esa-sceva/satcom-chunk-collection huggingface dataset:
# A Robust Mutual Authentication with a Key Agreement Scheme for Session Initiation Protocol
## 6 Formal Verification  
ProVerif is an automatic cryptographic protocol verifier, which is widely used to specify and analyze the security of authenticated key agreement protocols [23; 24; 25; 26; 27].  
In this section, we used the automatic cryptographic protocol tool ProVerif to show that our proposed scheme is secure. ProVerif was used because it can implement one-way hash function, symmetric and asymmetric encryption, digital signatures, etc. Moreover, various attacks can be reconstructed by ProVerif. The code of the scheme is illustrated below.  
There were two types of channels, the private channel for transmitting sensitive messages and the public channel for transmitting general messages. The declarations of variables, functions, keys, and other related parameters are shown in Figure 5. The processes performed by the client and server are presented in Figures 6 and 7, respectively. The main process is shown in Figure 8.  
Figure 5: The declarations of variables, functions, keys, and other related parameters.  
Figure 6: The client’s process.  
Figure 7: The server’s process.  
The results of our proposed scheme are presented in Figure 9. It can be concluded from these results that the session key is out of an attacker's reach.
## 5 Results and Discussion
### Effect of Baffle Structure on Particle Concentration Distribution  
In order to explore the uniformity of particle distribution in the flow field, Figure 17 presents the contour plot of particle volume fraction before and after the baffle modification. The sine baffles increased the solid volume fraction in the annular flow region between the impeller and the baffles. The homogenization of solid phase distribution was improved when \(A/W=0.333\), \(\lambda/\hbar=0.104\) and \(A/W=0.333\), \(\lambda/\hbar=0.028\), and the latter was more perceptible. However, the fluid tended to move out of the wall, and the particle concentration in the blade gap decreased because of the slotted baffle. Additionally, compared with the standard baffle, the particle uniformity behind the baffle was reduced when \(A/W=0.333\), \(\lambda/\hbar=0.104\) (0.25 h). Since the sine baffle was still installed vertically on the wall, compared with before modification, the intensification of the sawtooth baffle in the axial direction was not clear, and only the change of the particle flow direction caused by the baffle slot was shown (0.6 h).  
Figure 18 depicts the distribution of solid particle concentration in the radial direction of the modified baffle, with the curve showing two peaks. From r/R = 0.4 to r/R = 0.6, the solid particle concentration was higher due to being in the watershed where the impeller blades directly operated. The reason for the peak value at r/R = 0.8\(\sim\)1.0 is that in the typical flow field of the axial-flow impeller, the liquid flowed out of the impeller tip and contacted the wall of tank for diversion, and a large-scale main circulation vortex and inverted conical circulation induction zone were formed at the circumferential direction and bottom of the impeller, respectively (as shown in Figure 6). The annular vortex rolled up the particles and moved upward along the wall of tank, and the sawtooth structure strengthened the degree of turbulence at the baffle, making the peak more obvious. Figure 18 shows that the sinusoidal sawtooth baffle had the beneficial effect of increasing the particle concentration, and between r/R = 0.5\(\sim\)0.8, the volume fraction had a higher level due to the larger fluid velocity after the baffle was cut. When the relative tooth height (\(A/W\)) increased and the relative tooth width (\(\lambda/\hbar\)) decreased, the particle volume fraction increased accordingly. Further, the relative tooth width (\(\lambda/\hbar\)) was more prominent than the relative tooth height (\(A/W\)).  
Figure 16: The effect of relative tooth width on local radial turbulence dynamics (\(A/W=0.333\)). (**a**) The turbulent kinetic energy; (**b**) the turbulent dissipation rate.  
Figure 17: The particle volume fraction comparison before and after baffle modification. (**a**) The standard baffle; (**b**) \(A/W=0.333\), \(\lambda/h=0.104\); and (**c**) \(A/W=0.333\), \(\lambda/h=0.028\).  
Figure 18: The particle concentration distribution in the radial direction. (**a**) The effect of relative tooth height (\(A/W\)); (**b**) the effect of relative tooth width (\(\lambda/h\)).
# Systematic Literature Review of Challenges in Blockchain Scalability
## 8 RQ3: How Researchers Address the Scalability Issue in Blockchain?
### On-Chain Solution
#### 8.1.3 Sharding  
Sharding was traditionally proposed in the field of database for storage optimization in commercial databases. It was later adopted in public blockchains to address the scalability issue. It is considered to be one of the effective methods. In the sharding technique, the nodes are broken into several chunks called shards [121]. Each shard possesses a small part of nodes. Every shard is responsible for processing small portions of a transaction. Therefore, the transaction is processed in parallel. The parallel processing of the transaction improves the authentication mechanism that ultimately maximizes the throughput of the entire blockchain network. The Byzantine consensus algorithm is used in between the nodes within shards to agree on the state of the transaction [25]. There is an immense need for the inter-shard communication protocol for cross-shard transaction. The total computation energy increases with the increasing number of shards. In this technique, every shard process transaction with the same throughput, and by increasing the number of shards, results in a linear increase in throughput. Elastico [122] and OmniLedger [123] implemented the sharding technique to increase the throughput. The only difference between them is that Elastico is unable to process inter-shared transactions while OmniLedger processes them atomically using an atomic Commit Protocol. Another approach utilizing sharding is the rapid chain [79].  
Sharding technique is the only viable solution if transaction stays in the same shard, which is considered to be the most significant limitation. As a matter of fact, inter-shard transaction cannot be automatically executed by Elastico. OmniLedger supports intershard transactions but high shard size is hard to select. However, large shards are not scalable because whole network executes the Byzantine consensus, and small shards are not scalable because they would result in a huge number of shards. Furthermore, small shards are more vulnerable to security risk. Each shard is fault-tolerant, up to, at the most, a third of the shard's size. Moreover, the shard size would decrease if the number of malicious nodes remained constant, making the shard highly likely to fail.
# Phtyoremediation and Bioremediation of Pesticide-Contaminated Soil
## 8 Implementing and Monitoring Phytoremediation  
There are some inexpensive approaches to implementing and monitoring phytoremediation. The initial starting point may be pesticide-contaminated soil where there is knowledge of a spill or where plant growth is poor or plants do not survive because of pesticides. Soil amendments such as compost, manure, and plant residues (such as leaves) can be added to the contaminated soil to add microorganisms, nutrients, and organic carbon to obtain an amended soil with about 5% to 10% amendments. One can blend this amended soil into clean productive soil to obtain a 50/50 soil and a 10/90 soil with 10% of the contaminated soil to reduce toxicity. The different soils can be placed in pots and plants can be transplanted so there is one plant in each pot. The experiment may include (1) The original polluted soil, (2) The original soil with amendments, (3) The 50/50 soil with amendments, and (4) The 10/90 soil with 10% polluted soil with amendments. With three replicates of each soil mixture, there would be 12 pots with one plant transplanted into each pot. These pots can be watered regularly and managed in an environment with good natural light and appropriate temperature.  
The fate of the vegetation can be monitored with respect to plant survival and plant growth. The height and mass of the plants can be measured and recorded. There is value in measuring the concentrations of nitrogen, phosphorus, organic carbon, and microbial numbers in the original polluted soil, the clean soil, and the soils with amendments. The concentration of the pesticide should be measured in the original contaminated soil and after the plant growth experiments. Since nematodes, arthropods, and earthworms are measures of soil health, experiments with soil fauna may be beneficial to understanding the fate of these organisms in pesticide contaminated soil.  
Once the initial experiments are finished, one may wish to begin the field experiment; however, the results may also suggest that further experimentation is needed. For example, experiments could be carried out with several different plants to try to find a plant that grows well in the polluted soil with amendments and without soil dilution.
# Predictive Analytics for Sucker Rod Pump Failures in Kazakhstan Oil Wells Using Machine Learning
## 3 Data
### Data Preparation  
Data preparation is a crucial step in machine learning modeling. Using High-quality data ensure model accuracy and meaningful insights. The following steps were followed in data preparation:  
1. Data collection and acquisition: gathering and verifying data accuracy and completeness.
2. Data cleaning and preprocessing: detecting and processing missing data (outliers). For example, the work considered wells operating with the help of SRPs; these wells are not equipped with telemetry sensors that record well performance indicators in real time. Therefore, some anomaly values were present, which we removed from the dataset.
3. Feature selection: choosing the most appropriate features for the problem. SHAP values provide a measure of the contribution of each feature to each prediction. After training, we analyzed SHAP values to rank features by their overall impact and select features accordingly.
4. Data spliting: dividing data into training and testing sets to evaluate model performance.
5. Handling imbalanced data: addressing imbalanced classes using various methods, as described below.  
The above data preparation stages were followed to develop a correct model for predicting downhole pumping equipment failures in the pilot field.
# A Variational Neural Network Based on Algorithm Unfolding for Image Blind Deblurring
## 4 Experiments and Conclusions
A notable strength of the proposed model is its capacity to significantly reduce the ringing effect, a common artifact associated with image deblurring. The reduction of this effect contributes to a more realistic and smooth appearance in the restored images. Moreover, the proposed model excels in preserving and maximally restoring the ideal edge structures of the images.
## 2 Accuracy of Predictive Methods and What Is Required  
### On the Assessment of the Quality of the Methods Evaluating Physicochemical Properties  
When we develop or validate predictive methods, we need a way to judge about the quality of a method. Often the accuracy of methods is quantified by quantities such as the quality of fit, for example R\({}^{2}\) as the proportion of variability in a data set that is accounted for by a statistical method, \(0\leq\) R\({}^{2}\leq 1\), where for R\({}^{2}\) =1 we have perfect correlation. There are other quantities that may be preferred or that provide additional information on the quality of the method but the discussion below actually applies to most of these. As often used in statistical analysis, quantities like R\({}^{2}\) can be a good means to assess the quality of one method but depending on the application this may be a rather meaningless quantity as we will see further below. In many references, as is common in statistics, R\({}^{2}\) values in the range 0.6-0.8 are considered a good correlation. A typical interpretation for different fields of application is given in the table (Table 2) below.  
A correlation greater than 0.8 is generally described as _strong_, whereas a correlation less than 0.5 is generally described as _weak_. A study in chemistry reporting R\({}^{2}\)=0.85 could therefore be considered as state-of-the-art.  
Despite what on average is considered 'good' in the different disciplines, the absolute quality of prediction is of course worse with decreasing R\({}^{2}\) values. Is not a method with R\({}^{2}\) = 0.6 far from sufficient when we look at what this means in a practical sense? And would a true human expert not make an equally good prediction without the use of methods but based on experience? (see also Table 3 further below for an explicit example). Another important factor is whether (i) the predictions should be quantitatively correct over the entire numerical data range or (ii) it is only of interest to see whether values fall within in a certain domain. We will elaborate on this in the following two sub-sections.
# Seismic and Geodetic Crustal Moment-Rates Comparison: New Insights on the Seismic Hazard of Egypt
## 2 Tectonic Setting  
The primary tectonic features of the surrounding plate boundaries of Egypt were studied and discussed by several researchers [22; 23; 24; 25; 26]. The Nubian-Eurasian plate margin in the north, and the Gulf of Suez-Red Sea plate margin, and the Gulf of Aqaba-Dead Sea Transform Fault system in the east represent the three main tectonic features in the vicinity of Egypt (Figure 1). On one hand, based on the analysis of fault systems, oceanic spreading, and earthquake slip vectors, the global kinematic models by [PERSON] et al. [27] show that across a wide zone in the Mediterranean Sea, both the Nubian and Eurasian plates are converging. The Nubian plate is moving in a northerly direction relative to the Eurasian plate at a rate of about 10 mm/year [27]. This form of closeness is translated in the form of the Cyprian and the Hellenic Arcs along the Eastern Mediterranean region (Figure 1). On the other hand, the Arabian plate is continuing to rotate away, in a northeastward divergent movement, from the Nubian plate along the Gulf of Suez-Red Sea Rift [28; 29; 30] (Figure 1). This rift is considered one of the premier examples of the creation of a new ocean and ongoing rifting. This rift system may extend further towards the north beneath the Suez Canal area, but this possible extension is probably masked by the alluvial Nile deposits [31]. Finally, the differential motion between the Nubian and the Arabian plates (about 15 mm/year) is thought to be taken up by the Gulf of Aqaba-Dead Sea Transform Fault System [32]. This is a seismically active 1100 km long left-lateral strike-slip transform boundary (Figure 1) that connects the northern Mediterranean triple junction in the north to the Red Sea spreading center in the south [33]. Geological evidence suggests that a pure strike-slip motion with slip rates between 5 and 10 mm/yr occurs along this transform fault [34; 35; 36]. This left-lateral sense of motion was recognized by the minor pull-apart in young sediments, cut and offset of man-made structures, and drainage lines [37; 38; 39; 40; 41; 42].
# Experimental Evaluation of Unicast and Multicast CoAP Group Communication
## 2 Group Communication  
In this section, we provide the motivation for this work. We start by briefly showing the importance of group communication in various IoT application domains, followed by an introduction to CoAP-based group communication. Finally, we introduce our hybrid group communication, which is the new contribution of this paper.
###### Abstract  
Key distribution is a growing concern for symmetric cryptography. Most of the current key-distribution mechanisms assume the use of the Internet and WAN networks, which are exposed to security hazards. To overcome them, the use of comprehensive and robust cryptographic mechanisms such as Diffie-Hellman (DH) and RSA (Rivest-Shamir-Adleman) algorithms are proposed. These solutions are limited. DH and RSA have been under threat since the introduction of quantum computing. Hence, new ideas are required. This paper introduces a new security approach for a safe key transmission using undetected high-speed optical camera communication (OCC), which is based on the rolling shutter effect of modern smart cameras. The key transfer is done by blinking light-emitting diode (LED) lights in a specific sequence and frequency, following an encoding pattern. The receiver decodes the received blinks to a bit string using a corresponding image processing application. This optical media ensures secure transfer without the ability to quote it. We demonstrate in an experiment the feasibility of using basic wireless optical communication for key transmission, eliminating the need for a permanent, long, and costly setup. It is mobile, available everywhere anytime, and requires only simple connections to operate. The results show that this method is feasible, robust, efficient, and implementable.  
encryption key; secured key distribution; optical communications; symmetric and asymmetric encryption +
Footnote †: journal:
## 6 Application of Plasma Treatment in Advanced and Emerging Packages
### Through-Silicon Vias (TSVs)  
TSVs are vertical interconnects that pass through a silicon die, enabling 3D stacking of chips. This technology is an alternative to other bonding techniques, such as wire bonding or flip-chip technologies. TSVs are used to create high interconnection and device density. The literature has shown evidence of plasma being used for various roles, such as the removal of the fluoropolymer and the photoresist residues. This type of bulk material removal is beyond the scope of this paper, since the focus is on cleaning. However, plasmas are also used for the cleaning of the inner surfaces of the TSVs to ensure good adhesion and electrical contact with subsequent layers.  
For cleaning the inner surfaces of TSVs, the reviewed literature indicates that three main types of plasma gases are typically used. Oxygen plasma primarily removes organic contaminants (such as polymer or photoresist residues) through chemical reactions. Argon plasma, working via physical sputtering, is used to remove both organic and inorganic contaminants, including thin native oxides. Hydrogen, on the other hand, chemically reduces and removes metal oxide contaminants and can also address some organic contamination. The use of gas mixtures incorporating these gases is also frequently reported in the literature [146; 147].
# LSANet: Lightweight Super Resolution via Large Separable Kernel Attention for Edge Remote Sensing
## 2 Related Works  
### Attention Mechanism  
The attention mechanism has gained widespread adoption in deep learning tasks owing to its exceptional capability to capture long-range contextual information, emerging as a pivotal component in computer vision [16; 17]. The squeeze-and-excitation (SE) network [18] pioneered channel attention by modeling inter-channel dependencies to recalibrate feature maps. Building on this, CBAM [19] integrated spatial and channel attention modules to further optimize feature representation. Non-local neural networks [20] introduced global feature learning, enabling more effective modeling of long-range dependencies. In super resolution (SR), attention mechanisms have significantly enhanced reconstruction quality: RCAN [8] used residual channel attention blocks (RCAB) to refine high-frequency details, while SAN [21] leveraged second-order channel attention (SOCA) to exploit feature statistical information. SwinIR [22] adopted the Swin Transformer architecture with W-MSA to model local-global feature dependencies, and CAS-ViT [23] combined CNN and Transformer structures with cross-scale channel attention (CCSA) for efficiency-performance balance. Concurrently, efforts to reduce attention's computational cost have emerged: IMDN [10] proposed contrast-aware channel attention (CCA) to minimize complexity while focusing on key information, and RFANet [24] introduced the efficient spatial attention (ESA) module to enhance region-of-interest focus for improved SR quality. These studies highlight that designing efficient lightweight attention mechanisms remains a critical research direction in SR, with broad practical implications. While attention modules like RCAB, CCA, and ESA differ in architecture, their core mechanisms all focus on constructing global channel dependencies. [PERSON] et al. [25] proposed a remote sensing image super-resolution reconstruction model integrating multiple attention mechanisms, attempting to enhance feature interaction. However, it fails to adequately address the balance between long-range dependency modeling and lightweight deployment for remote sensing images.  
However, the existing methods often fail to balance long-range dependency modeling with lightweight deployment, which is critical for remote sensing applications constrained by limited onboard resources. To address this, we incorporate a novel attention mechanism, large separable kernel attention, which expands the receptive field by combining depthwise convolution and dilated convolution in a separable form. This design allows the network to capture fine-grained spatial details--such as road edges and building structures--while maintaining low computational cost. Integrated into an improved feature distillation framework, LSKA, together with a contrast-aware channel attention module, enables our LSANet to achieve a better trade-off between reconstruction quality and efficiency, addressing key limitations of prior attention-based SR models.
# Resource Mapping Allocation Scheme in 6G Satellite Twin Network
## 5 Serverless-Based Decentralized Simulation Development Model
### Control-management layer:  
This layer includes the functions for network architecture. The simulation network initialization, the connection between different docker containers, and the transmission of communication data and control information between docker containers are carried by this layer.  
**Service-resource layer:** This layer consists of task requirement discovery functions and resource orchestration mapping functions. The hardware resources are allocated to the functions. This layer completes the load balancing between different servers and docker containers.  
**Network ontology layer:** This layer includes the ontology functions of the 6G satellite twin network. The network topology, simulation scenarios, node models, constellations, network traffic, and protocols are simulated.  
A 6G satellite twin network is decomposed into multiple sub-simulation task sets distributed into different docker containers. Different simulation tasks are run in different docker containers. The differences in the functions that make up the simulation tasks lead to task differences between the docker containers. The consistency of the functions in the docker containers varies.