esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Receptor Binding with the Collagen Fibril  
Various ligands bind to specific regions (i.e., domains) within the hierarchical structure of the collagen fibril. Certain ligands with immunological function seem to have clear and unobstructed access to their collagen receptor sites, such as glycoprotein VI (GPVI), Endo180 [4; 5], and the human osteoclast-associated receptor (OSCAR) [6]. Others require the \"removal\" of specific molecular segments within the microfibrils/fibrils to reveal the underlying binding site (through peptide cleavage, damage, molecular motion, or mechanical relocation). Examples of the latter are the discoidin domain receptor (DDR), the matrix metalloproteinase (MMP), and some (but not all) integrin sites [7]. The C-terminal telopeptide region and nearby domains (described below) are the most significant matrix-matrix and matrix-cell interaction domains of the D-periodic collagen fibril [8; 9]. This is partially due to this D-periodic region being the most \"exposed\" relative to the rest of the D-periodic structure [1; 10]. It contains the C-telopeptide and cross-linking domain, the site of triple helix polymerization, and nearby, a significant fibronectin domain which overlaps a DDR binding domain and the MMP interaction domain and cleavage site [1; 8].  
Modeling and docking studies have indicated that a minimum of two GPO (glycine-proline -hydroxyproline) triplets are necessary for GPVI-collagen ligation [11] supported by favorable protein-protein contacts, as well as strongly supported by experimental studies [12; 13]. There is just one limited region on the collagen fibril surface where two GPO repeats occur, located just prior to the non-helical C-telopeptide. This highly accessible location is also between the fibronectin/collagenase interaction domain and the start of the C-terminal telopeptide. This region is structurally exposed and prominently placed for environmental interaction, according to the collagen fibril structure first put forward by [PERSON] et al. [2; 3; 14]. It also appears to possess favorable structural characteristics for interaction with immunoglobulin-like collagen receptors, such as OSCAR [6]. These receptors play in important role in the modulation of matrix remodeling and in antigen recognition [6; 15]. Again, it should be noted that important domains are present in close proximity to the C-telopeptide region, such as that for collagenase cleavage, DDR, and fibronectin interaction.  
The presence of these ligand-binding domains makes the C-terminal domain of the mature collagen fibril an important region for receptor mediated activation of cellular cascades, inflammation, cellular recruitment, and prevention of apoptosis.
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 1 Introduction
The remainder of this paper is organized as follows: In Section 2, we discuss related works. A review of Kd-tree and wavelet tree for compact data structures is given in Section 3. In Section 4, the proposed image compression and encryption scheme is described. The experimental results and security analysis of the proposed scheme are presented in Section 5. Finally, the conclusions are presented in Section 6.
## 1 Introduction
### Motivation  
The central authority in charge of access control may serve as a primary target for assault. Additionally, centralized control is widely favored in order to protect and guarantee the reliability of renewable-energy operations. However, adaptability and system integration at each organization is equally crucial. In order to secure the greatest return on investment and simplicity of installation, the access-control system should also provide interoperability with other third-party and legacy systems. To the best of our knowledge, the current access-control schemes suffer from the following main points:  
* Categorizing the users into a limited number of roles that can be used to access the encrypted data, which leads to the role explosion problem.
* Allowing the individual authorities to decrypt the ciphertexts, which violates data privacy.
* Placing high importance on the central authority in managing huge computations, which makes it a bottleneck and vulnerable to attack.
* Revoking the users only based on the attributes of the user. However, the revocation should be based on the user's behavior.  
We were inspired by the aforementioned points to suggest a token-revocation access-control system for the cloud-hosted renewable-energy data and energy optimization utility. TR-AC has the ability to block malicious users at the authorization stage using the authentic token algorithm.
## 1 Introduction
By regulating robot waiting time at the process steps, schedulability conditions are established for both SACTs and DACTs with WRTCs in [15; 16]. According to these conditions, if a tool is schedulable, a feasible and optimal schedule can be found by parameterizing robot waiting time. Then, a robot idle time regulating method can be extended to find a feasible schedule for both SACTs and DACTs with WRTCs and bounded activity time variations as in [17; 18; 19; 20; 21]. In practice, after a PM completes a number of wafers, the processing environment in the chamber at the PM is contaminated to some extent. Hence, for high quality wafer  
Figure 1: Cluster tools: (**a**) single-arm robot and (**b**) dual-arm robot. fabrication, it should be cleaned at this time. Chamber cleaning is necessary for wafer quality, but this makes the scheduling problem more challenging. [PERSON] et al. [22; 23] present an interesting idea of partially loading wafers into parallel PMs at a step for SACTs and DACTs with chamber cleaning requirements. By doing so, parallel PMs can be alternately cleaned. Their studies were conducted based on the assumption that a chamber cleaning operation is performed for a PM after every wafer is processed in the PM, which is also called a purge operation. However, it is also important to consider a more general case in which a chamber in a PM is required to be cleaned after a specified quantity of wafers is consecutively processed, which arises from practical wafer fabrication cases. For such a case with WRTCs being considered at the same time, the existing method in [22; 23] is no longer applicable. Thus, this work aims to solve such a new and challenging problem for SACTs with WRTCs and chamber cleaning operations by proposing a virtual wafer strategy. The rest of the paper is organized as follows. The virtual wafer-based method is introduced in Section 2. Then, based on the virtual wafer method, a genetic algorithm is constructed to find an approximate solution in Section 4. Finally, extensive experiments are described to verify the proposed method.
## 3 Methodology
### Recurrent Neural Network
#### 3.3.2 Lstm  
LSTM was improved to overcome the gradient disappearance problem [41] in standard RNN [42]. Figure 5b shows the basic structure of LSTM. A typical LSTM cell consists of one unit state and three types of gates: Input gate (\(i_{t}\)), output gate (\(o_{t}\)), and forget gate (\(f_{t}\)). These three gates act as filters, serving different purposes. The input gate (\(i_{t}\)) determines what new information is going to be stored in the cell state (\(C_{t}\)). The output gate (\(o_{t}\)) specifies what information from the cell state (\(C_{t}\)) is used as output. The forget gate (\(f_{t}\)) determines  
Figure 5: Basic structures of RNN units: (**a**) Standard RNN; (**b**) LSTM; (**c**) GRU.  
what information will be moved away from the cell state (\(C_{t}\)). More formally, the outputs of the input gate (\(i_{t}\)), output gate (\(o_{t}\)), and forget gate (\(f_{t}\)) can be formulated as follows:  
\[f_{t}=\sigma\Big{(}W_{fx}x_{t}+W_{fh}h_{t-1}+b_{f}\Big{)} \tag{7}\]  
\[i_{t}=\sigma(W_{ix}x_{t}+W_{ih}h_{t-1}+b_{i}) \tag{8}\]  
\[o_{t}=\sigma(W_{ox}x_{t}+W_{oh}h_{t-1}+b_{o}) \tag{9}\]  
The current cell state (\(C_{t}\)) can be formulated as follows:  
\[C_{t}=f_{t}\odot C_{t-1}+i_{t}\odot\widetilde{C}_{t} \tag{10}\]  
The unit state \(\widetilde{C}_{t}\) can be described by the following formula:  
\[\widetilde{C}_{t}=\tan\ln(W_{Cx}x_{t}+W_{Ch}h_{t-1}+b_{C}) \tag{11}\]  
The LSTM unit (\(h_{t}\)) can be formulated as follows:  
\[h_{t}=o_{t}\odot\tan\ln(C_{t}) \tag{12}\]  
where \(W_{fh}\), \(W_{ih}\), \(W_{oh}\), and \(W_{Ch}\) are the linear correlation coefficient matrices; \(W_{fx}\), \(W_{ix}\), \(W_{ox}\), and \(W_{Cx}\) are the coefficient matrices of the input variable; \(\sigma(\cdot)\) denotes the sigmoid activation function; and \(b_{f}\), \(b_{i}\), \(b_{o}\), and \(b_{C}\) are the bias terms of the corresponding formula.
## 3 Optimization Problem Formulation
### Constraints  
All the objective functions given by Equations (1)-(4) are subject to several constraints. Equation (5) ensures that the number of EVs charging and discharging does not exceed the total number of EVs in the system. Equations (6) and (7) control EV charging and discharging, and Equations (8) and (9) ensure that EVs will not charge and discharge at the same time.  
\[N_{\text{C}bg}+N_{\text{D}cbg}=N_{total} \tag{5}\]  
\[x_{\text{C}bg,i}\ P_{\text{C}bgMin}\leq P_{\text{C}bg,i}\leq x_{\text{C}bg,i} \ P_{\text{C}bgMax} \tag{6}\]  
\[x_{\text{D}cbg,i}\ P_{\text{D}cbgMin}\leq P_{\text{D}cbg,i}\leq x_{\text{D}cbg,i}\ P_{\text{D}cbgMax} \tag{7}\]  
\[x_{\text{C}bg,i}\in(0,1) \tag{8}\]\[x_{Dchg,i}\in(0,1) \tag{9}\]  
The constraints for the maximum efficiency of charging and discharging are presented in Equations (10) and (11), respectively. Equation (12) computes the maximum amount of available reactive power exchange from an EV, which is taken as 58% of the maximum rated power of EVs in this study [39; 45]. Equation (13) defines the range of the reactive power discharge.  
\[P_{EV\_Chg,i}=\eta_{Chg}\times P_{Clg,i} \tag{10}\]  
\[P_{EV\_Dchg,i}=\frac{P_{Dchg,i}}{\eta_{Dchg}} \tag{11}\]  
\[Q_{DchgMax,i}=(0.58\times P_{EV\_Chg/Dchg,i}) \tag{12}\]  
\[-Q_{DchgMax,i}\leq Q_{Dchg,i}\leq Q_{DchgMax,i} \tag{13}\]  
Equations (14)-(16) calculate the SOC values and helps them to be in the customer desired range.  
\[SOC_{i,t}=SOC_{i,t-1}+\frac{\left(\eta_{ch}\times P_{EV\_Chg,i}-\frac{P_{EV\_ Chg,i}}{\eta_{Dch}}\right)}{P_{NC,i}} \tag{14}\]  
\[SOC_{\min}\leq SOC_{i,t}\leq SOC_{\max} \tag{15}\]  
\[SOC_{final}=SOC_{desired} \tag{16}\]  
Equations (17) and (18) are the power flow constraints.  
\[P_{i,t}-P_{d,i,t}-V_{i,t}\sum_{j=1}^{N_{\text{low}}}V_{j,t}(G_{ij}\cos\phi_{ ij,t}+B_{ij}\sin\phi_{ij,t})=0 \tag{17}\]  
\[Q_{i,t}-Q_{d,i,t}-V_{i,t}\sum_{j=1}^{N_{\text{low}}}V_{j,t}(G_{ij}\cos\phi_{ ij,t}+B_{ij}\sin\phi_{ij,t})=0 \tag{18}\]
# Investigation of Nanocutting Characteristics of Off-Axis 4H-SiC Substrate by Molecular Dynamics
## 4 Conclusions  
In this paper, the nanomachining characteristics of off-axis 4H-SiC have been investigated by molecular dynamics. Different off-axis MD models were built. The influence of lattice orientation, cutting depth, and cutting speed was investigated. A defect was induced by cutting propagates in the basal plane, and it was identified as a stacking fault. Bond reconstruction was also detected near the slip plane. If the angle between the cutting direction and the c-axis was smaller than 90\({}^{\circ}\), the defect propagates deep into the workpiece. Cutting depth had significant impact on structure transformation, cutting force, and workpiece temperature. With a larger cutting depth, more defects were induced by cutting. Cutting force and the workpiece temperature were also larger. To select an appropriate cutting depth, material removal rate and defects are the things to consider. The effect of cutting speed is mainly reflected in the workpiece temperature. The workpiece temperature increases with the increase of the cutting speed. And it may further affect the structure transformation and the cutting force. Through this work, we aimed to reveal the machining characteristics of off-axis 4H-SiC and provide suggestions on SiC substrate thinning.  
conceptualization, F.Z. and S.L.; methodology, M.W.; formal analysis, M.W. and Y.X.; investigation, M.W. and Y.X.; writing--original draft preparation, M.W.; funding acquisition, F.Z.  
This work was supported by the 973 project (Number: 2015 CB057203) and the National Natural Science Foundation of China (Number: 51675211).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 11: Workpiece temperature of cutting with different cutting speeds.
# MA_W-Net-Based Dual-Output Method for Microseismic Localization in Strong Noise Environments
## 2 Methods and Principles
### Network Architecture  
The MA_W-Net network introduced in this study based on an enhanced U-Net framework, shown in Figure 8. This architecture consists a single encoder and dual decoders. The two decoders are responsible for seismic source localization and noise suppression, respectively. They collaboratively utilize the encoder for the end-to-end parallel training of features extracted from noisy microseismic data. The encoder is tasked with isolating crucial features from these recordings. Utilizing these features, Decoder 1 constructs a Gaussian mixture model to provide the location details of microseismic events. Subsequently, Decoder 2 then attempts to reconstruct the clean microseismic record using the extracted features and outputs a noise-compressed microseismic record. In this study, it is assumed that microseismic events have been detected, and the input data are microseismic recordings with a low SNR received by geophones with a size of 512 \(\times\) 64 \(\times\) 1. In the encoder part, we first resize the input data using 4 convolutional layers, each with a filter of 64 and a convolutional kernel of 5 \(\times\) 5. After two more RES modules, the output size is 64 \(\times\) 64 \(\times\) 64. The remainder of the encoder contains three down-sampling modules, each of which consists of a 2 \(\times\) 2 max-pooling [30] and two RES modules. This configuration  
Figure 6: The architecture of the CSEA module.  
Figure 7: The architecture of the SE Attention Module.  
effectively doubles the channel count while reducing the feature map's dimensions by half with each down-sampling iteration.  
The decoder contains a series of up-sampling processes. In Decoder 1, each process is up-sampled using a 2 \(\times\) 2 transposed convolution, doubling the size of the feature map and halving the number of channels. This process involves integrating feature maps from both the encoder and decoder, following a single convolution and two CCA modules. The final output of Decoder 1 has a size of 64 \(\times\) 128 and represents a Gaussian mixture distribution containing information about the location of the seismic source. In Decoder 2, each process is up-sampled using a 2 \(\times\) 2 transposed convolution, doubling the size of the feature map and halving the number of channels. Here, after merging encoder and decoder feature maps through one convolution and two CSEA modules, transposed convolution is then applied to revert the feature maps back to the original input dimensions. The final output size of Decoder 2 is 512 \(\times\) 64, representing a microseismic record after noise suppression. Except  
Figure 8: The architecture of the proposed MA_W-Net network.  
for the two output convolutional layers, which use a Sigmoid activation function [31], all other convolutional layers are activated with a ReLU activation function.
## 3 Results and Discussion  
### Dispersion Measurement of Alumina Nanofluids  
A uniform dispersion and stable suspension of nanoparticles in the dispersing media are essential for nanofluid applications since they determine the final properties [30]. Especially, solar absorption capacity depends on the dispersion of nanoparticles in nanofluids. Therefore, the dispersion of alumina nanofluids can be characterized using UV-vis spectroscopy in this study.  
The dispersions of alumina nanofluids at concentrations of 0.5, 0.3, and 0.1 wt% were measured by UV-vis spectroscopy. Figure 2 shows that the higher the concentration, the higher the absorbance, which indicates that increasing the concentration results in a higher solar absorption capacity.
# Ant Colony Optimization Task Scheduling Algorithm for SWIM Based on Load Balancing
## 1 Introduction  
In 1997, EUROCONTROL proposed the System Wide Information Management (SWIM) concept to the Federal Aviation Administration [1]. The International Civil Aviation Organization accepted this concept in 2002 [2]. The System Wide Information Management is a large-scale network system with high integration, which integrates and manages internal resources. SWIM, as an information sharing platform, provides a unified data transmission and exchange mechanism for different subsystems of civil aviation businesses. In order to achieve interoperability and consistency among the relevant units of civil aviation, as well as reduce the difficulty of integrated dispatching, this system enables data to be processed and integrated among independent systems. The basic idea of SWIM is to allow all air traffic participants, such as airports, air traffic control, airlines and other related civil aviation units, to share and exchange the latest information. Moreover, it ensures that information can be shared safely, effectively and in a timely manner. Thus, for accurate and timely cooperative decision-making, SWIM can greatly improve the predictability and effectiveness of decisions and meet the needs of civil aviation in terms of efficient and coordinated operation. The comparison of the traditional civil aviation mesh communication network and the SWIM bus communication structure is shown in Figure 1[3].  
In order to improve SWIM resource utilization and sharing rate, it is necessary to solve the problem of system task scheduling. Because of the distribution, heterogeneity and autonomy of resources in a SWIM environment, SWIM task scheduling is more complex and difficult. As an algorithm for solving combinatorial optimization problems, the ant colony algorithm has the characteristics of parallelism and strong robustness. It can quickly obtain high-quality solutions and is very suitable for solving SWIM task scheduling problems.  
Although the existing improved ant colony algorithm can improve the efficiency of task scheduling, it does not significantly improve the system load imbalance. This paper summarizes the existing network task scheduling technology in combination with SWIM's own characteristics, on the basis of the classical ant colony algorithm, focusing on solving the problem of unbalanced resource load of service nodes in task scheduling. According to the hardware performance and load situation of service node update pheromones, the SWIM ant colony task scheduling algorithm based on load balancing (ACTS-LB) is proposed. This ACTS-LB algorithm can reduce task execution time and improve system load balancing, thus ensuring that the user's task scheduling needs can be met as much as possible.  
The remainder of the paper is structured as follows. We give some related works in Section 2. We introduce SWIM load balancing requirements and related definitions in Section 3. Section 4 presents the details of the ACTS-LB algorithm. We give the simulation results and analysis in Section 5 and conclude this paper in Section 6.
###### Abstract  
Tungsten oxide (WO\({}_{3}\)) is a high-density material with exceptional radiation attenuation properties, making it a strong candidate for advanced shielding applications. This study explores the structural, mechanical, and shielding performance of WO\({}_{3}\)-reinforced polyisoprene composites. Morphological analysis reveals a plate-like structure, indicating robust interfacial interactions that enhance mechanical integrity and thermal stability. X-ray diffraction confirms the crystalline nature of WO\({}_{3}\), while Fourier transform infrared spectroscopy detects distinct W-O bond absorption bands, validating uniform dispersion. Computational analysis using XCOM demonstrates remarkable improvements in attenuation properties, particularly at intermediate- and high-photon energies. While PbO\({}_{2}\) outperforms at lower energies due to the photoelectric effect, Phy-X/PSD analysis confirms that composites with \(\geq\)75% WO\({}_{3}\) offer strong shielding capabilities. Variations in effective atomic number, linear attenuation coefficient, and mass attenuation coefficient establish WO\({}_{3}\)-reinforced NR as a compelling lead-free alternative, especially for Tc-99m applications. Experimental findings further reveal that increasing WO\({}_{3}\) content significantly reduces Tc-99m gamma radiation dose equivalents Hp(0.07), Hp(3), and Hp(10), emphasizing the potential of WO\({}_{3}\)-reinforced composites for next-generation radiation shielding solutions.  
Keywords:technium-99m; radiation shielding; polyisoprene; gamma attenuation; tungsten oxide; gamma radiation shielding; radiation exposure; radiation protection; low dose ionizing radiation; radiation attenuation _Article_
## 2 Experimental Investigation of Column
### Axial Compression Test Results and Discussions  
#### 2.4.1 Basic Behavior  
The test results of all samples are presented in Table 1. Figure 7a,b show the residual bearing capacity (\(P_{\mathrm{u}}\)) and mid-span lateral displacement (\(\Delta_{\mathrm{r}}\)) with the variations of \(H\) and the cross-section dimensions respectively. It can be seen from Table 1 and Figure 7a that for CFCSTCs, the residual bearing capacity (\(P_{\mathrm{u}}\)) decreases with the increasing \(H\). Furthermore, for samples with same \(D\) and \(t\), the highest \(P_{\mathrm{u}}\) is obtained by the sample that is not subjected to impact loading before the axial compression test, while the lowest \(P_{\mathrm{u}}\) is achieved by the sample with \(H\) of 4 m. It also can be found that for C159\(\times\)6-R26.5-H0 to H4 samples, the reduction of \(P_{\mathrm{u}}\) is 35%. However, for C159\(\times\)8-R19.9-H0 to H4, C219\(\times\)6-R36.5-H0 to H4, C219\(\times\)8-R27.4-H0 to H4, the reduction of \(P_{\mathrm{u}}\) is 29%, 13% and 4%, respectively. This means that the influence of \(H\) on \(P_{\mathrm{u}}\) is obviously smaller as a result of the increasing \(D\) or \(t\). In addition, under the same \(H\), the \(P_{\mathrm{u}}\) decreases with increasing \(D\)/\(t\) ratio under the same \(D\), while the \(P_{\mathrm{u}}\) decreases with decreasing \(D\)/\(t\) ratio under the same \(t\). It may be observed that a larger \(D\) or \(t\) can significantly improve the flexural rigidity, and thus \(P_{\mathrm{u}}\) can be increased.  
It can be noted from Table 1 and Figure 7b that under axial compression, the residual mid-span lateral displacement (\(\Delta_{\mathrm{r}}\)) increases with increasing \(H\). In addition, under the same \(H\), \(\Delta_{\mathrm{r}}\) increases with increasing \(D\)/\(t\) ratio under the same \(D\), while \(\Delta_{\mathrm{r}}\) decreases with an increasing \(D\)/\(t\) ratio under the same \(t\). This may attribute to the fact that the capacity to resist deformation of CFCSTC can be improved significantly by increasing the \(t\) or \(D\).  
Figure 6: Impact force (\(F\)) versus time (\(T\)) curves of samples in impact test. (**a**) C159\(\times\)6-R26.5-H2 to H4; (**b**) C159\(\times\)8-R19.9-H2 to H4; (**c**) C219\(\times\)6-R36.5-H2 to H4; (**d**) C219\(\times\)8-R27.4-H2 to H4.