esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Technique for High-Efficiency Characterization of Complex Cracks with Visual Artifacts
## 4 Results
### Performance of PPU  
Accuracy: These binary images were further analyzed to estimate crack width, crack number, and crack length using the PPU. To evaluate the performance of these computed characteristics, the original images were also analyzed manually to compute these crack characteristics. An example of the results of crack features measured during testing is shown in Figure 8, the PPU shows that the relative error is 0.4%, 1.2%, and 5.6% for crack number, length, and width, respectively when compared to those computed manually.  
Efficiency: To understand the impact of the proposed technique, the total time it takes to estimate various crack parameters by (a) analyzing the image through our technology and (b) analyzing images with the current state of the art (i.e., with crack scope) was monitored. Table 4 reports our findings for a typical 20 MP image. It is observed that the proposed method significantly (~90%) reduces the time required for computing various crack parameters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Deep Learning Model**} & \multicolumn{5}{c}{**Performance**} \\ \cline{2-7}  & **Accuracy** & **Precision** & **Recall** & **IOU** & **BF Score** & **Param.** \\ \hline Typical-RC-AI & 0.66 & 0.74 & 0.46 & 0.56 & 0.56 & 34M \\ CrackUnet7 & 0.71 & 0.51 & 0.64 & 0.59 & 0.63 & \\ CrackUnet11 & 0.78 & 0.58 & 0.71 & 0.71 & 0.72 & \\ CrackUnet15 & 0.82 & 0.70 & 0.84 & 0.77 & 0.78 & 30M \\ DeepLabv3+ & 0.78 & 0.79 & 0.72 & 0.74 & 0.76 & 16M \\ Song’s Model & 0.74 & 0.75 & 0.67 & 0.71 & 0.73 & 9M \\ FCN & 0.62 & 0.53 & 0.56 & 0.53 & 0.51 & 132M \\ CrackSegnet & 0.76 & 0.75 & 0.76 & 0.64 & 0.67 & 15M \\
**SHSnet** & **0.88** & **0.85** & **0.83** & **0.81** & **0.84** & **4M** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative performance on SHCC microcracks.  
Figure 7: Using AI from other cementitious composites for SHCC cracks: (**a**) original photograph and results using (**b**) SHSnet, (**c**) Typical-RC-AI, and (**d**) accuracy of SHSnet at different ROIs. Note: cracks (in red) are overlaid on the original picture for better visualization.
## 1 Introduction
[16] used LES-based CFD program to simulate the interior flow field of a five-stage centrifugal pump, developed a data-interface tool to solve the problem of data exchange between fluid and structural grids, and proved it is feasible to use fluid-structure weakly coupled simulations to estimate the flow-induced noise generated in turbomachinery. [PERSON] et al. [17; 18] proposed a numerical calculation method of flow-induced noise in a centrifugal pump combining CFD and CA (computational acoustics). It can be used to calculate the radiated noise after considering the vibro-acoustic effect of pump casing, which are applied on radiated noise prediction of a multi-stage pump under different flow rates and a washing machine pump under two-phase flow. [PERSON] et al. [19] developed the unsteady continuous adjoint method by using an incompressible improved delayed DES and the acoustic Kirchhoff integral method to predict the flow-induced sound radiation from a body in free-stream. The flow-induced noise prediction is also a very attractive theme in other turbo machinery such as fans and blowers. [PERSON] and [PERSON] [20] numerically studied the tonal noise of a centrifugal fan by solving the Powell wave equation. [PERSON]'s theory of votex sound has an advantage in explaining the mechanism of flow-induced noise, which expresses noise generation as a function of velocity and vorticity fields. [PERSON] and [PERSON] [21] developed a CFD procedure to accurately predict the tonal noise in radial bowers. Accurate flow noise prediction based on analogy theory is the development trend, but numerical methods that consider both fluid-borne noise and radiated noise still need development at present. In this paper, a CFD/CFA (computational fluid dynamics/computational fluid acoustic) method is proposed to calculate the fluid-borne noise and flow-induced radiated noise of high-speed centrifugal pumps based on [PERSON]'s acoustic analogy theory. A test rig was set up in the semi-anechoic room to give the initial boundary conditions and verify the calculation results. The pump performance and sound pressure levels of different speeds are measured. Moreover, the noise spectrum characteristicsand directivity characteristics under different working conditions are also obtained. The numerical flow-induced noise characteristics of the high-speed centrifugal pumps under the required working conditions are studied then.
## 2 Related Work
### Surveillance Video Analysis System
In particular, studies utilizing edge computing based on edge devices reduce the burden of data transmission on the central servers by reducing the load on the network and improving the processing speed. The system proposed in this research extends these existing works even further. It maximizes the efficiency and scalability of real-time analysis by linking the edge devices with the GPU servers. The system performs an initial analysis on the edge devices. It sends only filtered data to the GPU server for high-precision analysis, which increases the data processing efficiency and provides a scalable network structure. This approach revolutionizes the management of large-scale CCTV networks and can potentially improve pedestrian safety while significantly reduce the burden on monitoring personnel.
# Cybersecurity for Blockchain-Based IoT Systems: A Review
## 5 Discussion
### Blockchain for IoT: Potential Applications for Smart Cities
#### 5.2.6 Microtransactions and Tokenization  
Blockchain-based IoT systems can enable micropayments and value exchange between devices and entities. By leveraging cryptocurrencies or tokens, IoT devices can autonomously exchange value or services based on predefined conditions [53; 54; 55]. For example, a smart parking system in a smart city can utilize blockchain and tokens to facilitate automated payments for parking spaces based on real-time availability and demand. This tokenization of IoT interactions can simplify and streamline payment processes while reducing transaction costs.  
The combination of blockchain and IoT has the potential to revolutionize various aspects of urban living, including transportation, energy management, waste management, public safety, and more [56]. However, it's important to consider the scalability, interoperability, and governance challenges associated with integrating blockchain into large-scale IoT deployments.
# Structural and Biochemical Characterization of Thioredoxin-2 from _Deinococcus radiotrans_
## 3 Results and Discussion
### Structural Comparison with Other Trx2 Proteins
The C-terminal Trx-fold domains are shown as a ribbon diagram and the N-terminal zinc-finger domains are represented in cartoon. Lys43 and Pro35 from DrTrx2 and Leu41, Ile42, and Thr43 from RcTrx2 are shown in sticks. The bound zinc ion for each protein is shown as a sphere, and the difference between the domain orientations of two proteins is marked with an arrow. domain of RcTrx2 close to the Trx-fold domain, while Pro35 of DrTrx2 impose a twist in the relative orientation of its zinc-finger domain, by the restricted flexibility of the proline peptide bond. The different orientations of the N-terminal zinc-finger domain seen among these proteins also affected the interaction between N- and C-terminal domains of each protein. In RcTrx2, Arg11, and Thr13 from \(\beta\)1 make hydrogen bonds with Arg56 in \(\alpha\)1, Asp60, and Asp61 in the loop between \(\alpha\)1 and \(\beta\)3, and Arg95 in \(\beta\)4. In addition, Gly18 and Leu41 in the N-terminal domain make hydrogen bonds with the side chains of Arg56 and Arg95, respectively. YbTrx2 shows similar inter-domain interactions with RcTrx2. In YbTrx2, Thr3, Asp51, Asp52, and Arg86 are equivalent to the Thr13, Asp60, Asp61, and Arg95 of RcTrx2, in forming intensive hydrogen bonds, while there is no residue equivalent to RcTrx2, Arg11, and Arg56. Instead, Arg18 in the N-terminal domain interacts with the backbone oxygen atom of Gly83 in YbTrx2. Notably, DrTrx2 differs from the other two Trx2 proteins, in that DrTrx2 does not display a clear hydrogen bond between N- and C-terminal domains, except for water-mediated polar interactions between Asp3 and Asp39 (Figure S6). The N-terminal zinc-finger domain of bacterial Trx2 proteins is thought to be involved in the interaction between Trx2 and its target. Our preliminary results for the identification of DrTrx2 targets showed that DrTrx2 might interact with thiosulfate sulfurtransferase (TST), which is known to be a target of human Trx (data not shown). In contrast, DrTrx2 did not show clear interactions with DnaK or DnaJ, which were reported to interact with EcTrx2 and RcTrx2 [23; 59]. The different orientation of DrTrx2 N-terminal zinc-finger domain compared to RcTrx2 and YbTrx2 might cause, not only the distinct interdomain interactions, but also differences in the surface exposed region that are important for protein-protein interactions, and thus may be implicated in the recognition of the unique targets of DrTrx2. Further investigation of the structural flexibility of the zinc-finger domain of DrTrx2 and its effect on the function and target recognition of DrTrx2 may yield important evidence regarding the role of Trx in many vital cellular processes.
## 2 Problem Formulation
### Determining the Sub-Mission Time Windows  
In order to reduce the complexity of the weather conditions in the main problem, the first sub-problem considered assumes that a given time period with changing weather conditions is to be divided into several sub-mission time windows (STWs). The STWs consist of analogous weather conditions. The goal is to determine the length of STWs and create a sequence of STWs, such that each window consists of a range of wind speed and a wind direction.  
The weather forecast is constant for the assumed time period, and the weather data is used to determine the STWs, in which the weather conditions are held constant, such as the speed and direction of the wind. The minimum and maximum ranges of wind speed for each sub-mission time window are known in advance. The wind direction is the same inside any given sub-mission time window. The sub-mission time windows can be subdivided into flying-time windows (FTWs) assuming that a given time duration with similar weather conditions is to be divided into several flying time windows. The intention is to determine the length of FTWs depending on the time used in the flying of UAVs, considering the maximum energy limit and maximum carrying payload.  
The UAV airspeed is the speed of a UAV relative to the air. Because the UAV needs to maintain a constant ground speed, the UAV needs to adjust its airspeed to fly in the desired flight path. Each sub-mission time window consists of a minimum and a maximum range of wind speed and a specific wind direction. This information leads to a min and max range of airspeed because of the effect of the wind speed and direction. Using the min and max range of air speed, the range of energy consumption is calculated. Every travelled route of a UAV starts and finishes within a given FTW. All UAVs are filled with full energy capacity before they start to fly and one UAV can fly only one time during the FTW. Each UAV has enough energy capacity to travel directly to the farthest customer in the network and come back directly to the depot in the worst acceptable weather conditions. The results of this sub-problem provide a sequence of STWs where the time period is divided into several STWs where each STW is divided into FTWs.
## 2 Materials and Methods
### Characterization Methods  
The electrical characteristics were thoroughly analyzed using an Agilent 4156B precision semiconductor parameter analyzer. This involves applying voltage to the top gate while keeping the bottom n-doped Si substrate grounded, ensuring accurate and reliable readings. Measurements are conducted within an enclosed space devoid of external factors to prevent any outside influences. During program and erase operations, a voltage is applied to the floating gate (FG), while the bottom gate (BG) is connected to the ground.
# Implementing Federated Governance in Data Mesh Architecture +
## 4 Introducing Blockchain-Powered Mesh Platform
### Defining the Data Mesh Governance Types
#### 4.1.1 Type I--Centralized Metadata Repository  
The centralized repository implies that a set of all metadata records \(m_{1},\ldots m_{n}=M\) is stored in a single system \(\mathcal{R}\). Upon the release of a data product \(p_{x}\), the new metadata record \(m_{x}\) is pushed to the repository in order to make the product discoverable and accessible by other domains. Since data products can be consumed by multiple domains, we introduce the formal notion of a 'data contract' \(c\) that describes data product-to-product (DP2P) relations. To have control over the records, a repository operator can define the visibility map function \(v:(u,m)\rightarrow\{1,0\}\), and the access function \(a:(u,m)\rightarrow\{Read,Write,Delete\}\) that are associated with user profiles \(U\).  
The metadata repository is defined as \(\mathcal{R}=\{\mathcal{P},\mathcal{M},C,U,a_{u,m},c_{p,p},d_{p,m},v_{u,m}\}\), where:  
* \(\mathcal{P}=\{P_{1},P_{2}\ldots\}\) is a set of data products and \(P_{i}=\{p_{1},p_{2}\ldots\}\) is a set of product versions* \(\mathcal{M}=\{M_{1},M_{2}\ldots\}\) is a set of metadata records and \(M_{i}=\{m_{1},m_{2}\ldots\}\) is a set of metadata versions
* \(C=\{c_{1},c_{2}\ldots\}\) is a set of data contracts
* \(U=\{u_{1},u_{2}\ldots\}\) is a set of system users
* \(a_{u,m}:U\times\mathcal{M}\to\{Read,Write,Delete\}\) is a function returning a permissions map for any given pair \((u,m)\)
* \(c_{p,p}:\mathcal{P}\times\mathcal{P}\to\mathcal{C}\cup\emptyset\) is a function returning the contract for any given pair \((p_{i},p_{j})\) of data products or an empty set if it does not exist
* \(d_{p,m}:\mathcal{P}\to\mathcal{M}\) is a function returning the metadata description of a data product
* \(v_{u,m}:U\times\mathcal{M}\to\{1,0\}\) is a function returning a visibility map for any given pair \((u,m)\) of user and metadata
## 4 Anchor Bolt Pull-Out Analysis  
### Verification of the Proposed Method  
In general, the fracture pattern when pulling out an anchor bolt embedded in a concrete member is complicated, and, as shown in experiment [6], the actual fracture pattern is often a combination of three fracture patterns (bond fracture, corn fracture, and bolt fracture). Therefore, in this study, we conducted a simulation analysis of the anchor bolt pull-out test and confirmed whether the fracture process and the pull-out strength in various fracture cases can be reproduced. The analytical model is shown in Figure 10a. Each particle in the model is 3 mm in diameter, and the total number of particles is about 200,000. An anchor bolt is embedded in the center of the concrete block. As a boundary condition, the vertical degrees of freedom of the particles (black area in the figure) at the four corners of upper surface were constrained. Table 2 shows the material constants used in the analysis.  
Figure 10: SPH analysis for the pull-out process in experiment [6]. (**a**) Analysis model. (**b**) Complex fracture pattern by numerical analysis. (**c**) Comparison of load-displacement relationship.  
Figure 9: Stress-strain relationship of steel.  
Figure 10b,c shows the final fracture pattern and the load-displacement relationship obtained from the proposed numerical analysis method. It can be found that the maximum load of the experiment is well reproduced by the numerical analysis, and the fracture pattern shown in the experiment is also reproduced by the numerical analysis. Bond fracture occurs at the lower part of the anchor, and cone fracture occurs at the upper part of the anchor. From these results, it is shown that the pull-out strength and fracture type of anchor bolts can be grasped by the proposed method.
## 1 Introduction  
The use of plasmas for biomedical applications in encountering a growing interest, especially in the framework of so-called \"plasma medicine\", which aims at exploiting the action of low-power, atmospheric pressure plasmas for therapeutic purposes [1; 2; 3; 4]. Several applications have already reached the stage of clinical trials, while others are on their way, a large set of different plasma sources able to work at atmospheric pressure with low dissipated power have been created, and some of them are already certified as medical devices. From the scientific viewpoint, action mechanisms for the interaction of plasmas with cells, tissues and pathogens are being elucidated, although this is a slower process which still requires great efforts. Furthermore, the indirect action through the use of plasma-treated liquids is also being explored, presenting promising possibilities. Finally, one should mention the possibility of plasma-cell interactions not directly related to a therapeutic action of the plasma, but of great importance for facilitating other therapeutic approaches, such as plasma-mediated gene transfection and drug penetration.  
The plasmas used in this kind of applications have two main requirements: To be produced at atmospheric pressure, and to keep the treated substrate at temperatures below 37 \({}^{\circ}\)C. These two requirements imply that we are dealing with cold atmospheric plasmas (CAP), where only the electrons have a high temperature (of the order of 1 eV, that is 11,600 K), while ions and the neutral molecules are at or near room temperature. These are weakly ionized plasmas, where most of the gas molecules are neutral, and electron-neutral collisions are the main drive of transport processes. In order to keep the power deposition, and thus the gas heating, to low values, a method for limiting the current needs to be employed in the plasma generation, so as to avoid transition to an electric arc: The two most widespread approaches are the dielectric barrier discharge (DBD), where a dielectric layer separating the electrodes rapidly extinguishes the current when enough charge deposits on it, and the radiofrequency (RF) discharge, where the voltage is reversed very fast, at frequencies above 1 MHz [5].  
This special issue was launched to collect the latest advancements in this exciting and interdisciplinary field of research. There were 13 papers submitted, of which 11 papers were accepted. When looking back to this special issue, various topics have been addressed: Mechanisms of interaction of plasma with substrate (two papers), technologies for production of plasma-activated water (two papers), and applications to cancer treatment (three papers), disinfection (two papers), regenerative medicine (one paper) and dentistry (one paper).
## 4 Multihoming and Interworking
### Traffic Engineering Support
#### 4.5.1 Gateway Selection for Self-Initialized Communication  
We assume that GLI-node 1 wants to establish a real-time connection with another node outside its own domain. It selects outgoing GLI-gateway F using the method described in Section 4.2.1. When sending the packet to F, it activates the GAP-bit in the global GLI-address \"_F(g)._*\" to indicate that F should set the GAP-bit in the global source address. Thus, gateway F substitutes the local GLI-address \"_a.1_\" in the source field of the packet with the global GLI-address \"_F(g).1_.\" As a result, the corresponding node of node 1 will send return data to \"_F(g).1_\" and not to another global address of 1. This is important for destination nodes in GLI-domains with upgraded networking stacks, as they could send return data to \"_E.1_.\" Hence, client node 1 has successfully selected gateway F for outgoing and incoming traffic.
## 2 Drill Core Fidelity Performance Model
### Mechanical Specific Energy  
The mechanical specific energy (MSE) refers to the amount of energy consumed in breaking a unit volume of rock. More detailed work indicates a more significant energy input per unit volume when a drill bit breaks rock. This increased energy input results in more substantial core damage and rock fracture. As a result, the core becomes less capable of accurately representing the mechanical and physical properties of the rock in its original position, leading to a decrease in fidelity. Therefore, the mechanical-specific energy is a crucial indicator for evaluating fidelity performance.  
Calculating the actual volume of crushing is complex, but [PERSON] et al., found through simulation and experimental studies that the results were similar when comparing the existing and projected crushing volume [26]. For the sake of convenience in the calculation, this paper characterizes the rock-crushing ratio based on the projected volume of crushing using the following equation:  
\[MSE=\frac{W}{V}=\frac{F_{h}d}{Ad}=\frac{F_{h}}{A} \tag{10}\]  
\(MSE\) is required to break the rock. The work (\(W\)) represents the energy consumed in breaking the rock, while the volume of rock broken (\(V\)) corresponds to the amount of rock that has been fragmented. The work (\(W\)) can be calculated by multiplying the average cutting force (\(F\)) by the cutting stroke (\(d\)). Similarly, the volume of rock broken (\(V\)) is determined by multiplying the projected area (\(A\)) of the cutting surface by the cutting stroke (\(d\))  
\[A=\bigg{[}r^{2}\cos^{-1}\bigg{(}\frac{r-d/\cos\theta}{r}\bigg{)}-r\bigg{(}r- \frac{d}{\cos\theta}\bigg{)}\sin\bigg{(}\cos^{-1}\bigg{(}\frac{r-d/\cos\theta }{r}\bigg{)}\bigg{)}\bigg{]}\cos\beta \tag{11}\]  
In this paper, the radius of the cutting tooth (\(r\)) and the cut's (\(d\)) depth are considered fixed parameters. The projected area of the cutting surface, which determines the rock-breaking ratio work, is primarily influenced by the back-rake angle (\(\theta\)) and the side-rake angle (\(\beta\)). To achieve a high-fidelity performance, controlling the magnitude of the rock-breaking specific work is crucial. This can be achieved by carefully designing and selecting drill structure parameters, aiming to minimize core damage and fragmentation.
# Pathogenic Roles of RNA-Binding Proteins in Sarcomas
## 3 Role of RBPs in Sarcomas
### Survival Sarcoma  
Snyonial sarcomas account for 8-10% of all soft-tissue sarcoma cases and most commonly affect the AYA generation younger than 30 years old. Clinical prognosis of adult synovial sarcoma remains poor; the expected 5 year survival is 50-60% despite intensive multimodality treatment [82]. Synovial sarcoma is molecularly characterized by translocation t(X;18)(p11;q11), which forms a fusion gene between SS18 (SYT) on chromosome 18 and SSX1, SSX2, or SSX4 on chromosome X. SS18 is a subunit of SWI/SNF (BAF) complexes, and the SS18/SSX fusion protein alters SWI/SNF composition, in which a tumor suppressor, SMARCB1, is lacking [83]. The SWI/SNF complex is a chromatin remodeler and regulates gene expression together with polycomb repressive complexes. The aberrant SWI/SNF complex dysregulated immune evasion, cellular plasticity, and cell cycle in synovial sarcoma [84]. Recent research showed that SMARCB1 interacts with lncRNA and controls target gene expression at active promoter regions. An RNA immunoprecipitation assay identified that SMARCB1 is bound to both coding RNA and long noncoding RNA (lncRNA) at active promoter regions. For example, SWINGN, a lncRNA, modulates a large network of pro-oncogenic genes by favoring SMRCB1 binding [85]. Although the alteration of RNA interaction with aberrant SWI/SNF in synovial sarcoma remains greatly unknown, disruption of the interaction may contribute to = oncogenic transcription.