esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### The Grating Constant \(\Lambda\)  
The most straightforward method for spatially resolved analysis of the Bragg selectivity is to visualize the course of the local periodicity in the lateral plane. This aims to evaluate the information derived from lateral scans with respect to the grating constant \(\Lambda\). Information about optical shrinkage can subsequently be obtained by comparison of the final structure, _i.e._, the measured value \(\Lambda\), with the initial interference pattern, defined by the recording geometry:  
\[\Lambda_{\textrm{exp}}=\frac{\lambda}{2n\sin\!\left(\frac{\Theta}{2}\right)} \tag{1}\]  
where \(n\) is the average refractive index of the medium, \(\lambda\) the wavelength of exposure and \(\Theta\) the angle, enclosed by the two recording beams in air.  
As a first result from lateral analysis (see Figure 1), it can be demonstrated that the period is almost constant along the lateral position, or rather independent on the recording intensity, in case of short exposure duration:\[\lim_{t_{\text{exp}}\to 0}\frac{\partial\Lambda}{\partial I}=0 \tag{2}\]  
This is consistent with previous results, obtained with regard the temporal development of the relative optical shrinkage (defined as ratio of optical shrinkage to diffraction efficiency), which has proven to be time invariant in the case of short exposure duration as well [13]. However, in cases of long exposure duration a certain dependency on the lateral position and on the duration of the exposure arises, _i.e._, \(\Lambda(I,t_{\text{exp}})\
eq\text{const.}\), respectively.
## 3 Results  
### Laboratory Test Results  
Figure 4 summarizes the variation of diesel fraction content (boiling \(\leq\) 360 \({}^{\circ}\)C) in H-Oil HAGO, and LVGO for 2021 and 2022, when ASTM D 7169 is used. It is evident from these data that diesel in HAGO varies between 10.4 and 53.4 wt.%, averaging at 21.7 wt.%. Whereas the diesel in LVGO varies between 18 and 42 wt.%, averaging at 24.8 wt.%. It is clear from these data that a sufficient amount of diesel fraction remains in the H-Oil heavy distillates, which is unfavorable from an oil refining economic point of viewm and presents a strong incentive to recover the diesel fraction from H-Oil HAGO, and LVGO. However, the physical distillation data obtained by the use of ASTM D 1160 exhibits much lower content of diesel fraction in H-Oil HAGO, and LVGO as shown in Figure 5. The data in Figure 5 indicate 2.3 vol.% diesel fraction content in HAGO, according to ASTM D 1160, versus 12.4 wt.% according to the ASTM D 7169 method. The data in Figure 5 b indicate 5.0 vol.% diesel fraction content in LVGO, according to ASTM D 1160, versus 18.2 wt.% according to the ASTM D 7169 method. The ASTM D 1160 results disprove the opinion of poor diesel extraction in the H-Oil fractionator columns based on the ASTM D 7169 results. In order to validate which method provides more reliable information about the content of diesel fraction in H-Oil heavy distillates, a decision was made to perform a commercial test in which the H-Oil heavy distillates were to be processed in the FCCPT main fractionator.  
Figure 4: Variation of diesel fraction content (boiling \(\leq\) 360 \({}^{\circ}\)C) in H-Oil HAGO, and LVGO.
# Metal-Organic Frameworks Toward Electrocatalytic Applications
## 4 Recent Progress in Utilizing MOFs for Electrocatalysis and Relevant Applications
### CO\({}_{2}\) Reduction  
Via electrochemical processes, carbon dioxide can be transformed into useful fuels such as carbon monoxide, formic acid, methanol, methane, ethanol, ethylene, and ethane [5; 113]. With the help of protons to render proton-coupled multi-electron steps, the electrochemical reduction of CO\({}_{2}\) is not thermodynamically unfavorable at all, but its high kinetic barrier strongly hinders such electrochemical reactions to occur. Moreover, proton reduction that generates hydrogen gas may take place simultaneously as a side reaction. Therefore, the design of efficient electrocatalysts that can reduce the overpotential for CO\({}_{2}\) reduction as well as facilitate the selectivity against hydrogen evolution becomes crucial. Metal complexes consisting of macrocyclic or bipyridine ligands are common homogeneous electrocatalysts for the reduction of CO\({}_{2}\)[5], while metallic copper, gold, and silver are common heterogeneous electrocatalysts [114]. Among these catalysts, copper is the unique candidate that shows a proclivity to produce C\({}_{2}\) or even C\({}_{3}\) hydrocarbons from CO\({}_{2}\) electrochemically [115]. MOFs constructed from copper-based nodes have been widely applied for electrocatalytic CO\({}_{2}\) reduction [116; 117; 118; 119; 120]. In 2015, [PERSON] et al. demonstrated the use of an aluminum-based porphyrinic MOF for electrocatalytic CO\({}_{2}\) reduction [121]. With the cobalt (II) porphyrinic linkers as the catalytically active units, the MOF thin film can catalyze the reduction of proton and CO\({}_{2}\) to produce hydrogen and carbon monoxide. At the same time, [PERSON] and coworkers also reported that a Zr-MOF constructed from iron (III) porphyrinic linkers, MOF-525(Fe), can be utilized as an active electrocatalyst to produce hydrogen and carbon monoxide from CO\({}_{2}\) and a proton source [44]. As reported by [PERSON] et al., another porphyrinic Zr-MOF, PCN-222(Fe), was also found to show electrocatalytic activity to convert CO\({}_{2}\) to CO [122]. Beside porphyrinic MOFs, [PERSON] et al. also utilized thin films of a Zr-MOF (NU-1000) as the support to install metallic copper nanoparticles [123]; the resulting MOF thin films incorporated with copper nanoparticles were found to exhibit electrocatalytic activity to convert CO\({}_{2}\) to carbon monoxide and formic acid. Recently, [PERSON] and coworkers reported the ZIF-8 doped with electrocatalystally active 1,10-phenanthroline, which can show significant electrocatalytic activity to convert CO\({}_{2}\) to CO [124].
# Pointer Meter Recognition Method Based on Yolov7 and Hough Transform
## 3 Pointer Meter Recognition Method  
This paper proposes a pointer meter recognition method based on Yolov7 and Hough transform. First, use Yolov7 for object detection to locate the position of the meter and crop the image: input the cropped image into the DeepLabv3+ image segmentation model to extract the pointer area, use Thinning and Hough transform to determine the precise position of the pointer, at the same time, use PGNet in the OCR (Optical Character Recognition) method to identify the scale value and model of the pointer meter, and use the maximum value of the obtained scale value as the range of the pointer; finally, use the precise position and span of the pointer to determine the reading of the meter. The flow chart of the pointer meter identification method is shown in Figure 2. The following will introduce Yolov7, DeepLabv3+, Thining, Hough transform, PGNet, and reading.
# Empirical Evaluation of Privacy Efficiency in Blockchain Networks: Review and Open Challenges
## 3 Componential Classification of Blockchain Privacy Protecting Techniques
### Effect of Blockchain's Secondary Cryptography on Privacy
#### 3.2.1 Multi-Party Computation for Achieving Blockchain Privacy  
Multi-Party Computation (MPC), also referred to as Secure Multi-Party Computation (SMPC) is a privacy preserving cryptographic protocol. SMPC enables mutually distrusting distributed parties to jointly compute an arbitrary functionality without revelation of their own private inputs and outputs [62]. Consider a distributed environment with multiple parties \(P_{i}\) where \(\{i=1,2,\ldots n\}\) having private inputs \(x_{i}\) wishing to compute an arbitrary functionality \(f(x)\) jointly, such that \(f(x_{1},\ldots,x_{n})=y_{1},\ldots,y_{n}\). As soon as the computation completes, each party \(P_{i}\) is required to acquire its own corresponding output \(y_{1}\) without obtaining any other kind of information [63].  
The basic goal of SMPC is the construction of secure protocols that allow several mutually distrusted participants to collaborate for computation of an objective function in  
Figure 3: Structure of block.  
a joint fashion, using their own set of inputs. A study presented in [64] proposed an SMPC based solution for strengthening blockchain based privacy. In this solution, the user would store his data on the public ledger after encrypting it with his own secret key. Further, the solution exploited the features of smart contracts to enhance the security. When a user needs his private data in a smart contract, he decrypts the value using his key and uses the decrypted value as its local input to the SMPC protocol. The demonstration of the idea was presented using three parties only. Another study [65] also implemented SMPC for better privacy protection in blockchain based application. The study claims to have 66% more efficiency than existing solutions. However, since the claim was not backed up by any experiments, the authenticity of the claim is questionable.
# A Cross-Chain Solution to Connect Multiple DNS Blockchains in Consensus Roots System
###### Abstract  
The Domain Name System (DNS) is a key part of the Internet, and it is used for global domain name resolution. However, it has security risks due to its centralized or semi-centralized design and reliance on a few root servers. To improve DNS security and long-term stability, this study proposes the consensus roots system, a blockchain-based distributed domain architecture. The system uses a 1 + N master-subchain structure to solve the problem of trust and data synchronization across blockchains. The master chain acts as a relay and uses Hyperledger Fabric, a consortium blockchain platform, to support semi-centralized cross-chain communication. Subchains are local blockchains that need to connect with the master chain. To ensure safe and reliable transactions, the system uses a staged-proposal atomic swap method on the master chain. Compared to prior approaches, this work introduces a cross-chain architecture that enables more efficient trust synchronization, reducing latency and improving scalability without compromising security.  
DNS; blockchain; atomic swap; cross-chain Article
## 3 The Proposed Semantic Internet of Things Middleware
### The AI Feedback and Analysis Module: Role in the Middleware  
The AI Feedback and Analysis Module is a crucial entity embedded at the core of SIM. It employs advanced machine learning techniques, including deep learning, natural language processing, clustering and classification, and reinforcement learning to improve the middleware's functional capabilities and efficiency. A schematic representation of this module is presented in Figure 3. The module primarily focuses on  
1. Enhanced Insights Analysis for Service Integration: The AI module derives complex insights from data by utilising deep learning algorithms, which enriches the Service Integration and Composition Module's ability to manage services effectively.  
2. Semantic Annotation Suggestions: Utilising natural language processing techniques, the module discerns the context and semantics of diverse IoT devices and data, thereby suggesting more accurate and relevant semantic annotations and enhancing the data processing efficiency.  
3. Event and Feedback Analysis: Using clustering and classification techniques, the AI module comprehensively analyses system events and users' feedback, leading to the identification of patterns, trends, and potential areas for improvement.  
4. Adaptive Learning and Continuous Improvement: Through reinforcement learning, the AI module perpetually learns and adapts from its interactions and experiences. It consistently monitors and assesses system operations, user feedback, and interactions, detecting potential areas for improvement and suggesting proactive modifications to other middleware components. This keeps the middleware adaptive, optimised, and user-centric.
## 3 Mathematical Modelling and Optimization
### Response Surface Methodology (RSM)  
The used RSM in modeling and analyzing of problems consists of statistical techniques and mathematical terminology and it is developed in 1951. Especially, RSM is widely used for the formulation of problems and process optimization. Due to unknown format of actual response function, a suitable approach for true functional relationship between the response and the independent variables must be found [17]. If the response of the system is a good fit as a linear function of independent variables, it is a model of the first order.  
\[y=\beta_{0}+\beta_{1}\cdot X_{1}+\beta_{2}\cdot X_{2}+\ldots\ldots\ldots \ldots\ldots+\beta_{k}\cdot X_{k}+e \tag{11}\]A second-order model may be more appropriate if there is a curvature in the response surface.  
\[y=\beta_{0}+\sum_{j=1}^{k}\beta_{j}\cdot X_{j}+\sum_{j=1}^{k}\beta_{jj}\cdot X _{j}^{2}+\sum_{i}^{k-1}\sum_{j=1}^{k}\beta_{ij}\cdot X_{i}\cdot X_{j}+e \tag{12}\]  
where \(y\) is response variable; \(\beta_{0}\), \(\beta_{1}\), \(\beta_{2}\)... \(\beta_{k}\) are unknown regression parameters; \(X_{i}\) and \(X_{j}\) are process variables; \(e\) is error term.  
The data obtained from experiments was transferred to Box Behnken design in RSM. The highest and lowest value of process variables was defined into system and 17 test results were entered. The other 10 tests were used to determine the validity of the produced equations. Then RSM produced optimum results of experiments depending on machining parameters.
# Solid-Phase Microextraction Arrow for the Sampling of Volatile Organic Compounds in Milk Samples
## 3 Results and Discussion
### Evaluation of the SPME Arrow Procedure
#### 3.1.3 Selection of Extraction Temperature  
The effect of the extraction temperature in the SPME Arrow extraction procedure was performed by heating the sample at different temperatures (i.e., 25, 40, 50 and 60 \({}^{\circ}\)C). An increase in extraction temperature is known to release more volatiles to the headspace and enhance the overall extraction procedure. However, at high temperature, adverse effects which prevent the extraction can also take place (e.g., decrease in partition coefficients) [16]. For the SPME Arrow method, the extraction was assisted by increasing the extraction temperature value from 25 to 50 \({}^{\circ}\)C; however, further temperature increase did not result in higher extraction efficiency. Therefore, 50 \({}^{\circ}\)C was selected as the optimum extraction temperature for the SPME Arrow extraction procedure. Figure 2 shows the effect of extraction temperature in the SPME Arrow extraction procedure.
## 3 Materials and Methods
### Data Refinement  
In addition to the data set containing complete HGCAL images, given in full precision, different ways of data refinement are considered. Although the size of the images is small (5 \(\times\) 5), a high number of channels (HGCAL layers) results in a high number of pixels-features. Depending on the studied number of layers, it is 350 (for the CE-E part) or 900 for the complete HGCAL. Therefore, although the new detector structure enables analysis of a detailed collision image, the possibility of dimensionality reduction (the process of reducing the number of features) is examined. In the case where image data are observed, the number of pixels can be considered the number of features; each pixel represents one feature. Thus, the aim of the data refinement process, consisting of data selection and data quantization, is to reduce the quantity of data (i.e., data complexity) that the network needs to deal with to achieve lower inference time while retaining a similar performance level. This, in turn, is driven by the strict timing constraints of L1T.  
Figure 7: HGCAL image example, covering (**a**) complete HGCAL (**b**) just CE-E layers.  
Two main approaches in dealing with high-dimensional data are feature extraction (FE) and feature selection (FS). FE creates new features combining the existing ones, while FS identifies the relevant features and removes the redundant and irrelevant features. In [48], the authors review FE methods that reduce processing time while providing higher recognition accuracy. Although FE is the preferred approach in image processing, because of time and resource constraints, both approaches are used to scale down the dimensionality of the data without performance degradation. The experiment uses ad-hoc solutions suitable for our specific physically motivated classification problem. Considering the low computational time, two techniques are applied: filtering out the irrelevant pixel values (FS approach) and combining/summing particular layers (FE approach). Further reduction in resource consumption is achieved by data quantization. Data refinement is conducted in two steps: first, data selection, followed by data quantization. Particular techniques were chosen based on physics-based and logical-based reasoning, which is presented for each respective approach, while their applicability was determined through exhaustive testing.
## 3 Results
### Protections Operation  
Figure 11 shows the overcurrent protections in action, with a load of approximately 10 \(\Omega\), for an output voltage of about 700 V in Channel 0. The first result, Figure 11a, shows the slow protection in action for a set value of 40 A, where this value can be set from 10 A to 65 A. The time response observed is about 150 ns, due to the current measurement loop from the current transformer to the main control board, which switches off all the stage in MM0. Instead, the fast protection (i.e., 10 ns to 20 ns response), switches off the individual stages that sense this current limit, as shown in Figure 11b.  
The value of the output overcurrent protection can be set via software and the protection can be turned on-off.  
Figure 11: (**a**) Slow overcurrent protection, operating at 40 A. (**b**) Fast overcurrent protection, operating at 65 A. (blue) output voltage, 100 V/div; (cyan) output current, 10 A/div; 100 ns/div.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**\# MM0** & **Pause (ns)** & **Pulse (ns)** & **Mask** & **Stages on** \\ \hline
1 & 250 & 250 & 510 & 8 \\
2 & 250 & 250 & 126 & 6 \\
3 & 250 & 250 & 30 & 4 \\
4 & 250 & 250 & 6 & 2 \\ \hline
**\# MM1** & **Pause (ns)** & **Pulse (ns)** & **Mask** & **Stages on** \\ \hline
1 & 500 & 250 & 254 & 7 \\
2 & 250 & 250 & 62 & 5 \\
3 & 250 & 250 & 14 & 3 \\
4 & 250 & 250 & 2 & 1 \\ \hline
**\# MM2** & **Pause (ns)** & **Pulse (ns)** & **Mask** & **Stages on** \\ \hline
1 & 750 & 250 & 510 & 8 \\
2 & 250 & 250 & 126 & 6 \\
3 & 250 & 250 & 30 & 4 \\
4 & 250 & 250 & 6 & 2 \\ \hline
**\# MM3** & **Pause (ns)** & **Pulse (ns)** & **Mask** & **Stages on** \\ \hline
1 & 1000 & 250 & 510 & 7 \\
2 & 250 & 250 & 126 & 5 \\
3 & 250 & 250 & 30 & 3 \\
4 & 250 & 250 & 6 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Multi-channel command line for the pulses sequence shown in Figure 10.
## 2 Original SIFT Algorithm
### Description of Key Points  
Three pieces of information were obtained from the above: position, scale, and orientation. A descriptor was then established for each key point, and a set of vectors was used to describe the key point so that it did not change because of changes in lighting or perspective. This descriptor included not only the key points, but also the pixels around the key points that affected it. At the same time, the descriptor had to have uniqueness to improve the probability of correctly matching of feature points. The generation of keypoint descriptors is shown in Figure 2. The red dots in Figure 2 represent feature points. First, a square pixel area of a \(16\times 16\) grid was selected around the feature points; second, the \(4\times 4\) grid was divided; finally, the cumulative gradient values of 8 directions (one direction every 45 degrees) were calculated in each sub-region, and each feature point generated a \(4\times 4\times 8\) (128) dimensional feature descriptor.  
As above, the \(4\times 4\times 8=128\) gradient information was the feature vector of the key point. To remove the influence of illumination changes after the eigenvectors were formed, they had to be normalized. The normalized eigenvectors are shown in Formula (8):  
\[w_{i}=\frac{f_{i}}{\sqrt{\sum\limits_{j=1}^{128}f_{j}}} \tag{8}\]  
where \(w_{i}\) is the normalized vector; \(f_{i}\) is the original feature vector; and \(j=1,2,3\dots\). Larger gradient values were truncated by setting the threshold value. After that, a normalization process was performed to improve the discrimination of the features.  
Figure 2: Depiction of the 128-dimensional feature descriptor.
## 3 Proposed Status-Byte-Assisted RDMA Transmission Mechanism
### Multi-Device Shared QP Strategy  
In the proposed status-byte-assisted RDMA transmission mechanism, the system scalability is improved by introducing device numbers and packet numbers to manage the memory blocks. Figure 9 provides a comparison of data transmission methods for different transmission mechanisms in the multitasking scenarios. As shown in Figure 9, the existing sliding window RDMA transmission techniques struggle to support multi-threaded sending and receiving on both ends when transmitting multiple data streams from multiple devices. The sliding window approach establishes multiple RDMA connections, which significantly consumes the on-chip cache resources of the RDMA NIC. When the number of devices is large, low-speed PCIe channels may be required to retrieve connection status from main memory, which will lead to performance bottlenecks. Additionally, due to the uncertainty in the sending times of data blocks, multiple memory blocks may be sent simultaneously or not at all, resulting in significant fluctuations in the system's instantaneous throughput and low bandwidth utilization.  
To address this issue, as shown in Figure 9, the proposed status-byte-assisted RDMA transmission technology enables multiple tasks to share a single RNIC QP, in contrast to existing multi-task RDMA transmission methods where each task uses a separate QP connection. Although the multi-task QP sharing can lead to parallel access to QP resources and result in resource contention, the status-byte-assisted RDMA transmission mechanism serializes the process of filling device data into the RNIC QP resource. During data filling, the filling thread adds device numbers and packet numbers to the memory blocks. The receiving end identifies data from different devices using device numbers and arranges the data in sequence based on the packet number to ensure data reliability.  
Moreover, by utilizing container mounting technology to share transmitted data among containers for processing, a single-edge server can simultaneously control multiple devices. Additionally, K8s is employed to manage these containers, which automates the deployment, scaling, and operations. This approach addresses the issue of low resource utilization and poor scalability typically encountered with traditional RDMA technologies when transmitting data streams from multiple devices.  
Figure 9: Comparison of data transmission methods for different transmission mechanisms facing multitasking.