### Simplification of the VT Plate  
The cross section of a VT plate with the length and the width of \(a\) and \(b\), respectively, is shown in Figure 2, where \(h(x)\) is the thickness. A Clamped-Free (C-F) boundary condition is satisfied in the present study, the edge \(A^{*}\) in Figure 2 is clamped and the edge \(B^{*}\) is free.  
Figure 2: The cantilever variable thickness plate. (**a**) parameters of the plate; (**b**) the cross section of the plate.  
It is obvious that a VT plate can be simplified as a Stepped Thickness (ST) plate with \(q\) steps, where the VT plate and the ST plate are equivalent when \(q\rightarrow+\infty\), as shown in Figure 3 The thickness of each step is  
\[h_{si}=2H_{si}+h(a)\,(i=1,\,2,\cdots,q), \tag{9}\]  
where \(H_{si}=\left[2\,(q-i)+1\right]\dfrac{h(0)-h(a)}{2q}\).  
For example, consider the VT plate made of 42 CrMo, whose geometric and material parameters are shown in Table 1, the previous six orders' natural frequencies, including the flexural vibration (F), the torsional vibration (T) and the chordwise bending vibration (EB), are compared in Table 3 with the simplified ST plate of \(q=5\), whose geometric and material parameters are shown in Table 2.  
The relative simplification error \(\eta_{\text{s}}\) between the VT plate and the ST plate is defined as  
\[\eta_{\text{s}}=\dfrac{\left|\omega_{\text{s}}-\omega_{\text{v}}\right|}{ \omega_{\text{v}}}\times 100\%. \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Length** & **Width** & **Max Thickness** & **Min Thickness** & **Young’s Modulus** & **Density** & **Poisson’s Ratio** \\
**a/mm** & **b/mm** & \(h(0)\)**/mm** & \(h(a)\)**/mm** & **E/Pa** & \(\rho\)**/(kg/m\({}^{3}\))** & \(\mu\) \\ \hline
125 & 90 & 6 & 2 & \(7\times 10^{10}\) & 2770 & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the variable thickness (VT) plate.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Order \(i_{\text{v}}\)** & **VT Plate** & **VT Plate** & **ST Plate** & **ST plate** & **Errors** \\
**Vibration Model** & \(\omega_{\text{v}}\)**/Hz** & **Vibration Model** & \(\omega_{\text{v}}\)**/Hz** & \(\eta_{\text{s}}\)**/Hz** \\ \hline
1F & & 366.93 & & 357.25 & 2.64 \\
1T & & 842.42 & & 830.95 & 1.36 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between the VT and the ST plates.  
Figure 3: Rectangular elements that approach the variable thickness.  
Table 3 indicates that the simplified ST plate has the same vibration modal as the corresponding VT plate. The natural frequency results show small relative errors between the two types of plates as \(\eta_{\text{s}}\leq 5\%\), especially in the torsional vibration and the chordwise bending vibration, indicating that the equivalent method is applicable.
# Self-Gated Respiratory Motion Rejection for Optoacoustic Tomography
## 2 Materials and Methods
### Image Reconstruction and Processing  
In both scanning systems, the acquired signals were band-pass filtered (cut-off frequencies 0.25-6 MHz for SVOT and 0.5-8 MHz for cross-sectional OAT) and deconvolved with the impulse response of the array elements before reconstruction. For SVOT, tomographic reconstructions of single volumes (\(15\times 15\times 15\) mm\({}^{3}\)) for each scanning position of the spherical array transducer were done using a 3D back-projection-based algorithm [34; 35]. Volumetric images reconstructed at every transducer position were stitched together to render images from a larger field of view (whole-body scale). For cross-sectional OAT, the same back-projection algorithm was modified to account for the heterogeneous distribution of the speed of sound in the mouse versus the coupling medium (water) [36]. For this, an initial image was first reconstructed by considering a uniform speed of sound corresponding to the speed of sound in water (determined from the measured water temperature). The animal's surface was then manually segmented, and the reconstruction was fine-tuned by assigning a different speed of sound to the segmented tissue volume in order to optimize image quality. The processing was executed with a self-developed MATLAB code. Universal image quality index (QI) was calculated for the resulting images. QI is an objective image quality index that combines three models: loss of correlation, luminance distortion and contrast distortion--a detailed description and efficient MATLAB implementation was reported in [37].
## 2 Introduction  
Data Compression is crucial for the transmission and storage of digital data. It is possible to improve the compression performance when we can use previous knowledge of the emitting source in the compression process. In this case in the fundamental source coding theorem we can substitute entropy with conditional entropy and we have a new theoretical limit that allows for better compression.  
When data compression is used for data transmission we might assume some degree of interaction between the compressor and the decompressor that allows a more efficient usage of the previous knowledge of the source they both might have. The price we accept to pay, in some cases, is a negligible probability of errors.  
In this paper we review recent work that applies previous knowledge of the emitting source to data compression and interactive approaches and discuss these possibilities. We will concentrate on lossless, dictionary based, compression algorithms.  
In the next section we review relevant concepts about the theoretical limits of data compression and about the dictionary based compression methods. Section 3 discusses how to use the previous knowledge of the source when the shared knowledge is represented by common, identical files that both sides have available but there is no possibility of interaction between the Sender and the Receiver that need to communicate. Section 4 reviews the situation in which both Sender and Receiver have knowledge of the source, but do not share necessarily the same, identical, files. In this case, by taking advantage of the interaction between the two communication sides, Sender and Receiver can improve the compression process by using the shared knowledge if they accept a small possibility of communication errors. Section 5 presents our conclusions and points to new research directions.
## 6 Discussion
### Benefits
#### 6.1.3 High Security and Privacy  
Sixth-Generation (6G) networks embrace AI and machine learning for service delivery, so they inherit properties from the KDN paradigm. Due to the massive amount of data, which results in enormous processing and analysis using ML for knowledge generation, these networks are vulnerable to contamination by attackers. Recent works have demon strated how KDNs, such as 6G networks, can leverage blockchain technology integrated with AI to improve the security concerns existing in intelligent networks [390]. As centralized machine learning is vulnerable to attacks, decentralized machine learning with the aid of blockchain technology has improved security in contrast to the centralized approach, as the sole spot of attack is prevented using the distributed approach. Furthermore, sensitive data on the blockchain can be cryptographically encrypted, and access control can be implemented with the aid of smart contracts to prevent unauthorized people from accessing sensitive data. Thus, blockchain secures the privacy of stored data by partitioning data based on identity and access control rights. Furthermore, blockchain prevents malicious nodes from contributing to the data pool thanks to consensus approaches such as voting-based consensus, where consensus from the majority of the nodes is required to validate transactions. As blockchain transactions are traceable, malicious data-tampering attempts can be readily identified and given negative rewards or isolated to protect the intelligent network. Blockchains reduce privacy exposure by utilizing pseudonymous addresses for nodes; however, they cannot guarantee full privacy since blockchain transactions are traceable. Zero-knowledge proofs can be incorporated into the blockchain for privacy-preserving data and machine learning model transfer, and anonymous authentication. In addition, the traceability of transactions enables KDN systems to trace the origin of the data and knowledge as well as confirm the validity and immutability of the data.
# STN-Homography: Direct Estimation of Homography Parameters for Image Pairs
## 4 Hierarchical STN-Homography
### Time Consumption and Predicted Results  
We used TensorFlow [29] to implement our proposed hierarchical STN-homography model. During the test time, we achieved an average processing time of 4.87 ms for a single STN-Homography model on a GPU. When the same STN-Homography model is used at each stage of the hierarchical method, the overall computational complexity can be expressed by  
\[d_{e}=(l_{m}+l_{w})\times n \tag{4}\]  
where \(d_{e}\) is the end-to-end delay of the entire hierarchical model, \(l_{m}\) is the average latency for each STN-Homography model, \(l_{w}\) is the overhead of warping used to generate a new image pair, and \(n\) is the number of the stages used in the framework.  
Table 2 shows the time consumption of our hierarchical STN-Homography model on a GPU. It can be observed that our three-stage hierarchical STN-Homography model results in an average processing time of 17.85 ms on a GPU. The real-time processing speed satisfies the requirements of most potential applications.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Model Name** & **Time Consumption on a GPU [ms]** \\ \hline One-stage hierarchical STN-Homography & 4.87 \\ Two-stage hierarchical STN-Homography & 11.46 \\ Three-stage hierarchical STN-Homography & 17.85 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time consumption of our hierarchical STN-homography model.  
Figure 4: Hierarchical STN-Homography architecture.  
Figure 5 shows the predicted results of our three-stage hierarchical STN-homography model in some test samples. The green boxes in image_a and image_b represent the corresponding points for the ground truth, and the red boxes in image_a are transformed from the green boxes in image_b with the predicted homography matrix of image_a and image_b. As it can be observed from the figure, our model will achieve very small mean corner errors in stage 3.
# Determination of Hydrogen's Thermophysical Properties Using a Statistical Thermodynamic Method
## 2 Theoretical Model and Implementation
### Implementation of Different Methods for Hydrogen's Thermophysical Properties  
Based on the model in Section 2.1, statistical thermodynamic calculation can be implemented by developing MATLAB programs for determining hydrogen's thermophysical properties. For example, a function file can be compiled based on Equations (6)-(10) in thecompiler of MATLAB to obtain para-H\({}_{2}\) enthalpy at a certain pressure and temperature. The pressure and temperature are the two independent variables of the function, and para-H\({}_{2}\) enthalpy, the required thermophysical property, is set as the dependent variable. After the function compilation is finished, the function file should be saved. As long as the function name and the values of the independent variables are given, the function can be called in a main program by assigning the function value to a variable representing the required property. After the main program runs, the variable can be output and saved in the workspace of MATLAB.  
Reading REFPROP data is another common method for determining thermophysical properties by calling the function \"refpropm\" in a MATLAB program. The parameters of the function include the required property, temperature, pressure and the fluid type. They are sequenced from left to right after \"refpropm\". The value of \"refpropm\" is determined by the given parameters in REFPROP, and it will be returned as a variable representing the property after the program runs. It is worth noting that the interaction between MATLAB and REFPROP may slow down the running speed of the computational program.  
Spline interpolation is also employed for determining hydrogen properties. The process of spline interpolation is divided into two steps: (1) acquiring property data from REFPROP in 1 K increments and importing them to the MATLAB workspace before the program runs; (2) calling the function \"interp1\" in the main program to accomplish spline interpolation, and choosing the fourth parameter of \"interp1\" as the \"spline\". The procedures for determining hydrogen properties using the aforementioned three methods are shown in Figure 1.  
Figure 1: Procedures for determining hydrogen properties using different methods.  
Integrating isobaric specific heat (\(c_{\rm p}\)) with temperature is feasible when para-H\({}_{2}\) enthalpy differences between two temperatures are calculated. \(c_{\rm p}\) is obtained via polynomial fitting of REFPROP data, which are given in 1 K increments from 20 K to 300 K.
# Lymphedema Surgical Treatment Using BioBridgeTM: A Preliminary Experience
###### Abstract  
Lymphedema treatment has traditionally been based on conservative therapies. In recent years, the introduction of two microsurgical techniques--venolymphatic anastomoses and lymphnode transplantation--have revolutionized the therapeutic approach to this disease. However, these surgical techniques only provide a partial volume reduction, and thus, they require further improvement. A new device called BioBridgeTM, an implantable biocompatible and biodegradable mesh, made up of aligned nanofibrillar collagen scaffolds that mimic the collagen matrix, could significantly improve the benefits of these surgical strategies, favoring lymphatic regeneration. In this article, we describe our experience with three patients affected by primary lymphedema, two treated with a lymph-node transplant, and one with venolymphatic anastomosis. All interventions involved the placement of nanocollagen scaffolds. With each patient, non-contrast magnetic resonance lymphography was performed for surgical planning and there was an imaging and a clinical follow-up at 1 year. All patients showed a significant reduction in the affected areas with the demonstration of new lymphatic vessels at MR imaging follow-up. This was a preliminary experience, and it showed the potential benefit of positioning nonfibrillar scaffolds in combination with other microsurgical therapies for lymphedema.  
BioBridgeTM; lymphedema; lymphedema treatment; venolymphatic anastomosis; non-contrast magnetic resonance lymphography